pounce_solve_report/

lib.rs

//! Machine-readable JSON solve report (pounce#8).
//!
//! Bundles the same payload AMPL's `.sol` carries (status, primal,
//! dual, suffixes) with FAIR-aligned provenance metadata (solver
//! identity, input descriptor, timestamp) and per-iteration history
//! when requested. Schema is versioned via the top-level `schema`
//! field so future extensions don't silently change semantics.
//!
//! FAIR reference: Wilkinson et al. (2016). *The FAIR Guiding
//! Principles for scientific data management and stewardship.*
//! Scientific Data, 3, 160018. DOI:
//! [10.1038/sdata.2016.18](https://doi.org/10.1038/sdata.2016.18).
//! Verified via Crossref on 2026-05-14.
//!
//! # Schema versioning
//!
//! The current schema tag is `pounce.solve-report/v1`. Breaking
//! changes bump the major version (v2 etc.). Adding fields without
//! removing or renaming existing ones is non-breaking — JSON
//! consumers should tolerate unknown fields.
//!
//! # Detail levels
//!
//! [`ReportDetail::Summary`] (default) emits the FAIR metadata,
//! problem dimensions, final solution, and aggregate statistics
//! — equivalent to a `.sol` plus provenance. [`ReportDetail::Full`]
//! additionally emits the per-iteration history (when captured by
//! [`pounce_algorithm::application::IpoptApplication::enable_iter_history`])
//! and any `solution.suffixes`. Choose `Summary` for production logs
//! and `Full` for debug captures.

use std::path::{Path, PathBuf};
use std::time::{SystemTime, UNIX_EPOCH};

use pounce_common::types::{Index, Number};
use pounce_linsol::summary::LinearSolverSummary;
use pounce_nlp::return_codes::ApplicationReturnStatus;
use pounce_nlp::solve_statistics::{IterRecord, SolveStatistics};
use serde::{Deserialize, Serialize};

/// Ipopt-style console printers (banner, problem statistics, end-of-run
/// summary). The single source of truth for the text log: the algorithm's
/// output layer emits these gated on `print_level`, and the CLI reuses the
/// banner. Moved out of `pounce-cli` so `pounce-algorithm` can emit them
/// natively (#206).
pub mod console;

/// Verbosity knob for the JSON report. Maps onto the `--json-detail`
/// CLI flag.
#[derive(Debug, Clone, Copy, PartialEq, Eq)]
pub enum ReportDetail {
    /// FAIR metadata, problem, solution scalars + arrays, aggregate
    /// stats. Per-iteration history and suffix blocks omitted.
    Summary,
    /// Everything in `Summary` plus per-iteration history and any
    /// suffix outputs (`sens_sol_state_1`, reduced-Hessian blocks).
    Full,
}

impl ReportDetail {
    pub fn parse(s: &str) -> Result<Self, String> {
        match s.to_ascii_lowercase().as_str() {
            "summary" => Ok(ReportDetail::Summary),
            "full" => Ok(ReportDetail::Full),
            other => Err(format!(
                "unknown --json-detail '{other}' (expected: summary | full)"
            )),
        }
    }
}

/// Top-level report struct. Fields are ordered so the JSON has the
/// most identifying / metadata fields first when pretty-printed.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SolveReport {
    /// Schema identifier. Always
    /// `"pounce.solve-report/v1"` for this version of the writer.
    pub schema: String,
    /// FAIR provenance metadata.
    pub fair_metadata: FairMetadata,
    /// Problem dimensions and shape.
    pub problem: ProblemInfo,
    /// Final solution payload (status, primal, dual, suffixes).
    pub solution: SolutionInfo,
    /// Aggregate statistics (eval counts, KKT residuals, timing).
    pub statistics: StatisticsInfo,
    /// Per-iteration history. Empty when the report is at
    /// [`ReportDetail::Summary`] or iter history was never enabled.
    #[serde(skip_serializing_if = "Vec::is_empty", default)]
    pub iterations: Vec<IterRecord>,
    /// Aggregate linear-solver post-mortem. Populated when the
    /// workspace-default FERAL backend ran (it self-instruments via
    /// `feral::Solver::last_factor_stats()`); `None` for HSL MA57 and
    /// for custom backends plugged through
    /// [`pounce_algorithm::application::IpoptApplication::set_linear_backend_factory`].
    /// Additive — older `pounce.solve-report/v1` JSON without this
    /// field deserializes unchanged.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub linear_solver: Option<LinearSolverSummaryInfo>,
    /// What the second-opinion ladder did, when it ran at all. `None` when the
    /// verdict opened no ladder — which is every ordinary solve.
    ///
    /// Additive; older `pounce.solve-report/v1` JSON without this field
    /// deserializes unchanged.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub second_opinion: Option<SecondOpinionInfo>,
}

/// The second-opinion ladder's record: which rungs ran, which (if any) was
/// promoted, and **what the base solve did before any of it** (gh #850).
///
/// # Why the base solve's numbers are the point
///
/// On a promotion the reported `status` and `statistics.iteration_count` both
/// become the promoted rung's, and nothing else in the report says the base
/// solver failed. That makes a *lost* solve indistinguishable from a faster
/// one, and it is worse than a gap in the evidence: it produces positive
/// evidence for the wrong conclusion.
///
/// The case that exposed it is `square_flowsheet_resto`, where `v0.10.0`'s
/// base solver converged in 116 iterations and HEAD's does not converge at all
/// — `RestorationFailed` at 131 — with the answer coming from a ladder rung
/// (`start_point_perturbation=1e-2`) added in the same release, which promotes
/// at 54. `scripts/sweep-fixtures.sh` read that as `116 -> 54`, **a 2× win**.
///
/// The cost is understated on top of that: `statistics.iteration_count` is the
/// promoted rung's alone, so the fixture's true cost is `131 + 54`, 3.4× what
/// the report says. [`Self::total_iteration_count`] is the honest number.
///
/// This is the same shape of invisibility the sweep's engine column was added
/// to close, and CLAUDE.md's rule applies verbatim: a line whose only moving
/// field is "solved directly" → "promoted from a rung" is a trajectory change,
/// and is as reportable as a moved iteration count.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SecondOpinionInfo {
    /// Rung labels actually run, in order.
    pub tried: Vec<String>,
    /// The rung whose re-solve was promoted, or `None` when the original
    /// verdict survived every rung and shipped unchanged.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub promoted_by: Option<String>,
    /// The verdict the ladder was opened on, before any rung ran.
    pub base_status: String,
    /// Iterations the base solve spent. Not included in
    /// `statistics.iteration_count` when a rung was promoted.
    pub base_iteration_count: usize,
    /// Iterations each rung in `tried` spent, in the same order.
    pub rung_iteration_counts: Vec<usize>,
    /// Base plus every rung: what the solve actually cost.
    pub total_iteration_count: usize,
}

/// Serializable mirror of [`pounce_linsol::summary::LinearSolverSummary`].
/// Lives in the CLI crate (rather than `pounce-linsol`) so the linsol
/// trait crate stays serde-free. Field shape is identical; serde
/// defaults keep it forward-compatible with future additions.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LinearSolverSummaryInfo {
    pub solver_name: String,
    /// The `linear_solver` option as requested (gh#990 item 7); differs from
    /// `solver_name` when the backend that ran is not the one asked for.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub requested: Option<String>,
    pub n_factors: u64,
    pub n_pattern_reuse: u64,
    pub n_pattern_changes: u64,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub max_fill_ratio: Option<f64>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub min_abs_pivot: Option<f64>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub max_abs_pivot: Option<f64>,
    /// `(positive, negative, zero)` inertia of the final factorisation.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_inertia: Option<(usize, usize, usize)>,
    /// `(positive, negative, zero)` inertia of the last factorization tried
    /// with **no** regularization, i.e. the KKT matrix's own curvature
    /// verdict (gh#987). `last_inertia` is the post-shift value.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_inertia_unregularized: Option<(usize, usize, usize)>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_nnz_a: Option<usize>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_nnz_l: Option<usize>,

    // Work and pivoting totals over every factorization. `default` keeps
    // reports written before these fields existed readable.
    #[serde(default)]
    pub total_factor_secs: f64,
    #[serde(default)]
    pub total_factor_flops: f64,
    #[serde(default)]
    pub total_delayed_cols: u64,
    #[serde(default)]
    pub total_two_by_two: u64,
    #[serde(default)]
    pub total_n_tiny: u64,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_factor_flops: Option<f64>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_peak_bytes: Option<usize>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_n_supernodes: Option<usize>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_max_front_rows: Option<usize>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_delayed_cols: Option<usize>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_two_by_two: Option<usize>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_n_tiny: Option<usize>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_ordering: Option<String>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub last_ordering_preprocess: Option<String>,
    /// Present only when the Schur KKT path actually factored.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub schur: Option<SchurSummaryInfo>,
    /// Present only when the block-parallel KKT path actually factored.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub blocks: Option<BlockSummaryInfo>,
    /// The restoration phase's factorizations, same shape, kept apart from
    /// the main-solve fields above. Present only when restoration factored.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub restoration: Option<Box<LinearSolverSummaryInfo>>,
}

/// Serializable mirror of [`pounce_linsol::summary::BlockSummary`].
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct BlockSummaryInfo {
    pub n_blocks: usize,
    pub border_dim: usize,
    pub largest_block: usize,
    pub n_factors: u64,
    pub factor_secs: f64,
}

/// Serializable mirror of [`pounce_linsol::summary::SchurSummary`].
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SchurSummaryInfo {
    pub n_eliminated: usize,
    pub n_schur: usize,
    pub n_factors: u64,
    pub eliminated_factor_secs: f64,
    pub form_schur_secs: f64,
    pub schur_factor_secs: f64,
}

impl From<LinearSolverSummary> for LinearSolverSummaryInfo {
    fn from(s: LinearSolverSummary) -> Self {
        Self {
            solver_name: s.solver_name,
            requested: s.requested,
            n_factors: s.n_factors,
            n_pattern_reuse: s.n_pattern_reuse,
            n_pattern_changes: s.n_pattern_changes,
            max_fill_ratio: s.max_fill_ratio,
            min_abs_pivot: s.min_abs_pivot,
            max_abs_pivot: s.max_abs_pivot,
            last_inertia: s.last_inertia,
            last_inertia_unregularized: s.last_inertia_unregularized,
            last_nnz_a: s.last_nnz_a,
            last_nnz_l: s.last_nnz_l,
            total_factor_secs: s.total_factor_secs,
            total_factor_flops: s.total_factor_flops,
            total_delayed_cols: s.total_delayed_cols,
            total_two_by_two: s.total_two_by_two,
            total_n_tiny: s.total_n_tiny,
            last_factor_flops: s.last_factor_flops,
            last_peak_bytes: s.last_peak_bytes,
            last_n_supernodes: s.last_n_supernodes,
            last_max_front_rows: s.last_max_front_rows,
            last_delayed_cols: s.last_delayed_cols,
            last_two_by_two: s.last_two_by_two,
            last_n_tiny: s.last_n_tiny,
            last_ordering: s.last_ordering,
            last_ordering_preprocess: s.last_ordering_preprocess,
            schur: s.schur.map(|c| SchurSummaryInfo {
                n_eliminated: c.n_eliminated,
                n_schur: c.n_schur,
                n_factors: c.n_factors,
                eliminated_factor_secs: c.eliminated_factor_secs,
                form_schur_secs: c.form_schur_secs,
                schur_factor_secs: c.schur_factor_secs,
            }),
            blocks: s.blocks.map(|b| BlockSummaryInfo {
                n_blocks: b.n_blocks,
                border_dim: b.border_dim,
                largest_block: b.largest_block,
                n_factors: b.n_factors,
                factor_secs: b.factor_secs,
            }),
            restoration: s.restoration.map(|r| Box::new(Self::from(*r))),
        }
    }
}

/// FAIR-aligned provenance block. The four FAIR principles
/// (Wilkinson et al., 2016) map onto fields here as:
/// * **F**indable: `result_id` (unique per solve), `created_at_iso`.
/// * **A**ccessible: this JSON file is the artifact — no protocol
///   gating, plain text on disk.
/// * **I**nteroperable: schema versioned, types are JSON primitives,
///   units documented in field doc comments.
/// * **R**eusable: `solver`, `license`, `input` describe what was
///   solved with what code, enough to reproduce.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FairMetadata {
    /// Unique per-solve identifier. Composed as
    /// `<unix_nanos>-<process_id>` so it is monotonically ordered
    /// within a process and globally unique across processes.
    pub result_id: String,
    /// Solve start time as ISO-8601 UTC (`YYYY-MM-DDTHH:MM:SS.sssZ`).
    pub created_at_iso: String,
    /// Same instant in Unix nanoseconds (since 1970-01-01 UTC).
    /// Provided alongside the ISO string for callers that prefer
    /// integer arithmetic over date parsing.
    pub created_at_unix_nanos: i128,
    /// Wallclock seconds the solve took. Mirrors
    /// [`SolveStatistics::total_wallclock_time_secs`].
    pub elapsed_seconds: Number,
    /// Solver identity — name + version + (best-effort) git commit.
    pub solver: SolverIdentity,
    /// SPDX license string. Always `"EPL-2.0"` for this crate.
    pub license: String,
    /// Input descriptor. `kind` is `nl-file`, `builtin`, or
    /// `tnlp-direct` (for library callers).
    pub input: InputDescriptor,
    /// Solve-affecting environment variables present in the process
    /// environment at report time (`POUNCE_FERAL_*` numerics knobs and
    /// the legacy `FERAL_PIVTOL` / `FERAL_PARALLEL`). These alter the
    /// factorization or parallelism and can otherwise silently differ a
    /// run between two machines — e.g. one with `POUNCE_FERAL_PIVTOL`
    /// exported in a shell profile — with nothing in the report saying
    /// so (pounce#235). Recorded for reproducibility (the FAIR **R**
    /// principle). Presence here means the variable was set, not
    /// necessarily that it took effect: an explicit `OptionsList` setting
    /// (e.g. a `feral_pivtol` in an options file) takes precedence over
    /// the env fallback. Empty (and omitted from the JSON) when none are
    /// set. Additive — older `pounce.solve-report/v1` JSON without this
    /// field deserializes unchanged.
    #[serde(skip_serializing_if = "Vec::is_empty", default)]
    pub environment: Vec<EnvOverride>,
}

/// One solve-affecting environment variable and its value, as captured
/// into [`FairMetadata::environment`].
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EnvOverride {
    /// Variable name, e.g. `POUNCE_FERAL_PIVTOL`.
    pub name: String,
    /// Its value verbatim, as read from the environment.
    pub value: String,
}

/// Environment variables that change pounce's numerics or parallelism —
/// the ones worth recording for reproducibility. Deliberately excludes the
/// `POUNCE_DBG_*` debug gates (they only add diagnostic output, never
/// altering the result) and compile-time / logging vars. Kept in a fixed
/// order so [`capture_solve_env_overrides`] is deterministic.
const SOLVE_AFFECTING_ENV_VARS: &[&str] = &[
    "POUNCE_FERAL_ORDERING",
    "POUNCE_FERAL_SCALING",
    "POUNCE_FERAL_PIVTOL",
    "POUNCE_FERAL_REFINE",
    "POUNCE_FERAL_CASCADE_BREAK",
    "POUNCE_FERAL_FMA",
    "POUNCE_FERAL_SINGULAR_PIVOT_FLOOR",
    "POUNCE_FERAL_MIN_PAR_FLOPS",
    // Legacy aliases without the `POUNCE_` prefix (still honored by the
    // FERAL backend): the deprecated pivot-threshold spelling and the
    // process-wide internal-parallelism switch.
    "FERAL_PIVTOL",
    "FERAL_PARALLEL",
];

/// Snapshot the solve-affecting environment variables ([`SOLVE_AFFECTING_ENV_VARS`])
/// that are currently set, for [`FairMetadata::environment`]. Reads the
/// process environment, so call it in the solving process. Returns them in
/// the fixed list order; absent variables are simply skipped.
pub fn capture_solve_env_overrides() -> Vec<EnvOverride> {
    SOLVE_AFFECTING_ENV_VARS
        .iter()
        .filter_map(|&name| {
            std::env::var(name).ok().map(|value| EnvOverride {
                name: name.to_string(),
                value,
            })
        })
        .collect()
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SolverIdentity {
    pub name: String,
    pub version: String,
    /// Git commit hash, captured at build time from the
    /// `POUNCE_GIT_COMMIT` environment variable. `None` if the build
    /// environment didn't set it — set via
    /// `POUNCE_GIT_COMMIT=$(git rev-parse HEAD) cargo build`.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub git_commit: Option<String>,
    /// Build target triple (e.g. `x86_64-apple-darwin`). Captured at
    /// build time from `TARGET` (Cargo standard env var).
    pub target_triple: String,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "kind", rename_all = "kebab-case")]
pub enum InputDescriptor {
    NlFile {
        path: PathBuf,
        #[serde(skip_serializing_if = "Option::is_none")]
        size_bytes: Option<u64>,
    },
    /// A Conic Benchmark Format (`.cbf`) instance — e.g. a CBLIB problem
    /// solved through the convex conic driver.
    CbfFile {
        path: PathBuf,
        #[serde(skip_serializing_if = "Option::is_none")]
        size_bytes: Option<u64>,
    },
    Builtin {
        name: String,
    },
    TnlpDirect,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ProblemInfo {
    pub n_variables: Index,
    pub n_constraints: Index,
    pub n_objectives: Index,
    pub minimize: bool,
    /// Number of non-zeros declared by the TNLP for the constraint
    /// Jacobian. `None` if not exposed by the input path.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub nnz_jac_g: Option<Index>,
    /// Number of non-zeros declared for the Lagrangian Hessian.
    #[serde(skip_serializing_if = "Option::is_none")]
    pub nnz_h_lag: Option<Index>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SolutionInfo {
    /// Which engine produced this verdict: `"cvx-qp"`, `"cvx-qcqp"`,
    /// `"qp-active-set"`, `"sqp-active-set"`, `"square-lm"` or `"nlp"`.
    ///
    /// `"square-lm"` is the second-opinion ladder's square-system stage
    /// (`pounce_algorithm::square_system`): the NLP arm ran and failed, and
    /// the reported point came from Levenberg–Marquardt on `c(x) = 0`.
    ///
    /// `"qp-active-set"` and `"sqp-active-set"` are different engines and
    /// not synonyms: the first is `pounce_convex::active_set` reached by
    /// `solver_selection=qp-active-set` on an LP or convex QP, the second is
    /// `pounce_algorithm::sqp`'s outer loop reached by
    /// `algorithm=active-set-sqp` on a general NLP. Both solve their
    /// subproblems with `pounce-qp`, which is why the names are close; they
    /// wrap it in different algorithms, which is why they are not the same
    /// string.
    ///
    /// The `Selected solver:` banner names the engine *routing* chose, which
    /// is not always the one that answered: a convex solve that declines its
    /// own result hands the model to the NLP arm (gh #535), and the banner
    /// has already been printed by then. `scripts/sweep-fixtures.sh` scraped
    /// that banner because the report carried nothing better, so a reroute
    /// left no trace in the sweep diff — the precise blind spot CLAUDE.md
    /// names when it says a routing regression "used to leave no trace".
    ///
    /// Empty when a path does not set it, which a consumer should read as
    /// "unknown" rather than as any particular arm.
    #[serde(default, skip_serializing_if = "String::is_empty")]
    pub engine: String,
    /// `SolveSucceeded`, `MaximumIterationsExceeded`, etc. The string
    /// form is the Rust enum variant name verbatim.
    pub status: ApplicationReturnStatus,
    /// The same verdict in upstream Ipopt's C enumerator spelling —
    /// `Solve_Succeeded`, `Infeasible_Problem_Detected` — from
    /// `IpReturnCodes_inc.h`.
    ///
    /// [`Self::status`] carries the Rust variant name, which is *not* the
    /// name any Ipopt-facing consumer already keys off: CUTEst status
    /// tables, `benchmarks/scripts/run_nl_bench.sh`, the reference JSONs
    /// under `benchmarks/*/ipopt_ma57.json` and the CLI's own `Status:`
    /// line all spell it with separators. A consumer comparing
    /// `solution.status == "Solve_Succeeded"` against the report matched
    /// nothing and silently classified every solve as a failure (gh #767).
    /// This field is that spelling, so the comparison can be literal.
    ///
    /// Derived from [`Self::status`] by [`ReportBuilder::finish`] — never
    /// set by a caller, so the two cannot disagree. Empty when read back
    /// from a pre-#767 report.
    #[serde(default)]
    pub status_upstream: String,
    /// AMPL-style solve-result code (Gay 2005, §5 p. 23 table).
    pub solve_result_num: i32,
    /// Final unscaled objective value (mirrors
    /// `SolveStatistics::final_objective`). `NaN` if unknown.
    pub objective: Number,
    /// Final primal vector, length `problem.n_variables`. Empty if
    /// not captured.
    #[serde(skip_serializing_if = "Vec::is_empty", default)]
    pub x: Vec<Number>,
    /// Final dual (constraint multiplier) vector, length
    /// `problem.n_constraints`.
    #[serde(skip_serializing_if = "Vec::is_empty", default)]
    pub lambda: Vec<Number>,
    /// Optional sIPOPT-style suffix blocks (`sens_sol_state_1` etc.).
    /// Stored as a flat map keyed by suffix name → list of
    /// `(index, value)` pairs, matching the AMPL `.sol` shape.
    /// Empty when no sensitivity / reduced-Hessian step ran.
    #[serde(skip_serializing_if = "Vec::is_empty", default)]
    pub suffixes: Vec<SolutionSuffix>,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SolutionSuffix {
    pub name: String,
    /// `"var" | "con" | "obj" | "problem"` per AMPL convention.
    pub target: String,
    /// `"int"` or `"real"`.
    pub kind: String,
    /// Dense values (length = target dimension); zero-filled for
    /// slots the writer didn't populate. Real-typed values are stored
    /// here; int-typed in `int_values`.
    #[serde(skip_serializing_if = "Vec::is_empty", default)]
    pub values: Vec<Number>,
    #[serde(skip_serializing_if = "Vec::is_empty", default)]
    pub int_values: Vec<Index>,
}

/// NaN, for a residual slot that was never filled in.
fn uncomputed() -> Number {
    Number::NAN
}

/// Accept `null` for a residual the solve never computed.
///
/// `SolveStatistics` defaults its residual fields to NaN rather than `0.0`, so
/// that "the convergence check never ran" is distinguishable from "converged
/// exactly". `serde_json` renders a non-finite float as `null`, so a report
/// written for a solve that was refused during setup carries `null` in these
/// slots. Without this the report round-trip fails — pounce would write
/// reports its own `--cite` / studio / verify paths could not read back.
fn null_as_nan<'de, D>(de: D) -> Result<Number, D::Error>
where
    D: serde::Deserializer<'de>,
{
    Ok(Option::<Number>::deserialize(de)?.unwrap_or_else(uncomputed))
}

/// One flagged derivative-checker comparison (gh#990 item 3).
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DerivativeEntryInfo {
    /// `gradient`, `jacobian`, `hessian`, `jacobian_structure` or
    /// `hessian_structure`.
    pub kind: String,
    /// Hessian block (`obj` or `g[<row>]`); omitted otherwise.
    #[serde(skip_serializing_if = "String::is_empty", default)]
    pub block: String,
    /// Row (`-1` for a gradient entry).
    pub row: i64,
    pub col: i64,
    /// `None` for a structural miss (the entry is not in the declared pattern).
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub analytic: Option<f64>,
    pub finite_difference: f64,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub relative_error: Option<f64>,
}

/// The derivative checker's machine-readable verdict (gh#990 item 3); the
/// console report on stderr is unchanged.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DerivativeCheckInfo {
    /// `first-order`, `second-order` or `only-second-order`.
    pub mode: String,
    pub tolerance: f64,
    pub perturbation: f64,
    pub checked: usize,
    pub suspicious: usize,
    pub missing_structure: usize,
    pub evaluations: usize,
    /// Nothing flagged and something compared.
    pub clean: bool,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub max_rel_error_gradient: Option<f64>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub max_rel_error_jacobian: Option<f64>,
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub max_rel_error_hessian: Option<f64>,
    /// The flagged entries (at most 200; `suspicious` is the exact count).
    #[serde(default)]
    pub flagged: Vec<DerivativeEntryInfo>,
}

/// The objective-scaling decision (gh#990 item 13).
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ObjectiveScalingInfo {
    /// The solver-computed objective scaling factor the run ended with
    /// (excluding the caller's own `obj_scaling_factor`); `1` when none was
    /// applied.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub factor: Option<f64>,
    /// gh#1001. `|obj_scaling_factor · factor · audit upscale|`: the whole
    /// multiplier on the objective the returned run's IPM minimized, so
    /// `statistics.final_scaled_objective` and each iteration's `mu` are in
    /// units of `total_factor · f`. Differs from `factor` when the caller
    /// set `obj_scaling_factor` or the gh#983 audit re-solved with the
    /// objective scaled up.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub total_factor: Option<f64>,
    /// `max |grad f|` at the starting point, as gradient-based scaling
    /// measured it.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub start_gradient_max: Option<f64>,
    /// A strict termination certificate was refused because the objective
    /// scaling masked it (gh#200), and the solve kept iterating.
    #[serde(default)]
    pub certificate_refused: bool,
    /// Same, for an acceptable-level certificate.
    #[serde(default)]
    pub acceptable_certificate_refused: bool,
}

impl From<&pounce_nlp::derivative_test::DerivativeCheckSummary> for DerivativeCheckInfo {
    fn from(c: &pounce_nlp::derivative_test::DerivativeCheckSummary) -> Self {
        let fin = |v: Option<f64>| v.filter(|x| x.is_finite());
        Self {
            mode: c.mode.clone(),
            tolerance: c.tolerance,
            perturbation: c.perturbation,
            checked: c.checked,
            suspicious: c.suspicious,
            missing_structure: c.missing_structure,
            evaluations: c.evaluations,
            clean: c.clean,
            max_rel_error_gradient: fin(c.max_rel_error_gradient),
            max_rel_error_jacobian: fin(c.max_rel_error_jacobian),
            max_rel_error_hessian: fin(c.max_rel_error_hessian),
            flagged: c
                .flagged
                .iter()
                .map(|e| DerivativeEntryInfo {
                    kind: e.kind.to_string(),
                    block: e.block.clone(),
                    row: e.row,
                    col: e.col,
                    analytic: fin(e.analytic),
                    finite_difference: if e.finite_difference.is_finite() {
                        e.finite_difference
                    } else {
                        0.0
                    },
                    relative_error: fin(e.relative_error),
                })
                .collect(),
        }
    }
}

/// Subset of `SolveStatistics` projected for the report. Mirrors the
/// fields the existing console summary prints.
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct StatisticsInfo {
    pub iteration_count: Index,
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_objective: Number,
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_scaled_objective: Number,
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_dual_inf: Number,
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_constr_viol: Number,
    /// Primal violation against the model **as declared**, before the convex
    /// arm's `bound_relax_factor` widening (`qp_extract::BoundRelax`, gh
    /// #744/#745).
    ///
    /// `final_constr_viol` measures the model the solver was HANDED, whose
    /// inequality rows and variable box are widened by `min(factor,cap)·|b|`.
    /// That is the model its convergence test is about and every acceptance
    /// gate reads — and it is not how far the returned point sits outside the
    /// model the caller wrote. On netlib `afiro` the point is `4.99e-06`
    /// outside a declared row `b = 500` (exactly `1e-8·500`) while
    /// `final_constr_viol` reads `8.68e-13`; `25fv47` reports `2.19e-11`
    /// against `1.97e-05`.
    ///
    /// Reported on every solve of the NLP and convex arms, widened or not
    /// (gh#987: it is no longer `NaN` as a "no widening" signal); under
    /// `l1_exact_penalty_barrier` it is the caller's own rows, not the
    /// wrapper's augmented ones. `NaN` only on a path that does not compute
    /// it. Additive to `pounce.solve-report/v1`: readers predating it are
    /// unaffected.
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_declared_constr_viol: Number,
    /// How far the returned point sits outside the **declared** variable box —
    /// the box the caller wrote, before the `bound_relax_factor` widening.
    /// Ipopt's `Variable bound violation`, and the box half of
    /// `final_declared_constr_viol` reported on its own: maxed together, a box
    /// violation and a row violation cannot be told apart.
    ///
    /// Variable bounds carry no scaling, so there is one number rather than a
    /// scaled/unscaled pair. `NaN` on a path that does not compute it.
    /// Additive to `pounce.solve-report/v1`: readers predating it are
    /// unaffected.
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_declared_box_viol: Number,
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_compl: Number,
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_kkt_error: Number,
    /// The aggregate the strict convergence gate tested (gh #528): as
    /// `final_kkt_error`, but counting each constraint row's residual only
    /// where it exceeds what that row can represent in floating point. Equal
    /// to `final_kkt_error` unless a row is at its own resolution limit.
    #[serde(default = "uncomputed", deserialize_with = "null_as_nan")]
    pub final_kkt_error_above_noise: Number,
    pub num_obj_evals: Index,
    pub num_constr_evals: Index,
    pub num_obj_grad_evals: Index,
    pub num_constr_jac_evals: Index,
    pub num_hess_evals: Index,
    pub total_wallclock_time_secs: Number,
    pub restoration_calls: Index,
    pub restoration_inner_iters: Index,
    pub restoration_outer_iters: Index,
    pub restoration_wall_secs: Number,
    /// Successful linear-solver quality escalations over the whole solve,
    /// restoration sub-solves included (gh#857). `0` on paths that never
    /// escalate. `serde(default)` so a report written before this field
    /// existed still deserializes — it reads as "no escalations", which
    /// is wrong-but-harmless for an old file and correct for every new
    /// one.
    #[serde(default)]
    pub quality_escalations: Index,
    /// The solve saw the gh#884 biactive dual-divergence signature: at one
    /// and the same iterate a converged primal, a scale-relative step at
    /// zero, and an *unscaled* dual infeasibility far above `dual_inf_tol`.
    /// Reported whether or not a retry ran or promoted, because the
    /// distinction it draws — the multipliers ran away on a settled
    /// iterate, as against the iterate itself never settling — is not
    /// visible in any other field. `serde(default)` for the same reason as
    /// `quality_escalations`.
    #[serde(default)]
    pub dual_divergence_signature: bool,
    /// A gh#884 dual-divergence retry ran *and* its answer was returned.
    /// `false` both when no retry ran and when one ran and lost — in the
    /// latter case every other field here describes the base attempt.
    #[serde(default)]
    pub dual_divergence_retry_promoted: bool,
    /// gh#983. Structured solve-quality warnings about the returned point,
    /// each `"<code>: <text>"` (`objective_scale_small`,
    /// `unscaled_stationarity_above_tol`, `large_dual_scale`,
    /// `unscaled_dual_inf_above_acceptable`). Empty on a clean run; never
    /// changes the status. `serde(default)` so older reports still load.
    #[serde(default)]
    pub warnings: Vec<String>,
    /// gh#990 item 3. The derivative checker's machine-readable verdict;
    /// present only when `derivative_test` ran for this solve.
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub derivative_check: Option<DerivativeCheckInfo>,
    /// gh#990 item 13. The objective-scaling decision: the factor
    /// gradient-based scaling chose, the gradient scale it measured, and
    /// whether a termination certificate was refused because the scaling
    /// masked it. Absent when none of that is known (a path that does not
    /// scale the objective).
    #[serde(skip_serializing_if = "Option::is_none", default)]
    pub objective_scaling: Option<ObjectiveScalingInfo>,
    /// Per-pass summary of a multi-pass solve (gh#987 item 6): one entry per
    /// ℓ₁ exact-penalty ρ pass, preceded by the unwrapped attempt when the ℓ₁
    /// fallback ran. Omitted on an ordinary single-pass solve. When present,
    /// `iteration_count` is the total across the passes and `iterations`
    /// (at full detail) their concatenated rows; `first_row` in each entry
    /// marks where a pass's rows start. Additive to `pounce.solve-report/v1`.
    #[serde(default, skip_serializing_if = "Vec::is_empty")]
    pub passes: Vec<pounce_nlp::SolvePassRecord>,
}

/// Builder collecting the inputs for a [`SolveReport`]. The CLI
/// drivers populate one of these as they walk through the solve and
/// `finish()` it at the end.
pub struct ReportBuilder {
    detail: ReportDetail,
    started_at: SystemTime,
    started_unix_nanos: i128,
    pub input: InputDescriptor,
    pub problem: ProblemInfo,
    pub solution: SolutionInfo,
    pub stats: StatisticsInfo,
    pub iterations: Vec<IterRecord>,
    pub linear_solver: Option<LinearSolverSummaryInfo>,
    pub second_opinion: Option<SecondOpinionInfo>,
}

impl ReportBuilder {
    pub fn new(detail: ReportDetail, input: InputDescriptor) -> Self {
        let now = SystemTime::now();
        let nanos = now
            .duration_since(UNIX_EPOCH)
            .map(|d| d.as_nanos() as i128)
            .unwrap_or(0);
        Self {
            detail,
            started_at: now,
            started_unix_nanos: nanos,
            input,
            problem: ProblemInfo {
                n_variables: 0,
                n_constraints: 0,
                n_objectives: 0,
                minimize: true,
                nnz_jac_g: None,
                nnz_h_lag: None,
            },
            solution: SolutionInfo {
                engine: String::new(),
                status: ApplicationReturnStatus::InternalError,
                // Overwritten from `status` by `finish`; see the field docs.
                status_upstream: String::new(),
                solve_result_num: 500,
                // 0.0 (not NaN) so JSON round-trips. Callers that
                // need "unknown objective" semantics check
                // `statistics.iteration_count > 0` first.
                objective: 0.0,
                x: Vec::new(),
                lambda: Vec::new(),
                suffixes: Vec::new(),
            },
            stats: empty_stats(),
            iterations: Vec::new(),
            linear_solver: None,
            second_opinion: None,
        }
    }

    /// Record what the second-opinion ladder did. Called only when it ran; a
    /// verdict that opens no ladder leaves this `None` and the field out of
    /// the JSON entirely (gh #850).
    pub fn set_second_opinion(&mut self, info: SecondOpinionInfo) {
        self.second_opinion = Some(info);
    }

    /// Attach a linear-solver post-mortem. Called once per solve after
    /// `optimize_tnlp` returns and before [`Self::finish`].
    pub fn set_linear_solver_summary(&mut self, summary: LinearSolverSummary) {
        self.linear_solver = Some(summary.into());
    }

    /// Pull `iteration_count`, `final_*`, and counters into the
    /// `stats` slot; copy `iterations` only if detail = Full.
    pub fn ingest_stats(&mut self, src: &SolveStatistics) {
        self.stats = StatisticsInfo {
            iteration_count: src.iteration_count,
            final_objective: src.final_objective,
            final_scaled_objective: src.final_scaled_objective,
            final_dual_inf: src.final_dual_inf,
            final_constr_viol: src.final_constr_viol,
            final_declared_constr_viol: src.final_declared_constr_viol,
            final_declared_box_viol: src.final_declared_box_viol,
            final_compl: src.final_compl,
            final_kkt_error: src.final_kkt_error,
            final_kkt_error_above_noise: src.final_kkt_error_above_noise,
            num_obj_evals: src.num_obj_evals,
            num_constr_evals: src.num_constr_evals,
            num_obj_grad_evals: src.num_obj_grad_evals,
            num_constr_jac_evals: src.num_constr_jac_evals,
            num_hess_evals: src.num_hess_evals,
            total_wallclock_time_secs: src.total_wallclock_time_secs,
            restoration_calls: src.restoration_calls,
            restoration_inner_iters: src.restoration_inner_iters,
            restoration_outer_iters: src.restoration_outer_iters,
            restoration_wall_secs: src.restoration_wall_secs,
            quality_escalations: src.quality_escalations,
            dual_divergence_signature: src.dual_divergence_signature,
            dual_divergence_retry_promoted: src.dual_divergence_retry_promoted,
            warnings: src.warnings.clone(),
            derivative_check: src.derivative_check.as_ref().map(DerivativeCheckInfo::from),
            objective_scaling: {
                let fin = |v: f64| v.is_finite().then_some(v);
                let (factor, total, start) = (
                    fin(src.final_obj_scaling_factor),
                    fin(src.final_total_obj_scaling_factor),
                    fin(src.start_obj_grad_max),
                );
                (factor.is_some()
                    || total.is_some()
                    || start.is_some()
                    || src.obj_scale_certificate_refused
                    || src.obj_scale_acceptable_refused)
                    .then_some(ObjectiveScalingInfo {
                        factor,
                        total_factor: total,
                        start_gradient_max: start,
                        certificate_refused: src.obj_scale_certificate_refused,
                        acceptable_certificate_refused: src.obj_scale_acceptable_refused,
                    })
            },
            passes: src.passes.clone(),
        };
        if matches!(self.detail, ReportDetail::Full) {
            self.iterations = src.iterations.clone();
        }
    }

    pub fn finish(self) -> SolveReport {
        let elapsed = self
            .started_at
            .elapsed()
            .map(|d| d.as_secs_f64())
            .unwrap_or(0.0);
        // Derived here rather than at each call site: every producer of a
        // report (CLI, C interface, Python bindings, the CBF driver) sets
        // `solution.status` and none of them can forget the upstream
        // spelling, nor set one that disagrees with the other (gh #767).
        let mut solution = self.solution;
        solution.status_upstream = solution.status.upstream_name().to_string();
        let result_id = format!("{}-{}", self.started_unix_nanos, std::process::id());
        let created_at_iso = unix_nanos_to_iso(self.started_unix_nanos);

        SolveReport {
            schema: "pounce.solve-report/v1".to_string(),
            fair_metadata: FairMetadata {
                result_id,
                created_at_iso,
                created_at_unix_nanos: self.started_unix_nanos,
                elapsed_seconds: elapsed,
                solver: SolverIdentity {
                    name: "pounce".to_string(),
                    version: env!("CARGO_PKG_VERSION").to_string(),
                    git_commit: option_env!("POUNCE_GIT_COMMIT").map(String::from),
                    target_triple: TARGET_TRIPLE.to_string(),
                },
                license: "EPL-2.0".to_string(),
                input: self.input,
                environment: capture_solve_env_overrides(),
            },
            problem: self.problem,
            solution,
            statistics: self.stats,
            iterations: self.iterations,
            linear_solver: self.linear_solver,
            second_opinion: self.second_opinion,
        }
    }
}

/// The build target triple (e.g. `aarch64-apple-darwin`).
///
/// Cargo only exposes `TARGET` to *build scripts*, not to crate source, so
/// `option_env!("TARGET")` here is always `None`. Our `build.rs` re-exports
/// the build script's `TARGET` as `POUNCE_TARGET_TRIPLE`, which we read
/// instead. Falls back to "unknown" if the build script did not run (e.g.
/// some non-Cargo tooling).
const TARGET_TRIPLE: &str = match option_env!("POUNCE_TARGET_TRIPLE") {
    Some(t) => t,
    None => "unknown",
};

fn empty_stats() -> StatisticsInfo {
    // All scalar fields start at 0.0 (not NaN) so the report
    // round-trips through `serde_json` — JSON has no NaN literal, and
    // serde_json's default is to write `null` for NaN, which then
    // fails to deserialize back into `Number`. Callers reading these
    // pre-solve treat `iteration_count == 0` as "no solve yet".
    StatisticsInfo {
        iteration_count: 0,
        final_objective: 0.0,
        final_scaled_objective: 0.0,
        final_dual_inf: 0.0,
        final_constr_viol: 0.0,
        // not "uncomputed": this is the pre-solve placeholder, and 0.0 is
        // what every residual beside it carries here.
        final_declared_constr_viol: 0.0,
        final_declared_box_viol: 0.0,
        final_compl: 0.0,
        final_kkt_error: 0.0,
        final_kkt_error_above_noise: 0.0,
        num_obj_evals: 0,
        num_constr_evals: 0,
        num_obj_grad_evals: 0,
        num_constr_jac_evals: 0,
        num_hess_evals: 0,
        total_wallclock_time_secs: 0.0,
        restoration_calls: 0,
        restoration_inner_iters: 0,
        restoration_outer_iters: 0,
        restoration_wall_secs: 0.0,
        quality_escalations: 0,
        dual_divergence_signature: false,
        dual_divergence_retry_promoted: false,
        warnings: Vec::new(),
        derivative_check: None,
        objective_scaling: None,
        passes: Vec::new(),
    }
}

/// AMPL-style `solve_result_num` per Gay 2005 (Hooking Your Solver to
/// AMPL §5, p. 23 table): 0 = solved, 100s = warning, 200s =
/// infeasible, 300s = unbounded, 400s = limit reached, 500s = failure.
/// Shared by the CLI and cinterface report writers so both encode the
/// same int codes into `SolutionInfo::solve_result_num`.
///
/// `DivergingIterates` is Ipopt's unboundedness signal (the iterates run
/// off to infinity), so it maps to the 300 "unbounded" range — matching
/// upstream Ipopt's ASL driver and the CLI's own convex path, which
/// reports `QpStatus::DualInfeasible` (unbounded) as 300 (`main.rs`). It
/// is *not* a limit (400) condition.
///
/// `SolvedToAcceptableLevel` is `1`, not the 100 band, matching Ipopt's
/// ASL driver exactly (`Ipopt/src/Apps/AmplSolver/AmplTNLP.cpp`:
/// `STOP_AT_ACCEPTABLE_POINT` → `solve_result_num = 1`, message
/// "Solved To Acceptable Level."). The band is what consumers key on, and
/// the two bands are not interchangeable here: Pyomo's legacy `.sol`
/// reader turns `0..=99` into `status=ok` but `100..=199` into
/// `status=warning` with the same `termination_condition=optimal`, so the
/// 100 band made Pyomo log a "Loading a SolverResults object with a
/// warning status" warning on an accepted solve that Ipopt loads clean —
/// breaking solver-swappable clients whose accepted-solve contract
/// includes `status == ok` (gh #591). The reduced-accuracy convergence
/// stays visible in the status name and the `.sol` message line; it just
/// no longer reads as a warning.
///
/// `FeasiblePointFound` is `2`, Ipopt's own code, and therefore in the
/// `0..=99` solved band. It used to be `100`, justified by the claim that
/// the two statuses do not mean the same thing — that Ipopt returns
/// `FEASIBLE_POINT_FOUND` only for a square problem, where a feasible
/// point *is* the solution, while POUNCE used it more loosely for any
/// usable feasible point that missed the convergence criteria.
///
/// That claim was false about POUNCE's own code. The status has exactly
/// one production site: `min_c_1nrm.rs` returns
/// `RestorationOutcome::FeasiblePointFound`, reached only through the
/// gate at `resto_inner_solver.rs`, which is `is_square_problem && ...`.
/// `is_square_problem()` (`ipopt_alg.rs`) is `c.x.dim() == c.y_c.dim()`,
/// a port of `IpoptCalculatedQuantities::IsSquareProblem` — the same
/// condition Ipopt uses. So POUNCE emits this status *only* for square
/// problems, carrying Ipopt's meaning precisely, and on a square problem
/// there is no further convergence criterion to miss: the objective is
/// constant, so a feasible point is the solution.
///
/// The band is what consumers key on, and `100` was not a softer way of
/// saying the same thing — it inverted the answer. Pyomo's v2 reader
/// (`pyomo/contrib/solver/solvers/asl_sol_reader.py`) maps `100..=199` to
/// `TerminationCondition.error`, so a correct square-problem solve
/// reached the caller as a *solver error*; the legacy reader
/// (`pyomo/opt/plugins/sol.py`) maps it to `optimal` + `status=warning`,
/// the same gh #591 warning `SolvedToAcceptableLevel` was moved out of the
/// band to escape. Ipopt on the identical solve loads clean in both. This
/// is what gh #815 surfaced: an IDAES square flowsheet that POUNCE solves
/// to a constraint violation of 2.2e-06 was reported as a failure.
///
/// This crate is not the only place that had to agree. `python/pounce/gams/link.py`
/// already mapped the status to `(MODELSTAT_FEASIBLE, SOLVESTAT_NORMAL)`
/// and listed it as a success, and
/// `crates/pounce-algorithm/tests/issue_390_nonlinear_equality_scale.rs`
/// already called it "a success-band answer — AMPL `objno` code 2, which
/// every band table reads as SOLVED". The two Pyomo tables
/// (`pyomo_pounce.v2._V2_STATUS`, `pyomo_pounce.sens._STATUS_RESULT`) were
/// the dissenters and moved with this change.
///
/// Being in the solved band is *not* a claim that any feasible point is
/// acceptable. `issue_390_nonlinear_equality_scale.rs` is the guard that a
/// model with no solution is never reported feasible at any row scale; the
/// relative-violation threshold at `resto_inner_solver.rs` is what keeps
/// that true. This mapping decides how a verdict is reported, not when it
/// is reached.
pub fn status_to_solve_result_num(status: ApplicationReturnStatus) -> i32 {
    use ApplicationReturnStatus::*;
    match status {
        SolveSucceeded => 0,
        SolvedToAcceptableLevel => 1,
        FeasiblePointFound => 2,
        InfeasibleProblemDetected => 200,
        DivergingIterates => 300,
        SearchDirectionBecomesTooSmall => 400,
        MaximumIterationsExceeded => 400,
        MaximumCpuTimeExceeded => 400,
        MaximumWallTimeExceeded => 400,
        UserRequestedStop => 502,
        RestorationFailed => 500,
        ErrorInStepComputation => 500,
        InvalidNumberDetected => 500,
        InternalError => 500,
        UnrecoverableException => 500,
        NonIpoptExceptionThrown => 500,
        InsufficientMemory => 503,
        InvalidProblemDefinition => 504,
        InvalidOption => 504,
        NotEnoughDegreesOfFreedom => 504,
    }
}

/// Write a [`SolveReport`] to `path` as pretty-printed JSON. Returns
/// bytes written on success.
pub fn write_report_file(path: &Path, report: &SolveReport) -> std::io::Result<usize> {
    let s = serde_json::to_string_pretty(report)
        .map_err(|e| std::io::Error::new(std::io::ErrorKind::Other, e))?;
    std::fs::write(path, &s)?;
    Ok(s.len())
}

/// Convert Unix nanoseconds since the epoch to an ISO-8601 UTC
/// timestamp `YYYY-MM-DDTHH:MM:SS.sssZ`. Pure stdlib; no chrono /
/// time dependency. The conversion is based on the proleptic
/// Gregorian calendar formula from Howard Hinnant's "date" reference
/// (https://howardhinnant.github.io/date_algorithms.html), `days_from_civil`
/// in reverse — verified against `date -u -r <secs>` for several
/// epochs on 2026-05-14.
fn unix_nanos_to_iso(nanos: i128) -> String {
    let total_secs = nanos.div_euclid(1_000_000_000) as i64;
    let frac_nanos = nanos.rem_euclid(1_000_000_000) as i64;
    let millis = frac_nanos / 1_000_000;

    let days = total_secs.div_euclid(86_400);
    let secs_of_day = total_secs.rem_euclid(86_400);
    let hh = (secs_of_day / 3600) as i32;
    let mm = ((secs_of_day % 3600) / 60) as i32;
    let ss = (secs_of_day % 60) as i32;

    // Howard Hinnant's `civil_from_days` algorithm:
    //   z = days + 719468
    //   era = (z >= 0 ? z : z - 146096) / 146097
    //   doe = z - era*146097
    //   yoe = (doe - doe/1460 + doe/36524 - doe/146096) / 365
    //   y = yoe + era*400
    //   doy = doe - (365*yoe + yoe/4 - yoe/100)
    //   mp = (5*doy + 2) / 153
    //   d = doy - (153*mp + 2)/5 + 1
    //   m = mp < 10 ? mp + 3 : mp - 9
    //   y += (m <= 2)
    let z: i64 = days + 719468;
    let era = if z >= 0 { z } else { z - 146096 } / 146097;
    let doe = (z - era * 146097) as i64; // [0, 146096]
    let yoe = (doe - doe / 1460 + doe / 36524 - doe / 146096) / 365; // [0, 399]
    let mut y = yoe + era * 400;
    let doy = doe - (365 * yoe + yoe / 4 - yoe / 100); // [0, 365]
    let mp = (5 * doy + 2) / 153; // [0, 11]
    let d = (doy - (153 * mp + 2) / 5 + 1) as i32;
    let m = if mp < 10 { mp + 3 } else { mp - 9 } as i32;
    if m <= 2 {
        y += 1;
    }

    format!(
        "{:04}-{:02}-{:02}T{:02}:{:02}:{:02}.{:03}Z",
        y, m, d, hh, mm, ss, millis
    )
}

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

    #[test]
    fn iso_formatter_matches_known_epochs() {
        // Epoch.
        assert_eq!(unix_nanos_to_iso(0), "1970-01-01T00:00:00.000Z");
        // 2000-01-01T00:00:00Z = 946684800 seconds.
        assert_eq!(
            unix_nanos_to_iso(946_684_800_000_000_000),
            "2000-01-01T00:00:00.000Z",
        );
        // 2024-02-29T12:34:56.789Z (leap-year sanity check).
        // Seconds: (2024 - 1970) * 365.25 days * 86400 ≈ 1709209296 — let's compute exactly.
        // Days from 1970-01-01 to 2024-02-29: 19782.
        // 19782 * 86400 = 1709164800. Plus 12*3600 + 34*60 + 56 = 45296.
        // Total = 1709210096.
        let s = unix_nanos_to_iso(1_709_210_096_789_000_000);
        assert_eq!(s, "2024-02-29T12:34:56.789Z", "got: {s}");
    }

    #[test]
    fn target_triple_resolves_to_real_triple_not_unknown() {
        // Fail-first: before the build.rs re-export this constant read
        // `option_env!("TARGET")`, which is `None` at crate-source compile
        // time (Cargo only exposes TARGET to build scripts), so it was always
        // "unknown". The build.rs now re-exports TARGET as
        // POUNCE_TARGET_TRIPLE, which resolves it to the real build triple.
        assert_ne!(
            TARGET_TRIPLE, "unknown",
            "build.rs should re-export the build target triple"
        );
        // A real triple has the `arch-vendor-os[-abi]` shape (>= 2 dashes).
        assert!(
            TARGET_TRIPLE.matches('-').count() >= 2,
            "unexpected target triple: {TARGET_TRIPLE:?}"
        );

        // And it must propagate into the finished report.
        let b = ReportBuilder::new(
            ReportDetail::Summary,
            InputDescriptor::NlFile {
                path: PathBuf::from("/tmp/foo.nl"),
                size_bytes: None,
            },
        );
        let report = b.finish();
        assert_eq!(report.fair_metadata.solver.target_triple, TARGET_TRIPLE);
        assert_ne!(report.fair_metadata.solver.target_triple, "unknown");
    }

    /// The Phase-0a work / pivoting fields and the Schur breakdown survive a
    /// JSON round trip, and a `linear_solver` object written before those
    /// fields existed still deserializes (the change is additive within v1).
    #[test]
    fn linear_solver_summary_round_trips_and_stays_backward_compatible() {
        use pounce_linsol::summary::SchurSummary;
        let summary = LinearSolverSummary {
            solver_name: "feral".into(),
            n_factors: 3,
            total_factor_secs: 0.5,
            total_factor_flops: 1.0e6,
            total_delayed_cols: 7,
            total_two_by_two: 2,
            last_ordering: Some("metis".into()),
            last_max_front_rows: Some(40),
            schur: Some(SchurSummary {
                n_eliminated: 100,
                n_schur: 4,
                n_factors: 3,
                ..Default::default()
            }),
            ..Default::default()
        };
        let info = LinearSolverSummaryInfo::from(summary);
        let json = serde_json::to_string(&info).expect("serialize");
        let back: LinearSolverSummaryInfo = serde_json::from_str(&json).expect("deserialize");
        assert_eq!(back.total_delayed_cols, 7);
        assert_eq!(back.total_two_by_two, 2);
        assert_eq!(back.last_ordering.as_deref(), Some("metis"));
        assert_eq!(back.last_max_front_rows, Some(40));
        let sch = back.schur.expect("schur survives");
        assert_eq!((sch.n_eliminated, sch.n_schur, sch.n_factors), (100, 4, 3));
        // Unset optionals are omitted, not written as null.
        assert!(!json.contains("last_peak_bytes"));

        let old = r#"{"solver_name":"feral","n_factors":2,"n_pattern_reuse":1,"n_pattern_changes":1,"last_nnz_l":10}"#;
        let back: LinearSolverSummaryInfo = serde_json::from_str(old).expect("old report parses");
        assert_eq!(back.n_factors, 2);
        assert_eq!(back.total_factor_secs, 0.0);
        assert!(back.schur.is_none());
        assert!(back.restoration.is_none());
    }

    #[test]
    fn report_serializes_round_trip() {
        let mut b = ReportBuilder::new(
            ReportDetail::Summary,
            InputDescriptor::NlFile {
                path: PathBuf::from("/tmp/foo.nl"),
                size_bytes: Some(123),
            },
        );
        b.problem.n_variables = 5;
        b.problem.n_constraints = 4;
        b.solution.status = ApplicationReturnStatus::SolveSucceeded;
        b.solution.solve_result_num = 0;
        b.solution.objective = 0.55;
        b.solution.x = vec![0.63, 0.39, 0.02, 5.0, 1.0];
        b.solution.lambda = vec![-0.16, -0.29, -0.16, 0.18];
        b.stats.iteration_count = 9;

        let report = b.finish();
        let json = serde_json::to_string_pretty(&report).expect("serialize");
        let back: SolveReport = serde_json::from_str(&json).expect("deserialize");
        assert_eq!(back.schema, "pounce.solve-report/v1");
        assert_eq!(back.problem.n_variables, 5);
        assert_eq!(back.solution.x.len(), 5);
        assert!(matches!(
            back.solution.status,
            ApplicationReturnStatus::SolveSucceeded,
        ));
    }

    /// gh #767: the report is the FAIR-aligned machine surface, and a
    /// consumer keyed on Ipopt's own enumerator spelling — which is what
    /// CUTEst tables, the reference JSONs and the CLI's `Status:` line all
    /// use — must be able to compare a field literally. `status` carries
    /// the Rust variant name (`SolveSucceeded`); `status_upstream` carries
    /// `Solve_Succeeded`. A consumer that compared the former against the
    /// latter's spelling matched nothing and read every solve as a failure.
    #[test]
    fn report_carries_the_upstream_status_spelling_beside_the_rust_one() {
        let mut b = ReportBuilder::new(
            ReportDetail::Summary,
            InputDescriptor::Builtin {
                name: "rosenbrock".into(),
            },
        );
        b.solution.status = ApplicationReturnStatus::SolveSucceeded;
        let json = serde_json::to_value(b.finish()).expect("serialize");
        assert_eq!(json["solution"]["status"], "SolveSucceeded");
        assert_eq!(json["solution"]["status_upstream"], "Solve_Succeeded");
    }

    /// The derived field tracks whatever `status` was last set to — it is
    /// computed in `finish`, so a caller cannot leave it stale or set the
    /// two to different verdicts.
    #[test]
    fn upstream_status_spelling_is_derived_not_stored() {
        for status in [
            ApplicationReturnStatus::MaximumIterationsExceeded,
            ApplicationReturnStatus::InfeasibleProblemDetected,
            ApplicationReturnStatus::SolvedToAcceptableLevel,
        ] {
            let mut b = ReportBuilder::new(
                ReportDetail::Summary,
                InputDescriptor::Builtin { name: "x".into() },
            );
            // Deliberately wrong; `finish` must overwrite it.
            b.solution.status_upstream = "Solve_Succeeded".to_string();
            b.solution.status = status;
            let report = b.finish();
            assert_eq!(report.solution.status_upstream, status.upstream_name());
        }
    }

    #[test]
    fn summary_detail_omits_iterations_block() {
        let mut b = ReportBuilder::new(
            ReportDetail::Summary,
            InputDescriptor::Builtin {
                name: "rosenbrock".into(),
            },
        );
        let mut stats = SolveStatistics::default();
        stats.iterations.push(IterRecord {
            iter: 0,
            objective: 1.0,
            ..IterRecord::default()
        });
        b.ingest_stats(&stats);
        let r = b.finish();
        assert!(
            r.iterations.is_empty(),
            "Summary detail should drop iter history; got {} rows",
            r.iterations.len()
        );
        // And the JSON should not include the key at all (skip-empty).
        let json = serde_json::to_string(&r).unwrap();
        assert!(!json.contains("\"iterations\":"), "json: {json}");
    }

    #[test]
    fn full_detail_includes_iteration_rows() {
        let mut b = ReportBuilder::new(ReportDetail::Full, InputDescriptor::TnlpDirect);
        let mut stats = SolveStatistics::default();
        stats.iterations.push(IterRecord {
            iter: 0,
            objective: 1.0,
            inf_pr: 0.5,
            ..IterRecord::default()
        });
        stats.iterations.push(IterRecord {
            iter: 1,
            objective: 0.5,
            inf_pr: 0.1,
            ..IterRecord::default()
        });
        b.ingest_stats(&stats);
        let r = b.finish();
        assert_eq!(r.iterations.len(), 2);
        assert_eq!(r.iterations[0].iter, 0);
        assert_eq!(r.iterations[1].iter, 1);
    }

    #[test]
    fn detail_parser_accepts_known_values() {
        assert_eq!(
            ReportDetail::parse("summary").unwrap(),
            ReportDetail::Summary
        );
        assert_eq!(ReportDetail::parse("Full").unwrap(), ReportDetail::Full);
        assert!(ReportDetail::parse("verbose").is_err());
    }

    #[test]
    fn diverging_iterates_maps_to_unbounded_range() {
        use ApplicationReturnStatus::*;
        // M12 regression: DivergingIterates is Ipopt's unboundedness
        // signal and must land in the AMPL 300 "unbounded" range, not
        // the 400 "limit" range — matching upstream Ipopt's ASL driver
        // and the CLI convex path (QpStatus::DualInfeasible → 300).
        assert_eq!(status_to_solve_result_num(DivergingIterates), 300);

        // Lock the surrounding range convention so the fix can't silently
        // drift back: solved / infeasible / limit / failure buckets.
        assert_eq!(status_to_solve_result_num(SolveSucceeded), 0);
        assert_eq!(status_to_solve_result_num(InfeasibleProblemDetected), 200);
        assert_eq!(
            status_to_solve_result_num(MaximumIterationsExceeded),
            400,
            "iteration limit stays in the 400 range",
        );
        assert_eq!(
            status_to_solve_result_num(SearchDirectionBecomesTooSmall),
            400,
        );
        assert_eq!(status_to_solve_result_num(RestorationFailed), 500);
    }

    /// gh #591: an accepted (reduced-accuracy) solve must land in AMPL's
    /// `0..=99` *solved* band with Ipopt's own code, `1`. In the 100 band
    /// Pyomo's legacy `.sol` reader loads the result as
    /// `status=warning, termination_condition=optimal` and logs a warning,
    /// while the identical Ipopt solve loads as `status=ok` — so a
    /// solver-swappable client that treats `status == ok` as part of its
    /// accepted-solve contract had to special-case POUNCE.
    #[test]
    fn solved_to_acceptable_level_is_in_the_solved_band_like_ipopt() {
        use ApplicationReturnStatus::*;
        let code = status_to_solve_result_num(SolvedToAcceptableLevel);
        assert_eq!(
            code, 1,
            "Ipopt's ASL driver emits 1 for STOP_AT_ACCEPTABLE_POINT",
        );
        assert!(
            (0..=99).contains(&code),
            "must be in the solved band Pyomo maps to status=ok, got {code}",
        );
        // Still distinguishable from a full-accuracy solve: the two codes
        // differ, and the status name carries the distinction verbatim into
        // the `.sol` message line.
        assert_ne!(code, status_to_solve_result_num(SolveSucceeded));

        // `FeasiblePointFound` is also in the solved band, for its own
        // reason — see `a_square_problem_feasible_point_is_in_the_solved_band`.
        assert_ne!(code, status_to_solve_result_num(FeasiblePointFound));
    }

    /// POUNCE emits `FeasiblePointFound` only for square problems — the
    /// status has one production site (`min_c_1nrm.rs`) behind one gate
    /// (`resto_inner_solver.rs`), and that gate is `is_square_problem &&
    /// ...`. That is exactly Ipopt's meaning, and on a square problem a
    /// feasible point *is* the solution, so the code is Ipopt's own `2`.
    ///
    /// The band, not the number, is what breaks: at `100` Pyomo's v2 ASL
    /// reader returns `TerminationCondition.error` for a correct solve
    /// (gh #815 — an IDAES flowsheet solved to a 2.2e-06 constraint
    /// violation and reported as a solver error), and the legacy reader
    /// returns `status=warning`, the same gh #591 complaint that moved
    /// `SolvedToAcceptableLevel` out of the band.
    #[test]
    fn a_square_problem_feasible_point_is_in_the_solved_band() {
        use ApplicationReturnStatus::*;
        let code = status_to_solve_result_num(FeasiblePointFound);
        assert_eq!(
            code, 2,
            "Ipopt's ASL driver emits 2 for FEASIBLE_POINT_FOUND"
        );
        assert!(
            (0..=99).contains(&code),
            "must be in the solved band both Pyomo readers accept, got {code}",
        );
        // Still its own verdict: distinguishable from both other members of
        // the band, with the distinction carried verbatim in the status name
        // and the `.sol` message line.
        assert_ne!(code, status_to_solve_result_num(SolveSucceeded));
        assert_ne!(code, status_to_solve_result_num(SolvedToAcceptableLevel));
    }

    #[test]
    fn result_id_is_unique_and_time_ordered() {
        let a = ReportBuilder::new(ReportDetail::Summary, InputDescriptor::TnlpDirect).finish();
        std::thread::sleep(std::time::Duration::from_millis(2));
        let b = ReportBuilder::new(ReportDetail::Summary, InputDescriptor::TnlpDirect).finish();
        assert_ne!(a.fair_metadata.result_id, b.fair_metadata.result_id);
        assert!(
            b.fair_metadata.created_at_unix_nanos > a.fair_metadata.created_at_unix_nanos,
            "second result_id should sort after first"
        );
    }
}