pounce_algorithm/

alg_builder.rs

//! Algorithm builder — port of `Algorithm/IpAlgBuilder.{hpp,cpp}`.
//!
//! Reads `OptionsList`, walks the dependency order documented in
//! `ref/Ipopt/AGENT_REFERENCE/ARCHITECTURE.md` §"BuildBasicAlgorithm",
//! and assembles the strategy objects needed by `IpoptAlgorithm`:
//!
//! * `SymLinearSolver` (MA57 / MUMPS / FERAL) → `AugSystemSolver`
//!   (`StdAugSystemSolver`) → `PdSystemSolver` (`PdFullSpaceSolver`)
//!   → `SearchDirCalculator` (`PdSearchDirCalc`).
//! * `BacktrackingLsAcceptor` (filter / penalty / cg-penalty) →
//!   `BacktrackingLineSearch`.
//! * `MuUpdate` (monotone / adaptive[+oracle]).
//! * `ConvCheck` (`OptErrorConvCheck`).
//! * `IterateInitializer` (default / warm-start) and
//!   `EqMultCalculator` (`LeastSquareMults`).
//! * `HessianUpdater` (exact / limited-memory).
//! * `IterationOutput` (`OrigIterationOutput`).
//! * `NLPScalingObject` (none / user / gradient-based / equilibration-based).
//!
//! Phase 7 ships the option-driven dispatch surface; the assembled
//! `IpoptAlgorithm` lands once each strategy's arithmetic does.

use crate::conv_check::opt_error::OptErrorConvCheck;
use crate::eq_mult::least_square::LeastSquareMults;
use crate::hess::exact::ExactHessianUpdater;
use crate::hess::lim_mem_quasi_newton::{InitialApprox, LimMemQuasiNewtonUpdater, UpdateType};
use crate::init::default::DefaultIterateInitializer;
use crate::init::warm_start::WarmStartIterateInitializer;
use crate::kkt::aug_system_solver::AugSystemSolver;
use crate::kkt::low_rank_aug_system_solver::LowRankAugSystemSolver;
use crate::kkt::pd_full_space_solver::PdFullSpaceSolver;
use crate::kkt::pd_search_dir_calc::PdSearchDirCalc;
use crate::kkt::perturbation_handler::PdPerturbationHandler;
use crate::kkt::std_aug_system_solver::StdAugSystemSolver;
use crate::line_search::backtracking::BacktrackingLineSearch;
use crate::line_search::filter_acceptor::FilterLsAcceptor;
use crate::line_search::ls_acceptor::BacktrackingLsAcceptor;
use crate::line_search::penalty_acceptor::PenaltyLsAcceptor;
use crate::mu::adaptive::{AdaptiveMuUpdate, MuOracleKind};
use crate::mu::monotone::MonotoneMuUpdate;
use crate::output::orig::OrigIterationOutput;
use pounce_common::types::{Index, Number};
use pounce_linsol::{SparseSymLinearSolverInterface, TSymLinearSolver};
use std::cell::RefCell;
use std::rc::Rc;

/// Backend factory — the application supplies one before calling
/// [`AlgorithmBuilder::build`]. Mirrors upstream's
/// `SymLinearSolverFactory` knob in `IpAlgBuilder.cpp`. The default
/// factory wires in FERAL; MA57 is selectable when the `ma57` cargo
/// feature is enabled.
pub type LinearBackendFactory =
    Box<dyn FnMut(LinearSolverChoice) -> Box<dyn SparseSymLinearSolverInterface>>;

/// Top-level algorithm choice. `InteriorPoint` is pounce's default
/// (the existing `IpoptAlgorithm`); `ActiveSetSqp` is the
/// Phase 5b SQP driver in `crate::sqp::SqpAlgorithm`, which uses
/// `pounce-qp` for QP subproblem solves and reuses
/// `FilterLsAcceptor` for globalization.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
pub enum AlgorithmChoice {
    #[default]
    InteriorPoint,
    ActiveSetSqp,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LinearSolverChoice {
    Ma57,
    Feral,
}

/// Symmetric scaling method applied to the augmented KKT system by
/// [`TSymLinearSolver`]. Mirrors the `linear_system_scaling` option
/// in `IpAlgBuilder.cpp:302-318` and the `RuizTSymScalingMethod` /
/// `Mc19TSymScalingMethod` strategies in upstream Ipopt.
///
/// * `None` (default) — no scaling; `TSymLinearSolver` runs with a
///   null scaling method. Matches upstream's default.
/// * `Ruiz` — iterative symmetric ∞-norm equilibration (Ruiz, 2001).
///   Implemented in `pounce_linsol::RuizTSymScalingMethod`.
/// * `Mc19` — Curtis-Reid (HSL MC19) scaling. Not yet implemented;
///   falls back to `None` with a warning.
#[derive(Debug, Clone, Copy, PartialEq, Eq, Default)]
pub enum LinearSystemScalingChoice {
    #[default]
    None,
    Ruiz,
    Mc19,
    /// `slack-based` — `IpSlackBasedTSymScalingMethod`. Unlike the
    /// others this one is a function of the iterate, not of the matrix,
    /// so the algorithm pushes the `s`-block factors down each
    /// iteration (see `IpoptAlgorithm::push_slack_scaling`).
    SlackBased,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum MuStrategyChoice {
    Monotone,
    Adaptive,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum HessianApproxChoice {
    Exact,
    LimitedMemory,
    /// Partitioned quasi-Newton: one small dense block per element
    /// function (the objective and each constraint row), assembled into
    /// a genuine sparse `SymTMatrix`. See
    /// [`crate::hess::partitioned_quasi_newton`].
    Partitioned,
    /// Sparse finite-difference Lagrangian Hessian, recovered by graph
    /// coloring from the analytic Jacobian. See
    /// [`crate::hess::fd_hessian`].
    FiniteDifference,
}

#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum LineSearchChoice {
    Filter,
    CgPenalty,
    Penalty,
}

/// Assembled strategy bundle. Phase 7 ships the structural bundle;
/// `IpoptAlgorithm::new` reads from this when it lands.
pub struct AlgorithmBundle {
    pub mu_update: Box<dyn crate::mu::r#trait::MuUpdate>,
    pub conv_check: Box<dyn crate::conv_check::r#trait::ConvCheck>,
    pub init: Box<dyn crate::init::r#trait::IterateInitializer>,
    pub eq_mult: Box<dyn crate::eq_mult::r#trait::EqMultCalculator>,
    pub hess: Box<dyn crate::hess::r#trait::HessianUpdater>,
    pub line_search: BacktrackingLineSearch,
    pub iter_output: Box<dyn crate::output::r#trait::IterationOutput>,
    /// `Some` when the builder was given a [`LinearBackendFactory`];
    /// `None` for the bare structural bundle that pre-Phase-6 unit
    /// tests still rely on.
    pub search_dir: Option<PdSearchDirCalc>,
}

/// Knobs read off `OptionsList` and baked into the assembled
/// `OptErrorConvCheck`. Defaults mirror
/// `IpOptErrorConvCheck.cpp:RegisterOptions`.
#[derive(Debug, Clone)]
pub struct ConvCheckOptions {
    pub tol: Number,
    pub dual_inf_tol: Number,
    pub constr_viol_tol: Number,
    pub compl_inf_tol: Number,
    pub acceptable_tol: Number,
    pub acceptable_dual_inf_tol: Number,
    pub acceptable_constr_viol_tol: Number,
    pub acceptable_compl_inf_tol: Number,
    pub acceptable_obj_change_tol: Number,
    pub acceptable_iter: Index,
    pub max_iter: Index,
    pub max_cpu_time: Number,
    pub max_wall_time: Number,
    pub infeas_stationarity_tol: Number,
    pub infeas_viol_kappa: Number,
    pub infeas_max_streak: Index,
    /// Objective-scale floor below which a strict termination certificate is
    /// refused while the unscaled KKT error is still above `acceptable_tol`
    /// (gh #200). `0` disables the mechanism.
    pub obj_scale_certificate_threshold: Number,
    /// Safety factor on the per-row floor the **strict** gate uses to decide
    /// when a constraint residual is finer than the row can represent
    /// (gh #528). `0` disables the floor, restoring upstream Ipopt's
    /// bare-absolute primal term.
    pub primal_noise_floor_kappa: Number,
    /// Fraction of `acceptable_tol` the KKT error and the objective may drift
    /// across the acceptable-level streak's window while the streak still
    /// counts as settled (gh #533). `0` disables the progress test, leaving
    /// acceptable-level termination the bare consecutive-count criterion.
    pub acceptable_progress_kappa: Number,
    /// Safety factor on the scale-relative floor under `dual_inf_tol` the
    /// **strict** gate judges the dual infeasibility against (gh #532). `0`
    /// disables the floor, restoring upstream Ipopt's bare-absolute bound.
    pub dual_inf_scale_kappa: Number,
}

impl Default for ConvCheckOptions {
    fn default() -> Self {
        Self {
            tol: 1e-8,
            dual_inf_tol: 1.0,
            constr_viol_tol: 1e-4,
            compl_inf_tol: 1e-4,
            acceptable_tol: 1e-6,
            acceptable_dual_inf_tol: 1e10,
            acceptable_constr_viol_tol: 1e-2,
            acceptable_compl_inf_tol: 1e-2,
            acceptable_obj_change_tol: 1e20,
            acceptable_iter: 15,
            max_iter: 3000,
            max_cpu_time: 1e6,
            max_wall_time: 1e6,
            infeas_stationarity_tol: 1e-8,
            infeas_viol_kappa: 1e2,
            infeas_max_streak: 5,
            obj_scale_certificate_threshold: 1e-4,
            primal_noise_floor_kappa: 64.0,
            acceptable_progress_kappa: 1e-1,
            dual_inf_scale_kappa: 1.0,
        }
    }
}

#[derive(Debug, Clone)]
pub struct AlgorithmBuilder {
    /// Top-level algorithm dispatch. Default `InteriorPoint` ⇒
    /// `build_with_backend` returns the existing `AlgorithmBundle`
    /// (consumed by `IpoptAlgorithm`). `ActiveSetSqp` ⇒ caller
    /// must use `build_sqp_with_backend` to assemble the Phase 5b
    /// `SqpAlgorithm`. The two builder methods sit side by side
    /// because the assembled algorithm shape differs (IPM bundle
    /// vs SQP struct).
    pub algorithm: AlgorithmChoice,
    pub linear_solver: LinearSolverChoice,
    /// Symmetric scaling method for the augmented KKT system. Wired
    /// into [`TSymLinearSolver`] by [`Self::build_with_backend`].
    /// Mirrors upstream `linear_system_scaling` (`IpAlgBuilder.cpp:538-560`).
    pub linear_system_scaling: LinearSystemScalingChoice,
    /// Lazy-vs-eager scaling toggle (`linear_scaling_on_demand`,
    /// `IpTSymLinearSolver.cpp:50-58`). Only consulted when
    /// `linear_system_scaling != None`. Upstream default is `true`
    /// (compute scaling only on the first solve that fails / shows
    /// poor conditioning); pounce mirrors that. Set to `false` to
    /// scale every factorization.
    pub linear_scaling_on_demand: bool,
    pub mu_strategy: MuStrategyChoice,
    /// Selector forwarded to [`AdaptiveMuUpdate`] when
    /// `mu_strategy = Adaptive`. Ignored for `Monotone`. Defaults to
    /// `QualityFunction` per upstream's `RegisterOptions` default.
    pub mu_oracle: MuOracleKind,
    pub hessian_approximation: HessianApproxChoice,

    /// Element update formula for
    /// [`HessianApproxChoice::Partitioned`] (`partitioned_update_type`).
    /// SR1 by default: a single constraint is not convex, so damped
    /// BFGS would force every `∇²c_j` model PSD and then scale it by a
    /// multiplier of either sign.
    pub partitioned_update_type: UpdateType,
    /// Whether the caller named `partitioned_update_type` explicitly, so
    /// the block mode's BFGS default does not override them.
    pub partitioned_update_type_was_set: bool,
    /// Widest element that keeps a dense block under
    /// [`HessianApproxChoice::Partitioned`]; wider elements degrade to a
    /// diagonal approximation (`partitioned_max_element`).
    pub partitioned_max_element: usize,
    /// Variables the **objective** is nonlinear in, in the compressed
    /// `x_var` space — `TNLPAdapter::objective_nonlinear_vars`. Consumed
    /// by both the partitioned updater (as its objective element's
    /// support) and the finite-difference updater (as the objective's
    /// contribution to a Jacobian-derived Hessian pattern, which the
    /// constraint Jacobian cannot supply). `None` leaves each to fall
    /// back on the first `∇f`'s nonzeros, which is value-derived; see
    /// that method for what it costs.
    pub objective_nonlinear_vars: Option<Vec<Index>>,
    /// `partitioned_curvature_cap` — multiple of an element's implied
    /// curvature that one update may reach. See
    /// [`crate::hess::partitioned_quasi_newton`].
    pub partitioned_curvature_cap: Number,
    /// How the Lagrangian is split into elements under
    /// [`HessianApproxChoice::Partitioned`] (`partitioned_elements`).
    pub partitioned_elements: crate::hess::partitioned_quasi_newton::ElementMode,
    /// Target primal-block width when `partitioned_elements` is
    /// `blocks` (`partitioned_block_size`).
    pub partitioned_block_size: usize,
    /// Where [`HessianApproxChoice::FiniteDifference`] takes its
    /// sparsity pattern from (`fd_hessian_pattern`).
    pub fd_hessian_pattern: crate::hess::fd_hessian::FdPatternSource,
    /// How finite-difference probe groups are formed
    /// (`fd_hessian_coloring`).
    pub fd_hessian_coloring: crate::hess::fd_hessian::FdColoring,
    /// Relative movement in `x` AND `y` below which the previous Hessian
    /// is reused (`fd_hessian_reuse_tol`). `0` rebuilds every iteration.
    pub fd_hessian_reuse_tol: Number,
    pub limited_memory_update_type: UpdateType,
    /// History length for the limited-memory quasi-Newton approximation
    /// (`limited_memory_max_history`). Defaults to upstream's 6.
    pub limited_memory_max_history: i32,
    /// `limited_memory_init_val_max` / `_min` — the clamp on the initial
    /// Hessian scalar σ before the rank-2 updates. Upstream defaults 1e8
    /// / 1e-8, which `LimMemQuasiNewtonUpdater` has carried as hard-coded
    /// fields and consumed in `initial_hessian_scalar` all along; only
    /// the read sites were missing (gh#483, #191 round 2).
    pub limited_memory_init_val_max: Number,
    pub limited_memory_init_val_min: Number,
    /// `limited_memory_initialization` — which formula picks the initial
    /// Hessian scalar σ. Matches upstream's `scalar1` (σ = sᵀy/sᵀs).
    /// pounce shipped `scalar2` (σ = yᵀy/sᵀy) with no way to change it,
    /// because the option was registered and never read (#677).
    pub limited_memory_initialization: InitialApprox,
    /// `limited_memory_init_val` — σ on the first iteration, before any
    /// curvature pair exists, and every iteration under
    /// `InitialApprox::Constant`. Upstream default 1.0.
    pub limited_memory_init_val: Number,
    /// `limited_memory_max_skipping` — consecutive skipped curvature
    /// updates before the approximation is discarded (#686). Upstream
    /// default 2.
    pub limited_memory_max_skipping: Index,
    /// Positions in the algorithm's compressed `x_var` space that enter
    /// the problem *nonlinearly* (gh#624). `None` — the default —
    /// approximates the Hessian over every variable, which is what the
    /// limited-memory path has always done. When set, the quasi-Newton
    /// update is restricted to this subspace and the Hessian is exactly
    /// zero elsewhere. Comes from
    /// `TNLPAdapter::quasi_newton_nonlinear_vars` (the TNLP's
    /// `get_list_of_nonlinear_variables`, or the `num_linear_variables`
    /// prefix fallback) and is ignored on the exact-Hessian path.
    ///
    /// The restoration sub-IPM must clear this: the mask indexes the
    /// original NLP's variables, not the restoration compound primal.
    pub limited_memory_nonlinear_vars: Option<Vec<Index>>,
    pub line_search_method: LineSearchChoice,
    pub warm_start_init_point: bool,
    /// `mehrotra_algorithm` — when true, [`PdSearchDirCalc`] folds
    /// the Mehrotra second-order complementarity term into the
    /// search-direction RHS. Mirrors upstream's
    /// `IpAlgBuilder.cpp:Mehrotra` flag. Requires `mu_strategy =
    /// Adaptive` so that an affine step is computed each iteration;
    /// [`Self::build_with_backend`] does not enforce this — the
    /// option-parser in `application.rs` is responsible for the
    /// cascading defaults (`mu_oracle = probing` etc.).
    pub mehrotra_algorithm: bool,
    /// `fast_step_computation` — when true, [`PdSearchDirCalc`] accepts
    /// the search direction without the residual check and allows an
    /// inexact linear solve. Mirrors upstream's flag of the same name,
    /// default `no`. The field existed and was consumed from the day the
    /// search-direction calculator landed, hard-coded to `false`; only
    /// the option's read site was missing, so setting it did nothing
    /// (gh#483 follow-up, #191 round 2).
    pub fast_step_computation: bool,
    /// `kappa_sigma` — factor bounding how far the bound multipliers may
    /// deviate from their primal estimates. The clamp
    /// (`kappa_sigma_clamp`) runs after every accepted step; `< 1`
    /// disables the correction. Mirrors `IpIpoptAlg.cpp` (Eqn. (16)),
    /// default `1e10`. Baked onto [`crate::ipopt_alg::IpoptAlgorithm`] by
    /// the solve path.
    pub kappa_sigma: Number,
    /// `recalc_y` / `recalc_y_feas_tol` — least-square re-estimation of
    /// the equality multipliers once feasible (#677). Registered
    /// upstream, refused by pounce as unimplemented until now. Default
    /// `false` matches the registry; the limited-memory path turns it on
    /// for itself in `application.rs`, as upstream's own option text
    /// says it does.
    pub recalc_y: bool,
    pub recalc_y_feas_tol: Number,
    /// `kappa_d` — weight of the linear damping term added to the barrier
    /// objective/gradient (and dual-infeasibility) to handle one-sided
    /// bounds. Mirrors `IpIpoptCalculatedQuantities.cpp`, default `1e-5`.
    /// Baked onto [`crate::ipopt_cq::IpoptCalculatedQuantities`] by the
    /// solve path.
    pub kappa_d: Number,
    /// `s_max` — cap on the average multiplier magnitude used to build
    /// the `(s_d, s_c)` scaling factors of the KKT error test
    /// (`IpIpoptCalculatedQuantities.cpp:ComputeOptimalityErrorScaling`,
    /// the paragraph after Eqn. (6) of the implementation paper).
    /// Registered default `100`, which is what
    /// [`crate::ipopt_cq::IpoptCalculatedQuantities`] already carries as
    /// its struct default, so forwarding it is behaviour-neutral for a
    /// run that does not set it (#551 / #677). Baked onto the cq by the
    /// solve path, next to `kappa_d`.
    pub s_max: Number,
    /// `tiny_step_tol` — relative primal step size below which the full
    /// step is accepted without line search; repeated tiny steps
    /// terminate the solve. Mirrors `IpBacktrackingLineSearch.cpp`,
    /// default `10·EPSILON`. Baked onto
    /// [`crate::ipopt_alg::IpoptAlgorithm`] by the solve path.
    pub tiny_step_tol: Number,
    /// `tiny_step_y_tol` — dual-step threshold; when both primal and dual
    /// steps are tiny in consecutive iterations the algorithm stops at the
    /// best attainable accuracy. Default `1e-2`.
    pub tiny_step_y_tol: Number,
    /// `diverging_iterates_tol` — if `max_i |x_i|` exceeds this the solve
    /// aborts as diverging. Default `1e20`.
    pub diverging_iterates_tol: Number,
    /// `dual_diverging_streak` (pounce#246) — consecutive growing-dual-
    /// infeasibility iterations before the dual-divergence guard routes to
    /// restoration. **Default `0` (off).**
    ///
    /// It defaulted to `15` when introduced, on the strength of a reported
    /// emfl050 bad-warm-start grind. That justification did not survive being
    /// reproduced: the measurement was caller-side JAX compilation, and the
    /// build predating the guard solves both emfl050 instances to the same
    /// optimum in the same time (pounce#246 / pounce#250). What remained was a
    /// knife-edge, non-monotone effect on four of 1284 MINLPLib models — so it
    /// is opt-in rather than imposed. See `upstream_options.rs` for the full
    /// account.
    pub dual_diverging_streak: Index,
    /// `dual_divergence_retry_step_tol` (gh#884) — the scale-relative
    /// step `max_i |d_i| / (1 + |x_i|)` at or below which the biactive
    /// dual-divergence detector calls the primal iterate *settled*.
    /// Default `1e-5`; `0` disables the detector without disabling the
    /// `dual_divergence_retry` option. See `upstream_options.rs` for the
    /// measured population behind the default.
    pub dual_divergence_retry_step_tol: Number,
    /// `dual_divergence_retry_du_floor` (gh#884) — the *unscaled* dual
    /// infeasibility at or above which the same detector calls the
    /// multipliers *diverged*. Default `1e2`. Measured in the model's own
    /// units on purpose: the `s_d`-normalised aggregate is what hid the
    /// defect. See `upstream_options.rs`.
    pub dual_divergence_retry_du_floor: Number,
    /// `resto_decline_deferrals` (gh #534) — how many times the
    /// acceptable-point restoration decline may be deferred on a solve whose
    /// NLP error is still contracting. Default `1`; `0` restores the pre-#534
    /// behaviour (decline immediately, always). See `upstream_options.rs`.
    pub resto_decline_deferrals: Index,
    /// `resto_decline_progress_ratio` (gh #534) — required per-iteration
    /// contraction of the NLP error before a decline is deferred. Default
    /// `0.5`; at or above `1` the progress requirement is dropped entirely.
    pub resto_decline_progress_ratio: Number,
    /// `neg_curv_escapes` (gh #797) — how many times a certified stationary
    /// point with an indefinite reduced Hessian may be left along a direction
    /// of negative curvature instead of reported. Default `1`; `0` restores the
    /// pre-#797 behaviour. See `upstream_options.rs`.
    pub neg_curv_escapes: Index,
    /// `limited_memory_ls_failure_restarts` (gh #818) — how many times a
    /// line-search failure at an already-feasible point may re-anchor the
    /// quasi-Newton model and retry instead of entering restoration.
    /// Default `0`, i.e. the rung is off and a line-search failure always
    /// hands off, which is upstream's behaviour; see
    /// `DEFAULT_LBFGS_LS_FAILURE_RESTARTS` in `ipopt_alg.rs` for the
    /// measurement that put it there. See `upstream_options.rs`.
    pub limited_memory_ls_failure_restarts: Index,
    /// `kkt_fidelity_tol` (pounce#173). Read by the algorithm as well as by the
    /// post-solve gate, because the #200 fallback's tiebreak has to rank the two
    /// candidate points by the status each will be *reported* under. Default
    /// `0.0` (gate disabled).
    pub kkt_fidelity_tol: Number,
    pub conv_check: ConvCheckOptions,
    pub mu: MuOptions,
    pub line_search: LineSearchOptions,
    pub refinement: RefinementOptions,
    pub perturbation: PerturbationOptions,
    pub resto: RestoOptions,
    pub output: OutputOptions,
    pub warm: WarmStartOptions,
    /// SQP-specific options (consulted only when
    /// `algorithm = ActiveSetSqp`).
    pub sqp: crate::sqp::SqpOptions,
    /// QP-subproblem-solver options for the active-set SQP path
    /// (`pounce_qp::QpOptions`), threaded into the `SqpAlgorithm` via
    /// `with_qp_options`. Consulted only when `algorithm = ActiveSetSqp`.
    /// Populated from the `sqp_qp_*` CLI options by
    /// `application::apply_qp_subproblem_options`.
    pub sqp_qp: pounce_qp::QpOptions,
    pub init: InitOptions,
    /// Optional block-triangular / Schur KKT partition (pounce#180 item 2):
    /// `(schur_indices, feral_cfg)`. When `Some` and the IPM path is selected
    /// with the feral linear solver and an exact Hessian, `build_with_backend`
    /// wraps the standard aug-system solver in a
    /// [`crate::kkt::SchurAugSystemSolver`] over the given KKT-space indices.
    /// The Schur solver falls back to the standard solver transparently when
    /// the partition is unsuitable. Set via [`Self::set_kkt_schur`].
    pub kkt_schur: Option<(Vec<usize>, pounce_feral::FeralConfig)>,
    /// Shared tally of successful linear-solver quality escalations, handed
    /// to the assembled
    /// [`PdFullSpaceSolver`](crate::kkt::pd_full_space_solver::PdFullSpaceSolver)
    /// by [`Self::build_with_backend`]. `None` leaves that solver with its
    /// own private counter, which is what every test double and every
    /// direct builder user gets.
    ///
    /// The point of sharing it is the restoration sub-solve: its inner
    /// algorithm is assembled from a *clone* of this builder
    /// (`resto_inner_solver::run_inner_resto`), so a `Some` here makes the
    /// sub-solve's escalations land in the same total as the main loop's.
    /// gh#857's exact leg escalates once in each, and counting only the
    /// main loop would report half the trajectory change.
    pub quality_escalation_counter: Option<Rc<std::cell::Cell<u64>>>,
}

/// Knobs read off `OptionsList` and baked into
/// [`DefaultIterateInitializer`]. Defaults mirror
/// `IpDefaultIterateInitializer.cpp:RegisterOptions`. The Mehrotra
/// cascade in `application.rs` overrides `bound_push`, `bound_frac`,
/// and `bound_mult_init_val` to upstream's more-aggressive values
/// (`10`, `0.2`, `1.0`).
#[derive(Debug, Clone)]
pub struct InitOptions {
    pub bound_push: Number,
    pub bound_frac: Number,
    pub slack_bound_push: Number,
    pub slack_bound_frac: Number,
    pub constr_mult_init_max: Number,
    pub bound_mult_init_val: Number,
    /// `bound_mult_init_method`: `"constant"` (default) or `"mu-based"`
    /// (matches upstream's `IpDefaultIterateInitializer.cpp`).
    pub bound_mult_init_method: String,
    /// `least_square_init_primal` — replace the user's starting `x`
    /// with the min-norm primal that satisfies the linearized
    /// constraints. Used by the Mehrotra cascade in `application.rs`
    /// to drop iter-0 primal infeasibility on LP-shaped problems.
    /// Mirrors upstream `IpDefaultIterateInitializer.cpp:200-222`.
    pub least_square_init_primal: bool,
}

impl Default for InitOptions {
    fn default() -> Self {
        Self {
            bound_push: 1e-2,
            bound_frac: 1e-2,
            slack_bound_push: 1e-2,
            slack_bound_frac: 1e-2,
            constr_mult_init_max: 1e3,
            bound_mult_init_val: 1.0,
            bound_mult_init_method: "constant".into(),
            least_square_init_primal: false,
        }
    }
}

/// Knobs read off `OptionsList` and baked into
/// [`WarmStartIterateInitializer`]. Defaults mirror
/// `IpWarmStartIterateInitializer.cpp:RegisterOptions`.
///
/// Wired today: every knob above plus the gh#606 recentering pair.
/// `warm_start_entire_iterate` / `warm_start_same_structure` are
/// deliberately *not* here — they name the `GetWarmStartIterate` TNLP
/// surface pounce does not expose, and are refused by
/// [`crate::unimplemented_options`] rather than parsed into a field
/// nothing reads (gh#606).
#[derive(Debug, Clone)]
pub struct WarmStartOptions {
    pub bound_push: Number,
    pub bound_frac: Number,
    pub slack_bound_push: Number,
    pub slack_bound_frac: Number,
    pub mult_bound_push: Number,
    pub mult_init_max: Number,
    pub target_mu: Number,
    /// The value a NaN-seeded bound multiplier takes: NaN in a
    /// user-supplied `z`/`v` seed means "unseeded, use the default".
    /// Threaded from `builder.init.bound_mult_init_val` at build time
    /// so the Mehrotra override and any user setting stay the single
    /// source of truth.
    pub bound_mult_init_val: Number,
    /// `constr_mult_init_max`, threaded from the init options at build
    /// time (gh#606). The warm path's reconstruction of an unseeded
    /// equality-multiplier block runs the *cold* path's least-squares
    /// solve, so it is capped by the cold path's cap — not by
    /// `warm_start_mult_init_max`, which caps multipliers a caller
    /// actually supplied and is three orders looser (1e6 vs 1e3).
    /// Measured: on `redundant_rows`, whose duplicated equality rows
    /// make the least-squares system singular, the looser cap let an
    /// arbitrary estimate through and cost 7 -> 25 iterations.
    pub constr_mult_init_max: Number,
    /// `warm_start_recentering` (gh#606). Whether the initializer
    /// measures the supplied point and adapts μ / the multiplier fills
    /// to it, or keeps the pre-gh#606 universal constants.
    pub recentering: WarmStartRecentering,
}

/// Value of `warm_start_recentering` (gh#606).
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum WarmStartRecentering {
    /// Pre-gh#606 behaviour: constant pushes and floors, `y` left at
    /// zero when unseeded, μ untouched unless `warm_start_target_mu`
    /// is set. The kill switch.
    None,
    /// Measure the supplied iterate's residuals and derive μ, the
    /// bound-multiplier fills, and the equality-multiplier
    /// reconstruction from them.
    Residual,
}

impl WarmStartOptions {
    /// `mult_init_max` as a usable cap: the registered `0` sentinel
    /// means "no cap".
    pub(crate) fn mult_init_max_or_inf(&self) -> Number {
        if self.mult_init_max > 0.0 {
            self.mult_init_max
        } else {
            Number::INFINITY
        }
    }
}

impl Default for WarmStartOptions {
    fn default() -> Self {
        Self {
            bound_push: 1e-3,
            bound_frac: 1e-3,
            slack_bound_push: 1e-3,
            slack_bound_frac: 1e-3,
            mult_bound_push: 1e-3,
            mult_init_max: 1e6,
            target_mu: 0.0,
            // seeded from the init options so the default has one
            // home; build() re-resolves it from the live init options
            // anyway (see `resolved_warm_options`)
            bound_mult_init_val: InitOptions::default().bound_mult_init_val,
            constr_mult_init_max: InitOptions::default().constr_mult_init_max,
            recentering: WarmStartRecentering::Residual,
        }
    }
}

/// The warm-start options as the initializer actually receives them:
/// `bound_mult_init_val` and `constr_mult_init_max` come from the
/// (option-read, Mehrotra-resolved) init options, never from
/// `WarmStartOptions`'s own copy. Split out of `build()` so the threading is testable.
pub(crate) fn resolved_warm_options(
    warm: &WarmStartOptions,
    init: &InitOptions,
) -> WarmStartOptions {
    let mut w = warm.clone();
    w.bound_mult_init_val = init.bound_mult_init_val;
    w.constr_mult_init_max = init.constr_mult_init_max;
    w
}

/// Knobs read off `OptionsList` and baked into the assembled
/// `MonotoneMuUpdate` or `AdaptiveMuUpdate`. Defaults mirror
/// `IpMonotoneMuUpdate.cpp` / `IpAdaptiveMuUpdate.cpp:RegisterOptions`.
/// `mu_max` defaults to the sentinel `-1`; positive values are baked
/// into both updaters at build time (adaptive interprets `-1` as
/// "lazy-init from `mu_max_fact * avrg_compl`").
#[derive(Debug, Clone)]
pub struct MuOptions {
    pub mu_init: Number,
    pub mu_max: Number,
    pub mu_max_fact: Number,
    pub mu_min: Number,
    pub mu_target: Number,
    pub mu_linear_decrease_factor: Number,
    pub mu_superlinear_decrease_power: Number,
    pub mu_allow_fast_monotone_decrease: bool,
    pub barrier_tol_factor: Number,
    /// `tau_min` — floor on the fraction-to-the-boundary parameter
    /// τ = max(tau_min, 1 − μ). Registered default 0.99, which is what
    /// both `MonotoneMuUpdate` and `AdaptiveMuUpdate` already carry as
    /// their struct default, so forwarding it is behaviour-neutral for
    /// a run that does not set the option (#551 / #677). Consumed by
    /// both updaters; the adaptive one also uses it in the monotone
    /// mode and for the post-oracle τ = max(tau_min, 1 − NLP error).
    pub tau_min: Number,
    /// `sigma_max` / `sigma_min` — clamp on the centering parameter σ
    /// chosen by `QualityFunctionMuOracle`. Only consumed when
    /// `mu_strategy=adaptive` and `mu_oracle=quality-function`.
    /// Defaults from `IpQualityFunctionMuOracle.cpp:RegisterOptions`.
    pub sigma_max: Number,
    pub sigma_min: Number,
    /// `adaptive_mu_globalization` — globalization strategy for the
    /// adaptive μ-selection mode. Mirrors
    /// `IpAdaptiveMuUpdate.cpp:RegisterOptions`. Default is
    /// `ObjConstrFilter`; the Mehrotra cascade switches to
    /// `NeverMonotoneMode` to disable globalization entirely.
    pub adaptive_mu_globalization: crate::mu::adaptive::AdaptiveMuGlobalization,
    /// `quality_function_norm_type` — norm used inside the quality
    /// function to aggregate the three KKT components. Forwarded to
    /// `QualityFunctionMuOracle` when `mu_oracle=quality-function`.
    pub quality_function_norm_type: crate::mu::oracle::quality_function::NormType,
    /// `quality_function_centrality` — centrality penalty term added
    /// to the quality function.
    pub quality_function_centrality: crate::mu::oracle::quality_function::CentralityType,
    /// `quality_function_balancing_term` — balancing penalty term in
    /// the quality function (kicks in when complementarity is far
    /// below infeasibilities).
    pub quality_function_balancing_term: crate::mu::oracle::quality_function::BalancingTermType,
    /// `quality_function_max_section_steps` — cap on golden-section
    /// iterations when picking σ. Default 8.
    pub quality_function_max_section_steps: i32,
    /// `quality_function_section_sigma_tol` — width tolerance in
    /// σ-space for golden section. Default 1e-2.
    pub quality_function_section_sigma_tol: Number,
    /// `quality_function_section_qf_tol` — relative flatness
    /// tolerance for golden section. Default 0.0.
    pub quality_function_section_qf_tol: Number,
    /// `adaptive_mu_safeguard_factor` — guard for the LOQO fallback
    /// in adaptive mode. Default 0.0.
    pub adaptive_mu_safeguard_factor: Number,
    /// `adaptive_mu_monotone_init_factor` — multiplier on the
    /// average complementarity when seeding monotone mode after a
    /// free-mode bailout. Default 0.8.
    pub adaptive_mu_monotone_init_factor: Number,
    /// `adaptive_mu_restore_previous_iterate` — restore the most
    /// recent free-mode iterate when switching to fixed mode.
    /// Default `false`.
    pub adaptive_mu_restore_previous_iterate: bool,
    /// `adaptive_mu_max_free_returns` (pounce#749, POUNCE
    /// extension). Cap on how many times the adaptive strategy may
    /// leave fixed-mu mode. `-1` = unlimited (upstream behavior).
    pub adaptive_mu_max_free_returns: i32,
    /// `adaptive_mu_budget_pin_fraction` (pounce#753, POUNCE
    /// extension). Fraction of an explicitly-set `max_cpu_time` /
    /// `max_wall_time` after which the adaptive strategy stops
    /// returning to free-mu mode and finishes monotone. `1.0`
    /// disables. Inert unless the caller set a time budget.
    pub adaptive_mu_budget_pin_fraction: Number,
    /// `adaptive_mu_kkterror_red_iters` — window length for the
    /// `KKT_ERROR` globalization history. Default 4.
    pub adaptive_mu_kkterror_red_iters: usize,
    /// `adaptive_mu_kkterror_red_fact` — required relative reduction
    /// of the KKT error over the window. Default 0.9999.
    pub adaptive_mu_kkterror_red_fact: Number,
    /// `adaptive_mu_kkt_norm_type` — norm used to score the iterate
    /// in adaptive globalization decisions.
    pub adaptive_mu_kkt_norm_type: crate::mu::adaptive::AdaptiveMuKktNorm,
    /// `filter_margin_fact` — width factor of the margin an entry must
    /// clear in the `obj-constr-filter` adaptive globalization test
    /// (`margin = filter_margin_fact * min(filter_max_margin, err)`).
    /// Only consumed when `mu_strategy=adaptive` and
    /// `adaptive_mu_globalization=obj-constr-filter` (the default).
    /// Default 1e-5, from `IpAdaptiveMuUpdate.cpp:RegisterOptions`.
    pub filter_margin_fact: Number,
    /// `filter_max_margin` — cap on the margin above. Default 1.0,
    /// from `IpAdaptiveMuUpdate.cpp:RegisterOptions`.
    pub filter_max_margin: Number,
    /// `probing_iterate_quality_factor` (default 1e4, pounce-specific
    /// — see pounce#58). When the probing (Mehrotra) μ-oracle is
    /// about to read `curr_avrg_compl()` for its `mu_curr` input, a
    /// single imbalanced `(s_i, z_i)` pair can inflate the average
    /// 5+ orders above the stored `data.curr_mu`. The oracle then
    /// returns `σ · mu_curr` ≫ previous μ, throwing the iterate out
    /// of the convergence neighborhood. This guard short-circuits
    /// that case by signalling restoration when the ratio
    /// `curr_avrg_compl / curr_mu` exceeds the factor. Set to 0 or
    /// any non-positive value to disable.
    pub probing_iterate_quality_factor: Number,
}

impl Default for MuOptions {
    fn default() -> Self {
        Self {
            mu_init: 0.1,
            mu_max: -1.0,
            mu_max_fact: 1e3,
            mu_min: 1e-11,
            mu_target: 0.0,
            mu_linear_decrease_factor: 0.2,
            mu_superlinear_decrease_power: 1.5,
            mu_allow_fast_monotone_decrease: true,
            barrier_tol_factor: 10.0,
            tau_min: 0.99,
            sigma_max: 1e2,
            sigma_min: 1e-6,
            adaptive_mu_globalization:
                crate::mu::adaptive::AdaptiveMuGlobalization::ObjConstrFilter,
            quality_function_norm_type:
                crate::mu::oracle::quality_function::NormType::TwoNormSquared,
            quality_function_centrality: crate::mu::oracle::quality_function::CentralityType::None,
            quality_function_balancing_term:
                crate::mu::oracle::quality_function::BalancingTermType::None,
            quality_function_max_section_steps: 8,
            quality_function_section_sigma_tol: 1e-2,
            quality_function_section_qf_tol: 0.0,
            adaptive_mu_safeguard_factor: 0.0,
            adaptive_mu_monotone_init_factor: 0.8,
            adaptive_mu_restore_previous_iterate: false,
            adaptive_mu_max_free_returns: -1,
            adaptive_mu_budget_pin_fraction: 0.75,
            adaptive_mu_kkterror_red_iters: 4,
            adaptive_mu_kkterror_red_fact: 0.9999,
            adaptive_mu_kkt_norm_type: crate::mu::adaptive::AdaptiveMuKktNorm::TwoNormSquared,
            filter_margin_fact: 1e-5,
            filter_max_margin: 1.0,
            probing_iterate_quality_factor: 1e4,
        }
    }
}

/// Knobs baked into the assembled [`BacktrackingLineSearch`]. Defaults
/// mirror `IpBacktrackingLineSearch.cpp:RegisterOptions`.
#[derive(Debug, Clone)]
pub struct LineSearchOptions {
    /// `alpha_red_factor` — fractional reduction applied to the trial
    /// step size at every backtracking step
    /// (`alpha *= alpha_red_factor`). Mirrors upstream's
    /// `IpBacktrackingLineSearch::alpha_red_factor_`.
    pub alpha_red_factor: Number,
    /// `alpha_red_factor_min` — floor on one backtracking reduction,
    /// which turns the fixed geometric trial sequence into a
    /// safeguarded quadratic interpolation (gh#818). See
    /// `BacktrackingLineSearch::next_alpha`.
    ///
    /// `None` — the default — means "let the Hessian mode decide", and
    /// the two modes decide differently:
    ///
    /// * **limited-memory → `0.05`** (interpolation on). The
    ///   quasi-Newton model's *scale* can be wrong by orders of
    ///   magnitude in any direction its curvature pairs do not span, so
    ///   the acceptable `alpha` can be far below 1 and the fixed factor
    ///   spends `log(1/alpha)` objective evaluations walking to it.
    /// * **exact → off** (`alpha_red_factor`, i.e. upstream's fixed
    ///   sequence). A Newton step's length is meaningful, the
    ///   acceptable `alpha` is normally within a few halvings of 1, and
    ///   the trial sequence is not what costs.
    ///
    /// The split is measured, not assumed. Forcing
    /// `alpha_red_factor_min 0.05` onto the exact path moves 3 of the
    /// 156 fixture-legs in `scripts/sweep-fixtures.sh`, and the one
    /// that matters is a **status loss**: `eigena2` goes from
    /// `SolveSucceeded`/27 to `SolvedToAcceptableLevel`/32. The other
    /// two are `issue_508_infeasible_gap_1em4` 441 → 385 to the same
    /// certificate — a gain — and an objective digit on
    /// `hs13_bigstart`. One fixture giving up a solve is the whole
    /// argument; a faster infeasibility certificate does not buy it
    /// back. Before `ALPHA_INTERP_MIN_TRIALS` gated the interpolation
    /// the same experiment moved 9 legs and cost
    /// `infeasible_square_scaled_1em4` its infeasibility certificate
    /// (`InfeasibleProblemDetected`/17 → `ErrorInStepComputation`/12);
    /// the gate narrowed the damage, it did not remove the reason for
    /// the split.
    ///
    /// An explicit `alpha_red_factor_min` from the user is honoured on
    /// both paths — the mode-dependence is only in the default.
    pub alpha_red_factor_min: Option<Number>,
    pub watchdog_shortened_iter_trigger: Index,
    pub watchdog_trial_iter_max: Index,
    /// `soft_resto_pderror_reduction_factor` — required relative
    /// reduction in the primal-dual error for a soft-resto step.
    /// `0` disables the soft restoration phase.
    pub soft_resto_pderror_reduction_factor: Number,
    /// `max_soft_resto_iters` — cap on consecutive soft-resto
    /// iterations before full restoration is forced.
    pub max_soft_resto_iters: Index,
    /// `accept_every_trial_step` — short-circuits the filter / alpha
    /// loop and accepts the full fraction-to-the-boundary step every
    /// outer iteration. Mirrors upstream's
    /// `IpBacktrackingLineSearch::accept_every_trial_step_`. Drops
    /// global convergence guarantees; only safe for problems where the
    /// Newton step is already a descent step (LPs, convex QPs). The
    /// Mehrotra cascade in `application.rs` flips this on.
    pub accept_every_trial_step: bool,
    /// `alpha_for_y` — policy for the equality-multiplier (y_c / y_d)
    /// step length. Upstream default is `Primal`; the Mehrotra cascade
    /// switches to `BoundMult`.
    pub alpha_for_y: crate::line_search::backtracking::AlphaForY,
    /// `accept_after_max_steps` — accept a trial point once this many
    /// backtracking steps have been taken, even if it fails the
    /// acceptor's tests. `-1` (the default) disables it, which is why
    /// wiring it moves no default trajectory. Mirrors
    /// `IpBacktrackingLineSearch.cpp:759-770`.
    pub accept_after_max_steps: Index,

    // Filter switching / Armijo / margin constants baked onto the
    // assembled [`crate::line_search::filter_acceptor::FilterLsAcceptor`]
    // (only when `line_search_method = Filter`). All were registered but
    // never read (#191); defaults mirror `IpFilterLSAcceptor.cpp`.
    /// `eta_phi` — relaxation factor in the Armijo condition (Eqn. (20)).
    pub eta_phi: Number,
    /// `delta` — multiplier on the constraint violation in the filter's
    /// switching rule (Eqn. (19)); maps to
    /// [`FilterLsAcceptor::delta_armijo`]. Default 1.0, from
    /// `IpFilterLSAcceptor.cpp:RegisterOptions`.
    pub delta: Number,
    /// `theta_min_fact` — constraint-violation threshold factor in the
    /// switching rule.
    pub theta_min_fact: Number,
    /// `theta_max_fact` — upper-bound factor for constraint violation in
    /// the filter (Eqn. (21)).
    pub theta_max_fact: Number,
    /// `theta_max_row_scale_kappa` — multiplier on the constraint-row
    /// count used as the floor of the `theta_max` reference.
    /// **Opt-in**: default `0`, which is upstream's bare
    /// `max(1, theta_0)` floor bit-for-bit. Set to `1` on a large model
    /// that stalls from a feasible start. See
    /// [`FilterLsAcceptor::theta_max_row_scale_kappa`].
    pub theta_max_row_scale_kappa: Number,
    /// `theta_max_adaptive_trigger` — consecutive line searches whose
    /// every trial was refused at the `theta_max` gate before the
    /// ceiling is raised. `0` disables the rule. See
    /// [`FilterLsAcceptor::theta_max_adaptive_trigger`] (pounce#546).
    pub theta_max_adaptive_trigger: u32,
    /// Geometric factor applied to `theta_max` on each adaptive raise.
    /// See [`FilterLsAcceptor::theta_max_adaptive_factor`].
    pub theta_max_adaptive_factor: Number,
    /// Cap on adaptive raises per solve, which is what keeps `theta_max`
    /// finite. See [`FilterLsAcceptor::theta_max_adaptive_max_raises`].
    pub theta_max_adaptive_max_raises: u32,
    /// `gamma_phi` — filter margin factor for the barrier function
    /// (Eqn. (18a)).
    pub gamma_phi: Number,
    /// `gamma_theta` — filter margin factor for the constraint violation
    /// (Eqn. (18b)).
    pub gamma_theta: Number,
    /// `s_phi` — exponent for the linear barrier model in the switching
    /// rule (Eqn. (19)).
    pub s_phi: Number,
    /// `s_theta` — exponent for the current constraint violation in the
    /// switching rule (Eqn. (19)).
    pub s_theta: Number,
    /// `alpha_min_frac` — safety factor for the minimal step size before
    /// switching to restoration (gamma_alpha, Eqn. (23)).
    pub alpha_min_frac: Number,
    /// `obj_max_inc` — max acceptable increase (orders of magnitude) of
    /// the barrier objective for a trial point.
    pub obj_max_inc: Number,
    /// `max_filter_resets` — maximum number of filter resets allowed
    /// (`0` disables the reset heuristic).
    pub max_filter_resets: Index,
    /// `filter_reset_trigger` — successive filter-rejected iterations that
    /// trigger a filter reset.
    pub filter_reset_trigger: Index,

    // Penalty-acceptor constants baked onto the assembled
    // [`crate::line_search::penalty_acceptor::PenaltyLsAcceptor`] (only
    // when `line_search_method = penalty` / `cg-penalty`). Defaults
    // mirror `IpPenaltyLSAcceptor.cpp:RegisterOptions`.
    /// `nu_init` — initial value of the penalty parameter ν.
    pub nu_init: Number,
    /// `nu_inc` — increment added when ν is bumped.
    pub nu_inc: Number,
    /// `rho` — convex-combination weight in the ν update rule.
    pub rho: Number,
    /// `eta_penalty` — relaxation factor in the Armijo condition on the
    /// penalty merit function.
    pub eta_penalty: Number,

    // Second-order-correction constants baked onto the assembled
    // [`BacktrackingLineSearch`]. Registered but never read (#191);
    // defaults mirror `IpBacktrackingLineSearch.cpp`.
    /// `max_soc` — max second-order-correction trial steps per iteration;
    /// `0` disables SOC.
    pub max_soc: Index,
    /// `kappa_soc` — sufficient-reduction factor for a SOC step to be
    /// continued.
    pub kappa_soc: Number,
    /// `soc_method` — `0` (paper method) or `1` (alpha-on-rhs variant).
    pub soc_method: Index,
}

impl Default for LineSearchOptions {
    fn default() -> Self {
        Self {
            alpha_red_factor: 0.5,
            alpha_red_factor_min: None,
            watchdog_shortened_iter_trigger: 10,
            watchdog_trial_iter_max: 3,
            soft_resto_pderror_reduction_factor: 1.0 - 1e-4,
            max_soft_resto_iters: 10,
            accept_every_trial_step: false,
            alpha_for_y: crate::line_search::backtracking::AlphaForY::Primal,
            accept_after_max_steps: -1,
            eta_phi: 1e-8,
            delta: 1.0,
            theta_min_fact: 1e-4,
            theta_max_fact: 1e4,
            theta_max_row_scale_kappa: 0.0,
            theta_max_adaptive_trigger: 3,
            theta_max_adaptive_factor: 100.0,
            theta_max_adaptive_max_raises: 4,
            gamma_phi: 1e-8,
            gamma_theta: 1e-5,
            s_phi: 2.3,
            s_theta: 1.1,
            alpha_min_frac: 0.05,
            obj_max_inc: 5.0,
            max_filter_resets: 5,
            filter_reset_trigger: 5,
            nu_init: 1e-6,
            nu_inc: 1e-4,
            rho: 0.1,
            eta_penalty: 1e-8,
            max_soc: 4,
            kappa_soc: 0.99,
            soc_method: 0,
        }
    }
}

/// Inertia-correction / regularization knobs baked onto the assembled
/// [`crate::kkt::perturbation_handler::PdPerturbationHandler`]. Field
/// names use the option names; they map to the handler's `delta_xs_*` /
/// `delta_cd_*` fields. Defaults mirror
/// `IpPDPerturbationHandler.cpp:RegisterOptions`. All were registered but
/// never read (#191).
#[derive(Debug, Clone)]
pub struct PerturbationOptions {
    /// `max_hessian_perturbation` → `delta_xs_max`.
    pub max_hessian_perturbation: Number,
    /// `min_hessian_perturbation` → `delta_xs_min`.
    pub min_hessian_perturbation: Number,
    /// `perturb_inc_fact_first` → `delta_xs_first_inc_fact`.
    pub perturb_inc_fact_first: Number,
    /// `perturb_inc_fact` → `delta_xs_inc_fact`.
    pub perturb_inc_fact: Number,
    /// `perturb_dec_fact` → `delta_xs_dec_fact`.
    pub perturb_dec_fact: Number,
    /// `first_hessian_perturbation` → `delta_xs_init`.
    pub first_hessian_perturbation: Number,
    /// `jacobian_regularization_value` → `delta_cd_val`.
    pub jacobian_regularization_value: Number,
    /// `jacobian_regularization_exponent` → `delta_cd_exp`.
    pub jacobian_regularization_exponent: Number,
    /// `perturb_always_cd` — always regularize the c/d (Jacobian) block.
    pub perturb_always_cd: bool,
    /// `perturb_delta_c_max_rungs` → `delta_c_max_rungs` (pounce gh#592).
    pub perturb_delta_c_max_rungs: Index,
}

impl Default for PerturbationOptions {
    fn default() -> Self {
        Self {
            max_hessian_perturbation: 1e20,
            min_hessian_perturbation: 1e-20,
            perturb_inc_fact_first: 100.0,
            perturb_inc_fact: 8.0,
            perturb_dec_fact: 1.0 / 3.0,
            first_hessian_perturbation: 1e-4,
            jacobian_regularization_value: 1e-8,
            jacobian_regularization_exponent: 0.25,
            perturb_always_cd: false,
            perturb_delta_c_max_rungs: 3,
        }
    }
}

/// Restoration-phase knobs carried on the outer builder and copied into
/// the `RestoAlgorithmBuilder` when the restoration factory is minted
/// (`pounce-restoration`). The restoration builder is constructed with
/// defaults by each frontend and never options-configured, so these were
/// registered but never read (#191). Defaults mirror upstream's
/// restoration `RegisterOptions`.
#[derive(Debug, Clone)]
pub struct RestoOptions {
    /// `bound_mult_reset_threshold` — reset bound multipliers to 1 after
    /// restoration if the largest exceeds this.
    pub bound_mult_reset_threshold: Number,
    /// `constr_mult_reset_threshold` — ignore the least-square constraint
    /// multiplier estimate after restoration if its norm exceeds this
    /// (`0` keeps the estimate).
    pub constr_mult_reset_threshold: Number,
    /// `resto_penalty_parameter` — penalty on the slack 1-norm in the
    /// restoration objective (`rho`).
    pub resto_penalty_parameter: Number,
    /// `resto_proximity_weight` — proximity-term weight (`eta_factor`;
    /// `η = eta_factor · sqrt(μ)`).
    pub resto_proximity_weight: Number,
    /// `required_infeasibility_reduction` — the restoration sub-solve
    /// keeps iterating until the *original* NLP's infeasibility has been
    /// reduced to at most this fraction of its value at restoration entry
    /// (`κ_resto` in `IpRestoConvCheck.cpp:58`). `0` disables the guard,
    /// i.e. restoration runs until the sub-NLP itself converges.
    pub required_infeasibility_reduction: Number,
    /// `evaluate_orig_obj_at_resto_trial` — evaluate the *original*
    /// objective at every restoration trial point, so an iterate the
    /// restoration problem likes but the original cannot evaluate is
    /// rejected there rather than after the phase exits. Upstream default
    /// `yes`. `RestoAlgorithmBuilder` has consumed this since it landed;
    /// only the read site was missing (gh#483, #191 round 2).
    pub evaluate_orig_obj_at_resto_trial: bool,
    /// `expect_infeasible_problem` — enter restoration sooner and demand
    /// more infeasibility reduction before leaving it. Upstream default
    /// `no`. Same story: consumed, never read.
    pub expect_infeasible_problem: bool,
    /// `start_with_resto` — switch to restoration in the first iteration.
    /// Upstream default `no`. Same story.
    pub start_with_resto: bool,
    /// `max_resto_iter` — cap on *successive* restoration iterations
    /// (`IpRestoConvCheck.cpp:144`'s `maximum_resto_iters`). Consumed by
    /// `pounce_restoration::conv_check::RestoConvCheckAdapter`, which
    /// returns `MaxIterExceeded` once the count is reached; the value
    /// used to be the hard-coded `RESTO_MAX_SUCCESSIVE_ITERS` in
    /// `resto_inner_solver.rs`, so setting the option did nothing
    /// (#551 / #677). The field is named after the option here, but the
    /// consumer's field is `maximum_resto_iters` — which is why grepping
    /// for the option name found nothing (#551 caution 2).
    ///
    /// **This default deliberately differs from the registered one.**
    /// `upstream_options.rs` registers Ipopt's `3000000`; pounce has
    /// enforced `3000` since the cap landed. Wiring the option must not
    /// change what an unset option does, so the effective cap stays
    /// `3000` and only an explicit `max_resto_iter` moves it. Raising
    /// the default to upstream's number is a trajectory change (it
    /// lets a restoration that pounce currently cuts off at 3000 keep
    /// going) and belongs to a change that measures it.
    pub max_resto_iter: i32,
}

impl Default for RestoOptions {
    fn default() -> Self {
        Self {
            bound_mult_reset_threshold: 1e3,
            constr_mult_reset_threshold: 0.0,
            resto_penalty_parameter: 1e3,
            resto_proximity_weight: 1.0,
            required_infeasibility_reduction: 0.9,
            evaluate_orig_obj_at_resto_trial: true,
            expect_infeasible_problem: false,
            start_with_resto: false,
            // NOT the registered default (3000000) — see the field docs.
            max_resto_iter: 3000,
        }
    }
}

/// Iterative-refinement knobs baked onto the assembled
/// [`crate::kkt::pd_full_space_solver::PdFullSpaceSolver`]. Defaults
/// mirror `IpPDFullSpaceSolver.cpp:RegisterOptions`. All were registered
/// but never read (#191).
#[derive(Debug, Clone)]
pub struct RefinementOptions {
    /// `min_refinement_steps` — minimum iterative-refinement steps per
    /// linear solve.
    pub min_refinement_steps: Index,
    /// `max_refinement_steps` — maximum iterative-refinement steps.
    pub max_refinement_steps: Index,
    /// `residual_ratio_max` — refine until the residual test ratio drops
    /// below this (or `max_refinement_steps` is reached).
    pub residual_ratio_max: Number,
    /// `residual_ratio_singular` — above this ratio after failed
    /// refinement, the system is declared singular.
    pub residual_ratio_singular: Number,
    /// `residual_improvement_factor` — minimum per-step reduction of the
    /// residual test ratio before refinement is aborted.
    pub residual_improvement_factor: Number,
    /// `neg_curv_test_tol` — tolerance α_n of the inertia-free curvature
    /// test of Zavala & Chiang (2014). Zero (the registered default)
    /// disables the heuristic and keeps the inertia check; positive
    /// turns the inertia check off and accepts the factorization only
    /// when the computed direction passes the curvature test in
    /// `PdFullSpaceSolver::solve_once`.
    pub neg_curv_test_tol: Number,
    /// `neg_curv_test_reg` — whether the curvature test includes the
    /// primal regularization δ_x‖dx‖² + δ_s‖ds‖². Registered default
    /// `yes`; `no` reproduces the original Ipopt form that ignores it.
    /// Only consulted when `neg_curv_test_tol > 0`.
    pub neg_curv_test_reg: bool,
}

impl Default for RefinementOptions {
    fn default() -> Self {
        Self {
            min_refinement_steps: 1,
            max_refinement_steps: 10,
            residual_ratio_max: 1e-10,
            residual_ratio_singular: 1e-5,
            residual_improvement_factor: 0.999_999_999,
            neg_curv_test_tol: 0.0,
            neg_curv_test_reg: true,
        }
    }
}

/// Knobs baked into the assembled [`OrigIterationOutput`]. Defaults
/// mirror `IpOrigIterationOutput.cpp:RegisterOptions` /
/// `IpAlgorithmRegOp.cpp`.
#[derive(Debug, Clone)]
pub struct OutputOptions {
    pub print_frequency_iter: Index,
    pub print_frequency_time: Number,
    /// `print_info_string` (default `false`). When on, the iter row
    /// ends with the contents of `IpoptData::info_string` so users
    /// can read the per-iteration diagnostic tags.
    pub print_info_string: bool,
    /// `inf_pr_output` — `"original"` (default) prints the unscaled
    /// NLP primal infeasibility; `"internal"` prints the internal
    /// reformulated violation. Only meaningful once NLP-side scaling
    /// is in play; until then both modes produce the same number.
    pub inf_pr_output_internal: bool,
}

impl Default for OutputOptions {
    fn default() -> Self {
        Self {
            print_frequency_iter: 1,
            print_frequency_time: 0.0,
            print_info_string: false,
            inf_pr_output_internal: false,
        }
    }
}

impl Default for AlgorithmBuilder {
    fn default() -> Self {
        Self {
            algorithm: AlgorithmChoice::default(),
            linear_solver: LinearSolverChoice::Feral,
            linear_system_scaling: LinearSystemScalingChoice::None,
            linear_scaling_on_demand: true,
            mu_strategy: MuStrategyChoice::Monotone,
            mu_oracle: MuOracleKind::QualityFunction,
            hessian_approximation: HessianApproxChoice::Exact,
            partitioned_update_type: UpdateType::Sr1,
            partitioned_update_type_was_set: false,
            partitioned_max_element: 64,
            objective_nonlinear_vars: None,
            partitioned_curvature_cap: Number::INFINITY,
            partitioned_elements: crate::hess::partitioned_quasi_newton::ElementMode::PerConstraint,
            partitioned_block_size: 64,
            fd_hessian_pattern: crate::hess::fd_hessian::FdPatternSource::Declared,
            fd_hessian_coloring: crate::hess::fd_hessian::FdColoring::Cpr,
            fd_hessian_reuse_tol: 0.0,
            limited_memory_update_type: UpdateType::Bfgs,
            limited_memory_max_history: 6,
            limited_memory_init_val_max: 1e8,
            limited_memory_init_val_min: 1e-8,
            limited_memory_initialization: InitialApprox::Scalar1,
            limited_memory_init_val: 1.0,
            limited_memory_max_skipping: 2,
            limited_memory_nonlinear_vars: None,
            line_search_method: LineSearchChoice::Filter,
            warm_start_init_point: false,
            mehrotra_algorithm: false,
            fast_step_computation: false,
            kappa_sigma: 1e10,
            recalc_y: false,
            recalc_y_feas_tol: 1e-6,
            kappa_d: 1e-5,
            s_max: 100.0,
            tiny_step_tol: 10.0 * Number::EPSILON,
            tiny_step_y_tol: 1e-2,
            diverging_iterates_tol: 1e20,
            dual_divergence_retry_step_tol: 1e-5,
            dual_divergence_retry_du_floor: 1e2,
            dual_diverging_streak: 0,
            resto_decline_deferrals: 1,
            resto_decline_progress_ratio: 0.5,
            neg_curv_escapes: 1,
            limited_memory_ls_failure_restarts: 0,
            kkt_fidelity_tol: 0.0,
            conv_check: ConvCheckOptions::default(),
            mu: MuOptions::default(),
            line_search: LineSearchOptions::default(),
            refinement: RefinementOptions::default(),
            perturbation: PerturbationOptions::default(),
            resto: RestoOptions::default(),
            output: OutputOptions::default(),
            warm: WarmStartOptions::default(),
            sqp: crate::sqp::SqpOptions::default(),
            sqp_qp: pounce_qp::QpOptions::sqp_subproblem(),
            init: InitOptions::default(),
            kkt_schur: None,
            quality_escalation_counter: None,
        }
    }
}

impl AlgorithmBuilder {
    pub fn new() -> Self {
        Self::default()
    }

    /// Install a Schur KKT partition (pounce#180 item 2). `schur_indices` are
    /// KKT-space indices (`0..dim`, the `x,s,c,d` block order the aug-system
    /// solver assembles); `cfg` configures the per-block feral solvers. Only
    /// honored on the IPM + feral + exact-Hessian path by
    /// [`Self::build_with_backend`]; ignored otherwise.
    pub fn set_kkt_schur(&mut self, schur_indices: Vec<usize>, cfg: pounce_feral::FeralConfig) {
        self.kkt_schur = Some((schur_indices, cfg));
    }

    /// Assemble the strategy bundle without a search-direction
    /// calculator. Used by structural unit tests that don't want to
    /// pull in a linear-solver backend.
    pub fn build(&self) -> AlgorithmBundle {
        self.build_inner(None)
    }

    /// Same as [`Self::build`] but also constructs the
    /// `SymLinearSolver → AugSystemSolver → PdFullSpaceSolver →
    /// PdSearchDirCalc` chain via the supplied `factory`.
    pub fn build_with_backend(&self, mut factory: LinearBackendFactory) -> AlgorithmBundle {
        let backend = factory(self.linear_solver);
        let make_scaling = || -> Option<Box<dyn pounce_linsol::TSymScalingMethod>> {
            match self.linear_system_scaling {
                LinearSystemScalingChoice::None => None,
                LinearSystemScalingChoice::Ruiz => {
                    Some(Box::new(pounce_linsol::RuizTSymScalingMethod::new()))
                }
                LinearSystemScalingChoice::Mc19 => {
                    tracing::warn!(target: "pounce::algorithm",
                        "pounce: linear_system_scaling=mc19 not yet implemented; using no scaling"
                    );
                    None
                }
                LinearSystemScalingChoice::SlackBased => {
                    Some(Box::new(pounce_linsol::SlackBasedTSymScalingMethod::new()))
                }
            }
        };
        let linsol = TSymLinearSolver::new(backend, make_scaling(), self.linear_scaling_on_demand);
        let inner_aug = StdAugSystemSolver::new(linsol);
        // Limited-memory mode publishes the Hessian as a
        // `LowRankUpdateSymMatrix`; wrap the standard solver in the
        // Sherman-Morrison-Woodbury low-rank solver so the augmented
        // system factorizes only the diagonal `B0` and the quasi-Newton
        // update is applied as a rank-`m` correction (`O(n·m)` memory).
        let is_lbfgs = matches!(
            self.hessian_approximation,
            HessianApproxChoice::LimitedMemory
        );
        let aug_solver: Box<dyn AugSystemSolver> = if is_lbfgs {
            // A second, independent inner solver for the Hessian-free
            // solves. Both see one W shape each for their whole life, so
            // neither re-runs the backend's symbolic factorization when
            // the other's shape comes round — see
            // `LowRankAugSystemSolver::with_bypass_solver` (gh#730).
            let bypass_linsol = TSymLinearSolver::new(
                factory(self.linear_solver),
                make_scaling(),
                self.linear_scaling_on_demand,
            );
            Box::new(LowRankAugSystemSolver::with_bypass_solver(
                Box::new(inner_aug),
                Box::new(StdAugSystemSolver::new(bypass_linsol)),
            ))
        } else if let Some((indices, cfg)) = self.kkt_schur.clone() {
            // Block-triangular / Schur KKT path (pounce#180 item 2). Only on the
            // exact-Hessian feral path — the Schur backend is feral-specific,
            // and the L-BFGS low-rank Woodbury wrapper owns the (2,2) block.
            // The Schur solver falls back to `StdAugSystemSolver` transparently
            // when the partition is unsuitable, so a stray hook never breaks a
            // solve; we gate on `linear_solver == Feral` here to avoid silently
            // ignoring a user's explicit MA57 selection.
            if matches!(self.linear_solver, LinearSolverChoice::Feral) {
                Box::new(crate::kkt::SchurAugSystemSolver::new(
                    inner_aug, indices, cfg,
                ))
            } else {
                Box::new(inner_aug)
            }
        } else {
            Box::new(inner_aug)
        };
        // Inertia-correction / Jacobian-regularization constants (#191):
        // registered but previously never read. Defaults equal the
        // registered defaults. `perturb_always_cd` goes through the setter
        // because it also rebuilds the initial jac-degeneracy state.
        let mut ph = PdPerturbationHandler::new();
        ph.delta_xs_max = self.perturbation.max_hessian_perturbation;
        ph.delta_xs_min = self.perturbation.min_hessian_perturbation;
        ph.delta_xs_first_inc_fact = self.perturbation.perturb_inc_fact_first;
        ph.delta_xs_inc_fact = self.perturbation.perturb_inc_fact;
        ph.delta_xs_dec_fact = self.perturbation.perturb_dec_fact;
        ph.delta_xs_init = self.perturbation.first_hessian_perturbation;
        ph.delta_cd_val = self.perturbation.jacobian_regularization_value;
        ph.delta_cd_exp = self.perturbation.jacobian_regularization_exponent;
        ph.set_perturb_always_cd(self.perturbation.perturb_always_cd);
        ph.delta_c_max_rungs = self.perturbation.perturb_delta_c_max_rungs;
        let perturb = Rc::new(RefCell::new(ph));
        let mut pd_solver = PdFullSpaceSolver::new(aug_solver, perturb);
        // Iterative-refinement constants (#191): registered but previously
        // never read, so overrides were silently dropped. Defaults equal
        // the registered defaults.
        pd_solver.min_refinement_steps = self.refinement.min_refinement_steps;
        pd_solver.max_refinement_steps = self.refinement.max_refinement_steps;
        pd_solver.residual_ratio_max = self.refinement.residual_ratio_max;
        pd_solver.residual_ratio_singular = self.refinement.residual_ratio_singular;
        pd_solver.residual_improvement_factor = self.refinement.residual_improvement_factor;
        // Inertia-free curvature test (#551 / #677). Both were registered
        // and never read; `neg_curv_test_tol` defaults to 0, which leaves
        // the heuristic off and the inertia check on, so this changes
        // nothing for a run that does not set it.
        pd_solver.neg_curv_test_tol = self.refinement.neg_curv_test_tol;
        pd_solver.neg_curv_test_reg = self.refinement.neg_curv_test_reg;
        // gh#857: share the escalation tally with the caller, so the
        // restoration sub-solve built from a clone of this builder counts
        // into the same total.
        if let Some(counter) = self.quality_escalation_counter.as_ref() {
            pd_solver.set_quality_escalation_counter(Rc::clone(counter));
        }
        let mut search_dir = PdSearchDirCalc::new(pd_solver);
        search_dir.mehrotra_algorithm = self.mehrotra_algorithm;
        search_dir.fast_step_computation = self.fast_step_computation;
        self.build_inner(Some(search_dir))
    }

    /// Phase 5b assembly path for the SQP algorithm. Consults
    /// `self.algorithm`: when `ActiveSetSqp`, constructs an
    /// `SqpAlgorithm` using the supplied backend factory for the
    /// QP subproblem solver; otherwise returns `None` so the
    /// caller can fall back to the IPM `build_with_backend`.
    ///
    /// Sister to `build_with_backend`: the SQP algorithm doesn't
    /// share `AlgorithmBundle`'s shape (no mu_update / no IPM
    /// line search), so the two paths return different types.
    pub fn build_sqp_with_backend(
        &self,
        mut factory: LinearBackendFactory,
    ) -> Option<crate::sqp::SqpAlgorithm> {
        if !matches!(self.algorithm, AlgorithmChoice::ActiveSetSqp) {
            return None;
        }
        let backend = factory(self.linear_solver);
        let qp_solver = pounce_qp::ParametricActiveSetSolver::new(backend);
        Some(
            crate::sqp::SqpAlgorithm::new(qp_solver, self.sqp.clone())
                .with_qp_options(self.sqp_qp.clone()),
        )
    }

    fn build_inner(&self, search_dir: Option<PdSearchDirCalc>) -> AlgorithmBundle {
        let mu_update: Box<dyn crate::mu::r#trait::MuUpdate> = match self.mu_strategy {
            MuStrategyChoice::Monotone => {
                let mut m = MonotoneMuUpdate::new();
                m.mu_init = self.mu.mu_init;
                // `mu_max` sentinel `-1` keeps the monotone default
                // (1e5); only override on a user-supplied positive.
                if self.mu.mu_max > 0.0 {
                    m.mu_max = self.mu.mu_max;
                }
                m.mu_min = self.mu.mu_min;
                m.mu_target = self.mu.mu_target;
                m.mu_linear_decrease_factor = self.mu.mu_linear_decrease_factor;
                m.mu_superlinear_decrease_power = self.mu.mu_superlinear_decrease_power;
                m.mu_allow_fast_monotone_decrease = self.mu.mu_allow_fast_monotone_decrease;
                m.barrier_tol_factor = self.mu.barrier_tol_factor;
                m.tau_min = self.mu.tau_min;
                m.compl_inf_tol = self.conv_check.compl_inf_tol;
                Box::new(m)
            }
            MuStrategyChoice::Adaptive => {
                let mut adaptive = AdaptiveMuUpdate::new();
                adaptive.mu_oracle = self.mu_oracle;
                adaptive.mu_init = self.mu.mu_init;
                // Adaptive treats `mu_max == -1` as "lazy init from
                // `mu_max_fact * curr_avrg_compl`" — forward the
                // sentinel as-is.
                adaptive.mu_max = self.mu.mu_max;
                adaptive.mu_max_fact = self.mu.mu_max_fact;
                adaptive.mu_min = self.mu.mu_min;
                adaptive.compl_inf_tol = self.conv_check.compl_inf_tol;
                adaptive.mu_linear_decrease_factor = self.mu.mu_linear_decrease_factor;
                adaptive.mu_superlinear_decrease_power = self.mu.mu_superlinear_decrease_power;
                adaptive.barrier_tol_factor = self.mu.barrier_tol_factor;
                adaptive.tau_min = self.mu.tau_min;
                adaptive.sigma_min = self.mu.sigma_min;
                adaptive.sigma_max = self.mu.sigma_max;
                adaptive.adaptive_mu_globalization = self.mu.adaptive_mu_globalization;
                adaptive.qf_norm_type = self.mu.quality_function_norm_type;
                adaptive.qf_centrality_type = self.mu.quality_function_centrality;
                adaptive.qf_balancing_term = self.mu.quality_function_balancing_term;
                adaptive.qf_max_section_steps = self.mu.quality_function_max_section_steps;
                adaptive.qf_section_sigma_tol = self.mu.quality_function_section_sigma_tol;
                adaptive.qf_section_qf_tol = self.mu.quality_function_section_qf_tol;
                adaptive.probing_iterate_quality_factor = self.mu.probing_iterate_quality_factor;
                adaptive.adaptive_mu_safeguard_factor = self.mu.adaptive_mu_safeguard_factor;
                adaptive.adaptive_mu_monotone_init_factor =
                    self.mu.adaptive_mu_monotone_init_factor;
                adaptive.restore_accepted_iterate = self.mu.adaptive_mu_restore_previous_iterate;
                adaptive.max_free_returns = self.mu.adaptive_mu_max_free_returns;
                adaptive.budget_pin_fraction = self.mu.adaptive_mu_budget_pin_fraction;
                // The pin measures against the same budget the
                // convergence check enforces (pounce#753); the
                // `conv_check` copies are the ones the application
                // also hands to `Deadline::new`.
                adaptive.max_cpu_time = self.conv_check.max_cpu_time;
                adaptive.max_wall_time = self.conv_check.max_wall_time;
                adaptive.adaptive_mu_kkterror_red_iters = self.mu.adaptive_mu_kkterror_red_iters;
                adaptive.adaptive_mu_kkterror_red_fact = self.mu.adaptive_mu_kkterror_red_fact;
                adaptive.adaptive_mu_kkt_norm = self.mu.adaptive_mu_kkt_norm_type;
                adaptive.filter_margin_fact = self.mu.filter_margin_fact;
                adaptive.filter_max_margin = self.mu.filter_max_margin;
                Box::new(adaptive)
            }
        };

        let acceptor: Box<dyn BacktrackingLsAcceptor> = match self.line_search_method {
            LineSearchChoice::Filter => {
                // Filter switching / Armijo / margin constants (#191):
                // registered but previously never read. Set them on the
                // concrete acceptor before boxing; defaults equal the
                // registered defaults, so a run that doesn't set them is
                // unchanged.
                let mut f = FilterLsAcceptor::default();
                f.eta_phi = self.line_search.eta_phi;
                f.delta_armijo = self.line_search.delta;
                f.theta_min_fact = self.line_search.theta_min_fact;
                f.theta_max_fact = self.line_search.theta_max_fact;
                f.theta_max_row_scale_kappa = self.line_search.theta_max_row_scale_kappa;
                f.theta_max_adaptive_trigger = self.line_search.theta_max_adaptive_trigger;
                f.theta_max_adaptive_factor = self.line_search.theta_max_adaptive_factor;
                f.theta_max_adaptive_max_raises = self.line_search.theta_max_adaptive_max_raises;
                f.gamma_phi = self.line_search.gamma_phi;
                f.gamma_theta = self.line_search.gamma_theta;
                f.s_phi = self.line_search.s_phi;
                f.s_theta = self.line_search.s_theta;
                f.alpha_min_frac = self.line_search.alpha_min_frac;
                f.obj_max_inc = self.line_search.obj_max_inc;
                f.max_filter_resets = self.line_search.max_filter_resets;
                f.filter_reset_trigger = self.line_search.filter_reset_trigger;
                Box::new(f)
            }
            // Penalty-acceptor constants: same direct-field pattern as
            // the filter arm above. `reset()` re-seeds ν (and `last_nu`)
            // from the freshly-set `nu_init`, which `default()` had
            // seeded from the registered default.
            LineSearchChoice::Penalty | LineSearchChoice::CgPenalty => {
                // CG-penalty acceptor lands with the rest of the
                // CG-penalty path; fall back to the penalty acceptor's
                // surface for now.
                let mut p = PenaltyLsAcceptor::default();
                p.nu_init = self.line_search.nu_init;
                p.nu_inc = self.line_search.nu_inc;
                p.rho = self.line_search.rho;
                p.eta_penalty = self.line_search.eta_penalty;
                p.reset();
                Box::new(p)
            }
        };
        let mut line_search = BacktrackingLineSearch::new(acceptor);
        line_search.alpha_red_factor = self.line_search.alpha_red_factor;
        // Resolve `None` against the Hessian mode; see the field's doc
        // for the measurement behind the split (gh#818).
        line_search.alpha_red_factor_min =
            self.line_search
                .alpha_red_factor_min
                .unwrap_or(match self.hessian_approximation {
                    // The criterion in the field's doc is whether the
                    // model's *scale* is trustworthy, not whether it is
                    // the exact Hessian. A quasi-Newton `B` can be wrong
                    // by orders of magnitude in any direction its
                    // curvature pairs do not span, which is as true of
                    // the partitioned elements as of the limited-memory
                    // ones — they are the same update on a finer
                    // decomposition.
                    HessianApproxChoice::LimitedMemory | HessianApproxChoice::Partitioned => 0.05,
                    // Equal to `alpha_red_factor`, so the clamp in
                    // `next_alpha` collapses and the sequence is upstream's.
                    //
                    // `FiniteDifference` belongs here rather than above:
                    // it recovers the Lagrangian Hessian itself by
                    // probing the analytic Jacobian, so it carries no
                    // curvature history and its step is a Newton step
                    // whose length is meaningful. It is the same
                    // distinction `hessian_at_current` draws — a pure
                    // function of `(x, y)` on one side, a history-carrying
                    // `B` on the other. And the measurement behind the
                    // split cuts this way too: forcing `0.05` onto a path
                    // with a meaningful step length cost `eigena2` a
                    // solve.
                    HessianApproxChoice::Exact | HessianApproxChoice::FiniteDifference => {
                        self.line_search.alpha_red_factor
                    }
                });
        line_search.watchdog_shortened_iter_trigger =
            self.line_search.watchdog_shortened_iter_trigger;
        line_search.watchdog_trial_iter_max = self.line_search.watchdog_trial_iter_max;
        line_search.soft_resto_pderror_reduction_factor =
            self.line_search.soft_resto_pderror_reduction_factor;
        line_search.max_soft_resto_iters = self.line_search.max_soft_resto_iters;
        line_search.accept_every_trial_step = self.line_search.accept_every_trial_step;
        line_search.alpha_for_y = self.line_search.alpha_for_y;
        line_search.accept_after_max_steps = self.line_search.accept_after_max_steps;
        // Second-order-correction constants (#191): registered but
        // previously never read. Same direct-field pattern as the
        // watchdog knobs above.
        line_search.max_soc = self.line_search.max_soc;
        line_search.kappa_soc = self.line_search.kappa_soc;
        line_search.soc_method = self.line_search.soc_method;

        let conv_check: Box<dyn crate::conv_check::r#trait::ConvCheck> =
            Box::new(OptErrorConvCheck {
                tol: self.conv_check.tol,
                dual_inf_tol: self.conv_check.dual_inf_tol,
                constr_viol_tol: self.conv_check.constr_viol_tol,
                compl_inf_tol: self.conv_check.compl_inf_tol,
                acceptable_tol: self.conv_check.acceptable_tol,
                acceptable_dual_inf_tol: self.conv_check.acceptable_dual_inf_tol,
                acceptable_constr_viol_tol: self.conv_check.acceptable_constr_viol_tol,
                acceptable_compl_inf_tol: self.conv_check.acceptable_compl_inf_tol,
                acceptable_obj_change_tol: self.conv_check.acceptable_obj_change_tol,
                acceptable_iter: self.conv_check.acceptable_iter,
                max_iter: self.conv_check.max_iter,
                max_cpu_time: self.conv_check.max_cpu_time,
                max_wall_time: self.conv_check.max_wall_time,
                acceptable_count: 0,
                last_acceptable_obj: None,
                infeas_stationarity_tol: self.conv_check.infeas_stationarity_tol,
                infeas_viol_kappa: self.conv_check.infeas_viol_kappa,
                infeas_max_streak: self.conv_check.infeas_max_streak,
                infeas_streak: 0,
                obj_scale_certificate_threshold: self.conv_check.obj_scale_certificate_threshold,
                primal_noise_floor_kappa: self.conv_check.primal_noise_floor_kappa,
                acceptable_progress_kappa: self.conv_check.acceptable_progress_kappa,
                acceptable_window: std::collections::VecDeque::new(),
                acceptable_progress_refusals: 0,
                dual_inf_scale_kappa: self.conv_check.dual_inf_scale_kappa,
                dual_floor_reported: false,
                veto_fired: false,
                acceptable_veto_fired: false,
                masked_acceptable_veto_fired: false,
                veto_extra_iters: 0,
                rel_infeas_extra_iters: 0,
                prev_rel_viol: f64::NAN,
            });

        let init: Box<dyn crate::init::r#trait::IterateInitializer> = if self.warm_start_init_point
        {
            Box::new(WarmStartIterateInitializer::with_options(
                resolved_warm_options(&self.warm, &self.init),
            ))
        } else {
            let mut d = DefaultIterateInitializer::with_eq_mult_calculator(Box::new(
                LeastSquareMults::new(),
            ));
            d.bound_push = self.init.bound_push;
            d.bound_frac = self.init.bound_frac;
            d.slack_bound_push = self.init.slack_bound_push;
            d.slack_bound_frac = self.init.slack_bound_frac;
            d.constr_mult_init_max = self.init.constr_mult_init_max;
            d.bound_mult_init_val = self.init.bound_mult_init_val;
            d.bound_mult_init_method = self.init.bound_mult_init_method.clone();
            d.least_square_init_primal = self.init.least_square_init_primal;
            Box::new(d)
        };

        let eq_mult: Box<dyn crate::eq_mult::r#trait::EqMultCalculator> =
            Box::new(LeastSquareMults::new());

        let hess: Box<dyn crate::hess::r#trait::HessianUpdater> = match self.hessian_approximation {
            HessianApproxChoice::Exact => Box::new(ExactHessianUpdater::new()),
            HessianApproxChoice::LimitedMemory => Box::new(LimMemQuasiNewtonUpdater {
                update_type: self.limited_memory_update_type,
                max_history: self.limited_memory_max_history,
                init_val_max: self.limited_memory_init_val_max,
                init_val_min: self.limited_memory_init_val_min,
                initial_approx: self.limited_memory_initialization,
                init_val: self.limited_memory_init_val,
                max_skipping: self.limited_memory_max_skipping,
                nonlinear_vars: self.limited_memory_nonlinear_vars.clone(),
                ..LimMemQuasiNewtonUpdater::default()
            }),
            HessianApproxChoice::Partitioned => {
                let mut u =
                    crate::hess::partitioned_quasi_newton::PartitionedQuasiNewtonUpdater::new(
                        self.partitioned_update_type,
                    );
                u.max_element = self.partitioned_max_element;
                u.objective_vars = self.objective_nonlinear_vars.clone();
                u.curvature_cap = self.partitioned_curvature_cap;
                u.mode = self.partitioned_elements;
                u.block_size = self.partitioned_block_size;
                // Damped BFGS is the right pairing for a Lagrangian
                // block: unlike a single constraint's Hessian, the
                // Lagrangian's is the object the IPM wants a positive
                // definite model of, and the sign problem that makes
                // damping wrong per constraint does not arise. Only when
                // the caller has not named a formula.
                if self.partitioned_elements
                    == crate::hess::partitioned_quasi_newton::ElementMode::PrimalBlock
                    && !self.partitioned_update_type_was_set
                {
                    u.update_type = UpdateType::Bfgs;
                }
                u.init_val_min = self.limited_memory_init_val_min;
                u.init_val_max = self.limited_memory_init_val_max;
                Box::new(u)
            }
            HessianApproxChoice::FiniteDifference => {
                let mut u = crate::hess::fd_hessian::FdHessianUpdater::new(self.fd_hessian_pattern);
                u.coloring = self.fd_hessian_coloring;
                u.reuse_tol = self.fd_hessian_reuse_tol;
                u.objective_vars = self.objective_nonlinear_vars.clone();
                u.nonlinear_vars = self.limited_memory_nonlinear_vars.clone();
                Box::new(u)
            }
        };

        let iter_output: Box<dyn crate::output::r#trait::IterationOutput> = {
            use crate::output::orig::{InfPrTag, PrintInfoString};
            let mut o = OrigIterationOutput::new();
            o.print_frequency_iter = self.output.print_frequency_iter;
            o.print_frequency_time = self.output.print_frequency_time;
            o.print_info_string = if self.output.print_info_string {
                PrintInfoString::Yes
            } else {
                PrintInfoString::No
            };
            o.inf_pr_output = if self.output.inf_pr_output_internal {
                InfPrTag::Internal
            } else {
                InfPrTag::Original
            };
            Box::new(o)
        };

        AlgorithmBundle {
            mu_update,
            conv_check,
            init,
            eq_mult,
            hess,
            line_search,
            iter_output,
            search_dir,
        }
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn warm_options_take_the_init_default_not_their_own() {
        let mut init = InitOptions::default();
        init.bound_mult_init_val = 10.0; // the Mehrotra override value
        let mut warm = WarmStartOptions::default();
        warm.bound_mult_init_val = 123.0; // stale copy must lose
        let resolved = resolved_warm_options(&warm, &init);
        assert_eq!(resolved.bound_mult_init_val, 10.0);
        // everything else passes through untouched
        assert_eq!(resolved.mult_bound_push, warm.mult_bound_push);
        assert_eq!(resolved.target_mu, warm.target_mu);
    }

    #[test]
    fn default_builder_assembles() {
        let bundle = AlgorithmBuilder::new().build();
        // Sanity: the placeholder traits compile and the boxed
        // strategies don't panic on construction.
        let _ = bundle.line_search.acceptor();
        assert!(bundle.search_dir.is_none());
    }

    #[test]
    fn build_with_backend_assembles_search_dir_chain() {
        // Drive the builder with the FERAL backend factory; the
        // resulting bundle should expose a populated `PdSearchDirCalc`.
        let factory: LinearBackendFactory = Box::new(|_| {
            Box::new(pounce_feral::FeralSolverInterface::new())
                as Box<dyn SparseSymLinearSolverInterface>
        });
        let bundle = AlgorithmBuilder::new().build_with_backend(factory);
        assert!(bundle.search_dir.is_some());
    }

    #[test]
    fn limited_memory_sr1_propagates() {
        let b = AlgorithmBuilder {
            hessian_approximation: HessianApproxChoice::LimitedMemory,
            limited_memory_update_type: UpdateType::Sr1,
            ..AlgorithmBuilder::default()
        };
        let _bundle = b.build();
    }

    #[test]
    fn every_strategy_combination_assembles_without_panic() {
        let solvers = [LinearSolverChoice::Ma57, LinearSolverChoice::Feral];
        let mu = [MuStrategyChoice::Monotone, MuStrategyChoice::Adaptive];
        let hess = [
            HessianApproxChoice::Exact,
            HessianApproxChoice::LimitedMemory,
        ];
        let ls = [
            LineSearchChoice::Filter,
            LineSearchChoice::CgPenalty,
            LineSearchChoice::Penalty,
        ];
        for &linear_solver in &solvers {
            for &mu_strategy in &mu {
                for &hessian_approximation in &hess {
                    for &line_search_method in &ls {
                        let _ = AlgorithmBuilder {
                            algorithm: AlgorithmChoice::default(),
                            linear_solver,
                            linear_system_scaling: LinearSystemScalingChoice::None,
                            linear_scaling_on_demand: true,
                            mu_strategy,
                            mu_oracle: MuOracleKind::QualityFunction,
                            hessian_approximation,
                            partitioned_update_type: UpdateType::Sr1,
                            partitioned_update_type_was_set: false,
                            partitioned_max_element: 64,
                            objective_nonlinear_vars: None,
                            partitioned_curvature_cap: Number::INFINITY,
                            partitioned_elements:
                                crate::hess::partitioned_quasi_newton::ElementMode::PerConstraint,
                            partitioned_block_size: 64,
                            fd_hessian_pattern: crate::hess::fd_hessian::FdPatternSource::Declared,
                            fd_hessian_coloring: crate::hess::fd_hessian::FdColoring::Cpr,
                            fd_hessian_reuse_tol: 0.0,
                            limited_memory_update_type: UpdateType::Bfgs,
                            limited_memory_max_history: 6,
                            limited_memory_init_val_max: 1e8,
                            limited_memory_init_val_min: 1e-8,
                            limited_memory_initialization: InitialApprox::Scalar1,
                            limited_memory_init_val: 1.0,
                            limited_memory_max_skipping: 2,
                            limited_memory_nonlinear_vars: None,
                            line_search_method,
                            warm_start_init_point: false,
                            mehrotra_algorithm: false,
                            fast_step_computation: false,
                            kappa_sigma: 1e10,
                            recalc_y: false,
                            recalc_y_feas_tol: 1e-6,
                            kappa_d: 1e-5,
                            s_max: 100.0,
                            tiny_step_tol: 10.0 * Number::EPSILON,
                            tiny_step_y_tol: 1e-2,
                            diverging_iterates_tol: 1e20,
                            dual_diverging_streak: 0,
                            dual_divergence_retry_step_tol: 1e-5,
                            dual_divergence_retry_du_floor: 1e2,
                            resto_decline_deferrals: 1,
                            resto_decline_progress_ratio: 0.5,
                            neg_curv_escapes: 1,
                            limited_memory_ls_failure_restarts: 0,
                            kkt_fidelity_tol: 0.0,
                            conv_check: ConvCheckOptions::default(),
                            mu: MuOptions::default(),
                            line_search: LineSearchOptions::default(),
                            refinement: RefinementOptions::default(),
                            perturbation: PerturbationOptions::default(),
                            resto: RestoOptions::default(),
                            output: OutputOptions::default(),
                            warm: WarmStartOptions::default(),
                            sqp: crate::sqp::SqpOptions::default(),
                            sqp_qp: pounce_qp::QpOptions::sqp_subproblem(),
                            init: InitOptions::default(),
                            kkt_schur: None,
                            quality_escalation_counter: None,
                        }
                        .build();
                    }
                }
            }
        }
    }
}