gam_solve/

active_set.rs

use crate::estimate::EstimationError;
use faer::linalg::solvers::{Lblt as FaerLblt, Solve as FaerSolve, SolveLstsq};
use faer::Side;
use gam_linalg::faer_ndarray::{FaerArrayView, FaerLinalgError, FaerSvd, array1_to_col_matmut};
use gam_linalg::utils::{StableSolver, array_is_finite, boundary_hit_step_fraction};
use gam_problem::{
    ConstraintRowId, ConstraintSet, KhatriRaoConeConstraints, LinearInequalityConstraints,
};
use ndarray::{Array1, Array2, s};
use serde::{Deserialize, Serialize};
use std::cell::Cell;
use std::collections::HashSet;

/// Primal-feasibility tolerance the inequality-constrained active-set Newton
/// solver guarantees on its returned iterate, measured in the *scaled*
/// constraint-row coordinate system in which `A * beta >= b` is expressed.
///
/// The solver accepts a step when the worst scaled violation
/// `max_i (b_i - a_i^T beta)` is below this threshold (see the acceptance
/// gate in [`solve_linear_constrained_newton_step`] and the KKT diagnostics
/// in [`compute_constraint_kkt_diagnostics`]). Any consumer that re-derives a
/// raw (un-scaled) feasibility tolerance from a returned iterate must scale
/// this value by the per-row normalization that the constraint builder
/// applied; demanding tighter feasibility than this is inconsistent with the
/// solver contract and will spuriously reject valid boundary solutions.
pub const ACTIVE_SET_PRIMAL_FEASIBILITY_TOL: f64 = 1e-8;

/// Scaled slack tolerance for membership in an active working face.
///
/// This is intentionally tighter than the public primal-feasibility contract:
/// a row may be numerically feasible without being an equality at the current
/// point. Warm-start and terminal face provenance both use this value so a QP
/// endpoint row cannot remain active after globalization accepts an interior
/// subsegment of the endpoint chord.
pub const ACTIVE_SET_WORKING_FACE_TOL: f64 = 1e-10;

/// Step fraction to the EXACT constraint boundary. A strictly feasible row
/// (`slack > 0`) clips the step so the iterate lands ON the boundary — never
/// inside the `±ACTIVE_SET_PRIMAL_FEASIBILITY_TOL` certified band. The former
/// `slack + TOL` target deliberately overshot to the band's outer edge, which
/// (a) returned band-edge answers from problems whose true optimum is on the
/// boundary (`maxiter_accepts_current_boundary_solution` observed 0.1+1e-8),
/// (b) made every downstream feasibility re-check a rounding coin flip, and
/// (c) broke the strict-interior projection repair, whose own identity-QP
/// landed band-edge and was then rejected by its interior margin.
///
/// A row already at or marginally past the boundary (`slack <= 0`, moving
/// outward) clips to a zero step: the row is added as blocking, and the
/// zero-progress machinery (projected-gradient tangent escape at
/// `primal_step_norm <= tol_step`, plus the post-full-step multiplier
/// adjudication) inspects the escape the pre-#979 code refused — the original
/// reason the `+TOL` overshoot was introduced, now handled structurally.
#[inline]
fn active_set_boundary_hit_step_fraction(
    scaled_slack: f64,
    scaled_directional_change: f64,
    current_step_limit: f64,
) -> Option<f64> {
    boundary_hit_step_fraction(
        scaled_slack.max(0.0),
        scaled_directional_change,
        current_step_limit,
    )
}

/// Stationarity tolerance for the strong-KKT acceptance gate: the projected
/// (working-set) gradient residual ‖∇L − Aᵀλ‖∞, either absolute or relative to
/// `max(1, ‖∇L‖∞)`, must fall below this to certify a constrained stationary
/// point. Matched against `ACTIVE_SET_KKT_COMPLEMENTARITY_TOL` so both KKT
/// residual channels are certified at compatible scales.
const ACTIVE_SET_KKT_STATIONARITY_TOL: f64 = 2e-6;

/// Complementarity-slackness tolerance for the KKT acceptance gate:
/// `max_i |λ_i · slack_i|` must fall below this for the
/// active-inactive partition to be consistent.
const ACTIVE_SET_KKT_COMPLEMENTARITY_TOL: f64 = 1e-6;

/// Dual-feasibility tolerance for the KKT acceptance gate: every working-set
/// multiplier must satisfy `λ_i ≥ −ACTIVE_SET_KKT_DUAL_FEASIBILITY_TOL` (a
/// strictly-negative multiplier means the constraint should be released).
const ACTIVE_SET_KKT_DUAL_FEASIBILITY_TOL: f64 = 1e-8;

/// Relaxed stationarity tolerance accepted only on a *genuinely degenerate
/// boundary face* — one whose active rows are linearly dependent
/// (`rank(A_active) < n_active`), so the active-row multipliers are non-unique
/// and the exact projected gradient cannot reach
/// `ACTIVE_SET_KKT_STATIONARITY_TOL`. Still requires primal feasibility,
/// complementarity, and a relative-stationarity backstop.
///
/// Public so the outer REML / PIRLS validation gate can apply the same
/// relaxation when the diagnostic reports a rank-deficient active face — a
/// strict 5e-6 check there would otherwise refuse iterates that the inner
/// active-set solver legitimately certified via its own `degenerate_boundary_ok`
/// clause.
///
/// NOTE: this is *not* the mechanism that fixes the `shape=concave` /
/// `shape=convex` cold-vs-warm cache divergence (#873). The B-spline shape path
/// reparameterizes curvature into independent *coordinate lower bounds*
/// `γ_j ≥ 0` (see `shape_lower_bounds_local`); any subset of those active rows
/// is full rank, so `working_set_rank_deficient` stays `false` and this
/// relaxation never fires for them — and must not be widened to. That bug is a
/// *seed* problem (a cold seed landing on the cone vertex with every curvature
/// row tight); it is fixed at the source by
/// `project_point_strictly_into_feasible_cone`, which starts the inner solve
/// strictly inside the cone so the strict tolerance is reachable.
pub(crate) const ACTIVE_SET_KKT_DEGENERATE_STATIONARITY_TOL: f64 = 1e-3;

/// Relative scale on the predicted-decrease test `predicted_delta ≤
/// −ε·(1 + ‖∇L‖∞·‖d‖∞)`: when the working-set Newton step still buys a
/// quadratic-model decrease at this relative margin the step is a usable
/// descent direction even if the KKT residual has not yet tightened.
const ACTIVE_SET_MODEL_DESCENT_REL_TOL: f64 = 1e-10;

/// KKT diagnostics for inequality-constrained Newton subproblems.
///
/// Constraints are represented as `A * beta >= b` in the same coefficient
/// coordinate system as the returned `beta`.
///
/// **Invariants** (held by all producers; not enforced at consumer boundary):
/// - `n_active <= n_constraints` (a row cannot be active twice).
/// - All four residual components (`primal_feasibility`, `dual_feasibility`,
///   `complementarity`, `stationarity`) are `>= 0.0` and finite.
/// - `active_tolerance >= 0.0` and finite.
#[derive(Clone, Debug, Serialize, Deserialize)]
pub struct ConstraintKktDiagnostics {
    /// Number of inequality rows.
    pub n_constraints: usize,
    /// Number of rows considered active (`slack <= active_tolerance`).
    pub n_active: usize,
    /// Maximum primal feasibility violation: `max_i max(0, b_i - a_i^T beta)`.
    pub primal_feasibility: f64,
    /// Maximum dual feasibility violation: `max_i max(0, -lambda_i)`.
    pub dual_feasibility: f64,
    /// Maximum complementarity residual: `max_i |lambda_i * slack_i|`.
    pub complementarity: f64,
    /// Stationarity residual: `||grad - A^T lambda||_inf`.
    pub stationarity: f64,
    /// Tolerance used to classify active constraints from slacks.
    pub active_tolerance: f64,
    /// `true` when the active rows are linearly dependent (`rank(A_active) <
    /// n_active`) — a *degenerate boundary face*. On such a face the active-row
    /// multipliers are non-unique and the strict stationarity tolerance is
    /// unreachable by construction. The inner active-set solver certifies these
    /// iterates via its `ACTIVE_SET_KKT_DEGENERATE_STATIONARITY_TOL` relaxation;
    /// the outer validation gate must consult this flag to apply the matching
    /// relaxation, or it will refuse a legitimately-converged constrained
    /// optimum and abort the REML startup loop.
    ///
    /// NOTE: B-spline `shape=concave`/`shape=convex` faces are *not* degenerate
    /// — that path reparameterizes curvature into independent coordinate lower
    /// bounds `γ_j ≥ 0` (full-rank active subsets), so this flag stays `false`
    /// for them. Their cold-start fragility is a seed problem fixed by the
    /// strictly-interior seed, not by this relaxation.
    #[serde(default)]
    pub working_set_rank_deficient: bool,
    /// Inf-norm of the (raw, unprojected) gradient at `beta`, `‖gradient‖∞` —
    /// the natural scale of the stationarity residual. A converged constrained
    /// optimum drives `stationarity = ‖grad − Aᵀλ‖∞` to zero *relative to* this
    /// scale, not to a fixed absolute floor: the profiled REML latent objective
    /// carries an O(n) gradient magnitude even at a genuine stationary point
    /// (issue #879), so a bare absolute stationarity gate is unreachable there
    /// by construction. The inner active-set solver already certifies
    /// convergence on the scale-invariant ratio
    /// `stationarity / max(gradient_scale, 1)` (its `stationarity_rel` path
    /// against `ACTIVE_SET_KKT_STATIONARITY_TOL`); the outer validation gate
    /// [`crate::estimate::reml::outer_eval`]`::enforce_constraint_kkt` consults this
    /// field to apply the identical relative test, so the two stop on the same
    /// contract instead of the gate spuriously aborting a constrained optimum
    /// the solver legitimately reached (issue #989). Defaults to `0.0` when
    /// deserialized from a model saved before this field existed, which makes
    /// `max(gradient_scale, 1) = 1` and recovers the bare absolute test.
    #[serde(default)]
    pub gradient_scale: f64,
}

/// Inf-norm `‖g‖∞` used as the scale of the stationarity residual in the
/// relative KKT criterion shared by the inner active-set solver and the outer
/// validation gate (see [`ConstraintKktDiagnostics::gradient_scale`]).
fn gradient_inf_norm(gradient: &Array1<f64>) -> f64 {
    gradient.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()))
}

fn solve_newton_direction_dense(
    hessian: &Array2<f64>,
    gradient: &Array1<f64>,
    direction_out: &mut Array1<f64>,
) -> Result<(), EstimationError> {
    if direction_out.len() != gradient.len() {
        *direction_out = Array1::zeros(gradient.len());
    }

    let factor = StableSolver::new()
        .factorize(hessian)
        .map_err(EstimationError::LinearSystemSolveFailed)?;
    direction_out.assign(gradient);
    let mut rhsview = array1_to_col_matmut(direction_out);
    factor.solve_in_place(rhsview.as_mut());
    direction_out.mapv_inplace(|v| -v);
    if array_is_finite(direction_out) {
        return Ok(());
    }
    Err(EstimationError::LinearSystemSolveFailed(
        FaerLinalgError::FactorizationFailed {
            context: "active-set newton direction non-finite solve",
        },
    ))
}

fn solve_dense_system_via_pseudoinverse(
    matrix: &Array2<f64>,
    rhs: &Array1<f64>,
    out: &mut Array1<f64>,
) -> Result<(), EstimationError> {
    if matrix.nrows() != matrix.ncols() || rhs.len() != matrix.nrows() {
        crate::bail_invalid_estim!("dense pseudoinverse solve dimension mismatch");
    }

    let (u_opt, singular, vt_opt) = matrix.svd(true, true).map_err(|_| {
        EstimationError::InvalidInput("dense pseudoinverse solve SVD failed".to_string())
    })?;
    let (Some(u), Some(vt)) = (u_opt, vt_opt) else {
        crate::bail_invalid_estim!("dense pseudoinverse solve missing singular vectors");
    };

    let max_singular = singular.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
    let tol = 100.0
        * f64::EPSILON
        * (matrix.nrows().max(matrix.ncols()).max(1) as f64)
        * max_singular.max(1.0);
    let mut coeff = u.t().dot(rhs);
    for (idx, value) in coeff.iter_mut().enumerate() {
        let sigma = singular[idx];
        if sigma.abs() > tol {
            *value /= sigma;
        } else {
            *value = 0.0;
        }
    }
    let solution = vt.t().dot(&coeff);
    if !array_is_finite(&solution) {
        crate::bail_invalid_estim!("dense pseudoinverse solve produced non-finite values");
    }
    if out.len() != solution.len() {
        *out = Array1::zeros(solution.len());
    }
    out.assign(&solution);
    Ok(())
}

/// Least-squares `min_z ‖A z − b‖` for `A` of shape `(p, k)` and rhs `b`
/// (length `p`), returning `z` (length `k`) or `None` on numerical failure.
///
/// - Tall or square (`p ≥ k`): the rank-revealing col-pivoted QR (faer
///   `solve_lstsq`) — the exact prior behavior, byte-for-byte.
/// - Wide (`k > p`): the system is underdetermined. This arises on a DEGENERATE
///   active face where more constraint rows are active than the problem has
///   dimensions — e.g. a monotone coefficient cone plus many binding per-row
///   derivative guards. The minimum-norm solution `z = Aᵀ (A Aᵀ)⁺ b` is taken
///   via the SVD pseudoinverse of the square (possibly rank-deficient) Gram
///   `A Aᵀ`, matching what a wide-capable least-squares would return.
///
/// Faer's `solve_lstsq` asserts `nrows ≥ ncols`, so feeding it a wide matrix
/// panics — and here that panic would cross the Rust/Python FFI boundary,
/// violating the typed-error contract. Routing the wide case through this helper
/// keeps the failure typed: callers receive `None` and treat it as
/// "not certified" (conservative), never a process abort.
fn least_squares_min_norm_any_shape(a: &Array2<f64>, b: &Array1<f64>) -> Option<Array1<f64>> {
    let p = a.nrows();
    let k = a.ncols();
    if b.len() != p {
        return None;
    }
    if k == 0 {
        return Some(Array1::zeros(0));
    }
    if k <= p {
        let mut rhs = Array2::<f64>::zeros((p, 1));
        rhs.column_mut(0).assign(b);
        let a_view = FaerArrayView::new(a);
        let rhs_view = FaerArrayView::new(&rhs);
        let solved = a_view.as_ref().col_piv_qr().solve_lstsq(rhs_view.as_ref());
        let mut z = Array1::<f64>::zeros(k);
        for c in 0..k {
            let value = solved[(c, 0)];
            if !value.is_finite() {
                return None;
            }
            z[c] = value;
        }
        Some(z)
    } else {
        // Underdetermined: min-norm `z = Aᵀ (A Aᵀ)⁺ b`. `A Aᵀ` is `p × p`, so it
        // satisfies the square precondition of the SVD pseudoinverse solve, and
        // the pseudoinverse absorbs the rank deficiency of an over-complete face.
        let gram = a.dot(&a.t());
        let mut y = Array1::<f64>::zeros(p);
        solve_dense_system_via_pseudoinverse(&gram, b, &mut y).ok()?;
        let z = a.t().dot(&y);
        if z.iter().any(|value| !value.is_finite()) {
            return None;
        }
        Some(z)
    }
}

pub(crate) fn compute_constraint_kkt_diagnostics(
    beta: &Array1<f64>,
    gradient: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
) -> ConstraintKktDiagnostics {
    let m = constraints.a.nrows();
    let active_tolerance = ACTIVE_SET_PRIMAL_FEASIBILITY_TOL;

    // Measure feasibility in the *scaled* (geometric) coordinate system the
    // solver's tolerance is expressed in: normalize each inequality
    // `a_i·β ≥ b_i` by ‖a_i‖ so its slack becomes the signed Euclidean
    // distance from β to the constraint hyperplane. Without this, a row with a
    // large norm — e.g. a B-spline endpoint *derivative* clamp, whose rows
    // carry ‖a_i‖ ≫ 1 — reports a raw slack inflated by ‖a_i‖, so an iterate
    // that is feasible to the solver's scaled `ACTIVE_SET_PRIMAL_FEASIBILITY_TOL`
    // guarantee can still exceed a raw primal gate downstream and be spuriously
    // refused. Per-row normalization makes the diagnostic scale-invariant and
    // consistent with that contract. Dual/complementarity/stationarity are
    // invariant under this positive per-row rescaling (with λ̂_i = ‖a_i‖·λ_i:
    // Âᵀλ̂ = Aᵀλ and λ̂_i·ŝ_i = λ_i·s_i), so only primal feasibility and the
    // active-set threshold change meaning — both toward the geometric distance
    // the tolerance is meant to bound.
    let p = constraints.a.ncols();
    let mut a_scaled = constraints.a.clone();
    let mut b_scaled = constraints.b.clone();
    for i in 0..m {
        let n_i = constraints.a.row(i).dot(&constraints.a.row(i)).sqrt();
        if n_i > 0.0 {
            let inv = 1.0 / n_i;
            a_scaled.row_mut(i).mapv_inplace(|v| v * inv);
            b_scaled[i] *= inv;
        }
    }

    let mut slack = Array1::<f64>::zeros(m);
    let mut primal_feasibility: f64 = 0.0;
    for i in 0..m {
        let s_i = a_scaled.row(i).dot(beta) - b_scaled[i];
        slack[i] = s_i;
        primal_feasibility = primal_feasibility.max((-s_i).max(0.0));
    }

    let active_idx: Vec<usize> = (0..m).filter(|&i| slack[i] <= active_tolerance).collect();
    let mut lambda = Array1::<f64>::zeros(m);
    let mut working_set_rank_deficient = false;
    if !active_idx.is_empty() {
        let n_active = active_idx.len();
        let mut a_active = Array2::<f64>::zeros((n_active, p));
        for (r, &idx) in active_idx.iter().enumerate() {
            a_active.row_mut(r).assign(&a_scaled.row(idx));
        }
        if let Some((_, lambda_active)) =
            project_stationarity_residual_on_constraint_cone(gradient, &a_active)
        {
            for (r, &idx) in active_idx.iter().enumerate() {
                lambda[idx] = lambda_active[r];
            }
        }
        // Rank-deficiency detection on the (scaled) active rows. Per-row
        // positive scaling is rank-preserving, so this answers the same
        // question the inner solver's `CompressedActiveWorkingSet::
        // is_degenerate_face` does — `rank(A_active) < n_active`. For curvature
        // constraints the second-difference operator forces dependence
        // whenever more than `p` rows bind, and for monotonicity the
        // first-difference operator does so beyond a similar count. The
        // diagnostic exposes the flag so the outer validation gate can apply
        // the same `ACTIVE_SET_KKT_DEGENERATE_STATIONARITY_TOL` relaxation
        // the inner solver does, instead of refusing the iterate at strict
        // `ACTIVE_SET_KKT_STATIONARITY_TOL`.
        working_set_rank_deficient = if n_active > p {
            true
        } else if n_active > 1 {
            let groups: Vec<Vec<usize>> = (0..n_active).map(|i| vec![i]).collect();
            let b_dummy = Array1::<f64>::zeros(n_active);
            let (reduced_a, _, _, _) =
                rank_reduce_rows_pivoted_qr_with_dependence(a_active, b_dummy, groups);
            reduced_a.nrows() < n_active
        } else {
            false
        };
    }

    let mut dual_feasibility: f64 = 0.0;
    let mut complementarity: f64 = 0.0;
    for i in 0..m {
        dual_feasibility = dual_feasibility.max((-lambda[i]).max(0.0));
        complementarity = complementarity.max((lambda[i] * slack[i]).abs());
    }
    let stationarity = {
        let mut resid = gradient.to_owned();
        resid -= &a_scaled.t().dot(&lambda);
        resid.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()))
    };

    ConstraintKktDiagnostics {
        n_constraints: m,
        n_active: active_idx.len(),
        primal_feasibility,
        dual_feasibility,
        complementarity,
        stationarity,
        active_tolerance,
        working_set_rank_deficient,
        gradient_scale: gradient_inf_norm(gradient),
    }
}

/// Lawson–Hanson nonnegative least squares onto a finitely generated cone.
///
/// Solves `min_{λ ≥ 0} ‖rowsᵀ λ − target‖₂` for a row block `rows` (`m × p`,
/// original row units) and returns `(λ, projected)` with
/// `projected = target − rowsᵀ λ`. By the Moreau decomposition `rowsᵀ λ` is
/// the Euclidean projection of `target` onto the cone generated by the rows,
/// so `projected` is the projection onto that cone's polar.
///
/// This is the existence-form dual-feasibility certificate for degenerate
/// working faces: multipliers on a rank-deficient face are non-unique, and
/// any single reconstruction (KKT least-squares, per-group attribution) can
/// carry huge canceling ± components — reporting `dual ≫ 0` at a point where
/// a different `λ ≥ 0` closes stationarity exactly (#2298 survival
/// monotonicity faces, #979 CTN Khatri–Rao faces). NNLS answers the right
/// question: does ANY nonnegative multiplier close stationarity?
///
/// Rows are unit-normalized internally so pivot ordering and tolerances are
/// scale-invariant; the returned `λ` is in original row units. Zero rows
/// carry `λ = 0`. Classic LH terminates after finitely many passive-set
/// changes; a `3m + 30` outer guard bounds float pathologies and returns the
/// best iterate — callers treat an unclosed residual as "not certified", so
/// early return is conservative, never false-green.
pub(crate) fn nonnegative_cone_multipliers(
    rows: &Array2<f64>,
    target: &Array1<f64>,
) -> Option<(Array1<f64>, Array1<f64>)> {
    let p = target.len();
    let m = rows.nrows();
    if rows.ncols() != p {
        return None;
    }
    if m == 0 {
        return Some((Array1::zeros(0), target.clone()));
    }
    if target.iter().any(|v| !v.is_finite()) || rows.iter().any(|v| !v.is_finite()) {
        return None;
    }
    let mut norms = Array1::<f64>::zeros(m);
    let mut unit = Array2::<f64>::zeros((m, p));
    for i in 0..m {
        let norm = rows.row(i).dot(&rows.row(i)).sqrt();
        norms[i] = norm;
        if norm > 0.0 {
            unit.row_mut(i).assign(&(&rows.row(i) / norm));
        }
    }
    let target_inf = target.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
    if target_inf == 0.0 {
        return Some((Array1::zeros(m), target.clone()));
    }
    // Gradient tolerance in λ-space: with unit rows, `w_i = a_î·r` is bounded
    // by ‖r‖, so a relative band on the target scale is dimensionless.
    let tol_w = 1e-10 * target_inf;
    let lambda_floor = 1e-14 * target_inf;

    let mut lambda_unit = Array1::<f64>::zeros(m);
    let mut passive: Vec<usize> = Vec::new();
    let mut in_passive = vec![false; m];
    let mut residual = target.clone();
    // Rows whose trial coefficient collapsed to zero at the current residual;
    // re-eligible as soon as the residual moves. Prevents an add/drop loop on
    // exactly degenerate geometry.
    let mut banned = vec![false; m];

    let solve_passive = |passive: &[usize]| -> Option<Array1<f64>> {
        let k = passive.len();
        // `design` is `p × k` (each column is a unit active row). On a degenerate
        // over-complete face `k` can exceed `p` (more active rows than
        // dimensions); the min-norm least-squares helper handles that wide case
        // instead of panicking inside faer's tall-only `solve_lstsq`.
        let mut design = Array2::<f64>::zeros((p, k));
        for (col, &row) in passive.iter().enumerate() {
            design.column_mut(col).assign(&unit.row(row));
        }
        least_squares_min_norm_any_shape(&design, target)
    };

    let max_outer = 3 * m + 30;
    for _ in 0..max_outer {
        // Most-ascent candidate among non-passive, non-banned rows.
        let mut best: Option<(usize, f64)> = None;
        for i in 0..m {
            if in_passive[i] || banned[i] || norms[i] <= 0.0 {
                continue;
            }
            let w = unit.row(i).dot(&residual);
            if w > tol_w && best.map(|(_, bw)| w > bw).unwrap_or(true) {
                best = Some((i, w));
            }
        }
        let Some((entering, _)) = best else {
            break;
        };
        passive.push(entering);
        in_passive[entering] = true;

        let mut inner_ok = false;
        for _ in 0..(m + 2) {
            let Some(z) = solve_passive(&passive) else {
                return None;
            };
            let min_z = z.iter().copied().fold(f64::INFINITY, f64::min);
            if min_z > lambda_floor {
                for (pos, &row) in passive.iter().enumerate() {
                    lambda_unit[row] = z[pos];
                }
                inner_ok = true;
                break;
            }
            // Interpolate toward z until the first coefficient hits zero,
            // then drop every zeroed row from the passive set.
            let mut alpha = 1.0_f64;
            for (pos, &row) in passive.iter().enumerate() {
                if z[pos] <= lambda_floor {
                    let current = lambda_unit[row];
                    let denom = current - z[pos];
                    if denom > 0.0 {
                        alpha = alpha.min((current / denom).clamp(0.0, 1.0));
                    } else {
                        alpha = 0.0;
                    }
                }
            }
            for (pos, &row) in passive.iter().enumerate() {
                lambda_unit[row] += alpha * (z[pos] - lambda_unit[row]);
            }
            let mut retained = Vec::with_capacity(passive.len());
            for &row in &passive {
                if lambda_unit[row] > lambda_floor {
                    retained.push(row);
                } else {
                    lambda_unit[row] = 0.0;
                    in_passive[row] = false;
                    // The row failed at THIS residual; ban it until the
                    // residual moves so a degenerate add/drop pair cannot
                    // cycle within one outer round.
                    banned[row] = true;
                }
            }
            if retained.len() == passive.len() {
                // Nothing dropped despite a non-positive trial coefficient:
                // numerically stuck; stop refining this passive set.
                inner_ok = true;
                for (pos, &row) in passive.iter().enumerate() {
                    lambda_unit[row] = z[pos].max(0.0);
                }
                break;
            }
            passive = retained;
            if passive.is_empty() {
                break;
            }
        }
        // Refresh the residual; any movement re-enables banned rows.
        let mut fitted = Array1::<f64>::zeros(p);
        for &row in &passive {
            fitted.scaled_add(lambda_unit[row], &unit.row(row));
        }
        let new_residual = target - &fitted;
        let moved = new_residual
            .iter()
            .zip(residual.iter())
            .any(|(a, b)| (a - b).abs() > 1e-15 * target_inf);
        residual = new_residual;
        if moved {
            banned.iter_mut().for_each(|b| *b = false);
        } else if !inner_ok {
            break;
        }
    }

    let mut lambda = Array1::<f64>::zeros(m);
    for i in 0..m {
        if norms[i] > 0.0 {
            lambda[i] = lambda_unit[i] / norms[i];
        }
    }
    if !array_is_finite(&lambda) || !array_is_finite(&residual) {
        return None;
    }
    Some((lambda, residual))
}

pub fn project_stationarity_residual_on_constraint_cone(
    residual: &Array1<f64>,
    active_a: &Array2<f64>,
) -> Option<(Array1<f64>, Array1<f64>)> {
    let p = residual.len();
    if active_a.ncols() != p {
        return None;
    }
    if active_a.nrows() == 0 {
        return Some((residual.clone(), Array1::zeros(0)));
    }
    if let Some(result) = moreau_projection_via_primal_qp(residual, active_a) {
        return Some(result);
    }
    // The primal QP route can fail on degenerate faces (working-set cycling,
    // multiplier-reconstruction refusals). Projection onto a finitely
    // generated cone IS nonnegative least squares (Moreau), so the direct LH
    // solve is an exact fallback: `projected = residual − Aᵀλ*` with
    // `λ* = argmin_{λ≥0} ‖residual − Aᵀλ‖`. Reconstruction is exact by
    // construction here, so no cross-check gate is needed.
    nonnegative_cone_multipliers(active_a, residual).map(|(lambda, projected)| (projected, lambda))
}

fn moreau_projection_via_primal_qp(
    residual: &Array1<f64>,
    active_a: &Array2<f64>,
) -> Option<(Array1<f64>, Array1<f64>)> {
    let p = residual.len();

    let m = active_a.nrows();
    let constraints = LinearInequalityConstraints::new(active_a.clone(), Array1::<f64>::zeros(m))
        .ok()?
        .canonicalized()
        .ok()?;

    // Moreau projection onto the tangent cone is the strictly convex primal
    // QP
    //
    //   min_d  1/2 ||d + residual||^2    s.t. A d >= 0.
    //
    // The former implementation solved its dual with a second, nested
    // Lawson-Hanson active-set method. On the issue-979 CTN faces that nested
    // solver enumerated up to `3*m*m` passive sets, first with one dense SVD
    // and later with one fresh QR per pivot. Route the geometry through the
    // repository's single Bland-ordered, rank-compressed primal QP instead.
    // Its identity Hessian is strictly positive definite, so the returned face
    // and direction are unique and need no projected-gradient escape.
    let identity = Array2::<f64>::eye(p);
    let origin = Array1::<f64>::zeros(p);
    let mut tangent_direction = Array1::<f64>::zeros(p);
    let mut tangent_active = Vec::new();
    let max_iterations = (p + m + 8) * 4;
    solve_newton_direction_with_linear_constraints_impl(
        &identity,
        residual,
        &origin,
        &constraints,
        &mut tangent_direction,
        Some(&mut tangent_active),
        max_iterations,
        false,
    )
    .ok()?;
    if !array_is_finite(&tangent_direction) {
        return None;
    }
    let projected = -&tangent_direction;

    // Reconstruct the primal QP's nonnegative KKT multipliers once on its
    // canonical full-row-rank face: residual + d = A_active^T lambda. This is
    // a single rectangular least-squares solve, never normal equations and
    // never another active-set loop.
    let mut lambda_canonical = Array1::<f64>::zeros(m);
    if !tangent_active.is_empty() {
        let gathered = gather_linear_constraint_rows(&constraints, &tangent_active).ok()?;
        // `design` is `p × |tangent_active|`. The face is meant to be
        // full-row-rank (≤ p active rows), but a degenerate iterate can present
        // more active rows than dimensions; the min-norm helper handles that wide
        // case rather than panicking inside faer's tall-only `solve_lstsq`.
        let design = gathered.a.t().to_owned();
        let solved = least_squares_min_norm_any_shape(&design, &(residual + &tangent_direction))?;
        let scale = residual
            .iter()
            .fold(0.0_f64, |acc, &value| acc.max(value.abs()))
            .max(1.0);
        let tol = 100.0 * f64::EPSILON * (p.max(m) as f64) * scale;
        for (position, &row) in tangent_active.iter().enumerate() {
            let value = solved[position];
            if !value.is_finite() || value < -tol {
                return None;
            }
            lambda_canonical[row] = value.max(0.0);
        }
    }
    let reconstructed = residual - &constraints.a.t().dot(&lambda_canonical);
    let reconstruction_error = reconstructed
        .iter()
        .zip(projected.iter())
        .fold(0.0_f64, |acc, (&left, &right)| {
            acc.max((left - right).abs())
        });
    let scale = residual
        .iter()
        .fold(0.0_f64, |acc, &value| acc.max(value.abs()))
        .max(1.0);
    if reconstruction_error > 1e-8 * scale || !array_is_finite(&lambda_canonical) {
        return None;
    }

    // `constraints` has unit rows, but this public helper historically returns
    // multipliers in the caller's original row units. If a_i^canon = a_i/s_i,
    // then lambda_i^original = lambda_i^canon/s_i preserves
    // A_original^T lambda_original = A_canon^T lambda_canon.
    let mut lambda = Array1::<f64>::zeros(m);
    for row in 0..m {
        let norm = active_a.row(row).dot(&active_a.row(row)).sqrt();
        if norm > 0.0 {
            lambda[row] = lambda_canonical[row] / norm;
        }
    }
    Some((projected, lambda))
}

pub(crate) fn feasible_point_for_linear_constraints(
    constraints: &LinearInequalityConstraints,
    p: usize,
) -> Option<Array1<f64>> {
    if constraints.a.ncols() != p
        || constraints.a.nrows() == 0
        || constraints.b.len() != constraints.a.nrows()
    {
        return None;
    }
    // The zero-vector shortcut must compare `b` in GEOMETRIC (per-row-scaled)
    // units: on raw `b` alone, `1e-20·β ≥ 1e-20` — the same half-space as
    // `β ≥ 1` — would accept `β = 0`. A numerically-zero row is vacuous when
    // `b_i ≤ 0` and infeasible (no seed exists) when `b_i > 0`.
    let mut all_scaled_b_tiny = true;
    for i in 0..constraints.a.nrows() {
        let norm = constraints.a.row(i).dot(&constraints.a.row(i)).sqrt();
        if norm > 0.0 {
            if constraints.b[i].abs() > 1e-14 * norm {
                all_scaled_b_tiny = false;
            }
        } else if constraints.b[i] > 0.0 {
            return None;
        }
    }
    if all_scaled_b_tiny {
        return Some(Array1::zeros(p));
    }

    let gram = constraints.a.dot(&constraints.a.t());
    let (u_opt, singular, vt_opt) = gram.svd(true, true).ok()?;
    let (Some(u), Some(vt)) = (u_opt, vt_opt) else {
        return None;
    };
    let max_singular = singular.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
    // Rank tolerance relative to the LARGEST singular value only — an absolute
    // `max(σ_max, 1)` floor declares a uniformly small (but perfectly
    // well-conditioned) system rank-deficient purely because of its units.
    let tol = 100.0 * f64::EPSILON * constraints.a.nrows().max(1) as f64 * max_singular;
    let mut coeff = u.t().dot(&constraints.b);
    for (idx, value) in coeff.iter_mut().enumerate() {
        let sigma = singular[idx];
        if sigma.abs() > tol {
            *value /= sigma;
        } else {
            *value = 0.0;
        }
    }
    let dual = vt.t().dot(&coeff);
    let beta = constraints.a.t().dot(&dual);
    if beta.len() != p || beta.iter().any(|v| !v.is_finite()) {
        return None;
    }
    // Accept on per-row GEOMETRIC slack (raw slack over ‖a_i‖), the same
    // scale-invariant metric the active-set gates use.
    let feasible = (0..constraints.a.nrows()).all(|i| {
        let norm = constraints.a.row(i).dot(&constraints.a.row(i)).sqrt();
        if norm > 0.0 {
            (constraints.a.row(i).dot(&beta) - constraints.b[i]) / norm >= -1e-8
        } else {
            constraints.b[i] <= 0.0
        }
    });
    if feasible { Some(beta) } else { None }
}

/// Strictly-interior margin (in per-row geometric / scaled-slack units) required
/// of the projected cold-start seed produced by
/// [`project_point_strictly_into_feasible_cone`]. Each constraint row is shifted
/// to `a_iᵀβ ≥ b_i + ACTIVE_SET_INTERIOR_SEED_MARGIN·‖a_i‖` so that, scaled by
/// `‖a_i‖`, every row of the returned seed has slack `≥` this margin. The value
/// is far above the active-set activation threshold (`tol_active = 1e-10`) so the
/// initial working set the QP step solver builds from the seed is **empty** — no
/// row is mistaken for "on the boundary" — yet small enough that the seed stays a
/// negligible distance from the data-driven projection it is derived from.
const ACTIVE_SET_INTERIOR_SEED_MARGIN: f64 = 1e-6;

/// The strictly-interior cold-start margin (scaled-slack units) that
/// [`project_point_strictly_into_feasible_cone`] guarantees on its returned
/// seed. Exposed so the P-IRLS seed builder can decide, on the same scale,
/// whether the current seed is already strictly interior (and may be used as-is)
/// or sits on / outside the cone boundary (and must be projected).
#[inline]
pub(crate) fn interior_seed_margin() -> f64 {
    ACTIVE_SET_INTERIOR_SEED_MARGIN
}

/// Maximum nesting depth of the strictly-interior feasibility repair before the
/// solver stops re-projecting and surfaces an honest constraint-violation error.
///
/// [`project_point_strictly_into_feasible_cone`] and
/// [`solve_quadratic_with_linear_constraints`] are mutually recursive: the
/// quadratic solve's final feasibility contract (#1108) projects an infeasible
/// iterate back onto the cone, and that projection itself solves an inner
/// identity-Hessian QP whose *own* feasibility contract can project again. On a
/// well-conditioned cone the repair converges at depth 0–1 — each level shifts
/// every one-sided row strictly further inward. But on near-anti-parallel rows
/// (the clamped / anchored monotone time-warp constraints an interval-censored
/// survival fit emits, which are only *near* — not exactly — anti-parallel and so
/// slip past the zero-width equality lift below), the inward-shifted QP can keep
/// returning an infeasible candidate, so the `solve ↔ project` recursion never
/// bottoms out and exhausts the worker stack. A cone that cannot be certified
/// feasible within this many successive inward shifts is degenerate; the
/// projection then returns `None`, which the quadratic solve reports as
/// [`EstimationError::ParameterConstraintViolation`] rather than recursing.
const MAX_FEASIBILITY_REPAIR_DEPTH: u32 = 16;

thread_local! {
    /// Current nesting depth of the `solve ↔ project` feasibility-repair cycle on
    /// this thread. Every recursion path (the quadratic solve's repair step and
    /// the projected-gradient fallback alike) routes back through
    /// [`project_point_strictly_into_feasible_cone`], so bounding its re-entrancy
    /// bounds the whole cycle. Per-thread because each solve runs to completion on
    /// a single call stack; independent solves on other worker threads carry their
    /// own counter.
    static FEASIBILITY_REPAIR_DEPTH: Cell<u32> = const { Cell::new(0) };
}

/// RAII depth counter for the feasibility-repair recursion. [`enter`] increments
/// the per-thread depth and returns a guard whose `Drop` restores it on every
/// exit path — including the projection's many `return None` branches — so the
/// counter can never leak. It yields `None` once
/// [`MAX_FEASIBILITY_REPAIR_DEPTH`] is reached, so the caller bails out of the
/// recursion instead of descending another level.
///
/// [`enter`]: FeasibilityRepairGuard::enter
struct FeasibilityRepairGuard;

impl FeasibilityRepairGuard {
    fn enter() -> Option<Self> {
        FEASIBILITY_REPAIR_DEPTH.with(|depth| {
            let current = depth.get();
            if current >= MAX_FEASIBILITY_REPAIR_DEPTH {
                None
            } else {
                depth.set(current + 1);
                Some(Self)
            }
        })
    }
}

impl Drop for FeasibilityRepairGuard {
    fn drop(&mut self) {
        FEASIBILITY_REPAIR_DEPTH.with(|depth| depth.set(depth.get().saturating_sub(1)));
    }
}

/// Project `point` to a *strictly interior* feasible point of the polyhedron
/// `{β : A·β ≥ b}`: the solution of `min_β ½‖β − point‖²` subject to the
/// margin-shifted system `A·β ≥ b + δ·‖a_i‖`, with `δ =
/// ACTIVE_SET_INTERIOR_SEED_MARGIN`.
///
/// This is the principled feasible cold-start seed for a shape-constrained
/// (convex / concave / monotone) smooth. It is qualitatively different from
/// [`feasible_point_for_linear_constraints`], which returns the *minimum-norm*
/// feasible point — for a homogeneous cone (`b = 0`, as the second-difference
/// convexity / concavity constraints are) that minimum-norm point is the cone
/// **vertex** `β = 0` (a flat line) where every constraint row is tight. A
/// shape-constrained P-IRLS launched from that vertex hands the inner active-set
/// QP an all-rows-active working set (every row's slack is `0`), and the QP then
/// stalls on a degenerate, non-stationary face of the cone. The fit's success
/// then depends on whether a warm-start seed happens to drop it into the right
/// basin, so the same fit silently diverges (or aborts) between a cold and a
/// warm cache (#873).
///
/// Requiring a strictly-positive margin on every row makes the returned seed an
/// interior point: the QP step solver starts from an **empty** active set and
/// adds only the genuinely binding rows, converging to the certified constrained
/// stationary point regardless of cache state. The projection is the
/// identity-Hessian instance of [`solve_quadratic_with_linear_constraints`]
/// (`H = I`, `rhs = point` ⇒ minimizing `½‖β − point‖²`), so the interior seed is
/// also the *nearest* strictly-interior point to the supplied data-driven
/// `point` — it inherits whatever curvature `point` already carries. Returns
/// `None` if the constraints are malformed or the active-set QP cannot certify a
/// feasible solution, so callers can fall back.
pub fn project_point_strictly_into_feasible_cone(
    point: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
) -> Option<Array1<f64>> {
    // Bound the mutually-recursive `solve ↔ project` feasibility repair. Every
    // recursion path re-enters here, so a too-deep call returns `None` (a
    // degenerate cone the strictly-interior QP cannot certify) instead of
    // recursing until the worker stack overflows. The guard restores the
    // per-thread depth on every early return via its `Drop`.
    let repair_guard = FeasibilityRepairGuard::enter()?;
    let p = point.len();
    let m = constraints.a.nrows();
    if constraints.a.ncols() != p || m == 0 || constraints.b.len() != m {
        return None;
    }
    let norms: Vec<f64> = (0..m)
        .map(|i| constraints.a.row(i).dot(&constraints.a.row(i)).sqrt())
        .collect();

    // Classify rows. An *anti-parallel pair* with ~zero scaled feasible-slab
    // width is an EQUALITY `rᵀβ = t` encoded as `{rᵀβ ≥ t, −rᵀβ ≥ −t}` (the
    // canonical encoding emitted by a clamped / anchored boundary condition).
    // Representing an equality as two opposing inequalities makes the inequality
    // active-set QP CYCLE: it adds one side, the equality-split multiplier turns
    // the other negative, it removes it, and the working set repeats until cycle
    // detection aborts the solve — so the projection would fail and the caller
    // would fall back to the cone vertex, silently reintroducing the #873 seed
    // for the *combined* case (`shape=concave`/`convex` with `bc=clamped`). So we
    // lift such pairs out as genuine equalities, eliminate them through the null
    // space, and run the strictly-interior QP only on the one-sided rows. A pure
    // shape cone has no anti-parallel rows, so `equality_rows` is empty and this
    // reduces to the original single-QP path verbatim.
    const ANTIPARALLEL_COS_TOL: f64 = -1.0 + 1e-9;
    const EQUALITY_WIDTH_TOL: f64 = 1e-9;
    let mut is_equality_member = vec![false; m];
    let mut equality_rows: Vec<usize> = Vec::new();
    let mut margin = vec![ACTIVE_SET_INTERIOR_SEED_MARGIN; m];
    for i in 0..m {
        if norms[i] == 0.0 {
            margin[i] = 0.0;
            continue;
        }
        for j in (i + 1)..m {
            if norms[j] == 0.0 {
                continue;
            }
            let cos = constraints.a.row(i).dot(&constraints.a.row(j)) / (norms[i] * norms[j]);
            if cos > ANTIPARALLEL_COS_TOL {
                continue;
            }
            // Anti-parallel rows â and −â: row i is `âᵀβ ≥ b_i/‖a_i‖`, row j is
            // `âᵀβ ≤ −b_j/‖a_j‖`. Scaled feasible-slab width:
            let width = -constraints.b[j] / norms[j] - constraints.b[i] / norms[i];
            if width.abs() <= EQUALITY_WIDTH_TOL {
                // Zero width ⇒ equality. Record it once (row i's orientation) and
                // exclude both rows from the one-sided interior shift.
                if !is_equality_member[i] && !is_equality_member[j] {
                    equality_rows.push(i);
                }
                is_equality_member[i] = true;
                is_equality_member[j] = true;
            } else {
                // Genuine (wide) two-sided bound: cap each side's inward shift at
                // `w/3` so the shifted slab `s_i + s_j ≤ w` stays non-empty.
                let cap = (width / 3.0).max(0.0);
                margin[i] = margin[i].min(cap);
                margin[j] = margin[j].min(cap);
            }
        }
    }

    // One-sided rows (everything not lifted into an equality), shifted strictly
    // inward by `margin·‖a‖`.
    let ineq_rows: Vec<usize> = (0..m).filter(|&i| !is_equality_member[i]).collect();
    let mut a_ineq = Array2::<f64>::zeros((ineq_rows.len(), p));
    let mut b_ineq = Array1::<f64>::zeros(ineq_rows.len());
    for (r, &i) in ineq_rows.iter().enumerate() {
        a_ineq.row_mut(r).assign(&constraints.a.row(i));
        b_ineq[r] = constraints.b[i] + margin[i] * norms[i];
    }

    let beta = if equality_rows.is_empty() {
        // No equalities: the original single strictly-interior QP
        // (`min ½‖β − point‖²` s.t. the margin-shifted one-sided rows).
        let interior = LinearInequalityConstraints::new(a_ineq, b_ineq)
            .expect("shifted interior constraint shape invariant");
        let identity = Array2::<f64>::eye(p);
        solve_quadratic_with_linear_constraints(&identity, point, point, &interior, None)
            .ok()?
            .0
    } else {
        // Eliminate `E β = e` through its null space. From the thin SVD
        // `E = U Σ Vᵀ` (rank `r`): the row space is `span(v_0..v_{r-1})`, the
        // minimum-norm particular solution is `β_p = Σ_{i<r} (uᵢᵀe / σᵢ) vᵢ`, and
        // an orthonormal null basis `Z` (p × (p−r)) is the complement of the row
        // space (built by Gram-Schmidt of the standard axes — `p` is a single
        // smooth-term width, so this is cheap and exact). Writing `β = β_p + Z u`
        // and using `ZᵀZ = I`, the projection becomes the reduced strictly-
        // interior QP `min ½‖u − Zᵀ(point − β_p)‖²` s.t. `(A_ineq Z) u ≥ b_ineq −
        // A_ineq β_p`, whose rows carry no anti-parallel pair, so it can't cycle.
        let k = equality_rows.len();
        let mut e_mat = Array2::<f64>::zeros((k, p));
        let mut e_rhs = Array1::<f64>::zeros(k);
        for (r, &i) in equality_rows.iter().enumerate() {
            e_mat.row_mut(r).assign(&constraints.a.row(i));
            e_rhs[r] = constraints.b[i];
        }
        let (u_opt, sing, vt_opt) = e_mat.svd(true, true).ok()?;
        let (u_mat, vt) = (u_opt?, vt_opt?);
        let smax = sing.iter().fold(0.0_f64, |acc, &v| acc.max(v));
        let rank_tol = smax.max(1.0) * (k.max(p) as f64) * f64::EPSILON * 100.0;
        let rank = sing.iter().filter(|&&s| s > rank_tol).count();
        if rank == 0 || rank >= p {
            return None;
        }
        let mut beta_p = Array1::<f64>::zeros(p);
        for idx in 0..rank {
            let coeff = u_mat.column(idx).dot(&e_rhs) / sing[idx];
            beta_p.scaled_add(coeff, &vt.row(idx));
        }
        // Orthonormal null basis: Gram-Schmidt the standard axes against the row
        // space `vt[0..rank]` and the null vectors collected so far.
        let mut basis: Vec<Array1<f64>> = (0..rank).map(|i| vt.row(i).to_owned()).collect();
        let mut z = Array2::<f64>::zeros((p, p - rank));
        let mut collected = 0usize;
        for axis in 0..p {
            if collected == p - rank {
                break;
            }
            let mut v = Array1::<f64>::zeros(p);
            v[axis] = 1.0;
            for q in basis.iter() {
                let c = q.dot(&v);
                v.scaled_add(-c, q);
            }
            let nrm = v.dot(&v).sqrt();
            if nrm > 1e-8 {
                v /= nrm;
                z.column_mut(collected).assign(&v);
                basis.push(v);
                collected += 1;
            }
        }
        if collected != p - rank {
            return None;
        }
        let a_red = a_ineq.dot(&z);
        let b_red = &b_ineq - &a_ineq.dot(&beta_p);
        let u0 = z.t().dot(&(point - &beta_p));
        let reduced = LinearInequalityConstraints::new(a_red, b_red)
            .expect("reduced constraint shape invariant");
        let identity = Array2::<f64>::eye(z.ncols());
        let (u_sol, _active) =
            solve_quadratic_with_linear_constraints(&identity, &u0, &u0, &reduced, None).ok()?;
        &beta_p + &z.dot(&u_sol)
    };

    if beta.len() != p || beta.iter().any(|v| !v.is_finite()) {
        return None;
    }
    // Certify against the ORIGINAL constraints: every genuine one-sided row must
    // clear (most of) its requested margin so the QP step solver sees no spurious
    // active rows; equality-pair rows need only be feasible — they are
    // legitimately tight.
    const SEED_FEASIBILITY_TOL: f64 = 1e-9;
    for i in 0..m {
        let s = scaled_constraint_slack(&beta, constraints, i);
        let lower = if is_equality_member[i] {
            -SEED_FEASIBILITY_TOL
        } else {
            0.5 * margin[i] - SEED_FEASIBILITY_TOL
        };
        if s < lower {
            return None;
        }
    }
    // All mutually-recursive `solve ↔ project` calls are complete; release the
    // per-thread recursion-depth guard explicitly on the success path (early
    // returns above release it via `Drop`). Named + dropped (not `let _guard`)
    // to satisfy the underscore-binding ban without changing its lifetime.
    drop(repair_guard);
    Some(beta)
}

/// Worst primal-feasibility violation across all rows of `constraints`,
/// measured in the per-row-scaled (geometric) coordinate system documented on
/// [`ACTIVE_SET_PRIMAL_FEASIBILITY_TOL`]. A row's slack is divided by ‖a_i‖
/// so the returned value is the signed Euclidean distance from `beta` to the
/// constraint hyperplane, not the raw dot-product residual. This matches
/// [`compute_constraint_kkt_diagnostics`] and keeps the in-solver acceptance
/// gate, the downstream KKT report, and any post-fit feasibility check on a
/// single scale-invariant metric. A row with ‖a_i‖ = 38 (a typical B-spline
/// endpoint-derivative clamp at k = 12) has a 1e-6 raw violation that is only
/// 2.6e-8 in geometric units — accepting on raw alone is anisotropic in
/// input-space and breaks the published contract.
fn max_linear_constraint_violation(
    beta: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
) -> (f64, usize) {
    let mut worst = 0.0_f64;
    let mut worst_row = 0usize;
    for i in 0..constraints.a.nrows() {
        let slack = scaled_constraint_slack(beta, constraints, i);
        let viol = (-slack).max(0.0);
        if viol > worst {
            worst = viol;
            worst_row = i;
        }
    }
    (worst, worst_row)
}

/// Per-row signed scaled slack: `(a_i·beta - b_i) / ‖a_i‖`. A degenerate row
/// with `‖a_i‖ = 0` carries no direction, but it is NOT free of content: for
/// `b_i > 0` the row `0ᵀβ ≥ b_i` is unconditionally violated (−∞ slack), and
/// only for `b_i ≤ 0` is it vacuously satisfied (+∞ slack). Returning zero for
/// both let an impossible row report zero violation and pass every gate.
#[inline]
fn scaled_constraint_slack(
    beta: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
    i: usize,
) -> f64 {
    let norm = constraints.a.row(i).dot(&constraints.a.row(i)).sqrt();
    if norm > 0.0 {
        (constraints.a.row(i).dot(beta) - constraints.b[i]) / norm
    } else if constraints.b[i] > 0.0 {
        f64::NEG_INFINITY
    } else {
        f64::INFINITY
    }
}

pub(crate) fn solve_kkt_direction(
    hessian: &Array2<f64>,
    gradient: &Array1<f64>,
    active_a: &Array2<f64>,
    active_residual: Option<&Array1<f64>>,
) -> Result<(Array1<f64>, Array1<f64>), EstimationError> {
    let p = hessian.nrows();
    let m = active_a.nrows();
    if hessian.ncols() != p || gradient.len() != p || active_a.ncols() != p {
        crate::bail_invalid_estim!("KKT solve dimension mismatch");
    }
    if let Some(residual) = active_residual
        && residual.len() != m
    {
        crate::bail_invalid_estim!(
            "KKT active residual length mismatch: got {}, expected {}",
            residual.len(),
            m
        );
    }
    if m == 0 {
        let mut d = Array1::<f64>::zeros(p);
        solve_newton_direction_dense(hessian, gradient, &mut d)?;
        return Ok((d, Array1::zeros(0)));
    }
    let mut kkt = Array2::<f64>::zeros((p + m, p + m));
    kkt.slice_mut(s![0..p, 0..p]).assign(hessian);
    kkt.slice_mut(s![0..p, p..(p + m)]).assign(&active_a.t());
    kkt.slice_mut(s![p..(p + m), 0..p]).assign(active_a);

    let mut rhs = Array1::<f64>::zeros(p + m);
    for i in 0..p {
        rhs[i] = -gradient[i];
    }
    if let Some(residual) = active_residual {
        for i in 0..m {
            rhs[p + i] = residual[i];
        }
    }
    let rhs_target = rhs.clone();

    let kkt_view = FaerArrayView::new(&kkt);
    let factor = FaerLblt::new(kkt_view.as_ref(), Side::Lower);
    let mut rhs_col = array1_to_col_matmut(&mut rhs);
    factor.solve_in_place(rhs_col.as_mut());
    if !rhs.iter().all(|v| v.is_finite()) {
        solve_dense_system_via_pseudoinverse(&kkt, &rhs_target, &mut rhs)?;
    }
    let d = rhs.slice(s![0..p]).to_owned();
    let lambda = rhs.slice(s![p..(p + m)]).to_owned();
    Ok((d, lambda))
}

#[derive(Clone, Debug)]
pub(crate) struct CompressedActiveWorkingSet {
    pub(crate) constraints: LinearInequalityConstraints,
    /// Original active positions collapsed into each compressed (representative)
    /// row: `groups[g][0]` is the representative and the rest are its exactly-
    /// parallel (positively-aligned, same-constraint-up-to-scale) dependents. The
    /// whole group is released together when the representative's dual is negative.
    pub(crate) groups: Vec<Vec<usize>>,
    pub(crate) original_active_count: usize,
}

/// One dependent row of the WORKING SET expressed against its representative:
/// `a_dep ≈ coeff · a_rep`.
///
/// Recorded ONLY for exactly-parallel (positively-aligned scalar-multiple)
/// dependents; a general-position dependent is dropped from the working set with
/// NO entry and re-enters via the next feasibility scan (it never receives a
/// distributed/phantom multiplier).
///
/// `active_pos` is an ACTIVE-SET POSITION: an index into the caller's `active`
/// slice, so the original constraint id is `active[active_pos]` (see
/// [`compress_active_working_set`], which seeds `groups` with exactly these
/// positions). It is NOT a constraint-row id and NOT a coefficient index. The
/// reduced-face op reports its dependents in constraint-row space instead and
/// therefore uses its own [`ConstraintRowDependence`] — the two must not be
/// interchanged.
#[derive(Clone, Copy, Debug)]
pub struct ActiveRowDependence {
    pub active_pos: usize,
    pub coeff: f64,
}

/// One tight row of a REDUCED FACE expressed against its representative:
/// `a_dep ≈ coeff · a_rep`, with the dependent named in constraint-row space.
///
/// Same `(A)`-strict recording rule as [`ActiveRowDependence`], different index
/// space: `row` is the dependent's [`ConstraintRowId`] in the reduced set's own
/// row space. The representative is identified by the index of the owning
/// [`ReducedFace::dependence`] slot, which is aligned with
/// [`ReducedFace::representatives`].
#[derive(Clone, Copy, Debug)]
pub struct ConstraintRowDependence {
    pub row: ConstraintRowId,
    pub coeff: f64,
}

/// The result of reducing a tight active face to a minimal independent set — the
/// shared output of the `ConstraintSet` reduced-face op (Dense arm =
/// [`dense_reduced_face`]; KhatriRaoCone / BlockDiagonal arms produce the same
/// shape). Determinism: representatives are the lowest-flat-index row per
/// independent direction, ascending, with no float tie-break.
///
/// INDEX SPACE: every id here is a [`ConstraintRowId`] in the reduced set's own
/// constraint-row space (`0..nrows()`), addressing `values()` / `bound()` /
/// `row_norm()`. It is NOT a coefficient index; to reach β coordinates go
/// through [`gam_problem::ConstraintSet::row_column_support`].
#[derive(Clone, Debug)]
pub struct ReducedFace {
    /// Kept independent rows — the lowest-flat-index representative per direction,
    /// ascending. Flat id space is `0..nrows` (Dense) / `slot*n + obs` (cone) /
    /// the concatenation of the member row spaces (block-diagonal).
    pub representatives: Vec<ConstraintRowId>,
    /// Per-representative parallel-dependent map, index-aligned with
    /// `representatives`. `dependence[i]` lists the exactly-parallel dependents of
    /// `representatives[i]` (empty when it has none); general-position dependents
    /// are absent (dropped, re-enter on the next feasibility scan).
    pub dependence: Vec<Vec<ConstraintRowDependence>>,
    /// The full tight set that was reduced, ascending flat ids.
    pub tight_rows: Vec<ConstraintRowId>,
}

/// Reduce the tight active face of a Khatri–Rao monotonicity cone to its minimal
/// independent set — the `KhatriRaoCone` arm of the `ConstraintSet` reduced-face
/// op (gam#2306; the Dense arm is [`dense_reduced_face`]).
///
/// A cone row `(slot, i)` has normal `e_{k} ⊗ ψ_i` (`k = coupled_rows[slot]`),
/// so two normals' inner product is `δ_{slot,slot'}·(ψ_iᵀ ψ_{i'})`: cross-block
/// normals are ALWAYS orthogonal and never redundant, and redundancy occurs only
/// WITHIN a shape block among linearly dependent covariate rows `ψ_i`. The
/// reduction therefore decomposes into independent per-block Gram–Schmidt scans
/// over the block's tight `ψ_i` rows — never forming the `n·|coupled|` system.
///
/// Contract (matches the Dense arm): FULL rank cut (every dependent row is
/// dropped from `representatives`, parallel OR general-position); the dependence
/// map records `(A)`-strict — ONLY exactly-parallel dependents
/// (`|cos(ψ_dep, ψ_rep)| ≥ 1 − 1e-9`) get a [`ConstraintRowDependence`] against their
/// single representative (`coeff = ψ_depᵀψ_rep / ‖ψ_rep‖²`, so `a_dep ≈ coeff·a_rep`);
/// general-position drops get no entry and re-enter via the next feasibility
/// scan. Representatives are the lowest-flat-index row per direction (ascending
/// obs within a block), host-deterministic with no float tie-break. The rank
/// tolerance mirrors the Dense scan (`100·ε·max(n_tight, p_cov)·max‖ψ‖`), so the
/// two arms cut to the same numerical rank. Flat id is `slot*n + obs`, matching
/// [`KhatriRaoConeConstraints::values`].
pub fn khatri_rao_cone_reduced_face(
    cone: &KhatriRaoConeConstraints,
    beta: ndarray::ArrayView1<'_, f64>,
    membership_tol: f64,
) -> Result<ReducedFace, EstimationError> {
    let psi = cone.factor();
    let n = psi.nrows();
    let p_cov = psi.ncols();
    let coupled = cone.coupled_rows();
    let values = cone.values(beta).map_err(|error| {
        EstimationError::ParameterConstraintViolation(format!(
            "Khatri-Rao cone reduced-face values: {error}"
        ))
    })?;

    // ‖ψ_i‖ is shared across coupled slots (the same covariate factor).
    let row_norms: Vec<f64> = (0..n)
        .map(|i| {
            let row = psi.row(i);
            row.dot(&row).sqrt()
        })
        .collect();

    const RANK_ALPHA: f64 = 100.0;
    // Exactly-parallel threshold, matching the Dense scan's ±1e-9 cosine band.
    const PARALLEL_COS_TOL: f64 = 1.0 - 1e-9;

    let mut representatives: Vec<ConstraintRowId> = Vec::new();
    let mut dependence: Vec<Vec<ConstraintRowDependence>> = Vec::new();
    let mut tight_rows: Vec<ConstraintRowId> = Vec::new();

    for slot in 0..coupled.len() {
        // Tight obs in this block, ascending. A zero-norm ψ_i is a vacuous row
        // (0ᵀβ ≥ 0 always holds) — never a constraint direction, never a rep.
        let mut tight_obs: Vec<usize> = Vec::new();
        for i in 0..n {
            let norm_i = row_norms[i];
            if norm_i <= 0.0 {
                continue;
            }
            let scaled_slack = values[slot * n + i] / norm_i;
            if scaled_slack <= membership_tol {
                tight_rows.push(ConstraintRowId(slot * n + i));
                tight_obs.push(i);
            }
        }
        if tight_obs.is_empty() {
            continue;
        }

        let max_norm = tight_obs
            .iter()
            .map(|&i| row_norms[i])
            .fold(0.0_f64, f64::max);
        let rank_tol =
            RANK_ALPHA * f64::EPSILON * (tight_obs.len().max(p_cov).max(1) as f64) * max_norm;

        let mut ortho_basis: Vec<Array1<f64>> = Vec::new();
        // Kept representatives in THIS block: (obs, ψ_obs, index into representatives).
        let mut kept: Vec<(usize, Array1<f64>, usize)> = Vec::new();
        for &i in &tight_obs {
            let psi_i = psi.row(i).to_owned();
            let mut resid = psi_i.clone();
            for q in &ortho_basis {
                let proj = resid.dot(q);
                resid.scaled_add(-proj, q);
            }
            let resid_norm = resid.dot(&resid).sqrt();
            let flat = ConstraintRowId(slot * n + i);
            if resid_norm > rank_tol {
                ortho_basis.push(&resid / resid_norm);
                let out_idx = representatives.len();
                representatives.push(flat);
                dependence.push(Vec::new());
                kept.push((i, psi_i, out_idx));
            } else {
                // (A)-strict: record ONLY an exactly-parallel single-representative
                // dependence; general-position drops carry no multiplier.
                let mut best_abs_cos = 0.0_f64;
                let mut best: Option<(usize, f64)> = None;
                for (rep_obs, rep_psi, rep_out_idx) in &kept {
                    let rep_norm = row_norms[*rep_obs];
                    let dot = psi_i.dot(rep_psi);
                    let cos = if rep_norm > 0.0 {
                        dot / (row_norms[i] * rep_norm)
                    } else {
                        0.0
                    };
                    if cos.abs() > best_abs_cos {
                        best_abs_cos = cos.abs();
                        best = Some((*rep_out_idx, dot / (rep_norm * rep_norm)));
                    }
                }
                if best_abs_cos >= PARALLEL_COS_TOL {
                    if let Some((out_idx, coeff)) = best {
                        dependence[out_idx].push(ConstraintRowDependence {
                            row: flat,
                            coeff,
                        });
                    }
                }
            }
        }
    }

    Ok(ReducedFace {
        representatives,
        dependence,
        tight_rows,
    })
}

/// Dense arm of the reduced-face op: reduce the tight rows of an explicit
/// `A x ≥ b` set at `beta` to a minimal independent set. Mirrors
/// [`khatri_rao_cone_reduced_face`] exactly — ascending-index greedy MGS,
/// `RANK_ALPHA·ε·max(n_tight,p)·max‖a‖` tolerance, (A)-strict parallel-only
/// dependence (|cos| ≥ 1−1e-9, `coeff = a_depᵀa_rep/‖a_rep‖²`, `row` = the
/// dependent row's flat id) — so both carriers produce the same `ReducedFace`
/// contract. Flat id = the constraint row index. A zero-norm row is vacuous
/// (never a direction, never a representative).
pub fn dense_reduced_face(
    lin: &LinearInequalityConstraints,
    beta: ndarray::ArrayView1<'_, f64>,
    membership_tol: f64,
) -> Result<ReducedFace, EstimationError> {
    let a = &lin.a;
    let b = &lin.b;
    let n = a.nrows();
    let p = a.ncols();

    let row_norms: Vec<f64> = (0..n)
        .map(|i| {
            let row = a.row(i);
            row.dot(&row).sqrt()
        })
        .collect();

    const RANK_ALPHA: f64 = 100.0;
    const PARALLEL_COS_TOL: f64 = 1.0 - 1e-9;

    // The scan runs in raw row indices (they address `a` / `row_norms`); the
    // ids are wrapped into constraint-row space once, at the return boundary.
    let mut tight: Vec<usize> = Vec::new();
    for i in 0..n {
        let norm_i = row_norms[i];
        if norm_i <= 0.0 {
            continue;
        }
        let scaled_slack = (a.row(i).dot(&beta) - b[i]) / norm_i;
        if scaled_slack <= membership_tol {
            tight.push(i);
        }
    }

    let mut representatives: Vec<ConstraintRowId> = Vec::new();
    let mut dependence: Vec<Vec<ConstraintRowDependence>> = Vec::new();
    if tight.is_empty() {
        return Ok(ReducedFace {
            representatives,
            dependence,
            tight_rows: Vec::new(),
        });
    }

    let max_norm = tight
        .iter()
        .map(|&i| row_norms[i])
        .fold(0.0_f64, f64::max);
    let rank_tol = RANK_ALPHA * f64::EPSILON * (tight.len().max(p).max(1) as f64) * max_norm;

    let mut ortho_basis: Vec<Array1<f64>> = Vec::new();
    // Kept representatives: (row, a_row, index into `representatives`).
    let mut kept: Vec<(usize, Array1<f64>, usize)> = Vec::new();
    for &i in &tight {
        let a_i = a.row(i).to_owned();
        let mut resid = a_i.clone();
        for q in &ortho_basis {
            let proj = resid.dot(q);
            resid.scaled_add(-proj, q);
        }
        let resid_norm = resid.dot(&resid).sqrt();
        if resid_norm > rank_tol {
            ortho_basis.push(&resid / resid_norm);
            let out_idx = representatives.len();
            representatives.push(ConstraintRowId(i));
            dependence.push(Vec::new());
            kept.push((i, a_i, out_idx));
        } else {
            // (A)-strict: record ONLY an exactly-parallel single-representative
            // dependence; a general-position drop carries no multiplier and
            // re-enters via the next feasibility scan.
            let mut best_abs_cos = 0.0_f64;
            let mut best: Option<(usize, f64)> = None;
            for (rep_row, rep_a, rep_out_idx) in &kept {
                let rep_norm = row_norms[*rep_row];
                let dot = a_i.dot(rep_a);
                let cos = if rep_norm > 0.0 {
                    dot / (row_norms[i] * rep_norm)
                } else {
                    0.0
                };
                if cos.abs() > best_abs_cos {
                    best_abs_cos = cos.abs();
                    best = Some((*rep_out_idx, dot / (rep_norm * rep_norm)));
                }
            }
            if best_abs_cos >= PARALLEL_COS_TOL {
                if let Some((out_idx, coeff)) = best {
                    dependence[out_idx].push(ConstraintRowDependence {
                        row: ConstraintRowId(i),
                        coeff,
                    });
                }
            }
        }
    }

    Ok(ReducedFace {
        representatives,
        dependence,
        tight_rows: tight.into_iter().map(ConstraintRowId).collect(),
    })
}

/// Lift a MEMBER's constraint-row id into the JOINT block-diagonal row space.
///
/// Derivation: `ConstraintSet::BlockDiagonal` stacks its members' constraint
/// ROWS in block order — `ConstraintSet::values` writes member `m`'s values into
/// `out[off .. off + m.nrows()]`, and `bound` / `row_norm` decode a joint row by
/// walking the same running `nrows()` sum (`block_for_row`). So the joint id of
/// member row `local` is `local + Σ_{earlier m} m.nrows()`, which is what
/// `row_offset` accumulates.
///
/// The offset is deliberately NOT `col_start`. That is the COEFFICIENT offset,
/// and it advances by `ncols()`. Using one for the other is only invisible while
/// every member is square (`nrows() == ncols()`); the moment a member constrains
/// fewer rows than it has coefficients, the two sequences diverge and the ids
/// silently name the wrong block. To go from these ids to β coordinates, use
/// `ConstraintSet::row_column_support` — never arithmetic on the id.
#[inline]
fn lift_member_row(local: ConstraintRowId, row_offset: usize) -> ConstraintRowId {
    ConstraintRowId(local.index() + row_offset)
}

/// The shared tight-face reduction op over the `ConstraintSet` carrier union.
/// An extension trait (not an inherent method) so the numeric reduction
/// stays in gam-solve where the solvers consume it, keeping `gam-problem` a pure
/// data crate. All three arms produce the same `ReducedFace` contract.
pub trait ConstraintSetReducedFace {
    fn reduced_face(
        &self,
        beta: ndarray::ArrayView1<'_, f64>,
        membership_tol: f64,
    ) -> Result<ReducedFace, EstimationError>;
}

impl ConstraintSetReducedFace for ConstraintSet {
    fn reduced_face(
        &self,
        beta: ndarray::ArrayView1<'_, f64>,
        membership_tol: f64,
    ) -> Result<ReducedFace, EstimationError> {
        match self {
            ConstraintSet::Dense(lin) => dense_reduced_face(lin, beta, membership_tol),
            ConstraintSet::KhatriRaoCone(cone) => {
                khatri_rao_cone_reduced_face(cone, beta, membership_tol)
            }
            ConstraintSet::BlockDiagonal { blocks, .. } => {
                // Compose per inner block. TWO independent offsets are in play and
                // they are NOT interchangeable:
                //   * `block.col_start` slices β — COEFFICIENT space, advancing by
                //     each member's `ncols()`;
                //   * `row_offset` lifts the returned ids — CONSTRAINT-ROW space,
                //     advancing by each member's `nrows()`.
                // They coincide only when every member is a square carrier, which
                // is why a mixed block (a constrained sub-basis alongside
                // unconstrained intercept/covariate columns, `nrows() < ncols()`)
                // is the case that separates them. See `lift_member_row`.
                let mut representatives: Vec<ConstraintRowId> = Vec::new();
                let mut dependence: Vec<Vec<ConstraintRowDependence>> = Vec::new();
                let mut tight_rows: Vec<ConstraintRowId> = Vec::new();
                let mut row_offset = 0usize;
                for block in blocks {
                    let start = block.col_start;
                    let end = start + block.set.ncols();
                    let beta_block = beta.slice(ndarray::s![start..end]);
                    let sub = block.set.reduced_face(beta_block, membership_tol)?;
                    for r in sub.representatives {
                        representatives.push(lift_member_row(r, row_offset));
                    }
                    for deps in sub.dependence {
                        dependence.push(
                            deps.into_iter()
                                .map(|d| ConstraintRowDependence {
                                    row: lift_member_row(d.row, row_offset),
                                    coeff: d.coeff,
                                })
                                .collect(),
                        );
                    }
                    for t in sub.tight_rows {
                        tight_rows.push(lift_member_row(t, row_offset));
                    }
                    row_offset += block.set.nrows();
                }
                Ok(ReducedFace {
                    representatives,
                    dependence,
                    tight_rows,
                })
            }
        }
    }
}

impl CompressedActiveWorkingSet {
    fn is_degenerate_face(&self) -> bool {
        self.constraints.a.nrows() < self.original_active_count
            || self.groups.iter().any(|group| group.len() > 1)
    }

    /// The lowest-original-index representative direction whose compressed dual is
    /// negative beyond `tol_dual`, returned as the FULL set of original active
    /// positions collapsed into it (the representative plus its exactly-parallel
    /// dependents — the same constraint up to positive scale).
    ///
    /// Adjudicating the representative and releasing the WHOLE direction replaces
    /// the former per-original-row reconstruction, which divided the direction's
    /// dual by each dependent's `coeff` to synthesize a per-row multiplier and
    /// released ONE row at a time — a phantom ±1/ε dual on a redundant tight row
    /// (#2298/#979/#2132) and a churn source: releasing one row of an exactly-
    /// parallel group leaves the others pinning the same direction, so the working
    /// set oscillates. A negative representative dual means the aggregate pull along
    /// that independent direction is wrong-signed, so the whole group is released
    /// together; general-position dependents were never grouped (they carry no
    /// dependence entry and re-enter via the next feasibility scan).
    ///
    /// `active` maps `active_pos → original constraint id` so the choice is the
    /// deterministic lowest-original-index direction. `None` ⇒ every representative
    /// dual is non-negative: a KKT point on the reduced face.
    fn negative_representative_group(
        &self,
        lambda_system: &Array1<f64>,
        tol_dual: f64,
        active: &[usize],
    ) -> Option<Vec<usize>> {
        self.groups
            .iter()
            .enumerate()
            .filter(|&(group_pos, _)| {
                // lambda_true = -lambda_system[group_pos]; release iff < -tol_dual.
                lambda_system
                    .get(group_pos)
                    .is_some_and(|&value| -value < -tol_dual)
            })
            .min_by_key(|&(_, group)| {
                let first = group.first().copied().unwrap_or(usize::MAX);
                (active.get(first).copied().unwrap_or(usize::MAX), first)
            })
            .map(|(_, group)| group.clone())
    }

    /// True iff the active position `pos` is ENFORCED by this compressed face —
    /// it is a representative or an exactly-parallel dependent of one, so its
    /// half-space is carried by the enforced equality system. A general-position
    /// active row that was rank-reduced out of the face belongs to no group and
    /// is therefore NOT enforced (its violation cannot be closed by the current
    /// KKT solve, and it cannot be re-added because it is already active).
    fn position_enforced(&self, pos: usize) -> bool {
        self.groups.iter().any(|group| group.contains(&pos))
    }

    /// Over-complete-face adjudication (#2378). `violated` is the normal (in any
    /// scaling) of a constraint row that is ACTIVE yet was rank-reduced out of
    /// the enforced representative face: it is linearly dependent on the
    /// representatives, so the ordinary add/release transitions dead-end — it
    /// can be neither re-added (already active) nor released (not a
    /// representative). An over-complete face is inconsistent as EQUALITIES, so
    /// the method must adjudicate it by an active-set EXCHANGE rather than
    /// silently truncate it: release the representative the violated row is most
    /// positively aligned with, freeing that dependent direction so the violated
    /// row becomes an independent representative and binds on the next
    /// iteration. Returns the FULL representative group (representative +
    /// exactly-parallel dependents) to release, or `None` when no positively
    /// aligned representative exists (the caller then defers to the exit gate).
    /// Deterministic lowest-original-index tie-break.
    fn over_complete_release_group(
        &self,
        violated: ndarray::ArrayView1<'_, f64>,
        active: &[usize],
    ) -> Option<Vec<usize>> {
        let v_norm = violated.dot(&violated).sqrt();
        if !(v_norm > 0.0) {
            return None;
        }
        const COS_TIE_TOL: f64 = 1e-12;
        let mut best: Option<(f64, (usize, usize), usize)> = None;
        for (group_pos, group) in self.groups.iter().enumerate() {
            let rep = self.constraints.a.row(group_pos);
            let rep_norm = rep.dot(&rep).sqrt();
            if !(rep_norm > 0.0) {
                continue;
            }
            let cos = rep.dot(&violated) / (rep_norm * v_norm);
            if cos <= 0.0 {
                continue;
            }
            let first = group.first().copied().unwrap_or(usize::MAX);
            let key = (active.get(first).copied().unwrap_or(usize::MAX), first);
            let take = match &best {
                None => true,
                Some((best_cos, best_key, _)) => {
                    cos > best_cos + COS_TIE_TOL
                        || ((cos - best_cos).abs() <= COS_TIE_TOL && key < *best_key)
                }
            };
            if take {
                best = Some((cos, key, group_pos));
            }
        }
        best.map(|(_, _, group_pos)| self.groups[group_pos].clone())
    }
}

pub(crate) fn compress_active_working_set(
    x: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
    active: &[usize],
) -> Result<CompressedActiveWorkingSet, EstimationError> {
    let p = constraints.a.ncols();
    if x.len() != p {
        crate::bail_invalid_estim!("active working-set compression dimension mismatch");
    }

    let mut a_out = Array2::<f64>::zeros((active.len(), p));
    let mut b_out = Array1::<f64>::zeros(active.len());
    let mut groups_out: Vec<Vec<usize>> = Vec::with_capacity(active.len());
    for (pos, &idx) in active.iter().enumerate() {
        if idx >= constraints.a.nrows() {
            crate::bail_invalid_estim!(
                "active working-set index {} out of bounds for {} constraints",
                idx,
                constraints.a.nrows()
            );
        }
        a_out.row_mut(pos).assign(&constraints.a.row(idx));
        b_out[pos] = constraints.b[idx];
        groups_out.push(vec![pos]);
    }

    // The parallel-dependent map (4th return) is in ACTIVE-SET-POSITION space,
    // not the constraint-row space of the `ReducedFace` op; the working-set
    // release adjudicates whole representative groups, so it is not stored here.
    let (a_out, b_out, groups_out, _) =
        rank_reduce_rows_pivoted_qr_with_dependence(a_out, b_out, groups_out);

    Ok(CompressedActiveWorkingSet {
        constraints: LinearInequalityConstraints::new(a_out, b_out)
            .expect("compressed active constraint shape invariant"),
        groups: groups_out,
        original_active_count: active.len(),
    })
}

fn identity_multiplier_dependence(groups: &[Vec<usize>]) -> Vec<Vec<ActiveRowDependence>> {
    groups
        .iter()
        .map(|group| {
            group
                .iter()
                .copied()
                .map(|active_pos| ActiveRowDependence {
                    active_pos,
                    coeff: 1.0,
                })
                .collect()
        })
        .collect()
}

pub fn rank_reduce_rows_pivoted_qr_with_dependence(
    a: Array2<f64>,
    b: Array1<f64>,
    groups: Vec<Vec<usize>>,
) -> (
    Array2<f64>,
    Array1<f64>,
    Vec<Vec<usize>>,
    Vec<Vec<ActiveRowDependence>>,
) {
    let k = a.nrows();
    let p = a.ncols();
    if k <= 1 {
        let multiplier_dependence = identity_multiplier_dependence(&groups);
        return (a, b, groups, multiplier_dependence);
    }

    // DETERMINISTIC, host-independent representative selection. The former faer
    // `col_piv_qr` pivots by largest column norm; on an equal-norm tie
    // (near-parallel / identical active rows — the degenerate-face case) faer's
    // internal tie-break can differ across CPU arch / SIMD width / library
    // version, which would record a host-dependent active face and re-introduce
    // the nondeterministic cross-host certification the face feeds. Instead do a
    // greedy ASCENDING-original-index independence scan: iterate rows in index
    // order and keep row r iff its residual after projecting onto the orthonormal
    // span of the already-kept rows exceeds the rank tolerance; otherwise record
    // it dependent. This yields the lowest-index representative per independent
    // direction with no float-comparison tie-break.
    //
    // Rank tolerance is relative to the largest row norm — the same |R00| scale
    // (= largest column norm of Aᵀ = largest row norm of A) the pivoted QR used —
    // so the accepted COUNT matches the prior numerical rank; only WHICH
    // representative is chosen among tied near-parallel rows changes, and it
    // changes deterministically. The scale carries NO absolute floor, preserving
    // the unit-robustness of the prior tolerance (a perfectly independent system
    // in tiny units, e.g. A = 1e-20·I, keeps full rank rather than being dropped).
    const RANK_ALPHA: f64 = 100.0;
    let max_row_norm = (0..k)
        .map(|r| {
            let row = a.row(r);
            row.dot(&row).sqrt()
        })
        .fold(0.0_f64, f64::max);
    let tol = RANK_ALPHA * f64::EPSILON * (k.max(p).max(1) as f64) * max_row_norm;

    let mut ortho_basis: Vec<Array1<f64>> = Vec::new();
    let mut kept_orig: Vec<usize> = Vec::new();
    let mut dropped_orig: Vec<usize> = Vec::new();
    for r in 0..k {
        let mut resid = a.row(r).to_owned();
        for q in &ortho_basis {
            let proj = resid.dot(q);
            resid.scaled_add(-proj, q);
        }
        let resid_norm = resid.dot(&resid).sqrt();
        if resid_norm > tol {
            kept_orig.push(r);
            ortho_basis.push(&resid / resid_norm);
        } else {
            dropped_orig.push(r);
        }
    }
    let rank = kept_orig.len();
    if rank >= k {
        let multiplier_dependence = identity_multiplier_dependence(&groups);
        return (a, b, groups, multiplier_dependence);
    }
    if rank == 0 {
        log::debug!(
            "rank-reduced active constraints from {} to 0 rows (all active rows numerically zero)",
            k
        );
        return (
            Array2::<f64>::zeros((0, p)),
            Array1::<f64>::zeros(0),
            Vec::new(),
            Vec::new(),
        );
    }

    let mut orig_to_out = std::collections::HashMap::with_capacity(rank);
    let mut a_out = Array2::<f64>::zeros((rank, p));
    let mut b_out = Array1::<f64>::zeros(rank);
    let mut groups_out: Vec<Vec<usize>> = Vec::with_capacity(rank);
    let mut multiplier_dependence: Vec<Vec<ActiveRowDependence>> = Vec::with_capacity(rank);
    for (out_idx, &orig_idx) in kept_orig.iter().enumerate() {
        a_out.row_mut(out_idx).assign(&a.row(orig_idx));
        b_out[out_idx] = b[orig_idx];
        groups_out.push(groups[orig_idx].clone());
        multiplier_dependence.push(
            groups[orig_idx]
                .iter()
                .copied()
                .map(|active_pos| ActiveRowDependence {
                    active_pos,
                    coeff: 1.0,
                })
                .collect(),
        );
        orig_to_out.insert(orig_idx, out_idx);
    }

    // (A)-strict merge, matching the shared `dense_reduced_face` /
    // `khatri_rao_cone_reduced_face` `ReducedFace` contract. A dropped row joins
    // a representative's group — and receives a distributed multiplier — ONLY
    // when it is exactly PARALLEL to that representative (the same half-space up
    // to positive scale). A GENERAL-POSITION dependent — dependent only because
    // more normals bind than the face dimension (e.g. three normals inside a 2-D
    // coupled block) — is dropped outright with NO group entry and NO
    // multiplier: it re-enters the working set via the next feasibility scan and
    // is never conflated with a different half-space's dual (#979). The former
    // `best_positive_align` merge folded such a row into whichever kept row it
    // was most positively aligned with, silently truncating a general-position
    // active row out of the enforced face and pinning the wrong vertex (#2378).
    const PARALLEL_COS_TOL: f64 = 1.0 - 1e-9;
    for &dropped_idx in &dropped_orig {
        let dropped_row = a.row(dropped_idx);
        let dropped_norm = dropped_row.dot(&dropped_row).sqrt();
        let mut best_abs_cos = 0.0_f64;
        let mut best_target: Option<(usize, f64)> = None;
        for &kept_idx in &kept_orig {
            let kept_row = a.row(kept_idx);
            let kept_norm = kept_row.dot(&kept_row).sqrt();
            let dot = kept_row.dot(&dropped_row);
            let cos = if kept_norm > 0.0 && dropped_norm > 0.0 {
                dot / (kept_norm * dropped_norm)
            } else {
                0.0
            };
            let coeff = if kept_norm > 0.0 {
                dot / (kept_norm * kept_norm)
            } else {
                0.0
            };
            if cos.abs() > best_abs_cos {
                best_abs_cos = cos.abs();
                best_target = Some((kept_idx, coeff));
            }
        }
        // Only an exactly-parallel dependent is recorded; a general-position
        // drop carries no phantom distributed dual. The group (whose whole-set
        // release the working-set loop drives) additionally requires POSITIVE
        // parallelism — same constraint up to positive scale — so an opposing
        // (anti-parallel) tight row is never released together with it.
        if best_abs_cos >= PARALLEL_COS_TOL {
            if let Some((target, coeff)) = best_target {
                let &out_idx = orig_to_out
                    .get(&target)
                    .expect("merge target must be a kept row");
                for &active_pos in &groups[dropped_idx] {
                    multiplier_dependence[out_idx].push(ActiveRowDependence { active_pos, coeff });
                }
                if coeff > 0.0 {
                    groups_out[out_idx].extend_from_slice(&groups[dropped_idx]);
                }
            }
        }
    }

    for group in &mut groups_out {
        group.sort_unstable();
        group.dedup();
    }
    for dependencies in &mut multiplier_dependence {
        dependencies.sort_unstable_by_key(|dependency| dependency.active_pos);
        dependencies.dedup_by_key(|dependency| dependency.active_pos);
    }

    let mut row_order: Vec<usize> = (0..groups_out.len()).collect();
    row_order.sort_by_key(|&idx| groups_out[idx].first().copied().unwrap_or(usize::MAX));
    if row_order.iter().enumerate().any(|(idx, &orig)| idx != orig) {
        let mut a_sorted = Array2::<f64>::zeros((rank, p));
        let mut b_sorted = Array1::<f64>::zeros(rank);
        let mut groups_sorted = Vec::with_capacity(rank);
        let mut dependence_sorted = Vec::with_capacity(rank);
        for (out_idx, orig_idx) in row_order.into_iter().enumerate() {
            a_sorted.row_mut(out_idx).assign(&a_out.row(orig_idx));
            b_sorted[out_idx] = b_out[orig_idx];
            groups_sorted.push(groups_out[orig_idx].clone());
            dependence_sorted.push(multiplier_dependence[orig_idx].clone());
        }
        a_out = a_sorted;
        b_out = b_sorted;
        groups_out = groups_sorted;
        multiplier_dependence = dependence_sorted;
    }

    if rank < k {
        log::debug!(
            "rank-reduced active constraints from {} to {} rows (rank deficiency {})",
            k,
            rank,
            k - rank
        );
    }

    (a_out, b_out, groups_out, multiplier_dependence)
}

pub(crate) fn working_set_kkt_diagnostics_from_multipliers(
    x: &Array1<f64>,
    gradient: &Array1<f64>,
    working_constraints: &LinearInequalityConstraints,
    lambda_active_true: &Array1<f64>,
    n_total_constraints: usize,
) -> Result<ConstraintKktDiagnostics, EstimationError> {
    let p = working_constraints.a.ncols();
    if x.len() != p || gradient.len() != p {
        crate::bail_invalid_estim!("working-set KKT diagnostic dimension mismatch");
    }
    if lambda_active_true.len() != working_constraints.a.nrows() {
        crate::bail_invalid_estim!(
            "working-set KKT multiplier length mismatch: got {}, expected {}",
            lambda_active_true.len(),
            working_constraints.a.nrows()
        );
    }
    // Primal feasibility and complementarity are measured in the per-row-scaled
    // (geometric) coordinate system the public solver contract is expressed in
    // (see [`ACTIVE_SET_PRIMAL_FEASIBILITY_TOL`] and
    // [`compute_constraint_kkt_diagnostics`]). Without scaling, a row with
    // ‖a_i‖ ≫ 1 — e.g. a B-spline endpoint-derivative clamp — reports a raw
    // slack inflated by ‖a_i‖, and the in-solver acceptance gate
    // (`worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL`) becomes anisotropic across
    // rows. Complementarity is the SCALED product `λ̂_i · ŝ_i` with
    // `λ̂_i = ‖a_i‖·λ_i`; that's invariant under the same per-row rescaling, so
    // its semantics are unchanged while the units match the primal column.
    let m = working_constraints.a.nrows();
    let mut slack = Array1::<f64>::zeros(m);
    let mut primal_feasibility: f64 = 0.0;
    for i in 0..m {
        let s_i = scaled_constraint_slack(x, working_constraints, i);
        slack[i] = s_i;
        primal_feasibility = primal_feasibility.max((-s_i).max(0.0));
    }

    let lambda = lambda_active_true.to_owned();

    let mut dual_feasibility: f64 = 0.0;
    let mut complementarity: f64 = 0.0;
    for i in 0..m {
        dual_feasibility = dual_feasibility.max((-lambda[i]).max(0.0));
        // Scale-invariant complementarity `λ̂_i · ŝ_i` with `λ̂_i = ‖a_i‖·λ_i`
        // and `ŝ_i` the already-scaled slack: this product equals the raw
        // `λ_i · (a_iᵀx − b_i)`, invariant under per-row rescaling — matching the
        // documented contract above (`λ̂_i = ‖a_i‖·λ_i`). `lambda_active_true`
        // here is the RAW multiplier, so without the `‖a_i‖` factor this would
        // understate complementarity by `1/‖a_i‖` on high-norm rows (e.g. a
        // B-spline endpoint-derivative clamp, ‖a‖ ≈ 38).
        let norm_i = working_constraints
            .a
            .row(i)
            .dot(&working_constraints.a.row(i))
            .sqrt();
        complementarity = complementarity.max((norm_i * lambda[i] * slack[i]).abs());
    }
    let stationarity = {
        let mut resid = gradient.to_owned();
        resid -= &working_constraints.a.t().dot(&lambda);
        resid.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()))
    };

    Ok(ConstraintKktDiagnostics {
        n_constraints: n_total_constraints,
        n_active: m,
        primal_feasibility,
        dual_feasibility,
        complementarity,
        stationarity,
        active_tolerance: ACTIVE_SET_PRIMAL_FEASIBILITY_TOL,
        // `working_constraints` is the already-rank-reduced compressed
        // working set, so by construction `rank(working_constraints.a) ==
        // n_active`. Whether the *original* (uncompressed) active set was
        // rank-deficient is the caller's responsibility to track when it
        // needs to surface that to a downstream gate; here we report the
        // post-compression view honestly.
        working_set_rank_deficient: false,
        gradient_scale: gradient_inf_norm(gradient),
    })
}

fn canonicalize_active_constraint_ids(
    x: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
    active: &[usize],
) -> Result<Vec<usize>, EstimationError> {
    if active.is_empty() {
        return Ok(Vec::new());
    }
    let compressed_working = compress_active_working_set(x, constraints, active)?;
    let mut canonical = Vec::with_capacity(compressed_working.groups.len());
    for group in &compressed_working.groups {
        if let Some(&active_pos) = group.first() {
            canonical.push(active[active_pos]);
        }
    }
    Ok(canonical)
}

fn gather_linear_constraint_rows(
    constraints: &LinearInequalityConstraints,
    rows: &[usize],
) -> Result<LinearInequalityConstraints, EstimationError> {
    let p = constraints.a.ncols();
    let mut a = Array2::<f64>::zeros((rows.len(), p));
    let mut b = Array1::<f64>::zeros(rows.len());
    for (out, &row) in rows.iter().enumerate() {
        if row >= constraints.a.nrows() {
            crate::bail_invalid_estim!(
                "active constraint row {} out of bounds for {} rows",
                row,
                constraints.a.nrows()
            );
        }
        a.row_mut(out).assign(&constraints.a.row(row));
        b[out] = constraints.b[row];
    }
    LinearInequalityConstraints::new(a, b)
        .map_err(|error| EstimationError::ParameterConstraintViolation(error.to_string()))
}

fn fallback_projected_gradient_direction(
    beta: &Array1<f64>,
    x: &Array1<f64>,
    d_total: &Array1<f64>,
    gradient: &Array1<f64>,
    working_constraints: &LinearInequalityConstraints,
    constraints: &LinearInequalityConstraints,
) -> Result<Option<(Array1<f64>, Vec<usize>)>, EstimationError> {
    let p = gradient.len();
    if x.len() != p || d_total.len() != p || beta.len() != p || constraints.a.ncols() != p {
        crate::bail_invalid_estim!("projected-gradient fallback dimension mismatch");
    }

    // Project onto the FEASIBLE tangent cone `A_active d >= 0`, not merely
    // the equality tangent space `A_active d = 0`. A negative multiplier means
    // descent exists by moving away from that boundary into the cone; equality
    // projection erases exactly that direction and can mistake a wrong working
    // face for stationarity. The strictly convex primal cone QP projects the
    // gradient onto the nonnegative normal cone, so negating the residual is
    // the Euclidean projection of `-gradient` onto the feasible tangent cone
    // (Moreau).
    let tangent_direction = if working_constraints.a.nrows() == 0 {
        -gradient
    } else {
        let Some((stationarity_residual, _multipliers)) =
            project_stationarity_residual_on_constraint_cone(gradient, &working_constraints.a)
        else {
            return Ok(None);
        };
        -stationarity_residual
    };

    if !array_is_finite(&tangent_direction) {
        return Ok(None);
    }

    let step_inf = tangent_direction
        .iter()
        .fold(0.0_f64, |acc, &value| acc.max(value.abs()));
    if step_inf <= 1e-12 {
        // The projected-gradient tangent step has collapsed to ~0: the working
        // set holds the gradient in its nonnegative normal cone, so no descent
        // direction remains in the feasible tangent cone. Returning `d_total` as-is is ONLY correct
        // when the iterate `x = beta_start + d_total` is itself feasible. When
        // `x` is infeasible (an inactive row was violated by an earlier
        // `alpha`-clipped step and never repaired — the KKT solve only closes
        // residuals on ACTIVE rows), returning `d_total` leaks an infeasible
        // iterate that the downstream `check_linear_feasibility` gate rejects
        // as an "infeasible iterate" (#1108: the interval-censored survival
        // surrogate landed here at scaled-violation 5.7e-3..0.12 while the
        // exact projection of the SAME point reaches ~0 violation). Project the
        // stationary iterate onto the feasible cone and return the corresponding
        // direction so the solve returns a feasible point. The returned result
        // is `beta_start + dir`, and `x = beta_start + d_total`, so to return a
        // feasible `p` the direction is `dir = d_total + (p - x)`.
        let (worst, _) = max_linear_constraint_violation(x, constraints);
        if worst > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
            let projected = project_point_strictly_into_feasible_cone(x, constraints)
                .or_else(|| {
                    let identity = Array2::<f64>::eye(p);
                    solve_quadratic_with_linear_constraints(&identity, x, x, constraints, None)
                        .ok()
                        .map(|(beta, _active)| beta)
                })
                .filter(|p_candidate| {
                    max_linear_constraint_violation(p_candidate, constraints).0
                        <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
                });
            let Some(projected) = projected else {
                // No feasible repair available — let the caller report honestly
                // rather than returning an infeasible direction.
                return Ok(None);
            };
            let repair = &projected - x;
            let new_direction = d_total + &repair;
            // Certify the point the caller will reconstruct (`beta + dir`), not
            // the projection itself: the two differ by the rounding of the
            // x/d_total cancellation, and the caller's gate is what must pass.
            let candidate = beta + &new_direction;
            if max_linear_constraint_violation(&candidate, constraints).0
                > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
            {
                return Ok(None);
            }
            let active = canonicalize_active_constraint_ids(&candidate, constraints, &[])?;
            return Ok(Some((new_direction, active)));
        }
        let active = canonicalize_active_constraint_ids(x, constraints, &[])?;
        return Ok(Some((d_total.clone(), active)));
    }

    let directional_derivative = gradient.dot(&tangent_direction);
    if !directional_derivative.is_finite() || directional_derivative >= 0.0 {
        return Ok(None);
    }

    let mut alpha = 1.0_f64;
    for i in 0..constraints.a.nrows() {
        // Scale the step-fraction test by 1/‖a_i‖ so it matches the geometric
        // activation tolerance used elsewhere (see in-loop alpha computation in
        // `solve_newton_direction_with_linear_constraints_impl`).
        let norm = constraints.a.row(i).dot(&constraints.a.row(i)).sqrt();
        let inv = if norm > 0.0 { 1.0 / norm } else { 0.0 };
        let slack = (constraints.a.row(i).dot(x) - constraints.b[i]) * inv;
        let ai_d = constraints.a.row(i).dot(&tangent_direction) * inv;
        if let Some(candidate) = active_set_boundary_hit_step_fraction(slack, ai_d, alpha) {
            alpha = candidate;
        }
    }
    if !alpha.is_finite() || alpha <= 0.0 {
        return Ok(None);
    }

    let fallback_step = tangent_direction * alpha;
    let new_direction = d_total + &fallback_step;
    // Evaluate feasibility on the caller's reconstruction `beta + dir` so the
    // acceptance here and the caller's final gate see the same bits.
    let new_x = beta + &new_direction;
    // Per-row-scaled feasibility, matching ACTIVE_SET_PRIMAL_FEASIBILITY_TOL.
    let (worst, _) = max_linear_constraint_violation(&new_x, constraints);
    if worst > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
        return Ok(None);
    }
    let active = (0..constraints.a.nrows())
        .filter(|&i| scaled_constraint_slack(&new_x, constraints, i) <= 1e-10)
        .collect::<Vec<_>>();
    let active = canonicalize_active_constraint_ids(&new_x, constraints, &active)?;
    Ok(Some((new_direction, active)))
}

fn log_active_set_transition(
    event: &str,
    iteration: usize,
    active_len: usize,
    constraint: Option<usize>,
) {
    log::debug!(
        "[active-set/QP] iter={} event={} active={} constraint={}",
        iteration,
        event,
        active_len,
        constraint
            .map(|idx| idx.to_string())
            .unwrap_or_else(|| "NA".to_string()),
    );
}

/// Record the complete active-set state; returns `false` only when both the
/// canonical row ids and the primal point are bit-identical to a prior state.
/// A working set may legitimately recur after the primal iterate moved (leave
/// a face, descend, then re-enter it), so row ids alone are not a cycle witness.
/// The former row-only key sent such productive revisits to the projected-
/// gradient escape, which is exactly the issue-979 CTN trace: the 91-row face
/// recurred at a different coefficient point and the main QP was abandoned.
/// An identical `(face, x)` state is a genuine tolerance-band cycle and still
/// routes to the post-loop KKT gate. `to_bits` makes the decision deterministic
/// and admits no approximate/wall-clock notion of repetition.
fn record_active_working_set(
    visited: &mut HashSet<(Vec<usize>, Vec<u64>)>,
    active: &[usize],
    x: &Array1<f64>,
    iteration: usize,
) -> bool {
    let mut active_key = active.to_vec();
    active_key.sort_unstable();
    let point_key = x.iter().map(|value| value.to_bits()).collect::<Vec<_>>();
    if visited.insert((active_key.clone(), point_key)) {
        return true;
    }
    log::debug!(
        "[active-set/QP] iter={iteration} repeated working set at the identical primal point ({} rows); \
         deferring to the post-loop KKT exit gate",
        active_key.len()
    );
    false
}

fn solve_newton_direction_with_linear_constraints_impl(
    hessian: &Array2<f64>,
    gradient: &Array1<f64>,
    beta: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
    direction_out: &mut Array1<f64>,
    mut active_hint: Option<&mut Vec<usize>>,
    max_iterations: usize,
    allow_projected_gradient_fallback: bool,
) -> Result<(), EstimationError> {
    let p = gradient.len();
    if direction_out.len() != p {
        *direction_out = Array1::zeros(p);
    }
    let m = constraints.a.nrows();
    if constraints.a.ncols() != p || constraints.b.len() != m || beta.len() != p {
        crate::bail_invalid_estim!(
            "linear constraint shape mismatch: A={}x{}, b={}, p={}",
            constraints.a.nrows(),
            constraints.a.ncols(),
            constraints.b.len(),
            p
        );
    }

    let tol_active = ACTIVE_SET_WORKING_FACE_TOL;
    let tol_step = 1e-12;
    let tol_dual = 1e-10;
    let mut x = beta.to_owned();
    let mut d_total = Array1::<f64>::zeros(p);
    let mut g_cur = gradient.to_owned();

    // A warm working set describes the face at the point that produced it.
    // Trust-region globalization may accept only a strict subsegment of that
    // point's QP chord, in which case rows that bind at the QP endpoint are
    // slack at the accepted iterate. Treating those stale row ids as equality
    // constraints pins the next Newton solve to a face it has not reached and
    // turns a full Newton step into geometric boundary chasing (#979). A row
    // can enter the working set only if it is actually tight at this solve's
    // primal point; the ordinary ratio test rediscovers it when reached.
    if let Some(hint) = active_hint.as_mut() {
        hint.retain(|&idx| idx < m && scaled_constraint_slack(&x, constraints, idx) <= tol_active);
    }

    let has_active_hint = active_hint
        .as_ref()
        .map(|hint| !hint.is_empty())
        .unwrap_or(false);
    if !has_active_hint && solve_newton_direction_dense(hessian, gradient, direction_out).is_ok() {
        let candidate = beta + &*direction_out;
        let mut feasible = true;
        for i in 0..m {
            // Scaled (geometric) slack — matches `tol_active`'s semantics and
            // the public solver contract on `ACTIVE_SET_PRIMAL_FEASIBILITY_TOL`.
            let slack = scaled_constraint_slack(&candidate, constraints, i);
            if slack < -tol_active {
                feasible = false;
                break;
            }
        }
        if feasible {
            // Face provenance of the RETURNED point: the unconstrained optimum
            // can land exactly on a boundary (the warm filter above may have
            // just emptied the hint because the START was interior). Report the
            // rows tight at the answer so the next warm start carries the face
            // instead of an empty hint — but RANK-REDUCED to one representative
            // per independent direction, the same contract every other return
            // path honors via `canonicalize_active_constraint_ids`. The prior
            // code stored the RAW tight set here, so a degenerate face leaked all
            // K co-tight rows (e.g. K near-parallel `x >= 0` rows returned all K
            // instead of a single representative), re-seeding the next solve with
            // redundant rows and the phantom-dual release/re-add they drive.
            if let Some(hint) = active_hint.as_mut() {
                let mut tight: Vec<usize> = Vec::new();
                for i in 0..m {
                    if scaled_constraint_slack(&candidate, constraints, i) <= tol_active {
                        tight.push(i);
                    }
                }
                hint.clear();
                hint.extend(canonicalize_active_constraint_ids(
                    &candidate,
                    constraints,
                    &tight,
                )?);
            }
            return Ok(());
        }
    }

    let mut active: Vec<usize> = Vec::new();
    let mut is_active = vec![false; m];
    if let Some(hint) = active_hint.as_ref() {
        for &idx in hint.iter() {
            if idx < m && !is_active[idx] {
                active.push(idx);
                is_active[idx] = true;
                log_active_set_transition("warm-add", 0, active.len(), Some(idx));
            }
        }
    }
    for i in 0..m {
        // Scaled (geometric) slack: a row with ‖a_i‖ ≫ 1 (e.g. a B-spline
        // endpoint-derivative clamp at k = 12, ‖a_i‖ ≈ 38) would otherwise
        // require a raw slack of `tol_active·‖a_i‖` ≈ 4e-9 to activate, which
        // is below LBLT-solve precision on the KKT system and starves the
        // active set of rows that genuinely belong on the boundary.
        let slack = scaled_constraint_slack(&x, constraints, i);
        if slack <= tol_active && !is_active[i] {
            active.push(i);
            is_active[i] = true;
            log_active_set_transition("initial-boundary-add", 0, active.len(), Some(i));
        }
    }
    let mut visited_working_sets: HashSet<(Vec<usize>, Vec<u64>)> = HashSet::new();
    record_active_working_set(&mut visited_working_sets, &active, &x, 0);

    // See the operator loop's `face_minimized` doc: an unblocked full step
    // lands on the working-face minimizer, so the following iteration must
    // adjudicate multipliers rather than re-measure KKT-solve noise against
    // the absolute `tol_step` (#979 noise-chatter starvation).
    let mut face_minimized = false;

    for iteration in 0..max_iterations {
        let adjudicate_face = face_minimized;
        face_minimized = false;
        let compressed_working = compress_active_working_set(&x, constraints, &active)?;
        let mut residualw = Array1::<f64>::zeros(compressed_working.constraints.a.nrows());
        for r in 0..compressed_working.constraints.a.nrows() {
            residualw[r] = compressed_working.constraints.b[r]
                - compressed_working.constraints.a.row(r).dot(&x);
        }
        let (d, lambdaw) = solve_kkt_direction(
            hessian,
            &g_cur,
            &compressed_working.constraints.a,
            Some(&residualw),
        )?;
        let step_norm = d.iter().map(|v| v * v).sum::<f64>().sqrt();
        if step_norm <= tol_step || adjudicate_face {
            // A "stationary" iterate is only a genuine KKT point if it is
            // also primal-feasible on the FULL constraint set. The
            // "blocking-add" loop further down only catches rows that
            // become tight DURING a non-degenerate step, so a tiny step
            // entered with a residual violation on an inactive row would
            // otherwise leak an infeasible direction through this
            // short-circuit unchecked. Grow the active set with the
            // most-violated inactive row and re-solve; `residualw` for that
            // row will be non-zero on the next KKT solve, so the resulting
            // step is non-degenerate.
            let (worst, worst_row) = max_linear_constraint_violation(&x, constraints);
            if worst > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL && !is_active[worst_row] {
                active.push(worst_row);
                is_active[worst_row] = true;
                log_active_set_transition(
                    "stationary-infeasible-add",
                    iteration,
                    active.len(),
                    Some(worst_row),
                );
                if !record_active_working_set(&mut visited_working_sets, &active, &x, iteration) {
                    break;
                }
                continue;
            }
            if worst > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
                // The worst-violating row is already active. Distinguish two
                // cases by whether it is ENFORCED by the compressed face:
                let worst_pos = active.iter().position(|&idx| idx == worst_row);
                let enforced = worst_pos.is_some_and(|pos| compressed_working.position_enforced(pos));
                if !enforced {
                    // Over-complete face (#2378): the row is active but was
                    // rank-reduced out of the enforced representative face (it is
                    // linearly dependent on the reps). It can be neither re-added
                    // (already active) nor released (not a representative), so the
                    // plain add/release loop dead-ends here. Adjudicate by an
                    // active-set EXCHANGE — release the representative it is most
                    // aligned with so it becomes independent and binds next
                    // iteration — instead of truncating it.
                    if let Some(mut group) = compressed_working
                        .over_complete_release_group(constraints.a.row(worst_row), &active)
                    {
                        group.sort_unstable_by(|a, b| b.cmp(a));
                        let mut released = None;
                        for active_pos in group {
                            let idx = active.remove(active_pos);
                            is_active[idx] = false;
                            released = Some(idx);
                        }
                        log_active_set_transition(
                            "release-over-complete-face",
                            iteration,
                            active.len(),
                            released,
                        );
                        if !record_active_working_set(
                            &mut visited_working_sets,
                            &active,
                            &x,
                            iteration,
                        ) {
                            break;
                        }
                        continue;
                    }
                }
                // Enforced-but-unclosed (an `alpha`-clip prevented the full
                // residual closure) or no positively-aligned representative to
                // exchange: fall through to the post-loop exit gate, which
                // inspects the iterate's full KKT residuals and either tries the
                // projected-gradient fallback or returns an explicit error. Both
                // are correct outcomes; silently returning the infeasible
                // direction is not.
                break;
            }
            if compressed_working.groups.is_empty() {
                direction_out.assign(&d_total);
                return Ok(());
            }
            let remove_group =
                compressed_working.negative_representative_group(&lambdaw, tol_dual, &active);
            if let Some(mut group) = remove_group {
                // Release the whole independent direction. Remove positions in
                // DESCENDING order so each `active.remove` does not shift a
                // not-yet-removed position in the same group.
                group.sort_unstable_by(|a, b| b.cmp(a));
                let mut released = None;
                for active_pos in group {
                    let idx = active.remove(active_pos);
                    is_active[idx] = false;
                    released = Some(idx);
                }
                log_active_set_transition(
                    "release-negative-representative",
                    iteration,
                    active.len(),
                    released,
                );
                if !record_active_working_set(&mut visited_working_sets, &active, &x, iteration) {
                    break;
                }
                continue;
            }
            if let Some(hint) = active_hint {
                hint.clear();
                hint.extend(canonicalize_active_constraint_ids(
                    &x,
                    constraints,
                    &active,
                )?);
            }
            direction_out.assign(&d_total);
            return Ok(());
        }

        let mut alpha = 1.0_f64;
        for i in 0..m {
            if is_active[i] {
                continue;
            }
            // boundary_hit_step_fraction is scale-equivariant in `(slack, ai_d)`:
            // scaling both by 1/‖a_i‖ leaves `step = slack / -ai_d` unchanged,
            // and its directional-tol/finite checks operate on a max-magnitude
            // scale of the same triple. Doing the geometric rescale here keeps
            // the step-fraction's "moving toward the boundary" test in the same
            // scaled coordinates the activation tests use.
            let norm = constraints.a.row(i).dot(&constraints.a.row(i)).sqrt();
            let inv = if norm > 0.0 { 1.0 / norm } else { 0.0 };
            let slack = (constraints.a.row(i).dot(&x) - constraints.b[i]) * inv;
            let ai_d = constraints.a.row(i).dot(&d) * inv;
            if let Some(cand) = active_set_boundary_hit_step_fraction(slack, ai_d, alpha) {
                alpha = cand;
            }
        }

        ndarray::Zip::from(&mut d_total)
            .and(&d)
            .for_each(|dt_i, &d_i| {
                *dt_i += alpha * d_i;
            });
        // The public contract certifies `beta + d_total`, so the iterate must BE
        // that sum bitwise. Accumulating `x` by its own `+= alpha*d` walks a
        // different rounding path: a boundary-landing step (ratio test targets
        // scaled slack −TOL) then certifies feasible on the loop's `x` while the
        // caller's freshly summed candidate reads TOL + O(eps·scale) and the
        // solve is rejected as an "infeasible iterate" (#979 CTN cycle 86).
        x = beta + &d_total;
        g_cur = gradient + &hessian.dot(&d_total);

        let mut added_new_active = false;
        let mut working_set_repeated = false;
        for i in 0..m {
            if is_active[i] {
                continue;
            }
            let slack = scaled_constraint_slack(&x, constraints, i);
            if slack <= tol_active {
                active.push(i);
                is_active[i] = true;
                added_new_active = true;
                log_active_set_transition("blocking-add", iteration, active.len(), Some(i));
                working_set_repeated =
                    !record_active_working_set(&mut visited_working_sets, &active, &x, iteration);
                break;
            }
        }
        if !added_new_active {
            // Unblocked full step onto the working-face minimizer: adjudicate
            // multipliers next iteration instead of re-measuring solve noise.
            face_minimized = true;
        }
        if working_set_repeated {
            break;
        }

        if active.is_empty() && !added_new_active {
            if let Some(hint) = active_hint {
                hint.clear();
            }
            direction_out.assign(&d_total);
            return Ok(());
        }
    }

    let compressed_working = compress_active_working_set(&x, constraints, &active)?;
    let mut residualw = Array1::<f64>::zeros(compressed_working.constraints.a.nrows());
    for r in 0..compressed_working.constraints.a.nrows() {
        residualw[r] =
            compressed_working.constraints.b[r] - compressed_working.constraints.a.row(r).dot(&x);
    }
    let (_, lambdaw) = solve_kkt_direction(
        hessian,
        &g_cur,
        &compressed_working.constraints.a,
        Some(&residualw),
    )?;
    let lambda_true = lambdaw.mapv(|lam_sys| -lam_sys);
    let (worst, row) = max_linear_constraint_violation(&x, constraints);
    let working_kkt = working_set_kkt_diagnostics_from_multipliers(
        &x,
        &g_cur,
        &compressed_working.constraints,
        &lambda_true,
        m,
    )?;
    let grad_inf = gradient_inf_norm(&g_cur);
    let stationarity_rel = working_kkt.stationarity / grad_inf.max(1.0);
    let step_inf = d_total.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
    let hd_total = hessian.dot(&d_total);
    let predicted_delta = gradient.dot(&d_total)
        + 0.5
            * d_total
                .iter()
                .zip(hd_total.iter())
                .map(|(a, b)| a * b)
                .sum::<f64>();
    let kkt_strong_ok = (working_kkt.stationarity <= ACTIVE_SET_KKT_STATIONARITY_TOL
        || stationarity_rel <= ACTIVE_SET_KKT_STATIONARITY_TOL)
        && working_kkt.complementarity <= ACTIVE_SET_KKT_COMPLEMENTARITY_TOL;
    let model_descent_ok =
        predicted_delta <= -ACTIVE_SET_MODEL_DESCENT_REL_TOL * (1.0 + grad_inf * step_inf);
    let degenerate_boundary_ok = compressed_working.is_degenerate_face()
        && worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
        && working_kkt.primal_feasibility <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
        && working_kkt.complementarity <= ACTIVE_SET_KKT_COMPLEMENTARITY_TOL
        && (working_kkt.stationarity <= ACTIVE_SET_KKT_DEGENERATE_STATIONARITY_TOL
            || stationarity_rel <= ACTIVE_SET_KKT_STATIONARITY_TOL);
    // Existence-form KKT certificate over the rows tight at the terminal
    // iterate. The working-set multipliers above are one particular
    // reconstruction; on a degenerate face they can report `dual ≫ 0` while a
    // different λ ≥ 0 closes stationarity exactly (#2298 monotonicity faces).
    // NNLS over the tight rows has exact complementarity by construction
    // (only tight rows enter) and exact dual feasibility, so stationarity
    // closure of its projected residual is the entire remaining KKT question.
    // Skip it only when the strong path already ACCEPTS (stationarity AND dual
    // both certified): a strong-stationary point whose particular multiplier
    // reconstruction has phantom negative duals is exactly the case this
    // certificate exists for (#979 CTN cycle-95: stat=1.0e-8, comp=4.2e-8,
    // dual=7.6e4 on a 41-row degenerate face — refused with the old
    // `!kkt_strong_ok` gate although an NNLS λ ≥ 0 closes the point).
    let strong_path_accepts =
        kkt_strong_ok && working_kkt.dual_feasibility <= ACTIVE_SET_KKT_DUAL_FEASIBILITY_TOL;
    let nnls_certified = worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL && !strong_path_accepts && {
        let tight: Vec<usize> = (0..m)
            .filter(|&i| scaled_constraint_slack(&x, constraints, i) <= tol_active)
            .collect();
        match gather_linear_constraint_rows(constraints, &tight) {
            Ok(gathered) => nonnegative_cone_multipliers(&gathered.a, &g_cur)
                .map(|(_, projected)| {
                    let closure = projected.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
                    closure <= ACTIVE_SET_KKT_STATIONARITY_TOL
                        || closure / grad_inf.max(1.0) <= ACTIVE_SET_KKT_STATIONARITY_TOL
                })
                .unwrap_or(false),
            Err(_) => false,
        }
    };
    if worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
        && ((working_kkt.dual_feasibility <= ACTIVE_SET_KKT_DUAL_FEASIBILITY_TOL
            && (kkt_strong_ok || (allow_projected_gradient_fallback && model_descent_ok)))
            || degenerate_boundary_ok
            || nnls_certified)
    {
        if let Some(hint) = active_hint {
            hint.clear();
            hint.extend(canonicalize_active_constraint_ids(
                &x,
                constraints,
                &active,
            )?);
        }
        direction_out.assign(&d_total);
        return Ok(());
    }
    if !allow_projected_gradient_fallback {
        return Err(EstimationError::ParameterConstraintViolation(format!(
            "linear-constrained Newton active-set did not certify the strict-convex projection QP; max(Aβ-b violation)={worst:.3e} at row {row}; KKT[primal={:.3e}, dual={:.3e}, comp={:.3e}, stat={:.3e}, active={}/{}]",
            working_kkt.primal_feasibility,
            working_kkt.dual_feasibility,
            working_kkt.complementarity,
            working_kkt.stationarity,
            working_kkt.n_active,
            working_kkt.n_constraints,
        )));
    }
    let kkt = compute_constraint_kkt_diagnostics(&x, &g_cur, constraints);
    let fallback_working = gather_linear_constraint_rows(constraints, &active)?;
    if let Some((fallback_direction, fallback_active)) = fallback_projected_gradient_direction(
        beta,
        &x,
        &d_total,
        &g_cur,
        &fallback_working,
        constraints,
    )? {
        if let Some(hint) = active_hint {
            hint.clear();
            hint.extend(fallback_active);
        }
        direction_out.assign(&fallback_direction);
        return Ok(());
    }
    Err(EstimationError::ParameterConstraintViolation(format!(
        "linear-constrained Newton active-set failed to converge; max(Aβ-b violation)={worst:.3e} at row {row}; KKT[primal={:.3e}, dual={:.3e}, comp={:.3e}, stat={:.3e}, active={}/{}]; diagnostic-reconstruction[dual={:.3e}, stat={:.3e}]",
        working_kkt.primal_feasibility,
        working_kkt.dual_feasibility,
        working_kkt.complementarity,
        working_kkt.stationarity,
        working_kkt.n_active,
        working_kkt.n_constraints,
        kkt.dual_feasibility,
        kkt.stationarity
    )))
}

// ============================================================================
// Operator (ConstraintSet) active-set solver — gam#2306
//
// The factored Khatri-Rao monotonicity cone has `n · p_shape` rows over
// `p_resp · p_cov` coefficients; its dense materialization is gigabytes while
// every operation the primal active-set method performs factors through the
// `n × p_cov` covariate design. This section is the operator sibling of
// `solve_newton_direction_with_linear_constraints_impl`: identical
// per-row-scaled (geometric) tolerance semantics, identical KKT core
// (`solve_kkt_direction` on the rank-reduced working set) — but every
// full-row-set sweep (activation scan, ratio test, violation gate) runs on
// batched constraint values, never on explicit rows. Dense inputs delegate to
// the dense loop verbatim, so existing callers are byte-identical.
// ============================================================================

/// Batched full-row-set geometry for a [`ConstraintSet`].
///
/// `scaled_margin` shifts every non-vacuous row inward by that amount in
/// scaled (geometric) units — `a_iᵀβ ≥ b_i + scaled_margin·‖a_i‖` — which is
/// exactly the uniform interior-seed shift the dense strict projection
/// applies. The main QP solve uses `scaled_margin = 0`.
struct ConstraintSetOps<'a> {
    set: &'a ConstraintSet,
    norms: Vec<f64>,
    bounds: Vec<f64>,
    scaled_margin: f64,
}

impl<'a> ConstraintSetOps<'a> {
    fn new(set: &'a ConstraintSet, scaled_margin: f64) -> Result<Self, EstimationError> {
        let m = set.nrows();
        let mut norms = Vec::with_capacity(m);
        let mut bounds = Vec::with_capacity(m);
        for row in 0..m {
            norms.push(set.row_norm(row).map_err(|e| {
                EstimationError::ParameterConstraintViolation(format!(
                    "constraint-set row norm: {e}"
                ))
            })?);
            bounds.push(set.bound(row).map_err(|e| {
                EstimationError::ParameterConstraintViolation(format!(
                    "constraint-set row bound: {e}"
                ))
            })?);
        }
        Ok(Self {
            set,
            norms,
            bounds,
            scaled_margin,
        })
    }

    /// Operator view of only the rows that are tight at `beta`. Inactive rows
    /// do not constrain the tangent cone, so make them vacuous by zeroing both
    /// their cached norm and bound while retaining the original row indexing.
    /// This avoids materializing the potentially enormous tight submatrix and
    /// keeps returned active ids in the parent [`ConstraintSet`] coordinates.
    fn tangent_face(set: &'a ConstraintSet, beta: &Array1<f64>) -> Result<Self, EstimationError> {
        let mut ops = Self::new(set, 0.0)?;
        let values = ops.values(beta)?;
        for row in 0..ops.nrows() {
            if ops.norms[row] <= 0.0 {
                if ops.bounds[row] > 0.0 {
                    crate::bail_invalid_estim!(
                        "infeasible zero-norm constraint row {} entered tangent-face projection",
                        row
                    );
                }
                ops.bounds[row] = 0.0;
                continue;
            }
            let is_tight = ops.scaled_slack(&values, row) <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL;
            // Tangent directions are homogeneous even when the original
            // feasible set is affine: a_i^T d >= 0 on a tight row.
            ops.bounds[row] = 0.0;
            if !is_tight {
                ops.norms[row] = 0.0;
            }
        }
        Ok(ops)
    }

    fn nrows(&self) -> usize {
        self.norms.len()
    }

    fn values(&self, x: &Array1<f64>) -> Result<Array1<f64>, EstimationError> {
        self.set.values(x.view()).map_err(|e| {
            EstimationError::ParameterConstraintViolation(format!("constraint-set values: {e}"))
        })
    }

    /// Signed scaled slack of one row given the batched raw values, with the
    /// same ±∞ zero-norm semantics as [`scaled_constraint_slack`].
    #[inline]
    fn scaled_slack(&self, values: &Array1<f64>, row: usize) -> f64 {
        let norm = self.norms[row];
        if norm > 0.0 {
            (values[row] - self.bounds[row]) / norm - self.scaled_margin
        } else if self.bounds[row] > 0.0 {
            f64::NEG_INFINITY
        } else {
            f64::INFINITY
        }
    }

    fn max_violation(&self, values: &Array1<f64>) -> (f64, usize) {
        let mut worst = 0.0_f64;
        let mut worst_row = 0usize;
        for row in 0..self.nrows() {
            let violation = (-self.scaled_slack(values, row)).max(0.0);
            if violation > worst {
                worst = violation;
                worst_row = row;
            }
        }
        (worst, worst_row)
    }

    /// Gather the working rows as an explicit UNIT-normalized system (the
    /// per-row scale the dense path reaches via up-front canonicalization),
    /// with the margin shift folded into `b`. Zero-norm rows are rejected —
    /// they are vacuous and must never enter a working set.
    fn gather_unit_rows(
        &self,
        rows: &[usize],
    ) -> Result<LinearInequalityConstraints, EstimationError> {
        let mut gathered = self.set.gather_rows(rows).map_err(|e| {
            EstimationError::ParameterConstraintViolation(format!(
                "constraint-set working-row gather: {e}"
            ))
        })?;
        for (out_row, &row) in rows.iter().enumerate() {
            let norm = self.norms[row];
            if norm <= 0.0 {
                crate::bail_invalid_estim!(
                    "vacuous zero-norm constraint row {} entered the working set",
                    row
                );
            }
            let inv = 1.0 / norm;
            gathered.a.row_mut(out_row).mapv_inplace(|v| v * inv);
            gathered.b[out_row] = self.bounds[row] * inv + self.scaled_margin;
        }
        Ok(gathered)
    }

    /// Rank-reduced compressed working set over the gathered unit rows —
    /// the operator analogue of [`compress_active_working_set`].
    fn compress_working(
        &self,
        active: &[usize],
    ) -> Result<CompressedActiveWorkingSet, EstimationError> {
        let gathered = self.gather_unit_rows(active)?;
        let groups: Vec<Vec<usize>> = (0..active.len()).map(|pos| vec![pos]).collect();
        // 4th return (parallel-dependent map) is consumed only by the shared
        // `ReducedFace` op; whole-group release does not need it here.
        let (a_out, b_out, groups_out, _) =
            rank_reduce_rows_pivoted_qr_with_dependence(gathered.a, gathered.b, groups);
        Ok(CompressedActiveWorkingSet {
            constraints: LinearInequalityConstraints::new(a_out, b_out)
                .expect("compressed operator working-set shape invariant"),
            groups: groups_out,
            original_active_count: active.len(),
        })
    }
}

/// Retain only candidate row ids that are genuinely tight at `beta`.
///
/// Active-face provenance is point-local. A constrained QP reports its full
/// endpoint face, while trust-region globalization may accept a strict
/// subsegment whose endpoint-only rows are still slack. This helper is the
/// shared handoff for warm starts and terminal tangent-space evidence: it uses
/// the carrier's exact row scaling, preserves canonical input order, and never
/// scans rows outside the sparse candidate face.
pub fn constraint_set_rows_tight_at_point(
    set: &ConstraintSet,
    beta: &Array1<f64>,
    candidate_rows: &[usize],
) -> Result<Vec<usize>, EstimationError> {
    if set.ncols() != beta.len() {
        crate::bail_invalid_estim!(
            "active-face point dimension mismatch: set has {} columns, beta has {}",
            set.ncols(),
            beta.len()
        );
    }
    let mut seen = HashSet::with_capacity(candidate_rows.len());
    let mut unique = Vec::with_capacity(candidate_rows.len());
    for &row in candidate_rows {
        if row < set.nrows() && seen.insert(row) {
            unique.push(row);
        }
    }
    if unique.is_empty() {
        return Ok(Vec::new());
    }
    let gathered = set.gather_rows(&unique).map_err(|error| {
        EstimationError::ParameterConstraintViolation(format!(
            "active-face candidate-row gather failed: {error}"
        ))
    })?;
    let mut tight = Vec::with_capacity(unique.len());
    for (position, &row) in unique.iter().enumerate() {
        let constraint_row = gathered.a.row(position);
        let norm = constraint_row.dot(&constraint_row).sqrt();
        if norm > 0.0 {
            let scaled_slack = (constraint_row.dot(beta) - gathered.b[position]) / norm;
            if scaled_slack <= ACTIVE_SET_WORKING_FACE_TOL {
                tight.push(row);
            }
        }
    }
    Ok(tight)
}

/// Project a stationarity residual onto the normal cone of an operator-carried
/// constraint set without materializing its complete tight face.
///
/// `seed_active` is the QP's sparse face. The negative projected residual is a
/// candidate feasible-tangent direction. One operator-native active-set solve
/// discovers any omitted tight blocker with its full-set ratio test, while
/// gathering only rows that carry necessary cone geometry.
pub fn project_stationarity_residual_on_constraint_set(
    residual: &Array1<f64>,
    beta: &Array1<f64>,
    set: &ConstraintSet,
    seed_active: &[usize],
) -> Option<(Array1<f64>, Vec<usize>)> {
    let p = residual.len();
    if beta.len() != p || set.ncols() != p {
        return None;
    }
    match set {
        ConstraintSet::KhatriRaoCone(cone) if cone.p_left() != 1 || cone.coupled_rows() != &[0] => {
            // Each coupled response row occupies a disjoint `p_cov` slice,
            // and the projection Hessian is identity. The global projection is
            // therefore the exact direct sum of small row projections. Solving
            // all response rows in one `p_left*p_cov` KKT system needlessly
            // pays cubic global algebra at the all-tight CTN vertex.
            let p_cov = cone.factor().ncols();
            let n = cone.factor().nrows();
            let mut projected = residual.clone();
            let mut active = Vec::new();
            for (slot, &coefficient_row) in cone.coupled_rows().iter().enumerate() {
                let start = coefficient_row * p_cov;
                let end = start + p_cov;
                let local_residual = residual.slice(s![start..end]).to_owned();
                let local_beta = beta.slice(s![start..end]).to_owned();
                let local_set = ConstraintSet::KhatriRaoCone(cone.single_coupled_slot(slot).ok()?);
                let row_start = slot * n;
                let row_end = row_start + n;
                let local_seed: Vec<usize> = seed_active
                    .iter()
                    .copied()
                    .filter(|&row| row >= row_start && row < row_end)
                    .map(|row| row - row_start)
                    .collect();
                let (local_projected, local_active) =
                    project_stationarity_residual_on_constraint_set(
                        &local_residual,
                        &local_beta,
                        &local_set,
                        &local_seed,
                    )?;
                projected.slice_mut(s![start..end]).assign(&local_projected);
                active.extend(local_active.into_iter().map(|row| row_start + row));
            }
            Some((projected, active))
        }
        ConstraintSet::BlockDiagonal { blocks, .. } => {
            // The same direct-sum identity applies to explicitly placed blocks;
            // columns outside all blocks are unconstrained and retain their
            // original residual components.
            let mut projected = residual.clone();
            let mut active = Vec::new();
            let mut row_offset = 0usize;
            for block in blocks {
                let width = block.set.ncols();
                let start = block.col_start;
                let end = start + width;
                let local_residual = residual.slice(s![start..end]).to_owned();
                let local_beta = beta.slice(s![start..end]).to_owned();
                let row_end = row_offset + block.set.nrows();
                let local_seed: Vec<usize> = seed_active
                    .iter()
                    .copied()
                    .filter(|&row| row >= row_offset && row < row_end)
                    .map(|row| row - row_offset)
                    .collect();
                let (local_projected, local_active) =
                    project_stationarity_residual_on_constraint_set(
                        &local_residual,
                        &local_beta,
                        &block.set,
                        &local_seed,
                    )?;
                projected.slice_mut(s![start..end]).assign(&local_projected);
                active.extend(local_active.into_iter().map(|row| row_offset + row));
                row_offset = row_end;
            }
            Some((projected, active))
        }
        _ => project_stationarity_residual_on_constraint_set_undivided(
            residual,
            beta,
            set,
            seed_active,
        ),
    }
}

fn project_stationarity_residual_on_constraint_set_undivided(
    residual: &Array1<f64>,
    beta: &Array1<f64>,
    set: &ConstraintSet,
    seed_active: &[usize],
) -> Option<(Array1<f64>, Vec<usize>)> {
    let p = residual.len();
    let ops = ConstraintSetOps::tangent_face(set, beta).ok()?;
    let mut active = Vec::with_capacity(seed_active.len().min(p));
    for &row in seed_active {
        if row < ops.nrows() && ops.norms[row] > 0.0 && !active.contains(&row) {
            active.push(row);
        }
    }

    // Solve the Moreau tangent projection once in the operator-native primal
    // active set:
    //
    //   min_d  1/2 ||d + residual||^2    s.t. A d >= 0.
    //
    // The former separator gathered one extra factored row, invoked a complete
    // dense cone optimizer from the origin, and repeated. That nested a QP per
    // cut and discarded its face every time. The operator solver already owns
    // the same full-set ratio test without materializing A; carry `active`
    // through that single solve instead. Disable its projected-gradient escape
    // because that escape calls this projection oracle.
    let identity = Array2::<f64>::eye(p);
    let origin = Array1::<f64>::zeros(p);
    let mut tangent_direction = Array1::<f64>::zeros(p);
    let max_iterations = (p + active.len() + 8) * 4;
    if let Err(error) = solve_newton_direction_with_constraint_set_impl(
        &identity,
        residual,
        &origin,
        &ops,
        &mut tangent_direction,
        Some(&mut active),
        max_iterations,
        false,
    ) {
        log::warn!(
            "factored tangent-cone projection QP failed \
             (p={p}, rows={}, seed_active_rows={}, residual_inf={:.6e}): {error}; \
             attempting the direct Lawson-Hanson Moreau fallback on the final working face",
            ops.nrows(),
            seed_active.len(),
            residual
                .iter()
                .fold(0.0_f64, |scale, value| scale.max(value.abs())),
        );
        return nnls_tangent_cone_projection_fallback(residual, beta, set, &active, seed_active);
    }
    if !array_is_finite(&tangent_direction) {
        return nnls_tangent_cone_projection_fallback(residual, beta, set, &active, seed_active);
    }
    Some((-tangent_direction, active))
}

/// Exact Moreau fallback for the operator tangent-cone projection when the
/// strict-convex primal QP refuses to certify. Degenerate faces defeat that
/// QP's exit ritual: on the measured #979 CTN shape the projection pins a
/// fully determined 24-row vertex in R^24 and the single-face multiplier
/// reconstruction reports a phantom negative dual (KKT dual=1.2 with primal,
/// complementarity, and stationarity all at round-off), so the caller fell
/// back to the UNPROJECTED residual (1.447e3) and the convergence certificate
/// could never fire. Projection onto a finitely generated cone IS
/// nonnegative least squares (Moreau), so run Lawson-Hanson directly on the
/// rows of the QP's final working face that are genuinely tight at `beta`:
/// `projected = residual − Aᵀλ*` with `λ* = argmin_{λ≥0} ‖residual − Aᵀλ‖`.
///
/// Two properties make this sound as a certificate input:
/// - Restricting the generators to a subset of the true tangent-cone rows can
///   only ENLARGE the projected residual (the minimum runs over a smaller
///   λ-support), so a lost row biases toward refusal, never acceptance.
/// - Every generator is verified tight at `beta` before entering, so
///   `projected ≈ 0` states exactly `residual = Aᵀλ*, λ* ≥ 0` over active
///   rows — the existence form of the KKT stationarity condition.
fn nnls_tangent_cone_projection_fallback(
    residual: &Array1<f64>,
    beta: &Array1<f64>,
    set: &ConstraintSet,
    face_rows: &[usize],
    seed_active: &[usize],
) -> Option<(Array1<f64>, Vec<usize>)> {
    let mut candidates: Vec<usize> = Vec::with_capacity(face_rows.len() + seed_active.len());
    for &row in face_rows.iter().chain(seed_active.iter()) {
        if row < set.nrows() && !candidates.contains(&row) {
            candidates.push(row);
        }
    }
    if candidates.is_empty() {
        // No tight generators: the local tangent cone is the whole space and
        // the projected stationarity residual is the raw residual, exactly as
        // the primal QP would report for an interior point.
        return Some((residual.clone(), Vec::new()));
    }
    let candidate_rows = set.gather_rows(&candidates).ok()?;
    // Same tightness band the tangent-face carrier itself uses, so the
    // generator set matches the cone geometry the refused QP was solving.
    let mut tight = Vec::with_capacity(candidates.len());
    let mut tight_a = Vec::with_capacity(candidates.len());
    for (position, &row) in candidates.iter().enumerate() {
        let constraint_row = candidate_rows.a.row(position);
        let norm = constraint_row.dot(&constraint_row).sqrt();
        if norm > 0.0
            && (constraint_row.dot(beta) - candidate_rows.b[position]) / norm
                <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
        {
            tight.push(row);
            tight_a.push(position);
        }
    }
    if tight.is_empty() {
        return Some((residual.clone(), Vec::new()));
    }
    let mut generators = Array2::<f64>::zeros((tight.len(), residual.len()));
    for (out_row, &position) in tight_a.iter().enumerate() {
        generators
            .row_mut(out_row)
            .assign(&candidate_rows.a.row(position));
    }
    let (lambda, projected) = nonnegative_cone_multipliers(&generators, residual)?;
    let active: Vec<usize> = tight
        .iter()
        .zip(lambda.iter())
        .filter(|&(_, &multiplier)| multiplier > 0.0)
        .map(|(&row, _)| row)
        .collect();
    log::info!(
        "tangent-cone Moreau fallback certified the projection the primal QP refused: \
         face_rows={} tight_rows={} supported_rows={} projected_inf={:.6e}",
        face_rows.len(),
        tight.len(),
        active.len(),
        projected
            .iter()
            .fold(0.0_f64, |scale, value| scale.max(value.abs())),
    );
    Some((projected, active))
}

/// First-order escape from a tolerance-band working-set cycle for an operator
/// constraint carrier. This is the factored equivalent of
/// [`fallback_projected_gradient_direction`]: project `-gradient` into the
/// current face's tangent space, clip it at the first constraint boundary, and
/// accept only a finite, descending, fully feasible direction.
///
/// Keep the returned face sparse. A single coefficient row at zero can make
/// thousands of Khatri-Rao observation rows tight; rediscovering every tight
/// row here would recreate the all-face materialization this operator path is
/// specifically meant to avoid. The old working rows remain tight under the
/// tangent step. If a currently omitted tight row blocks at zero step, add
/// that separator and recompute the cone projection; this cutting-plane loop
/// discovers only the rows needed to describe a feasible tangent direction.
fn fallback_projected_gradient_direction_with_constraint_set(
    beta: &Array1<f64>,
    x: &Array1<f64>,
    d_total: &Array1<f64>,
    gradient: &Array1<f64>,
    active: &[usize],
    ops: &ConstraintSetOps<'_>,
) -> Result<Option<(Array1<f64>, Vec<usize>)>, EstimationError> {
    let p = gradient.len();
    if x.len() != p || d_total.len() != p || beta.len() != p || ops.set.ncols() != p {
        crate::bail_invalid_estim!("operator projected-gradient fallback dimension mismatch");
    }

    let values_x = ops.values(x)?;
    let Some((stationarity_residual, mut tangent_active)) =
        project_stationarity_residual_on_constraint_set(gradient, x, ops.set, active)
    else {
        return Ok(None);
    };
    let tangent_direction = -stationarity_residual;
    let step_inf = tangent_direction
        .iter()
        .fold(0.0_f64, |acc, &value| acc.max(value.abs()));
    if step_inf <= 1e-12 {
        let (worst, _) = ops.max_violation(&values_x);
        if worst > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
            let Some(projected) = project_point_strictly_into_feasible_constraint_set(x, ops.set)
                .ok()
                .filter(|candidate| {
                    ops.values(candidate)
                        .map(|candidate_values| {
                            ops.max_violation(&candidate_values).0
                                <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
                        })
                        .unwrap_or(false)
                })
            else {
                return Ok(None);
            };
            let repair = &projected - x;
            let new_direction = d_total + &repair;
            // Certify the caller's reconstruction `beta + dir`, not the
            // projection itself (they differ by the x/d_total cancellation
            // rounding, and the caller's gate is what must pass).
            let candidate = beta + &new_direction;
            let candidate_values = ops.values(&candidate)?;
            if ops.max_violation(&candidate_values).0 > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
                return Ok(None);
            }
            return Ok(Some((new_direction, Vec::new())));
        }
        return Ok(Some((d_total.clone(), tangent_active)));
    }

    let directional_derivative = gradient.dot(&tangent_direction);
    if !directional_derivative.is_finite() || directional_derivative >= 0.0 {
        return Ok(None);
    }
    let values_direction = ops.values(&tangent_direction)?;
    let mut alpha = 1.0_f64;
    let mut blocking_row = None;
    for row in 0..ops.nrows() {
        if ops.norms[row] <= 0.0 {
            continue;
        }
        let slack = ops.scaled_slack(&values_x, row);
        let rate = values_direction[row] / ops.norms[row];
        if let Some(candidate) = active_set_boundary_hit_step_fraction(slack, rate, alpha) {
            alpha = candidate;
            blocking_row = Some(row);
        }
    }
    if !alpha.is_finite() || alpha <= 0.0 {
        return Ok(None);
    }
    let fallback_step = tangent_direction * alpha;
    let new_direction = d_total + &fallback_step;
    // Evaluate feasibility on the caller's reconstruction `beta + dir` so the
    // acceptance here and the caller's final gate see the same bits.
    let new_x = beta + &new_direction;
    let new_values = ops.values(&new_x)?;
    if ops.max_violation(&new_values).0 > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
        return Ok(None);
    }
    if let Some(row) = blocking_row
        && !tangent_active.contains(&row)
    {
        tangent_active.push(row);
    }
    tangent_active.retain(|&row| ops.scaled_slack(&new_values, row) <= 1e-10);
    Ok(Some((new_direction, tangent_active)))
}

fn solve_newton_direction_with_constraint_set_impl(
    hessian: &Array2<f64>,
    gradient: &Array1<f64>,
    beta: &Array1<f64>,
    ops: &ConstraintSetOps<'_>,
    direction_out: &mut Array1<f64>,
    mut active_hint: Option<&mut Vec<usize>>,
    max_iterations: usize,
    allow_projected_gradient_fallback: bool,
) -> Result<(), EstimationError> {
    let p = gradient.len();
    if direction_out.len() != p {
        *direction_out = Array1::zeros(p);
    }
    let m = ops.nrows();
    if ops.set.ncols() != p || beta.len() != p {
        crate::bail_invalid_estim!(
            "constraint-set shape mismatch: set={}x{}, p={}",
            m,
            ops.set.ncols(),
            p
        );
    }

    let tol_active = ACTIVE_SET_WORKING_FACE_TOL;
    let tol_step = 1e-12;
    let tol_dual = 1e-10;
    let mut x = beta.to_owned();
    let mut d_total = Array1::<f64>::zeros(p);
    let mut g_cur = gradient.to_owned();
    let mut values_x = ops.values(&x)?;

    // Face provenance is point-local. If globalization accepted only part of
    // the previous QP chord, its endpoint rows are not active at the accepted
    // point. Retaining them as warm equalities makes the next operator solve
    // chase a still-slack face by the same small trust fraction every cycle.
    // Keep only rows tight at the current beta; the full-set ratio test adds
    // any discarded row exactly when the iterate actually reaches it.
    if let Some(hint) = active_hint.as_mut() {
        hint.retain(|&idx| {
            idx < m && ops.norms[idx] > 0.0 && ops.scaled_slack(&values_x, idx) <= tol_active
        });
    }

    let has_active_hint = active_hint
        .as_ref()
        .map(|hint| !hint.is_empty())
        .unwrap_or(false);
    if !has_active_hint && solve_newton_direction_dense(hessian, gradient, direction_out).is_ok() {
        let candidate = beta + &*direction_out;
        let candidate_values = ops.values(&candidate)?;
        let feasible = (0..m).all(|row| ops.scaled_slack(&candidate_values, row) >= -tol_active);
        if feasible {
            // Unlike the dense loop's fast path, the hint deliberately stays
            // empty here: a factored cone can have thousands of duplicate
            // geometrically-tight rows at a boundary landing, and eagerly
            // reporting them all would poison the next warm start with the
            // materialized face this operator path exists to avoid. The ratio
            // test rediscovers the one blocking row when it matters.
            return Ok(());
        }
    }

    let mut active: Vec<usize> = Vec::new();
    let mut is_active = vec![false; m];
    if let Some(hint) = active_hint.as_ref() {
        for &idx in hint.iter() {
            if idx < m && !is_active[idx] && ops.norms[idx] > 0.0 {
                active.push(idx);
                is_active[idx] = true;
                log_active_set_transition("warm-add", 0, active.len(), Some(idx));
            }
        }
    }
    // Do NOT eagerly classify every tight operator row as active.  A factored
    // cone can have tens of thousands of geometrically tight rows at a low-
    // dimensional face: for CTN, one coefficient row becoming zero makes all
    // `n` observation rows tight although their span has dimension at most
    // `p_cov`.  Gathering that entire face and rank-reducing it on every QP
    // cycle turns a 144-variable solve into minutes of redundant QR work.
    //
    // An active-set method only needs tight rows that block the proposed
    // direction.  Start from the warm working set (possibly empty); the ratio
    // test below adds the first boundary row whose directional rate would
    // leave the cone, and the negative-dual test releases rows normally.  This
    // is the standard feasible active-set invariant and changes neither the
    // feasible region nor the QP optimum.  Dense constraints retain their
    // existing eager initialization in the dense solver.
    let mut visited_working_sets: HashSet<(Vec<usize>, Vec<u64>)> = HashSet::new();
    record_active_working_set(&mut visited_working_sets, &active, &x, 0);

    // Terminal-diagnosis counters: dep-crate debug logs are filtered out by
    // CLI consumers, so a budget-exhausted refusal must name its own churn
    // mechanism (blocking-adds vs release cycling vs working-set repetition)
    // in the typed error text.
    let mut count_blocking_add = 0usize;
    let mut count_stationary_add = 0usize;
    let mut count_release = 0usize;
    let mut ws_repeat_break = false;
    let mut iterations_used = 0usize;
    // After an UNBLOCKED full step the iterate is the working-face minimizer
    // up to KKT-solve rounding, so the next iteration's direction is solver
    // noise, not progress. Measuring that noise against the absolute
    // `tol_step` starves the stationary branch (where releases and the
    // terminal acceptance live) on large-scale problems: the CTN cycle-95
    // witness spent 3146 of 3152 iterations re-solving noise steps with only
    // 6 transitions. An unblocked full step must be followed by multiplier
    // adjudication.
    let mut face_minimized = false;

    for iteration in 0..max_iterations {
        iterations_used = iteration + 1;
        let adjudicate_face = face_minimized;
        face_minimized = false;
        let compressed_working = ops.compress_working(&active)?;
        let mut residualw = Array1::<f64>::zeros(compressed_working.constraints.a.nrows());
        for r in 0..compressed_working.constraints.a.nrows() {
            residualw[r] = compressed_working.constraints.b[r]
                - compressed_working.constraints.a.row(r).dot(&x);
        }
        let (d, lambdaw) = solve_kkt_direction(
            hessian,
            &g_cur,
            &compressed_working.constraints.a,
            Some(&residualw),
        )?;
        let step_norm = d.iter().map(|v| v * v).sum::<f64>().sqrt();
        if step_norm <= tol_step || adjudicate_face {
            let (worst, worst_row) = ops.max_violation(&values_x);
            if worst > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL && !is_active[worst_row] {
                active.push(worst_row);
                is_active[worst_row] = true;
                count_stationary_add += 1;
                log_active_set_transition(
                    "stationary-infeasible-add",
                    iteration,
                    active.len(),
                    Some(worst_row),
                );
                if !record_active_working_set(&mut visited_working_sets, &active, &x, iteration) {
                    ws_repeat_break = true;
                    break;
                }
                continue;
            }
            if worst > ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
                // The worst-violating row is already active. If it is not
                // ENFORCED by the compressed face it was rank-reduced out as
                // linearly dependent on the representatives — an over-complete
                // face (#2378): three coupled-block normals bind but only two
                // are independent, so the third is neither re-addable (already
                // active) nor releasable (not a representative) and the loop
                // dead-ends. Adjudicate it by an active-set EXCHANGE: release the
                // representative it is most aligned with so it becomes an
                // independent representative and binds next iteration.
                let worst_pos = active.iter().position(|&idx| idx == worst_row);
                let enforced =
                    worst_pos.is_some_and(|pos| compressed_working.position_enforced(pos));
                if !enforced {
                    let violated_unit = ops.gather_unit_rows(&[worst_row])?;
                    if let Some(mut group) = compressed_working
                        .over_complete_release_group(violated_unit.a.row(0), &active)
                    {
                        group.sort_unstable_by(|a, b| b.cmp(a));
                        let mut released = None;
                        for active_pos in group {
                            let idx = active.remove(active_pos);
                            is_active[idx] = false;
                            count_release += 1;
                            released = Some(idx);
                        }
                        log_active_set_transition(
                            "release-over-complete-face",
                            iteration,
                            active.len(),
                            released,
                        );
                        if !record_active_working_set(
                            &mut visited_working_sets,
                            &active,
                            &x,
                            iteration,
                        ) {
                            ws_repeat_break = true;
                            break;
                        }
                        continue;
                    }
                }
                break;
            }
            if compressed_working.groups.is_empty() {
                direction_out.assign(&d_total);
                return Ok(());
            }
            let remove_group =
                compressed_working.negative_representative_group(&lambdaw, tol_dual, &active);
            if let Some(mut group) = remove_group {
                // Release the whole independent direction (representative +
                // exactly-parallel dependents). Descending removal so each
                // `active.remove` does not shift a not-yet-removed position.
                group.sort_unstable_by(|a, b| b.cmp(a));
                let mut released = None;
                for active_pos in group {
                    let idx = active.remove(active_pos);
                    is_active[idx] = false;
                    count_release += 1;
                    released = Some(idx);
                }
                log_active_set_transition(
                    "release-negative-representative",
                    iteration,
                    active.len(),
                    released,
                );
                if !record_active_working_set(&mut visited_working_sets, &active, &x, iteration) {
                    ws_repeat_break = true;
                    break;
                }
                continue;
            }
            if let Some(hint) = active_hint.as_mut() {
                hint.clear();
                let compressed = ops.compress_working(&active)?;
                for group in &compressed.groups {
                    if let Some(&active_pos) = group.first() {
                        hint.push(active[active_pos]);
                    }
                }
            }
            direction_out.assign(&d_total);
            return Ok(());
        }

        let values_d = ops.values(&d)?;
        let mut alpha = 1.0_f64;
        let mut blocking_row: Option<usize> = None;
        for row in 0..m {
            if is_active[row] || ops.norms[row] <= 0.0 {
                continue;
            }
            let slack = ops.scaled_slack(&values_x, row);
            let rate = values_d[row] / ops.norms[row];
            if let Some(cand) = active_set_boundary_hit_step_fraction(slack, rate, alpha) {
                alpha = cand;
                blocking_row = Some(row);
            }
        }

        ndarray::Zip::from(&mut d_total)
            .and(&d)
            .for_each(|dt_i, &d_i| {
                *dt_i += alpha * d_i;
            });
        // Same bitwise-identity requirement as the dense loop: the caller
        // certifies `beta + d_total`, so evaluate feasibility on exactly that
        // sum (#979 CTN cycle 86: boundary-landing iterate feasible in the
        // loop's arithmetic, 1.000e-8 > TOL in the wrapper's).
        x = beta + &d_total;
        g_cur = gradient + &hessian.dot(&d_total);
        values_x = ops.values(&x)?;

        let mut added_new_active = false;
        let mut working_set_repeated = false;
        if let Some(row) = blocking_row {
            active.push(row);
            is_active[row] = true;
            added_new_active = true;
            count_blocking_add += 1;
            log_active_set_transition("blocking-add", iteration, active.len(), Some(row));
            working_set_repeated =
                !record_active_working_set(&mut visited_working_sets, &active, &x, iteration);
        } else {
            // Unblocked full step: the iterate is now the minimizer of the
            // current working face — the next iteration must adjudicate
            // multipliers instead of re-measuring KKT-solve noise.
            face_minimized = true;
        }
        if working_set_repeated {
            ws_repeat_break = true;
            break;
        }

        // A zero-length blocking step changes only the row representation of
        // this active face; the primal point and its quadratic gradient have not
        // moved. A factored cone can have thousands of observation rows carrying
        // the same low-dimensional normal geometry, so continuing the add/drop
        // loop enumerates distinct row-ID combinations without primal progress.
        // The issue-979 CTN witness held a 92/93-row face and performed hundreds
        // of these swaps immediately; its row-count-derived ceiling permitted
        // roughly 96,000.
        //
        // This event is exactly a normal-cone identification problem at the
        // current x. Ask the shared factored separator for the tangent-cone
        // projection now. It adds only geometrically necessary omitted rows and
        // returns a full-set-feasible strict descent direction, or declines and
        // leaves the ordinary active-set loop in control. The trigger is a
        // mathematical no-progress event, not an iteration or wall-clock budget.
        let primal_step_norm = alpha.abs() * step_norm;
        if allow_projected_gradient_fallback && added_new_active && primal_step_norm <= tol_step {
            if let Some((fallback_direction, fallback_active)) =
                fallback_projected_gradient_direction_with_constraint_set(
                    beta, &x, &d_total, &g_cur, &active, ops,
                )?
            {
                if let Some(hint) = active_hint.as_mut() {
                    hint.clear();
                    hint.extend(fallback_active);
                }
                direction_out.assign(&fallback_direction);
                return Ok(());
            }
        }

        if active.is_empty() && !added_new_active {
            if let Some(hint) = active_hint.as_mut() {
                hint.clear();
            }
            direction_out.assign(&d_total);
            return Ok(());
        }
    }

    // Exit gate: same acceptance structure as the dense loop — primal
    // feasibility on the full set plus working-set KKT residuals on the
    // rank-reduced unit-row system.
    let compressed_working = ops.compress_working(&active)?;
    let mut residualw = Array1::<f64>::zeros(compressed_working.constraints.a.nrows());
    for r in 0..compressed_working.constraints.a.nrows() {
        residualw[r] =
            compressed_working.constraints.b[r] - compressed_working.constraints.a.row(r).dot(&x);
    }
    let (_, lambdaw) = solve_kkt_direction(
        hessian,
        &g_cur,
        &compressed_working.constraints.a,
        Some(&residualw),
    )?;
    let lambda_true = lambdaw.mapv(|lam_sys| -lam_sys);
    let (worst, row) = ops.max_violation(&values_x);
    let working_kkt = working_set_kkt_diagnostics_from_multipliers(
        &x,
        &g_cur,
        &compressed_working.constraints,
        &lambda_true,
        m,
    )?;
    let grad_inf = gradient_inf_norm(&g_cur);
    let stationarity_rel = working_kkt.stationarity / grad_inf.max(1.0);
    let step_inf = d_total.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
    let hd_total = hessian.dot(&d_total);
    let predicted_delta = gradient.dot(&d_total)
        + 0.5
            * d_total
                .iter()
                .zip(hd_total.iter())
                .map(|(a, b)| a * b)
                .sum::<f64>();
    let kkt_strong_ok = (working_kkt.stationarity <= ACTIVE_SET_KKT_STATIONARITY_TOL
        || stationarity_rel <= ACTIVE_SET_KKT_STATIONARITY_TOL)
        && working_kkt.complementarity <= ACTIVE_SET_KKT_COMPLEMENTARITY_TOL;
    let model_descent_ok =
        predicted_delta <= -ACTIVE_SET_MODEL_DESCENT_REL_TOL * (1.0 + grad_inf * step_inf);
    let degenerate_boundary_ok = compressed_working.is_degenerate_face()
        && worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
        && working_kkt.primal_feasibility <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
        && working_kkt.complementarity <= ACTIVE_SET_KKT_COMPLEMENTARITY_TOL
        && (working_kkt.stationarity <= ACTIVE_SET_KKT_DEGENERATE_STATIONARITY_TOL
            || stationarity_rel <= ACTIVE_SET_KKT_STATIONARITY_TOL);
    // Existence-form KKT certificate over the tight rows — the operator twin
    // of the dense loop's `nnls_certified` gate (see there for the full
    // rationale, including why it must run whenever the strong path does not
    // ACCEPT — strong stationarity with phantom negative duals is the target
    // case, not an exemption). Tightness is read off the batched values; only
    // the tight rows are gathered densely, so the factored cone never
    // materializes.
    let strong_path_accepts =
        kkt_strong_ok && working_kkt.dual_feasibility <= ACTIVE_SET_KKT_DUAL_FEASIBILITY_TOL;
    let mut nnls_closure: Option<(f64, usize)> = None;
    let nnls_certified = worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL && !strong_path_accepts && {
        let tight: Vec<usize> = (0..m)
            .filter(|&i| {
                ops.norms[i] > 0.0 && (values_x[i] - ops.bounds[i]) / ops.norms[i] <= tol_active
            })
            .collect();
        let tight_len = tight.len();
        match ops.set.gather_rows(&tight) {
            Ok(gathered) => nonnegative_cone_multipliers(&gathered.a, &g_cur)
                .map(|(_, projected)| {
                    let closure = projected.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
                    nnls_closure = Some((closure, tight_len));
                    closure <= ACTIVE_SET_KKT_STATIONARITY_TOL
                        || closure / grad_inf.max(1.0) <= ACTIVE_SET_KKT_STATIONARITY_TOL
                })
                .unwrap_or(false),
            Err(_) => false,
        }
    };
    if worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
        && ((working_kkt.dual_feasibility <= ACTIVE_SET_KKT_DUAL_FEASIBILITY_TOL
            && (kkt_strong_ok || (allow_projected_gradient_fallback && model_descent_ok)))
            || degenerate_boundary_ok
            || nnls_certified)
    {
        if let Some(hint) = active_hint.as_mut() {
            hint.clear();
            for group in &compressed_working.groups {
                if let Some(&active_pos) = group.first() {
                    hint.push(active[active_pos]);
                }
            }
        }
        direction_out.assign(&d_total);
        return Ok(());
    }
    let nnls_diag = match nnls_closure {
        Some((closure, tight_len)) => format!(
            "nnls_closure={closure:.3e} (tol={ACTIVE_SET_KKT_STATIONARITY_TOL:.1e}) over {tight_len} tight rows"
        ),
        None => "nnls_closure=not-evaluated".to_string(),
    };
    let churn_diag = format!(
        "iterations={iterations_used}/{max_iterations} transitions[blocking-add={count_blocking_add} stationary-add={count_stationary_add} release={count_release}] ws_repeat_break={ws_repeat_break}"
    );
    if !allow_projected_gradient_fallback {
        return Err(EstimationError::ParameterConstraintViolation(format!(
            "operator-constrained active-set did not certify the strict-convex projection QP; max scaled violation={worst:.3e} at row {row}; KKT[primal={:.3e}, dual={:.3e}, comp={:.3e}, stat={:.3e}, active={}/{}]; {nnls_diag}; {churn_diag}",
            working_kkt.primal_feasibility,
            working_kkt.dual_feasibility,
            working_kkt.complementarity,
            working_kkt.stationarity,
            working_kkt.n_active,
            working_kkt.n_constraints,
        )));
    }
    if let Some((fallback_direction, fallback_active)) =
        fallback_projected_gradient_direction_with_constraint_set(
            beta, &x, &d_total, &g_cur, &active, ops,
        )?
    {
        if let Some(hint) = active_hint.as_mut() {
            hint.clear();
            hint.extend(fallback_active);
        }
        direction_out.assign(&fallback_direction);
        return Ok(());
    }
    Err(EstimationError::ParameterConstraintViolation(format!(
        "operator-constrained Newton active-set failed to converge; max scaled violation={worst:.3e} at row {row}; KKT[primal={:.3e}, dual={:.3e}, comp={:.3e}, stat={:.3e}, active={}/{}]; {nnls_diag}; {churn_diag}; projected-gradient fallback declined",
        working_kkt.primal_feasibility,
        working_kkt.dual_feasibility,
        working_kkt.complementarity,
        working_kkt.stationarity,
        working_kkt.n_active,
        working_kkt.n_constraints,
    )))
}

/// Strictly-interior projection onto a [`ConstraintSet`]: the operator
/// analogue of [`project_point_strictly_into_feasible_cone`]. Dense sets
/// delegate to the dense projection (including its anti-parallel equality
/// lift); the factored cone is homogeneous and one-sided, so the projection
/// is a single identity-Hessian QP against the margin-shifted rows.
///
/// A refusal is a typed [`EstimationError::ParameterConstraintViolation`]
/// naming the failing condition (dimension mismatch, non-finite iterate, or
/// the specific row whose half-margin the projection could not clear), never a
/// bare `None`: the caller decides whether that refusal is fatal or a soft
/// fallback, but it is never a silent one.
pub fn project_point_strictly_into_feasible_constraint_set(
    point: &Array1<f64>,
    set: &ConstraintSet,
) -> Result<Array1<f64>, EstimationError> {
    match set {
        ConstraintSet::Dense(dense) => {
            // The dense arm keeps its `Option` contract (it has other callers);
            // its refusal is retyped here so this seam carries a diagnostic
            // rather than a bare `None`.
            project_point_strictly_into_feasible_cone(point, dense).ok_or_else(|| {
                EstimationError::ParameterConstraintViolation(
                    "dense strict-interior projection could not certify a feasible point"
                        .to_string(),
                )
            })
        }
        _ => {
            let repair_guard = FeasibilityRepairGuard::enter().ok_or_else(|| {
                EstimationError::ParameterConstraintViolation(format!(
                    "strict-interior projection exceeded feasibility-repair depth {MAX_FEASIBILITY_REPAIR_DEPTH}"
                ))
            })?;
            let p = point.len();
            if set.ncols() != p {
                return Err(EstimationError::ParameterConstraintViolation(format!(
                    "strict-interior projection dimension mismatch: point length {p} != constraint columns {}",
                    set.ncols()
                )));
            }
            let ops = ConstraintSetOps::new(set, ACTIVE_SET_INTERIOR_SEED_MARGIN)?;
            let identity = Array2::<f64>::eye(p);
            // min ½‖β − point‖² ⇒ Hessian = I, gradient at `point` = 0;
            // the margin-shifted rows carry the strict-interior shift.
            let mut direction = Array1::<f64>::zeros(p);
            let gradient = Array1::<f64>::zeros(p);
            let max_iterations = (p + set.nrows() + 8) * 4;
            solve_newton_direction_with_constraint_set_impl(
                &identity,
                &gradient,
                point,
                &ops,
                &mut direction,
                None,
                max_iterations,
                true,
            )?;
            let beta = point + &direction;
            if beta.iter().any(|v| !v.is_finite()) {
                return Err(EstimationError::ParameterConstraintViolation(
                    "strict-interior projection produced a non-finite iterate".to_string(),
                ));
            }
            // Certify against the ORIGINAL (unshifted) rows with half-margin
            // clearance, mirroring the dense projection's exit contract.
            const SEED_FEASIBILITY_TOL: f64 = 1e-9;
            let unshifted = ConstraintSetOps::new(set, 0.0)?;
            let values = unshifted.values(&beta)?;
            let half_margin = 0.5 * ACTIVE_SET_INTERIOR_SEED_MARGIN - SEED_FEASIBILITY_TOL;
            for row in 0..unshifted.nrows() {
                if unshifted.norms[row] <= 0.0 {
                    continue;
                }
                let slack = unshifted.scaled_slack(&values, row);
                if slack < half_margin {
                    return Err(EstimationError::ParameterConstraintViolation(format!(
                        "strict-interior projection could not clear the half-margin at row {row}: \
                         scaled slack {slack:.3e} < {half_margin:.3e}"
                    )));
                }
            }
            drop(repair_guard);
            Ok(beta)
        }
    }
}

/// Operator-carrier constrained quadratic solve: minimize
/// `½ βᵀHβ − rhsᵀβ` subject to the [`ConstraintSet`]. Dense sets take the
/// existing dense path byte-identically; the factored cone runs the operator
/// active-set loop. Same public feasibility contract as
/// [`solve_quadratic_with_linear_constraints`]: the returned point is
/// feasible to [`ACTIVE_SET_PRIMAL_FEASIBILITY_TOL`] or the solve errors.
pub fn solve_quadratic_with_constraint_set(
    hessian: &Array2<f64>,
    rhs: &Array1<f64>,
    beta_start: &Array1<f64>,
    set: &ConstraintSet,
    warm_active_set: Option<&[usize]>,
) -> Result<(Array1<f64>, Vec<usize>), EstimationError> {
    match set {
        ConstraintSet::Dense(dense) => solve_quadratic_with_linear_constraints(
            hessian,
            rhs,
            beta_start,
            dense,
            warm_active_set,
        ),
        _ => {
            if hessian.ncols() != hessian.nrows()
                || rhs.len() != hessian.nrows()
                || beta_start.len() != hessian.nrows()
                || set.ncols() != hessian.nrows()
            {
                crate::bail_invalid_estim!(
                    "operator-constrained quadratic solve: system dimension mismatch"
                );
            }
            let ops = ConstraintSetOps::new(set, 0.0)?;
            let gradient = hessian.dot(beta_start) - rhs;
            let mut delta = Array1::<f64>::zeros(beta_start.len());
            let mut active_hint = warm_active_set.map_or_else(Vec::new, |active| active.to_vec());
            let max_iterations = (beta_start.len() + set.nrows() + 8) * 4;
            solve_newton_direction_with_constraint_set_impl(
                hessian,
                &gradient,
                beta_start,
                &ops,
                &mut delta,
                Some(&mut active_hint),
                max_iterations,
                true,
            )?;
            let candidate = beta_start + &delta;
            let candidate_values = ops.values(&candidate)?;
            let (worst, _) = ops.max_violation(&candidate_values);
            if worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
                return Ok((candidate, active_hint));
            }
            let repaired = project_point_strictly_into_feasible_constraint_set(&candidate, set)
                .ok()
                .filter(|repaired_point| {
                    ops.values(repaired_point)
                        .map(|values| ops.max_violation(&values).0)
                        .map(|violation| violation <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL)
                        .unwrap_or(false)
                });
            match repaired {
                Some(feasible) => {
                    let feasible_values = ops.values(&feasible)?;
                    let active: Vec<usize> = (0..ops.nrows())
                        .filter(|&row| {
                            ops.norms[row] > 0.0
                                && ops.scaled_slack(&feasible_values, row)
                                    <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
                        })
                        .collect();
                    Ok((feasible, active))
                }
                None => Err(EstimationError::ParameterConstraintViolation(format!(
                    "operator-constrained quadratic solve returned an infeasible iterate \
                     (max scaled violation {worst:.3e}) and no feasible projection could be \
                     certified onto the constraint cone",
                ))),
            }
        }
    }
}

pub(crate) fn solve_newton_direction_with_linear_constraints(
    hessian: &Array2<f64>,
    gradient: &Array1<f64>,
    beta: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
    direction_out: &mut Array1<f64>,
    active_hint: Option<&mut Vec<usize>>,
) -> Result<(), EstimationError> {
    let max_iterations = (gradient.len() + constraints.a.nrows() + 8) * 4;
    solve_newton_direction_with_linear_constraints_impl(
        hessian,
        gradient,
        beta,
        constraints,
        direction_out,
        active_hint,
        max_iterations,
        true,
    )
}

pub fn solve_quadratic_with_linear_constraints(
    hessian: &Array2<f64>,
    rhs: &Array1<f64>,
    beta_start: &Array1<f64>,
    constraints: &LinearInequalityConstraints,
    warm_active_set: Option<&[usize]>,
) -> Result<(Array1<f64>, Vec<usize>), EstimationError> {
    if hessian.ncols() != hessian.nrows()
        || rhs.len() != hessian.nrows()
        || beta_start.len() != hessian.nrows()
        || constraints.a.ncols() != hessian.nrows()
    {
        crate::bail_invalid_estim!("constrained quadratic solve: system dimension mismatch");
    }
    // Canonicalize at the chokepoint: reject non-finite / infeasible-zero rows
    // and unit-normalize every row, so all downstream slack, activation, and
    // rank tolerances are geometric (scale-free) regardless of the units the
    // caller expressed the constraints in. Row order is preserved, so
    // `warm_active_set` indices and the returned active ids stay valid.
    let constraints = constraints.canonicalized().map_err(|e| {
        EstimationError::ParameterConstraintViolation(format!(
            "constrained quadratic solve: invalid constraint system: {e}"
        ))
    })?;
    let constraints = &constraints;
    let gradient = hessian.dot(beta_start) - rhs;
    let mut delta = Array1::<f64>::zeros(beta_start.len());
    let mut active_hint = warm_active_set.map_or_else(Vec::new, |active| active.to_vec());
    solve_newton_direction_with_linear_constraints(
        hessian,
        &gradient,
        beta_start,
        constraints,
        &mut delta,
        Some(&mut active_hint),
    )?;
    let candidate = beta_start + &delta;
    // FINAL FEASIBILITY CONTRACT (#1108). The active-set inner step computes its
    // Newton direction only against the ACTIVE working rows and line-searches
    // `alpha` against the inactive rows; a row whose approach rate falls inside
    // `boundary_hit_step_fraction`'s directional tolerance is not clipped, so the
    // returned `candidate` can overshoot a currently-inactive row and land
    // outside the cone by a small-but-gate-failing amount (the interval-censored
    // survival surrogate leaked 5.5e-3..2.2e-2 raw at cycles 3/9 here, accepted
    // into `states`, then rejected by the next cycle's `check_linear_feasibility`
    // — `infeasible iterate`). The solver's PUBLIC CONTRACT is a feasible point;
    // enforce it at the single chokepoint every caller flows through. A genuinely
    // converged feasible solve has `worst ~ 0`, so this is a no-op there and does
    // not perturb a well-conditioned constrained fit. When the step did leak,
    // project the iterate onto the feasible cone (the exact projection the #1108
    // diag proves reaches ~0 violation: the nearest strictly-interior point) and
    // return THAT. If no feasible repair is achievable, surface the active-set
    // error rather than returning an infeasible point.
    let (worst, _) = max_linear_constraint_violation(&candidate, constraints);
    if worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL {
        return Ok((candidate, active_hint));
    }
    let repaired = project_point_strictly_into_feasible_cone(&candidate, constraints).filter(|p| {
        max_linear_constraint_violation(p, constraints).0 <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL
    });
    match repaired {
        Some(feasible) => {
            let active = canonicalize_active_constraint_ids(&feasible, constraints, &[])?;
            Ok((feasible, active))
        }
        None => Err(EstimationError::ParameterConstraintViolation(format!(
            "constrained quadratic solve returned an infeasible iterate \
             (max scaled violation {worst:.3e}) and no feasible projection could be \
             certified onto the constraint cone",
        ))),
    }
}

#[cfg(test)]
mod tests {
    use super::{
        ACTIVE_SET_INTERIOR_SEED_MARGIN, ACTIVE_SET_PRIMAL_FEASIBILITY_TOL, ConstraintSet,
        ConstraintRowId, ConstraintSetOps, ConstraintSetReducedFace, LinearInequalityConstraints,
        active_set_boundary_hit_step_fraction, compute_constraint_kkt_diagnostics,
        constraint_set_rows_tight_at_point, fallback_projected_gradient_direction,
        khatri_rao_cone_reduced_face,
        fallback_projected_gradient_direction_with_constraint_set, moreau_projection_via_primal_qp,
        nnls_tangent_cone_projection_fallback, nonnegative_cone_multipliers,
        project_point_strictly_into_feasible_cone,
        project_point_strictly_into_feasible_constraint_set,
        project_stationarity_residual_on_constraint_cone,
        project_stationarity_residual_on_constraint_set,
        rank_reduce_rows_pivoted_qr_with_dependence, record_active_working_set,
        scaled_constraint_slack, solve_newton_direction_with_linear_constraints_impl,
        solve_quadratic_with_constraint_set, solve_quadratic_with_linear_constraints,
    };
    use approx::assert_relative_eq;
    use gam_problem::KhatriRaoConeConstraints;
    use ndarray::{Array1, Array2, array};

    #[test]
    fn working_set_cycle_detection_requires_the_same_primal_point() {
        let mut visited = std::collections::HashSet::new();
        let x0 = array![0.0_f64, 1.0];
        let x1 = array![0.5_f64, 1.0];

        assert!(record_active_working_set(&mut visited, &[3, 1], &x0, 0));
        assert!(record_active_working_set(&mut visited, &[1, 3], &x1, 1));
        assert!(!record_active_working_set(&mut visited, &[3, 1], &x1, 2));
    }

    #[test]
    fn boundary_ratio_lands_on_the_exact_boundary_and_blocks_at_it() {
        // Strictly feasible row: the clipped step lands ON the boundary, not
        // TOL past it — the exit gate then re-derives the same zero slack
        // instead of coin-flipping on band-edge rounding.
        let alpha = active_set_boundary_hit_step_fraction(0.1, -1.0, 1.0)
            .expect("a strictly feasible row moving toward its boundary must clip");
        assert_relative_eq!(alpha, 0.1, epsilon = 0.0);
        assert_relative_eq!(0.1 + alpha * -1.0, 0.0, epsilon = 0.0);

        // A row at (or a rounding hair past) its boundary and moving outward
        // clips the step to zero: it becomes blocking, and the escape is
        // adjudicated structurally (projected-gradient tangent fallback plus
        // post-full-step multiplier adjudication) rather than by overshooting
        // into the certified tolerance band.
        let blocked = active_set_boundary_hit_step_fraction(-2.5e-15, -1.0, 1.0)
            .expect("an at-boundary outward-moving row must block");
        assert_eq!(blocked, 0.0);
    }

    #[test]
    fn warm_face_rows_are_point_local_for_dense_and_operator_constraints() {
        // The previous QP endpoint was x=0, where x>=0 binds, but a trust
        // step accepted only an interior point x=1. Reusing row 0 as an
        // equality at x=1 would solve the wrong problem and drive x back to
        // the stale boundary. The actual quadratic has its feasible minimizer
        // at x=2, so both carriers must discard the slack warm row and return
        // the unconstrained interior minimizer with an empty face.
        let hessian = array![[1.0_f64]];
        let rhs = array![2.0_f64];
        let interior = array![1.0_f64];
        let dense = LinearInequalityConstraints::new(array![[1.0]], array![0.0])
            .expect("one-dimensional half-line");
        let (dense_solution, dense_active) =
            solve_quadratic_with_linear_constraints(&hessian, &rhs, &interior, &dense, Some(&[0]))
                .expect("dense stale-face solve");
        assert_relative_eq!(dense_solution[0], 2.0, epsilon = 1e-12);
        assert!(dense_active.is_empty());

        let factor = std::sync::Arc::new(array![[1.0_f64]]);
        let cone = KhatriRaoConeConstraints::new(factor, vec![0], 1)
            .expect("one-dimensional factored half-line");
        let operator = ConstraintSet::KhatriRaoCone(cone);
        let stale_terminal_face = constraint_set_rows_tight_at_point(&operator, &interior, &[0])
            .expect("terminal face classification");
        assert!(stale_terminal_face.is_empty());
        let (operator_solution, operator_active) =
            solve_quadratic_with_constraint_set(&hessian, &rhs, &interior, &operator, Some(&[0]))
                .expect("operator stale-face solve");
        assert_relative_eq!(operator_solution[0], 2.0, epsilon = 1e-12);
        assert!(operator_active.is_empty());
    }

    /// A `β = 0` seed sits on the boundary of EVERY row of a homogeneous
    /// (`b = 0`) convex/concave second-difference cone — it is the cone vertex.
    /// The strict-interior projection must move it to a point with a strictly
    /// positive scaled slack on every row, so the inner active-set QP starts
    /// from an EMPTY working set rather than an all-rows-active degenerate face
    /// (the #873 cache-dependence root cause). The zero seed is the worst case:
    /// the nearest interior point is unique up to the margin, and a buggy
    /// "min-norm" feasibility fallback would return `0` again.
    #[test]
    fn strict_interior_projection_lifts_vertex_seed_off_every_constraint_row() {
        // Signed second-difference rows of a 5-coefficient concave smooth:
        // -(β_{i+2} - 2β_{i+1} + β_i) ≥ 0 for i = 0..3.
        let p = 5usize;
        let rows = p - 2;
        let mut a = Array2::<f64>::zeros((rows, p));
        for i in 0..rows {
            a[[i, i]] = -1.0;
            a[[i, i + 1]] = 2.0;
            a[[i, i + 2]] = -1.0;
        }
        let constraints = LinearInequalityConstraints::new(a, Array1::zeros(rows))
            .expect("test constraint shape invariant");

        let vertex = Array1::<f64>::zeros(p);
        // The vertex is feasible (all rows exactly tight) but on every boundary.
        for i in 0..rows {
            assert!(
                scaled_constraint_slack(&vertex, &constraints, i).abs() < 1e-12,
                "vertex seed should sit exactly on row {i}"
            );
        }

        let interior = project_point_strictly_into_feasible_cone(&vertex, &constraints)
            .expect("strict-interior projection of the vertex must succeed");
        let min_slack = (0..rows)
            .map(|i| scaled_constraint_slack(&interior, &constraints, i))
            .fold(f64::INFINITY, f64::min);
        assert!(
            min_slack >= 0.5 * ACTIVE_SET_INTERIOR_SEED_MARGIN,
            "projected seed must be strictly interior on every row; min scaled slack = {min_slack:.3e}"
        );
    }

    /// Mirrors `s(x, shape=concave, bc=clamped)`: shape curvature reparameterized
    /// to independent coordinate lower bounds `γ_j ≥ 0` (genuine one-sided rows),
    /// MERGED with a boundary condition encoded as an anti-parallel inequality
    /// PAIR `{r·β ≥ t, −r·β ≥ −t}` (an equality `r·β = t`). A naive
    /// shift-every-row-inward projection turns that pair into the empty set
    /// `t+δ ≤ r·β ≤ t−δ`, fails, and the caller falls back to the cone vertex —
    /// silently reintroducing the #873 seed for the combined case. The
    /// anti-parallel-aware margin must leave the equality pair tight while still
    /// pushing the genuine shape rows strictly interior.
    #[test]
    fn strict_interior_projection_keeps_equality_pairs_tight_with_shape_bounds() {
        let p = 5usize;
        // Rows 0..3: shape lower bounds γ_2,γ_3,γ_4 ≥ 0 (homogeneous, b = 0).
        // Rows 3,4: endpoint equality β_0 = 0 as {e_0·β ≥ 0, −e_0·β ≥ 0}.
        let m = 3 + 2;
        let mut a = Array2::<f64>::zeros((m, p));
        a[[0, 2]] = 1.0;
        a[[1, 3]] = 1.0;
        a[[2, 4]] = 1.0;
        a[[3, 0]] = 1.0;
        a[[4, 0]] = -1.0;
        let constraints = LinearInequalityConstraints::new(a, Array1::zeros(m))
            .expect("test constraint shape invariant");

        // A seed that violates the shape bounds (negative curvature coords) and
        // the equality (β_0 ≠ 0).
        let point = Array1::from_vec(vec![0.7, -0.2, -0.5, -0.3, -0.1]);
        let seed = project_point_strictly_into_feasible_cone(&point, &constraints).expect(
            "strict-interior projection must succeed when an equality pair is present, \
             not collapse to the empty set and fall back to the vertex",
        );

        // Genuine one-sided shape rows are pushed strictly interior.
        for i in 0..3 {
            assert!(
                scaled_constraint_slack(&seed, &constraints, i)
                    >= 0.4 * ACTIVE_SET_INTERIOR_SEED_MARGIN,
                "shape row {i} not strictly interior: scaled slack = {:.3e}",
                scaled_constraint_slack(&seed, &constraints, i)
            );
        }
        // The equality pair stays tight (β_0 ≈ 0), i.e. the seed is projected
        // onto the boundary hyperplane rather than shifted off it.
        assert!(
            seed[0].abs() <= 1e-6,
            "boundary equality must be enforced, got β_0 = {:.3e}",
            seed[0]
        );
    }

    /// A seed that already carries genuine (concave) curvature and clears the
    /// interior margin is returned essentially unchanged — the projection only
    /// nudges boundary/violating seeds, it does not discard usable curvature.
    #[test]
    fn strict_interior_projection_preserves_a_curvature_carrying_seed() {
        let p = 5usize;
        let rows = p - 2;
        let mut a = Array2::<f64>::zeros((rows, p));
        for i in 0..rows {
            a[[i, i]] = -1.0;
            a[[i, i + 1]] = 2.0;
            a[[i, i + 2]] = -1.0;
        }
        let constraints = LinearInequalityConstraints::new(a, Array1::zeros(rows))
            .expect("test constraint shape invariant");
        // A strictly concave coefficient profile (-(j-2)^2): every second
        // difference is -(-2) = +2 > 0 after the concave sign flip, well above
        // the interior margin.
        let seed = Array1::from_iter((0..p).map(|j| -((j as f64 - 2.0).powi(2))));
        let projected = project_point_strictly_into_feasible_cone(&seed, &constraints)
            .expect("already-interior seed must project");
        let max_move = seed
            .iter()
            .zip(projected.iter())
            .map(|(a, b)| (a - b).abs())
            .fold(0.0_f64, f64::max);
        assert!(
            max_move < 1e-3,
            "strictly-interior curvature-carrying seed should be preserved; max move = {max_move:.3e}"
        );
    }

    #[test]
    fn maxiter_accepts_current_boundary_solution() {
        let hessian = array![[1.0]];
        let gradient = array![-1.0];
        let beta = array![0.0];
        let constraints = LinearInequalityConstraints {
            a: array![[-1.0]],
            b: array![-0.1],
        };
        let mut direction = Array1::zeros(1);
        let mut active_hint = Vec::new();

        solve_newton_direction_with_linear_constraints_impl(
            &hessian,
            &gradient,
            &beta,
            &constraints,
            &mut direction,
            Some(&mut active_hint),
            1,
            true,
        )
        .expect("solver should accept the current boundary solution at the iteration limit");

        assert_relative_eq!(direction[0], 0.1, epsilon = 1e-12);
        assert_eq!(active_hint, vec![0]);
    }

    #[test]
    fn projected_gradient_releases_a_boundary_with_negative_multiplier() {
        // At x=0 under x>=0, gradient=-1 points toward increasing x: the
        // boundary multiplier from an equality-only KKT projection is negative
        // and the correct feasible descent direction leaves the face. The old
        // equality-tangent projection erased this direction and could falsely
        // call the wrong active face stationary.
        let x = array![0.0_f64];
        let d_total = array![0.0_f64];
        let gradient = array![-1.0_f64];
        let constraints =
            LinearInequalityConstraints::new(array![[1.0]], array![0.0]).expect("one-sided bound");

        let (direction, active) = fallback_projected_gradient_direction(
            &x,
            &x,
            &d_total,
            &gradient,
            &constraints,
            &constraints,
        )
        .expect("fallback evaluation")
        .expect("negative-multiplier face must have a feasible descent escape");

        assert_relative_eq!(direction[0], 1.0, epsilon = 1e-12);
        assert!(gradient.dot(&direction) < 0.0);
        assert!(active.is_empty(), "descent moves strictly into the cone");
    }

    #[test]
    fn rank_reduce_zero_rows_returns_empty_working_set() {
        let a = array![[0.0, 0.0], [0.0, 0.0],];
        let b = array![0.0, 0.0];
        let groups = vec![vec![0], vec![1]];

        let (a_out, b_out, groups_out, _) =
            rank_reduce_rows_pivoted_qr_with_dependence(a, b, groups);

        assert_eq!(a_out.nrows(), 0);
        assert_eq!(a_out.ncols(), 2);
        assert_eq!(b_out.len(), 0);
        assert!(groups_out.is_empty());
    }

    #[test]
    fn cone_projection_solves_nonnegative_least_squares_not_one_way_pruning() {
        let active_a = array![
            [0.85258593, -0.77270261],
            [-1.22152485, 2.05129351],
            [0.22794844, 1.56987265],
        ];
        let residual = array![-0.50524761, -1.10104911];

        let (projected, multipliers) =
            project_stationarity_residual_on_constraint_cone(&residual, &active_a)
                .expect("cone projection should solve");

        let row0 = active_a.row(0);
        let expected_mu0 = row0.dot(&residual) / row0.dot(&row0);
        assert_relative_eq!(multipliers[0], expected_mu0, epsilon = 1e-8);
        assert_relative_eq!(multipliers[1], 0.0, epsilon = 1e-10);
        assert_relative_eq!(multipliers[2], 0.0, epsilon = 1e-10);

        let raw_norm2 = residual.dot(&residual);
        let projected_norm2 = projected.dot(&projected);
        assert!(
            projected_norm2 < raw_norm2 - 0.1,
            "NNLS projection should keep the improving active row: raw={raw_norm2:.6e}, projected={projected_norm2:.6e}"
        );
        let dual = active_a.dot(&projected);
        for (idx, (&mu, &w)) in multipliers.iter().zip(dual.iter()).enumerate() {
            if mu <= 1e-10 {
                assert!(
                    w <= 1e-8,
                    "inactive cone generator {idx} has positive reduced gradient {w:.3e}"
                );
            }
        }
    }

    /// The direct Lawson–Hanson route must agree with the primal-QP Moreau
    /// projection wherever the latter succeeds: both compute the projection
    /// of `residual` onto the polar of the generated cone.
    #[test]
    fn nnls_moreau_projection_matches_primal_qp_route() {
        let cases: Vec<(Array2<f64>, Array1<f64>)> = vec![
            (
                array![
                    [0.85258593, -0.77270261],
                    [-1.22152485, 2.05129351],
                    [0.22794844, 1.56987265],
                ],
                array![-0.50524761, -1.10104911],
            ),
            (array![[1.0, 0.0], [0.0, 1.0]], array![3.0, -2.0]),
            (
                array![[1.0, 1.0, 0.0], [1.0, -1.0, 0.0], [2.0, 2.0, 0.0]],
                array![1.5, 0.25, -0.75],
            ),
        ];
        for (rows, target) in cases {
            let qp = moreau_projection_via_primal_qp(&target, &rows)
                .expect("primal QP route must solve these well-posed instances");
            let (lambda, projected) = nonnegative_cone_multipliers(&rows, &target)
                .expect("LH route must solve the same instances");
            for (left, right) in qp.0.iter().zip(projected.iter()) {
                assert_relative_eq!(left, right, epsilon = 1e-8);
            }
            // λ ≥ 0 and exact reconstruction by construction.
            assert!(lambda.iter().all(|&v| v >= 0.0));
            let reconstructed = &target - &rows.t().dot(&lambda);
            for (left, right) in reconstructed.iter().zip(projected.iter()) {
                assert_relative_eq!(left, right, epsilon = 1e-12);
            }
        }
    }

    #[test]
    fn nnls_projects_axis_cone_exactly() {
        let rows = array![[1.0, 0.0], [0.0, 1.0]];
        let target = array![3.0, -2.0];
        let (lambda, projected) =
            nonnegative_cone_multipliers(&rows, &target).expect("axis cone NNLS");
        assert_relative_eq!(lambda[0], 3.0, epsilon = 1e-10);
        assert_relative_eq!(lambda[1], 0.0, epsilon = 1e-10);
        assert_relative_eq!(projected[0], 0.0, epsilon = 1e-10);
        assert_relative_eq!(projected[1], -2.0, epsilon = 1e-10);
    }

    /// A dependent active row that is weakly aligned with every kept row
    /// individually (`a3 = (a1 + a2)/(2ε)`, pairwise alignment ≈ ε) breaks the
    /// single-target multiplier attribution: `λ/coeff` explodes by `1/ε` and
    /// manufactures phantom huge duals. The existence-form certificate sees the
    /// exact nonnegative closure `g = 1·a3` and must certify stationarity.
    #[test]
    fn nnls_closes_stationarity_on_weakly_aligned_dependent_face() {
        let eps = 1e-8_f64;
        let rows = array![[1.0, eps], [-1.0, eps], [0.0, 1.0]];
        let target = array![0.0, 1.0];
        let (lambda, projected) =
            nonnegative_cone_multipliers(&rows, &target).expect("dependent-face NNLS");
        let closure = projected.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
        assert!(
            closure <= 1e-10,
            "λ = e3 closes stationarity exactly; got closure {closure:.3e}"
        );
        assert!(lambda.iter().all(|&v| v >= 0.0));
    }

    /// End-to-end: the constrained Newton solve on the same weakly-aligned
    /// degenerate face must certify the vertex instead of chasing phantom
    /// negative duals into a working-set cycle and refusing (#2298 survival
    /// monotonicity faces, #979 CTN faces).
    #[test]
    fn degenerate_face_with_weak_alignment_certifies_instead_of_cycling() {
        let eps = 1e-8_f64;
        let a = array![[1.0, eps], [-1.0, eps], [0.0, 1.0]];
        let b = array![0.0, 0.0, 0.0];
        let constraints = LinearInequalityConstraints::new(a.clone(), b).expect("constraints");
        let hessian = Array2::<f64>::eye(2);
        // KKT at d* = 0 with λ = e3 ≥ 0: gradient = A^T e3 = a3.
        let gradient = array![0.0, 1.0];
        let beta = array![0.0, 0.0];
        let mut direction = Array1::<f64>::zeros(2);
        solve_newton_direction_with_linear_constraints_impl(
            &hessian,
            &gradient,
            &beta,
            &constraints,
            &mut direction,
            None,
            64,
            false,
        )
        .expect("the vertex is a certified KKT point; refusal is the #2298 defect");
        let step = direction.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
        assert!(
            step <= 1e-8,
            "optimum is the vertex itself; got |d|∞ = {step:.3e}"
        );
    }

    /// #979 CTN plateau regression: when the operator-path primal projection
    /// QP refuses a degenerate fully-pinned vertex (its single-face multiplier
    /// attribution reports a phantom negative dual), the Lawson-Hanson Moreau
    /// fallback must certify the projection instead of surrendering to the
    /// unprojected residual — the measured 1.447e3 eternal plateau was exactly
    /// `residual` returned raw because this fallback did not exist.
    #[test]
    fn nnls_fallback_certifies_pinned_degenerate_vertex_projection_979() {
        // Four generators in R^3 (degenerate: a4 = a1 + a2), all tight at the
        // origin. The stationarity residual is a nonnegative combination, so
        // the projected residual is exactly zero.
        let a = array![
            [1.0_f64, 0.0, 0.0],
            [0.0, 1.0, 0.0],
            [0.0, 0.0, 1.0],
            [1.0, 1.0, 0.0],
        ];
        let b = array![0.0_f64, 0.0, 0.0, 0.0];
        let set = ConstraintSet::Dense(
            LinearInequalityConstraints::new(a, b).expect("degenerate vertex cone"),
        );
        let beta = array![0.0_f64, 0.0, 0.0];
        let residual = array![3.0_f64, 2.0, 0.0]; // = a1 + 2·a4
        let (projected, active) =
            nnls_tangent_cone_projection_fallback(&residual, &beta, &set, &[0, 1, 2, 3], &[0, 1])
                .expect("fallback must solve the degenerate vertex");
        let closure = projected.iter().fold(0.0_f64, |acc, &v| acc.max(v.abs()));
        assert!(
            closure <= 1e-9,
            "residual is in the cone; projection must close to zero, got {closure:.3e}"
        );
        assert!(!active.is_empty(), "a supported face must be reported");

        // A component outside the cone must survive the projection exactly.
        let outside = array![1.0_f64, 0.0, -1.0];
        let (projected_outside, _) =
            nnls_tangent_cone_projection_fallback(&outside, &beta, &set, &[0, 1, 2, 3], &[])
                .expect("fallback must solve the outside-component case");
        assert_relative_eq!(projected_outside[0], 0.0, epsilon = 1e-9);
        assert_relative_eq!(projected_outside[1], 0.0, epsilon = 1e-9);
        assert_relative_eq!(projected_outside[2], -1.0, epsilon = 1e-9);
    }

    /// The fallback is a KKT certificate input, so a working-set row that is
    /// NOT tight at `beta` must never enter the generator set: a residual
    /// aligned with a slack row must stay unprojected rather than be absorbed
    /// by a constraint that is not active at the iterate.
    #[test]
    fn nnls_fallback_excludes_rows_not_tight_at_beta() {
        let a = array![[1.0_f64, 0.0], [0.0, 1.0]];
        let b = array![0.0_f64, -1.0]; // row 1 has slack 1 at the origin
        let set = ConstraintSet::Dense(
            LinearInequalityConstraints::new(a, b).expect("half-tight system"),
        );
        let beta = array![0.0_f64, 0.0];
        let residual = array![0.0_f64, 1.0];
        let (projected, active) =
            nnls_tangent_cone_projection_fallback(&residual, &beta, &set, &[0, 1], &[])
                .expect("fallback must solve the half-tight system");
        assert_relative_eq!(projected[1], 1.0, epsilon = 1e-12);
        assert!(
            !active.contains(&1),
            "slack row 1 must not appear in the certified face"
        );
    }

    #[test]
    fn cone_projection_preserves_original_multiplier_units_after_row_canonicalization() {
        let residual = array![2.0, -1.0];
        let unit_row = array![[1.0, 0.0]];
        let scaled_row = array![[4.0, 0.0]];

        let (projected_unit, multiplier_unit) =
            project_stationarity_residual_on_constraint_cone(&residual, &unit_row)
                .expect("unit-row cone projection should solve");
        let (projected_scaled, multiplier_scaled) =
            project_stationarity_residual_on_constraint_cone(&residual, &scaled_row)
                .expect("scaled-row cone projection should solve");

        assert_relative_eq!(projected_unit[0], 0.0, epsilon = 1e-12);
        assert_relative_eq!(projected_unit[1], -1.0, epsilon = 1e-12);
        assert_relative_eq!(projected_scaled[0], projected_unit[0], epsilon = 1e-12);
        assert_relative_eq!(projected_scaled[1], projected_unit[1], epsilon = 1e-12);
        assert_relative_eq!(multiplier_unit[0], 2.0, epsilon = 1e-12);
        assert_relative_eq!(multiplier_scaled[0], 0.5, epsilon = 1e-12);

        let reconstructed_unit = &residual - &unit_row.t().dot(&multiplier_unit);
        let reconstructed_scaled = &residual - &scaled_row.t().dot(&multiplier_scaled);
        assert_relative_eq!(reconstructed_unit[0], projected_unit[0], epsilon = 1e-12);
        assert_relative_eq!(
            reconstructed_scaled[0],
            projected_scaled[0],
            epsilon = 1e-12
        );
    }

    // #500: the KKT primal residual must be the *geometric* distance to the
    // constraint hyperplane — invariant to how the constraint row is scaled.
    // A B-spline endpoint-derivative clamp carries a large row norm, so the
    // raw slack `a·β − b` of a near-feasible iterate is inflated by ‖a‖ and a
    // downstream raw primal gate would spuriously refuse it. The same geometry
    // expressed with a unit-norm row must yield the same primal.
    #[test]
    fn kkt_primal_is_per_row_scale_invariant() {
        // β sits 2.071e-8 on the infeasible side of the hyperplane `row·β ≥ 0`
        // (the exact geometric residual reported in #500's startup abort).
        let geometric_violation = 2.071e-8_f64;
        let gradient = Array1::<f64>::zeros(2);

        // Unit-norm row: raw slack == geometric distance.
        let beta_unit = array![-geometric_violation, 0.0];
        let unit = LinearInequalityConstraints {
            a: array![[1.0, 0.0]],
            b: array![0.0],
        };
        let diag_unit = compute_constraint_kkt_diagnostics(&beta_unit, &gradient, &unit);

        // Same hyperplane, row scaled ×1000: raw slack would be 2.071e-5, but
        // the *scaled* primal must still equal the geometric distance.
        let beta_big = array![-geometric_violation, 0.0];
        let big = LinearInequalityConstraints {
            a: array![[1000.0, 0.0]],
            b: array![0.0],
        };
        let diag_big = compute_constraint_kkt_diagnostics(&beta_big, &gradient, &big);

        assert_relative_eq!(
            diag_unit.primal_feasibility,
            geometric_violation,
            epsilon = 1e-14
        );
        assert_relative_eq!(
            diag_big.primal_feasibility,
            geometric_violation,
            epsilon = 1e-14
        );
        // The scaled diagnostic must NOT report the ‖a‖-inflated raw slack.
        assert!(
            diag_big.primal_feasibility < 1e-7,
            "scaled primal {:.3e} should pass a 1e-7 gate; raw slack would be {:.3e}",
            diag_big.primal_feasibility,
            1000.0 * geometric_violation
        );
    }

    // A B-spline `bc=clamped`/`bc=anchored` constraint is an EQUALITY
    // `a·β = b` encoded as two opposing inequalities `a·β ≥ b` and
    // `−a·β ≥ −b`. The active-set solver must drive the unconstrained
    // optimum back onto the hyperplane `a·β = b`. This is the isolated
    // analogue of the `bc=clamped` startup-validation abort: the exact
    // validation solve left `a·β ≈ 7.76` instead of 0, so the KKT primal
    // residual blew past tolerance and every seed was refused.
    #[test]
    fn opposing_inequality_pair_pins_equality_to_target() {
        // Minimize ½‖β‖² − rhs·β  (H = I) ⇒ unconstrained optimum β* = rhs.
        // rhs = [5,5,0,0] ⇒ a·β* = 10 with a = [1,1,0,0].
        // The opposing pair must pull a·β back to the target 0.
        let hessian = array![
            [1.0, 0.0, 0.0, 0.0],
            [0.0, 1.0, 0.0, 0.0],
            [0.0, 0.0, 1.0, 0.0],
            [0.0, 0.0, 0.0, 1.0],
        ];
        let rhs = array![5.0, 5.0, 0.0, 0.0];
        let beta_start = Array1::<f64>::zeros(4);
        let constraints = LinearInequalityConstraints {
            a: array![[1.0, 1.0, 0.0, 0.0], [-1.0, -1.0, 0.0, 0.0]],
            b: array![0.0, 0.0],
        };

        let (beta, _active) = solve_quadratic_with_linear_constraints(
            &hessian,
            &rhs,
            &beta_start,
            &constraints,
            None,
        )
        .expect("opposing-inequality equality QP must solve");

        let a_dot_beta = beta[0] + beta[1];
        assert!(
            a_dot_beta.abs() < 1e-8,
            "opposing inequalities must pin a·β to 0, got {a_dot_beta:.6e} (β = {beta:?})"
        );
    }

    // Same as above but with a non-zero target and a large row norm — the
    // exact shape of a B-spline endpoint-derivative clamp, whose rows carry
    // ‖a‖ ≫ 1. The equality must still be pinned in geometric coordinates.
    #[test]
    fn opposing_inequality_pair_pins_scaled_equality_to_nonzero_target() {
        let hessian = array![
            [1.0, 0.0, 0.0, 0.0],
            [0.0, 1.0, 0.0, 0.0],
            [0.0, 0.0, 1.0, 0.0],
            [0.0, 0.0, 0.0, 1.0],
        ];
        let rhs = array![5.0, 5.0, 0.0, 0.0];
        let beta_start = Array1::<f64>::zeros(4);
        // Row scaled ×1000 (mimics a derivative-clamp row norm) with target 3000
        // ⇒ geometric target a·β = 3.0 in unit coordinates.
        let constraints = LinearInequalityConstraints {
            a: array![[1000.0, 1000.0, 0.0, 0.0], [-1000.0, -1000.0, 0.0, 0.0]],
            b: array![3000.0, -3000.0],
        };

        let (beta, _active) = solve_quadratic_with_linear_constraints(
            &hessian,
            &rhs,
            &beta_start,
            &constraints,
            None,
        )
        .expect("scaled opposing-inequality equality QP must solve");

        let a_dot_beta = 1000.0 * (beta[0] + beta[1]);
        assert!(
            (a_dot_beta - 3000.0).abs() < 1e-5,
            "opposing inequalities must pin a·β to 3000, got {a_dot_beta:.6e} (β = {beta:?})"
        );
    }

    // `bc=clamped` at BOTH ends produces TWO opposing-inequality equalities
    // (4 rows total). The real abort reports `active=2/4` — only ONE of the
    // two equalities is being pinned. Reproduce two independent equalities
    // and require BOTH to be driven to their targets.
    #[test]
    fn two_opposing_inequality_equalities_both_pinned() {
        let hessian = array![
            [1.0, 0.0, 0.0, 0.0],
            [0.0, 1.0, 0.0, 0.0],
            [0.0, 0.0, 1.0, 0.0],
            [0.0, 0.0, 0.0, 1.0],
        ];
        let rhs = array![5.0, 5.0, 5.0, 5.0];
        let beta_start = Array1::<f64>::zeros(4);
        // Equality A: β0 + β1 = 0 (rows 0,1). Equality B: β2 + β3 = 0 (rows 2,3).
        let constraints = LinearInequalityConstraints {
            a: array![
                [1.0, 1.0, 0.0, 0.0],
                [-1.0, -1.0, 0.0, 0.0],
                [0.0, 0.0, 1.0, 1.0],
                [0.0, 0.0, -1.0, -1.0],
            ],
            b: array![0.0, 0.0, 0.0, 0.0],
        };

        let (beta, _active) = solve_quadratic_with_linear_constraints(
            &hessian,
            &rhs,
            &beta_start,
            &constraints,
            None,
        )
        .expect("two-equality QP must solve");

        assert!(
            (beta[0] + beta[1]).abs() < 1e-8,
            "equality A not pinned: β0+β1 = {:.6e}",
            beta[0] + beta[1]
        );
        assert!(
            (beta[2] + beta[3]).abs() < 1e-8,
            "equality B not pinned: β2+β3 = {:.6e}",
            beta[2] + beta[3]
        );
    }

    // Faithful to the failing fit: the penalized IRLS Hessian `X'WX + λS`
    // with λ at the over-smoothing ceiling is severely ill-conditioned — the
    // penalty `S` is rank-deficient (null space = the unpenalized polynomial
    // part), so directions in null(S) are governed by a tiny `X'WX` block
    // while penalized directions carry a huge λ. The opposing-inequality
    // equalities must STILL be pinned under this conditioning.
    #[test]
    fn opposing_inequality_equalities_pinned_under_ill_conditioned_penalty() {
        // H = diag(1, 1, λ, λ) with λ = 1e8 — penalized directions 2,3 are
        // ~1e8 stiffer than the data directions 0,1.
        let lam = 1.0e8_f64;
        let hessian = array![
            [1.0, 0.0, 0.0, 0.0],
            [0.0, 1.0, 0.0, 0.0],
            [0.0, 0.0, lam, 0.0],
            [0.0, 0.0, 0.0, lam],
        ];
        let rhs = array![5.0, 5.0, 5.0, 5.0];
        let beta_start = Array1::<f64>::zeros(4);
        // Two equalities that COUPLE a stiff and a soft coordinate, like a
        // B-spline derivative row spanning penalized and unpenalized parts:
        // A: β0 + β2 = 0, B: β1 + β3 = 0.
        let constraints = LinearInequalityConstraints {
            a: array![
                [1.0, 0.0, 1.0, 0.0],
                [-1.0, 0.0, -1.0, 0.0],
                [0.0, 1.0, 0.0, 1.0],
                [0.0, -1.0, 0.0, -1.0],
            ],
            b: array![0.0, 0.0, 0.0, 0.0],
        };

        let (beta, _active) = solve_quadratic_with_linear_constraints(
            &hessian,
            &rhs,
            &beta_start,
            &constraints,
            None,
        )
        .expect("ill-conditioned two-equality QP must solve");

        assert!(
            (beta[0] + beta[2]).abs() < 1e-6,
            "equality A not pinned under ill-conditioning: β0+β2 = {:.6e}",
            beta[0] + beta[2]
        );
        assert!(
            (beta[1] + beta[3]).abs() < 1e-6,
            "equality B not pinned under ill-conditioning: β1+β3 = {:.6e}",
            beta[1] + beta[3]
        );
    }

    // ==== gam#2306: operator (ConstraintSet) solver vs dense oracle ====

    /// Small Khatri-Rao cone whose dense materialization is exact: Ψ is
    /// 4 × 2, coefficient block is 3 × 2 (row 0 unconstrained location,
    /// rows 1–2 coupled), so p = 6 and the cone has 8 rows.
    fn small_cone() -> KhatriRaoConeConstraints {
        let psi = array![[1.0_f64, 0.2], [1.0, -0.4], [1.0, 1.3], [1.0, 0.8],];
        KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1, 2], 3).expect("small cone")
    }

    /// Parallel tight rows collapse to the lowest-index representative, and the
    /// duplicate is recorded in the dependence map with its scalar ratio.
    #[test]
    fn cone_reduced_face_collapses_parallel_rows_to_lowest_index() {
        // ψ_2 = 2·ψ_0 (parallel); ψ_1 independent. p_cov=2, one coupled row.
        let psi = array![[1.0_f64, 0.0], [0.0, 1.0], [2.0, 0.0]];
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1], 2)
            .expect("parallel cone");
        // β = 0 ⇒ every Γ = 0 ⇒ every row tight.
        let beta = Array1::<f64>::zeros(2 * 2);
        let face = khatri_rao_cone_reduced_face(&cone, beta.view(), 1e-8).expect("reduce");
        assert_eq!(face.tight_rows, rows(&[0, 1, 2]));
        // Rank 2: reps are the two independent directions at their lowest obs.
        assert_eq!(face.representatives, rows(&[0, 1]));
        assert_eq!(face.dependence.len(), 2);
        // ψ_2 (flat id 2) is parallel to representative ψ_0 (rep index 0), coeff 2.
        assert_eq!(face.dependence[0].len(), 1);
        assert_eq!(face.dependence[0][0].row.index(), 2);
        assert!((face.dependence[0][0].coeff - 2.0).abs() < 1e-12);
        assert!(face.dependence[1].is_empty());
    }

    /// A full-rank tight face keeps every row and records no dependence.
    #[test]
    fn cone_reduced_face_full_rank_has_no_dependence() {
        let psi = array![[1.0_f64, 0.0], [0.0, 1.0]];
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1], 2)
            .expect("full-rank cone");
        let beta = Array1::<f64>::zeros(2 * 2);
        let face = khatri_rao_cone_reduced_face(&cone, beta.view(), 1e-8).expect("reduce");
        assert_eq!(face.representatives, rows(&[0, 1]));
        assert!(face.dependence.iter().all(|d| d.is_empty()));
        assert_eq!(face.tight_rows, rows(&[0, 1]));
    }

    /// A general-position dependent (in the span but parallel to no single rep)
    /// is dropped from the working set (full rank cut) but gets NO dependence
    /// entry — the (A)-strict contract that avoids a phantom distributed dual.
    #[test]
    fn cone_reduced_face_general_combination_gets_no_dependence_entry() {
        // ψ_2 = ψ_0 + ψ_1: in the span, but cos with each rep is 1/√2 < 1.
        let psi = array![[1.0_f64, 0.0], [0.0, 1.0], [1.0, 1.0]];
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1], 2)
            .expect("general-combo cone");
        let beta = Array1::<f64>::zeros(2 * 2);
        let face = khatri_rao_cone_reduced_face(&cone, beta.view(), 1e-8).expect("reduce");
        assert_eq!(face.representatives, rows(&[0, 1])); // ψ_2 dropped
        assert_eq!(face.tight_rows, rows(&[0, 1, 2])); // but still in the tight set
        assert!(
            face.dependence.iter().all(|d| d.is_empty()),
            "a general-position drop must carry no distributed multiplier"
        );
    }

    /// Cross-block cone rows are automatically orthogonal (e_k ⊥ e_{k'}), so each
    /// shape block reduces independently — no cross-block dependence, and flat
    /// ids stay in the slot*n+obs space.
    #[test]
    fn cone_reduced_face_reduces_each_shape_block_independently() {
        let psi = array![[1.0_f64, 0.0], [0.0, 1.0]];
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1, 2], 3)
            .expect("two-block cone");
        let beta = Array1::<f64>::zeros(3 * 2);
        let face = khatri_rao_cone_reduced_face(&cone, beta.view(), 1e-8).expect("reduce");
        // Block 0 → flat 0,1; block 1 → flat 2,3 (slot*n+obs, n=2). All independent.
        assert_eq!(face.representatives, rows(&[0, 1, 2, 3]));
        assert!(face.dependence.iter().all(|d| d.is_empty()));
        assert_eq!(face.tight_rows, rows(&[0, 1, 2, 3]));
    }

    /// The Dense arm of the `ConstraintSet::reduced_face` dispatcher matches the
    /// cone arm's contract: parallel tight rows collapse to the lowest-index
    /// representative with the scalar ratio recorded; flat id = the row index.
    #[test]
    fn dense_reduced_face_via_dispatcher_collapses_parallel_rows() {
        // Row 2 = 2·row 0 (parallel); row 1 independent. b = 0 ⇒ every row tight
        // at β = 0 (scaled slack 0).
        let a = array![[1.0_f64, 0.0], [0.0, 1.0], [2.0, 0.0]];
        let set = ConstraintSet::Dense(
            LinearInequalityConstraints::new(a, Array1::<f64>::zeros(3)).expect("dense"),
        );
        let beta = Array1::<f64>::zeros(2);
        let face = set.reduced_face(beta.view(), 1e-8).expect("reduce");
        assert_eq!(face.tight_rows, rows(&[0, 1, 2]));
        assert_eq!(face.representatives, rows(&[0, 1]));
        assert_eq!(face.dependence[0].len(), 1);
        assert_eq!(face.dependence[0][0].row.index(), 2);
        assert!((face.dependence[0][0].coeff - 2.0).abs() < 1e-12);
        assert!(face.dependence[1].is_empty());
    }

    /// Constraint-row ids for the `ReducedFace` assertions below.
    fn rows(ids: &[usize]) -> Vec<ConstraintRowId> {
        ids.iter().copied().map(ConstraintRowId).collect()
    }

    /// A block-diagonal set whose FIRST member constrains fewer rows than it has
    /// coefficients — one `β₀ ≥ 0` row over a 3-wide block whose remaining two
    /// coordinates are unconstrained (intercept / covariate columns) — followed
    /// by a square 2×2 member at `col_start = 3`. This is the configuration that
    /// separates the constraint-row offset (`nrows`: 1) from the coefficient
    /// offset (`col_start`: 3); every pre-existing multi-block test used square
    /// members, where the two coincide and nothing can be distinguished.
    fn mixed_width_block_diagonal() -> ConstraintSet {
        let narrow = gam_problem::PlacedConstraintBlock {
            col_start: 0,
            set: ConstraintSet::Dense(
                LinearInequalityConstraints::new(
                    array![[1.0_f64, 0.0, 0.0]],
                    Array1::<f64>::zeros(1),
                )
                .expect("narrow block"),
            ),
        };
        let square = gam_problem::PlacedConstraintBlock {
            col_start: 3,
            set: ConstraintSet::Dense(
                LinearInequalityConstraints::new(
                    array![[1.0_f64, 0.0], [2.0, 0.0]],
                    Array1::<f64>::zeros(2),
                )
                .expect("square block"),
            ),
        };
        ConstraintSet::block_diagonal(vec![narrow, square], 5).expect("block-diagonal")
    }

    /// Every id a mixed-width block-diagonal reduced face emits addresses the
    /// JOINT CONSTRAINT-ROW space: it indexes `values()` and resolves through
    /// `bound()` / `row_norm()` to the member row it came from, and the tight
    /// rows really are tight there. This pins the id space that #2368 questioned
    /// — the running-`nrows()` shift is the one consistent with the rest of the
    /// `ConstraintSet` row API (`values` layout, `block_for_row` decoding).
    #[test]
    fn block_diagonal_reduced_face_row_ids_address_the_joint_constraint_row_space() {
        let set = mixed_width_block_diagonal();
        let beta = Array1::<f64>::zeros(5);
        let values = set.values(beta.view()).expect("values");
        let face = set.reduced_face(beta.view(), 1e-8).expect("reduce");

        // Joint rows: block 0 contributes row 0; block 1 contributes rows 1, 2.
        assert_eq!(set.nrows(), 3);
        assert_eq!(face.tight_rows, rows(&[0, 1, 2]));
        // Block 1's row 1 = 2·row 0, so it collapses onto joint representative 1.
        assert_eq!(face.representatives, rows(&[0, 1]));
        assert_eq!(face.dependence[1][0].row.index(), 2);

        for id in &face.tight_rows {
            let row = id.index();
            assert!(row < set.nrows(), "id {row} outside the joint row space");
            let norm = set.row_norm(row).expect("row norm resolves");
            let bound = set.bound(row).expect("bound resolves");
            assert!(
                (values[row] - bound) / norm <= 1e-8,
                "row {row} reported tight but has slack {}",
                (values[row] - bound) / norm
            );
        }
    }

    /// The same face, read as COEFFICIENT positions, is wrong — which is exactly
    /// why the ids are typed and why `row_column_support` exists.
    ///
    /// Block 1's representative is joint row 1, but it acts on β coordinate 3.
    /// Coordinate 1 is block 0's second column: an UNCONSTRAINED coefficient
    /// owned by a different block. A consumer that identified row ids with β
    /// positions (to build a free/pinned mask) would pin the wrong coordinate in
    /// the wrong block; the conversion recovers the right one.
    #[test]
    fn block_diagonal_reduced_face_row_ids_are_not_beta_coordinates() {
        let set = mixed_width_block_diagonal();
        let beta = Array1::<f64>::zeros(5);
        let face = set.reduced_face(beta.view(), 1e-8).expect("reduce");

        let block1_rep = face.representatives[1];
        assert_eq!(block1_rep.index(), 1);
        assert_eq!(
            set.row_column_support(block1_rep).expect("support"),
            vec![3],
            "block 1's row acts on the joint column 3 (col_start 3 + local 0)"
        );
        // The naive identity map would have named coordinate 1, which lies in
        // block 0's column range [0, 3) — a different block entirely.
        assert!(block1_rep.index() < 3, "id 1 falls inside block 0's columns");

        // Block 0's row is the one case where the two spaces agree; the
        // conversion must still be the thing that says so.
        assert_eq!(
            set.row_column_support(face.representatives[0])
                .expect("support"),
            vec![0]
        );
    }

    /// The BlockDiagonal arm composes member reductions and concatenates their
    /// row ids in order (each member's flat ids shift by the running member row
    /// count), so a parallel dependent in the second block reports its global id.
    #[test]
    fn block_diagonal_reduced_face_concatenates_member_row_ids() {
        // Two Dense blocks over disjoint columns; each: row0 independent, row1 =
        // 2·row0. b = 0 ⇒ all tight. Block 1's rows shift by block 0's 2 rows.
        let make = |c0: usize| gam_problem::PlacedConstraintBlock {
            col_start: c0,
            set: ConstraintSet::Dense(
                LinearInequalityConstraints::new(
                    array![[1.0_f64, 0.0], [2.0, 0.0]],
                    Array1::<f64>::zeros(2),
                )
                .expect("dense block"),
            ),
        };
        let set = ConstraintSet::block_diagonal(vec![make(0), make(2)], 4).expect("block-diagonal");
        let beta = Array1::<f64>::zeros(4);
        let face = set.reduced_face(beta.view(), 1e-8).expect("reduce");
        assert_eq!(face.tight_rows, rows(&[0, 1, 2, 3]));
        assert_eq!(face.representatives, rows(&[0, 2]));
        assert_eq!(face.dependence[0][0].row.index(), 1);
        assert_eq!(face.dependence[1][0].row.index(), 3);
    }

    /// Deterministic PD Hessian with off-diagonal coupling so active-set
    /// choices are not axis-trivial.
    fn coupled_pd_hessian(p: usize) -> Array2<f64> {
        let mut h = Array2::<f64>::eye(p) * 2.0;
        for i in 0..p {
            for j in 0..p {
                if i != j {
                    h[[i, j]] = 0.3 / (1.0 + (i as f64 - j as f64).abs());
                }
            }
        }
        h
    }

    #[test]
    fn operator_cone_qp_matches_dense_oracle_when_constraints_bind() {
        let cone = small_cone();
        let set = ConstraintSet::KhatriRaoCone(cone.clone());
        let dense = cone.to_dense().expect("dense oracle");
        let p = set.ncols();
        let hessian = coupled_pd_hessian(p);
        // rhs pulls the coupled rows negative so the unconstrained optimum
        // violates the cone and several rows must bind.
        let rhs = array![0.5_f64, -0.3, -2.0, 1.0, -1.5, -0.7];
        // Feasible start: coupled coefficient rows give strictly positive
        // functionals under every Ψ row (constant 1 with small slope loads).
        let beta_start = array![0.0_f64, 0.0, 1.0, 0.1, 1.0, 0.1];

        let (beta_op, mut active_op) =
            solve_quadratic_with_constraint_set(&hessian, &rhs, &beta_start, &set, None)
                .expect("operator solve");
        let (beta_dense, mut active_dense) =
            solve_quadratic_with_linear_constraints(&hessian, &rhs, &beta_start, &dense, None)
                .expect("dense solve");

        for j in 0..p {
            assert!(
                (beta_op[j] - beta_dense[j]).abs() < 1e-7,
                "operator/dense coefficient {j} mismatch: {} vs {}",
                beta_op[j],
                beta_dense[j]
            );
        }
        // The binding face must agree GEOMETRICALLY: both carriers land on the
        // same point (asserted above), so every reported active row must be
        // tight there, and both must carry the same number of independent
        // rows. Exact row-id equality is too strong — the fixture's coupled
        // rows admit alternate representations of the same face, and which
        // redundant row a carrier keeps is a tie-break, not semantics.
        active_op.sort_unstable();
        active_dense.sort_unstable();
        let values_at_solution = set.values(beta_op.view()).expect("values at solution");
        let tight_at_solution: Vec<usize> = (0..set.nrows())
            .filter(|&row| {
                let norm = set.row_norm(row).expect("norm");
                norm > 0.0 && values_at_solution[row] / norm <= 1e-7
            })
            .collect();
        for &row in active_op.iter().chain(active_dense.iter()) {
            assert!(
                tight_at_solution.contains(&row),
                "reported active row {row} is not tight at the common solution \
                 (op face {active_op:?}, dense face {active_dense:?}, tight {tight_at_solution:?})"
            );
        }
        assert_eq!(
            active_op.len(),
            active_dense.len(),
            "carriers disagree on the face dimension: op {active_op:?} vs dense {active_dense:?}"
        );
        assert!(
            !active_op.is_empty(),
            "fixture must actually bind at least one cone row"
        );
        // And the operator answer must be feasible on the full cone.
        let values = set.values(beta_op.view()).expect("values");
        let (worst, _) = set.max_scaled_violation(beta_op.view()).expect("violation");
        assert!(worst <= 1e-8, "operator answer infeasible: {worst:.3e}");
        assert_eq!(values.len(), 8);
    }

    #[test]
    fn separable_khatri_rao_tangent_projection_matches_dense_oracle() {
        let cone = small_cone();
        let set = ConstraintSet::KhatriRaoCone(cone.clone());
        let dense = cone.to_dense().expect("dense projection oracle");
        let beta = Array1::<f64>::zeros(set.ncols());
        let residual = array![0.4_f64, -0.2, 1.1, -0.7, -0.9, 0.8];

        let (operator_projected, _) =
            project_stationarity_residual_on_constraint_set(&residual, &beta, &set, &[])
                .expect("separable operator projection");
        let (dense_projected, _) =
            project_stationarity_residual_on_constraint_cone(&residual, &dense.a)
                .expect("dense cone projection");

        for index in 0..residual.len() {
            assert_relative_eq!(
                operator_projected[index],
                dense_projected[index],
                epsilon = 1e-8
            );
        }
    }

    #[test]
    fn operator_cone_qp_takes_unconstrained_path_when_interior() {
        let cone = small_cone();
        let set = ConstraintSet::KhatriRaoCone(cone);
        let p = set.ncols();
        let hessian = coupled_pd_hessian(p);
        // rhs pushing every coupled functional UP: unconstrained optimum is
        // strictly interior, so the operator path must equal the plain solve.
        let rhs = array![0.2_f64, 0.1, 3.0, 0.2, 2.5, 0.1];
        let beta_start = array![0.0_f64, 0.0, 1.0, 0.0, 1.0, 0.0];
        let (beta_op, active_op) =
            solve_quadratic_with_constraint_set(&hessian, &rhs, &beta_start, &set, None)
                .expect("operator solve");
        // Dense unconstrained oracle: H β = rhs.
        let mut beta_unconstrained = Array1::<f64>::zeros(p);
        super::solve_newton_direction_dense(
            &hessian,
            &(hessian.dot(&beta_start) - &rhs),
            &mut beta_unconstrained,
        )
        .expect("unconstrained newton");
        let beta_unconstrained = &beta_start + &beta_unconstrained;
        for j in 0..p {
            assert!(
                (beta_op[j] - beta_unconstrained[j]).abs() < 1e-8,
                "interior operator solve must match unconstrained optimum at {j}"
            );
        }
        assert!(
            active_op.is_empty(),
            "interior optimum must have empty face"
        );
    }

    #[test]
    fn operator_projection_returns_strictly_interior_point() {
        let cone = small_cone();
        let set = ConstraintSet::KhatriRaoCone(cone);
        // Infeasible point: coupled row 1 loaded negative everywhere.
        let point = array![0.4_f64, -0.2, -1.0, -0.5, 0.3, 0.05];
        let projected = project_point_strictly_into_feasible_constraint_set(&point, &set)
            .expect("projection must succeed on a one-sided homogeneous cone");
        let values = set.values(projected.view()).expect("values");
        for row in 0..set.nrows() {
            let norm = set.row_norm(row).expect("norm");
            if norm <= 0.0 {
                continue;
            }
            let slack = values[row] / norm;
            assert!(
                slack >= 0.5 * ACTIVE_SET_INTERIOR_SEED_MARGIN - 1e-9,
                "projected point not strictly interior on row {row}: slack {slack:.3e}"
            );
        }
        // The location coordinates (unconstrained) must be untouched by the
        // projection objective's optimum only if already optimal; at minimum
        // they must remain finite and close to the input (they carry no
        // constraint rows, and the identity-Hessian QP has no incentive to
        // move them).
        assert!((projected[0] - point[0]).abs() < 1e-8);
        assert!((projected[1] - point[1]).abs() < 1e-8);
    }

    /// #2378 regression, independent oracle. The operator strict-interior
    /// projection onto an OVER-COMPLETE cone face (three of a 2-D block's four
    /// half-spaces try to bind — rank 2) must not merely land on *a* feasible
    /// point; it must be the correct Euclidean projection. The former loose
    /// rank-reduction truncated the true binding extreme (row 2) out of the
    /// enforced face and refused the fit; a regression in the over-complete-face
    /// exchange would release the wrong representative and land on a different
    /// feasible vertex. Both are caught by matching the dense oracle over the
    /// same materialized rows AND by pinning which pair binds ({1,2}, not {1,3}).
    #[test]
    fn operator_projection_adjudicates_the_over_complete_face_2378() {
        let cone = small_cone();
        let set = ConstraintSet::KhatriRaoCone(cone.clone());
        // The #2378 witness point: coupled block 1 = coords[2..4] = (-1, -0.5)
        // is over-complete; block 2 is left feasible.
        let point = array![0.4_f64, -0.2, -1.0, -0.5, 0.3, 0.05];
        let projected = project_point_strictly_into_feasible_constraint_set(&point, &set)
            .expect("operator projection must certify the over-complete-face vertex");

        // Ground-truth oracle: the SAME projection over the dense materialization
        // of the cone rows, through the independent dense arm.
        let dense = ConstraintSet::Dense(cone.to_dense().expect("dense oracle"));
        let dense_proj = project_point_strictly_into_feasible_constraint_set(&point, &dense)
            .expect("dense projection oracle");
        for j in 0..point.len() {
            assert!(
                (projected[j] - dense_proj[j]).abs() < 1e-7,
                "operator projection diverged from the dense oracle at {j}: \
                 op={:.9e} dense={:.9e}",
                projected[j],
                dense_proj[j]
            );
        }

        // The correct binding pair is block-1 rows {1,2} (flat ids 1 and 2 in
        // slot 0). Row 2 — the extreme the old code truncated — must be TIGHT,
        // not violated. Rows are `slot*n + obs`, n = 4 Ψ rows, coupled slot 0.
        let values = set.values(projected.view()).expect("values");
        let scaled = |row: usize| values[row] / set.row_norm(row).expect("norm");
        // Rows 1 and 2 bind at (near) the strict-interior margin floor…
        for row in [1usize, 2] {
            assert!(
                scaled(row) < ACTIVE_SET_INTERIOR_SEED_MARGIN + 1e-7,
                "block-1 row {row} should bind, scaled slack {:.3e}",
                scaled(row)
            );
        }
        // …while rows 0 and 3 stay strictly slacker than the binding pair.
        for row in [0usize, 3] {
            assert!(
                scaled(row) > scaled(2) + 1e-9,
                "non-binding row {row} (slack {:.3e}) must exceed the binding \
                 row 2 (slack {:.3e})",
                scaled(row),
                scaled(2)
            );
        }
    }

    /// #2378 regression at the QP level (non-identity Hessian): the operator
    /// active-set loop's over-complete-face exchange must reach the same
    /// constrained minimizer as the dense oracle when a coupled block is loaded
    /// so that three of its half-spaces contend at the optimum.
    #[test]
    fn operator_cone_qp_over_complete_face_matches_dense_oracle_2378() {
        let cone = small_cone();
        let set = ConstraintSet::KhatriRaoCone(cone.clone());
        let dense = cone.to_dense().expect("dense oracle");
        let p = set.ncols();
        let hessian = coupled_pd_hessian(p);
        // Drive block-1's unconstrained optimum deep into the infeasible corner
        // where the extreme Ψ rows 1 and 2 both contend (the over-complete face),
        // and pin block-2 with its own mild load.
        let rhs = array![0.3_f64, -0.1, -2.5, -1.2, -0.4, 0.2];
        let beta_start = array![0.0_f64, 0.0, 1.0, 0.1, 1.0, 0.1];

        let (beta_op, _active_op) =
            solve_quadratic_with_constraint_set(&hessian, &rhs, &beta_start, &set, None)
                .expect("operator QP solve over an over-complete face");
        let (beta_dense, _active_dense) =
            solve_quadratic_with_linear_constraints(&hessian, &rhs, &beta_start, &dense, None)
                .expect("dense QP oracle");

        for j in 0..p {
            assert!(
                (beta_op[j] - beta_dense[j]).abs() < 1e-7,
                "operator/dense coefficient {j} mismatch: {} vs {}",
                beta_op[j],
                beta_dense[j]
            );
        }
        // The operator answer is feasible on the full factored cone.
        let values = set.values(beta_op.view()).expect("values");
        for row in 0..set.nrows() {
            let norm = set.row_norm(row).expect("norm");
            if norm > 0.0 {
                assert!(
                    values[row] / norm >= -ACTIVE_SET_PRIMAL_FEASIBILITY_TOL,
                    "row {row} violated at the operator optimum: {:.3e}",
                    values[row] / norm
                );
            }
        }
    }

    #[test]
    fn operator_cone_does_not_materialize_a_whole_tight_face() {
        // All 4,096 observation rows describe the same half-space.  At the
        // cone vertex every row is tight, but one warm row completely
        // describes the working face.  The operator solver must preserve that
        // compact working set instead of gathering/rank-reducing all 4,096
        // redundant rows before taking a step (the large-scale CTN cycle-2
        // stall from #979).
        let mut psi = Array2::<f64>::zeros((4096, 2));
        psi.column_mut(0).fill(1.0);
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1], 2)
            .expect("repeated-row cone");
        let set = ConstraintSet::KhatriRaoCone(cone);
        let hessian = Array2::<f64>::eye(4);
        let rhs = array![0.3_f64, -0.2, -1.0, 0.0];
        let beta_start = Array1::<f64>::zeros(4);

        let (beta, active) =
            solve_quadratic_with_constraint_set(&hessian, &rhs, &beta_start, &set, Some(&[0]))
                .expect("vertex solve");

        assert_eq!(
            active,
            vec![0],
            "redundant tight rows entered the working set"
        );
        assert!(beta[2].abs() <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL);
        assert!((beta[0] - 0.3).abs() < 1e-10);
        assert!((beta[1] + 0.2).abs() < 1e-10);
    }

    #[test]
    fn operator_cycle_escape_is_descending_feasible_and_sparse() {
        // A Khatri-Rao face can have several observation rows tight at the
        // same coefficient point even though only one row is needed to hold
        // the current tangent face. The dense solver already takes this
        // projected-gradient escape when tolerance-band add/drop transitions
        // revisit a working set; the operator solver must do the same without
        // expanding the returned hint to every currently-tight row.
        let psi = array![[1.0_f64, 0.0], [1.0, 1.0], [1.0, 2.0]];
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1], 2)
            .expect("cycle-escape cone");
        let set = ConstraintSet::KhatriRaoCone(cone);
        let ops = ConstraintSetOps::new(&set, 0.0).expect("operator geometry");
        let x = Array1::<f64>::zeros(4);
        let d_total = Array1::<f64>::zeros(4);
        // Row 0 pins the constant coefficient of the shaped response. The
        // remaining negative gradient points along its slope coefficient,
        // which lies in the face tangent and moves all other rows inward.
        let gradient = array![0.0_f64, 0.0, 0.0, -1.0];
        let (direction, active) = fallback_projected_gradient_direction_with_constraint_set(
            &x,
            &x,
            &d_total,
            &gradient,
            &[0],
            &ops,
        )
        .expect("operator fallback evaluation")
        .expect("a certified tangent descent direction must exist");

        assert!(
            gradient.dot(&direction) < 0.0,
            "escape must be a strict descent direction"
        );
        let candidate = &x + &direction;
        let (worst, _) = set
            .max_scaled_violation(candidate.view())
            .expect("full-set feasibility");
        assert!(
            worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL,
            "escape must remain feasible on every operator row: {worst:.3e}"
        );
        assert_eq!(
            active,
            vec![0],
            "operator escape expanded one sparse face row into all tight rows"
        );
    }

    #[test]
    fn operator_tangent_projection_does_not_constrain_interior_rows() {
        let psi = array![[1.0_f64, 0.0], [1.0, 1.0], [1.0, -1.0]];
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1], 2)
            .expect("interior tangent cone");
        let set = ConstraintSet::KhatriRaoCone(cone);
        // The shaped response row is strictly positive for every observation,
        // so its tangent cone is the complete coefficient space. A projection
        // against the original cone at the origin would incorrectly erase the
        // shaped constant component of this residual.
        let beta = array![0.0_f64, 0.0, 1.0, 0.0];
        let residual = array![0.0_f64, 0.0, 1.0, 0.0];
        let (projected, active) =
            project_stationarity_residual_on_constraint_set(&residual, &beta, &set, &[])
                .expect("interior tangent projection");

        for index in 0..residual.len() {
            assert_relative_eq!(projected[index], residual[index], epsilon = 1e-12);
        }
        assert!(active.is_empty(), "interior rows entered the tangent face");
    }

    #[test]
    fn operator_tangent_projection_homogenizes_an_affine_boundary() {
        let set = ConstraintSet::Dense(
            LinearInequalityConstraints::new(array![[1.0_f64, 0.0]], array![2.0])
                .expect("affine half-space"),
        );
        let beta = array![2.0_f64, 0.0];
        let residual = array![1.0_f64, -1.0];
        let (projected, active) =
            project_stationarity_residual_on_constraint_set(&residual, &beta, &set, &[0])
                .expect("affine-boundary tangent projection");

        assert_relative_eq!(projected[0], 0.0, epsilon = 1e-12);
        assert_relative_eq!(projected[1], -1.0, epsilon = 1e-12);
        assert_eq!(active, vec![0]);
    }

    #[test]
    fn operator_cycle_escape_discovers_a_zero_step_tangent_separator() {
        // All three rows are tight at the vertex. Row 0 alone permits a pure
        // positive-slope direction, but row 2 (`constant - slope >= 0`) blocks
        // it at alpha=0. The operator escape must add that one separator and
        // re-project, not give up and not materialize every tight row.
        let psi = array![[1.0_f64, 0.0], [1.0, 1.0], [1.0, -1.0]];
        let cone = KhatriRaoConeConstraints::new(std::sync::Arc::new(psi), vec![1], 2)
            .expect("separator cone");
        let set = ConstraintSet::KhatriRaoCone(cone);
        let ops = ConstraintSetOps::new(&set, 0.0).expect("operator geometry");
        let x = Array1::<f64>::zeros(4);
        let d_total = Array1::<f64>::zeros(4);
        let gradient = array![0.0_f64, 0.0, 0.0, -1.0];
        let (direction, active) = fallback_projected_gradient_direction_with_constraint_set(
            &x,
            &x,
            &d_total,
            &gradient,
            &[0],
            &ops,
        )
        .expect("operator separator evaluation")
        .expect("one omitted tight separator must not defeat the escape");

        assert!(gradient.dot(&direction) < 0.0);
        let candidate = &x + &direction;
        let (worst, _) = set
            .max_scaled_violation(candidate.view())
            .expect("full-set feasibility");
        assert!(worst <= ACTIVE_SET_PRIMAL_FEASIBILITY_TOL);
        assert!(
            active.len() <= 2,
            "separator discovery expanded a three-row vertex: {active:?}"
        );
    }
}