test: add adversarial-input coverage for exact arithmetic (#80)

.github/workflows/benchmarks.yml

@@ -114,8 +114,11 @@ jobs:
 
           if [ -d target/criterion/exact_d2/det/main ]; then
             echo "::notice::Baseline found — comparing against main"
+            # --baseline-lenient rather than --baseline: benches added on the
+            # PR branch that don't yet exist in the main baseline get a
+            # "no baseline data" notice instead of aborting the whole run.
             cargo bench --features bench,exact --bench exact \
-              -- --baseline main 2>&1 | tee bench-output.txt
+              -- --baseline-lenient main 2>&1 | tee bench-output.txt
           else
             echo "::notice::No baseline found — running without comparison"
             cargo bench --features bench,exact --bench exact \

AGENTS.md

@@ -221,7 +221,10 @@ When creating or updating issues:
 
 - `exact` — enables exact arithmetic methods via `BigRational`:
   `det_exact()`, `det_exact_f64()`, `det_sign_exact()`, `solve_exact()`, and `solve_exact_f64()`.
-  Also re-exports `BigRational` from the crate root and prelude.
+  Re-exports `BigInt` and `BigRational` from the crate root and prelude, plus
+  `FromPrimitive`, `ToPrimitive`, and `Signed` from `num-traits` in the prelude
+  (so consumers get usable `from_f64` / `to_f64` / `is_positive` etc. without adding
+  `num-bigint` / `num-rational` / `num-traits` as their own deps).
   Gates `src/exact.rs`, additional tests, and the `exact_det_3x3`/`exact_sign_3x3`/`exact_solve_3x3` examples.
   Clippy, doc builds, and test commands have dedicated `--features exact` variants.
 
@@ -245,7 +248,8 @@ When creating or updating issues:
 - The public API re-exports these items from `src/lib.rs`.
 - The `justfile` defines all dev workflows (see `just --list`).
 - Dev-only benchmarks live in `benches/vs_linalg.rs` (Criterion + nalgebra/faer comparison)
-  and `benches/exact.rs` (exact arithmetic across D=2–5).
+  and `benches/exact.rs` (exact arithmetic across D=2–5, plus adversarial-input groups
+  `exact_near_singular_3x3`, `exact_large_entries_3x3`, `exact_hilbert_4x4`, `exact_hilbert_5x5`).
 - Python scripts under `scripts/`:
   - `bench_compare.py`: exact-arithmetic benchmark comparison across releases (generates `docs/PERFORMANCE.md`)
   - `criterion_dim_plot.py`: benchmark plotting (CSV + SVG + README table update)

README.md

@@ -190,8 +190,13 @@ assert!((x[0] - 1.0).abs() <= f64::EPSILON);
 assert!((x[1] - 2.0).abs() <= f64::EPSILON);
 ```
 
-`BigRational` is re-exported from the crate root and prelude when the `exact`
-feature is enabled, so consumers don't need to depend on `num-rational` directly.
+With the `exact` feature enabled, `BigInt` and `BigRational` are re-exported
+from the crate root and prelude, alongside the most commonly needed
+`num-traits` items (`FromPrimitive`, `ToPrimitive`, `Signed`).  This lets
+consumers construct exact values (`BigRational::from_f64`, `from_i64`), query
+sign (`is_positive` / `is_negative`), and convert back to `f64` (`to_f64`)
+with a single `use la_stack::prelude::*;` — no need to add `num-bigint`,
+`num-rational`, or `num-traits` to their own `Cargo.toml`.
 
 For `det_sign_exact()`, D ≤ 4 matrices use a fast f64 filter (error-bounded
 `det_direct()`) that resolves the sign without allocating.  Only near-degenerate

benches/exact.rs

@@ -1,10 +1,22 @@
 //! Benchmarks for exact arithmetic operations.
 //!
 //! These benchmarks measure the performance of the `exact` feature's
-//! arbitrary-precision methods across dimensions D=2..5 (the primary
-//! target for geometric predicates).
+//! arbitrary-precision methods.  They are organised into two classes:
+//!
+//! 1. **General-case benches** (`exact_d{2..5}`) — a single
+//!    well-conditioned diagonally-dominant matrix per dimension.  These
+//!    measure typical-case performance and track regressions against a
+//!    reproducible input.
+//! 2. **Adversarial / extreme-input benches** — matrices chosen to
+//!    stress specific corners of the exact-arithmetic pipeline:
+//!    near-singularity (forces the Bareiss fallback), large f64 entries
+//!    (stresses intermediate `BigInt` growth), and Hilbert-style
+//!    ill-conditioning (wide range of `(mantissa, exponent)` pairs in
+//!    the `f64_decompose → BigInt` path).  These measure tail behaviour
+//!    that fixed well-conditioned inputs miss and provide stronger
+//!    empirical evidence for `docs/PERFORMANCE.md`.
 
-use criterion::Criterion;
+use criterion::{BenchmarkGroup, Criterion, measurement::WallTime};
 use la_stack::{Matrix, Vector};
 use pastey::paste;
 use std::hint::black_box;
@@ -47,8 +59,13 @@ fn make_vector_array<const D: usize>() -> [f64; D] {
 }
 
 /// Near-singular matrix: base singular matrix + tiny perturbation.
-/// This forces the exact Bareiss fallback in `det_sign_exact` (the fast
-/// f64 filter cannot resolve the sign).
+///
+/// The base `[[1,2,3],[4,5,6],[7,8,9]]` is exactly singular; adding
+/// `2^-50` to entry (0,0) makes `det = -3 × 2^-50 ≠ 0`.  The f64 filter
+/// in `det_sign_exact` cannot resolve this sign, so Bareiss is forced;
+/// `solve_exact` is the primary use case for near-degenerate inputs
+/// (exact circumcenter etc.) and exercises the largest intermediate
+/// `BigInt` values in the hybrid solve.
 #[inline]
 fn near_singular_3x3() -> Matrix<3> {
     let perturbation = f64::from_bits(0x3CD0_0000_0000_0000); // 2^-50
@@ -59,6 +76,97 @@ fn near_singular_3x3() -> Matrix<3> {
     ])
 }
 
+/// Large-entry 3×3: strictly diagonally-dominant matrix with diagonal
+/// entries near `f64::MAX / 2` and ones elsewhere.
+///
+/// Each big entry decomposes into a 53-bit mantissa with exponent `~970`;
+/// the unit off-diagonals have exponent `0`, so the shared `e_min = 0`
+/// shift in `component_to_bigint` produces `BigInt`s of `~1023` bits for
+/// the diagonal and small integers elsewhere.  Bareiss fraction-free
+/// updates then multiply these together, stressing the big-integer
+/// multiply and allocator along the full `O(D³)` elimination phase.  The
+/// matrix is non-singular (det ≈ `big³`) so both `det_*` and `solve_*`
+/// paths complete.
+#[inline]
+fn large_entries_3x3() -> Matrix<3> {
+    let big = f64::MAX / 2.0;
+    Matrix::<3>::from_rows([[big, 1.0, 1.0], [1.0, big, 1.0], [1.0, 1.0, big]])
+}
+
+/// Hilbert matrix `H[i][j] = 1 / (i + j + 1)`.
+///
+/// Most entries (`1/3`, `1/5`, `1/6`, `1/7`, …) are non-terminating in
+/// binary, so every cell has a distinct 53-bit mantissa and a small
+/// negative exponent.  `f64_decompose` therefore produces a wide mix of
+/// `(mantissa, exponent)` pairs with no shared power-of-two factors,
+/// and the scaling shift to the common `e_min` yields `BigInt` values
+/// of varied bit-lengths — a different kind of adversarial input from
+/// the large-entries case.  Hilbert matrices are also classically
+/// ill-conditioned (condition number grows exponentially with D), so
+/// they are a realistic stand-in for the near-degenerate geometric
+/// predicate inputs that motivate exact arithmetic.
+#[inline]
+#[allow(clippy::cast_precision_loss)]
+fn hilbert<const D: usize>() -> Matrix<D> {
+    let mut rows = [[0.0; D]; D];
+    let mut r = 0;
+    while r < D {
+        let mut c = 0;
+        while c < D {
+            rows[r][c] = 1.0 / ((r + c + 1) as f64);
+            c += 1;
+        }
+        r += 1;
+    }
+    Matrix::<D>::from_rows(rows)
+}
+
+/// Populate a Criterion group with the four headline exact-arithmetic
+/// benches on a single `(matrix, rhs)` pair: `det_sign_exact`,
+/// `det_exact`, `solve_exact`, `solve_exact_f64`.
+///
+/// Used by every adversarial-input group so each one measures the same
+/// operations, making the resulting tables directly comparable.
+fn bench_extreme_group<const D: usize>(
+    group: &mut BenchmarkGroup<'_, WallTime>,
+    m: Matrix<D>,
+    rhs: Vector<D>,
+) {
+    group.bench_function("det_sign_exact", |bencher| {
+        bencher.iter(|| {
+            let sign = black_box(m)
+                .det_sign_exact()
+                .expect("finite matrix entries");
+            black_box(sign);
+        });
+    });
+
+    group.bench_function("det_exact", |bencher| {
+        bencher.iter(|| {
+            let det = black_box(m).det_exact().expect("finite matrix entries");
+            black_box(det);
+        });
+    });
+
+    group.bench_function("solve_exact", |bencher| {
+        bencher.iter(|| {
+            let x = black_box(m)
+                .solve_exact(black_box(rhs))
+                .expect("non-singular matrix with finite entries");
+            let _ = black_box(x);
+        });
+    });
+
+    group.bench_function("solve_exact_f64", |bencher| {
+        bencher.iter(|| {
+            let x = black_box(m)
+                .solve_exact_f64(black_box(rhs))
+                .expect("solution representable in f64");
+            let _ = black_box(x);
+        });
+    });
+}
+
 macro_rules! gen_exact_benches_for_dim {
     ($c:expr, $d:literal) => {
         paste! {{
@@ -72,7 +180,7 @@ macro_rules! gen_exact_benches_for_dim {
                 bencher.iter(|| {
                     let det = black_box(a)
                         .det(la_stack::DEFAULT_PIVOT_TOL)
-                        .expect("should not fail");
+                        .expect("diagonally dominant matrix is non-singular");
                     black_box(det);
                 });
             });
@@ -87,39 +195,47 @@ macro_rules! gen_exact_benches_for_dim {
             // === det_exact (BigRational result) ===
             [<group_d $d>].bench_function("det_exact", |bencher| {
                 bencher.iter(|| {
-                    let det = black_box(a).det_exact().expect("should not fail");
+                    let det = black_box(a).det_exact().expect("finite matrix entries");
                     black_box(det);
                 });
             });
 
             // === det_exact_f64 (exact → f64) ===
             [<group_d $d>].bench_function("det_exact_f64", |bencher| {
                 bencher.iter(|| {
-                    let det = black_box(a).det_exact_f64().expect("should not fail");
+                    let det = black_box(a)
+                        .det_exact_f64()
+                        .expect("det representable in f64");
                     black_box(det);
                 });
             });
 
             // === det_sign_exact (adaptive: fast filter + exact fallback) ===
             [<group_d $d>].bench_function("det_sign_exact", |bencher| {
                 bencher.iter(|| {
-                    let sign = black_box(a).det_sign_exact().expect("should not fail");
+                    let sign = black_box(a)
+                        .det_sign_exact()
+                        .expect("finite matrix entries");
                     black_box(sign);
                 });
             });
 
             // === solve_exact (BigRational result) ===
             [<group_d $d>].bench_function("solve_exact", |bencher| {
                 bencher.iter(|| {
-                    let x = black_box(a).solve_exact(black_box(rhs)).expect("should not fail");
+                    let x = black_box(a)
+                        .solve_exact(black_box(rhs))
+                        .expect("diagonally dominant matrix is non-singular");
                     black_box(x);
                 });
             });
 
             // === solve_exact_f64 (exact → f64) ===
             [<group_d $d>].bench_function("solve_exact_f64", |bencher| {
                 bencher.iter(|| {
-                    let x = black_box(a).solve_exact_f64(black_box(rhs)).expect("should not fail");
+                    let x = black_box(a)
+                        .solve_exact_f64(black_box(rhs))
+                        .expect("solution representable in f64");
                     black_box(x);
                 });
             });
@@ -140,25 +256,50 @@ fn main() {
         gen_exact_benches_for_dim!(&mut c, 5);
     }
 
-    // Near-singular 3×3: forces Bareiss fallback in det_sign_exact.
+    // === Adversarial / extreme-input groups ===
+    //
+    // Each group runs the same four exact-arithmetic benches
+    // (`det_sign_exact`, `det_exact`, `solve_exact`, `solve_exact_f64`)
+    // via `bench_extreme_group`, so the resulting tables are directly
+    // comparable across input classes.
+
+    // Near-singular 3×3: forces Bareiss fallback in det_sign_exact and
+    // exercises the largest intermediate BigInt values in solve_exact
+    // (the primary motivating use case for exact solve).
     {
-        let m = near_singular_3x3();
         let mut group = c.benchmark_group("exact_near_singular_3x3");
+        bench_extreme_group(
+            &mut group,
+            near_singular_3x3(),
+            Vector::<3>::new([1.0, 2.0, 3.0]),
+        );
+        group.finish();
+    }
 
-        group.bench_function("det_sign_exact", |bencher| {
-            bencher.iter(|| {
-                let sign = black_box(m).det_sign_exact().expect("should not fail");
-                black_box(sign);
-            });
-        });
+    // Large-entry 3×3: diagonal entries near `f64::MAX / 2` stress
+    // BigInt growth during Bareiss forward elimination.
+    {
+        let mut group = c.benchmark_group("exact_large_entries_3x3");
+        bench_extreme_group(
+            &mut group,
+            large_entries_3x3(),
+            Vector::<3>::new([1.0, 1.0, 1.0]),
+        );
+        group.finish();
+    }
 
-        group.bench_function("det_exact", |bencher| {
-            bencher.iter(|| {
-                let det = black_box(m).det_exact().expect("should not fail");
-                black_box(det);
-            });
-        });
+    // Hilbert 4×4 and 5×5: classically ill-conditioned matrices whose
+    // entries span many orders of magnitude in `(mantissa, exponent)`
+    // space, exercising the f64 → BigInt scaling path.
+    {
+        let mut group = c.benchmark_group("exact_hilbert_4x4");
+        bench_extreme_group(&mut group, hilbert::<4>(), Vector::<4>::new([1.0; 4]));
+        group.finish();
+    }
 
+    {
+        let mut group = c.benchmark_group("exact_hilbert_5x5");
+        bench_extreme_group(&mut group, hilbert::<5>(), Vector::<5>::new([1.0; 5]));
         group.finish();
     }
 

docs/BENCHMARKING.md

@@ -14,6 +14,20 @@ la-stack has two Criterion benchmark suites:
   (`det_exact`, `solve_exact`, `det_sign_exact`, etc.) alongside f64
   baselines (`det`, `det_direct`) across D=2–5. Use this to understand
   the cost of exact arithmetic and track optimization progress.
+  In addition to the per-dimension groups (`exact_d{2..5}`), the suite
+  includes four adversarial-input groups designed to stress specific
+  corners of the pipeline:
+  - `exact_near_singular_3x3` — a 2^-50 perturbation of a singular base
+    matrix; forces the Bareiss fallback in `det_sign_exact` and
+    exercises the largest intermediate `BigInt` values in `solve_exact`.
+  - `exact_large_entries_3x3` — diagonal entries near `f64::MAX / 2`
+    stress `BigInt` growth during Bareiss forward elimination.
+  - `exact_hilbert_4x4` / `exact_hilbert_5x5` — classically
+    ill-conditioned matrices whose non-terminating-in-binary entries
+    stress the `f64_decompose → BigInt` scaling path.
+  Each adversarial group runs the same four benches (`det_sign_exact`,
+  `det_exact`, `solve_exact`, `solve_exact_f64`) so the resulting tables
+  are directly comparable across input classes.
 
 ## Quick reference
 

scripts/bench_compare.py

@@ -38,12 +38,22 @@
 # ---------------------------------------------------------------------------
 
 # Groups and the benchmarks within each group that we track.
+#
+# Mirrors the structure of `benches/exact.rs`: general-case per-dimension
+# groups (`exact_d{2..5}`) plus adversarial/extreme-input groups that
+# share a fixed four-bench layout (`det_sign_exact`, `det_exact`,
+# `solve_exact`, `solve_exact_f64`).
+_EXTREME_BENCHES: list[str] = ["det_sign_exact", "det_exact", "solve_exact", "solve_exact_f64"]
+
 EXACT_GROUPS: dict[str, list[str]] = {
     "exact_d2": ["det", "det_direct", "det_exact", "det_exact_f64", "det_sign_exact", "solve_exact", "solve_exact_f64"],
     "exact_d3": ["det", "det_direct", "det_exact", "det_exact_f64", "det_sign_exact", "solve_exact", "solve_exact_f64"],
     "exact_d4": ["det", "det_direct", "det_exact", "det_exact_f64", "det_sign_exact", "solve_exact", "solve_exact_f64"],
     "exact_d5": ["det", "det_direct", "det_exact", "det_exact_f64", "det_sign_exact", "solve_exact", "solve_exact_f64"],
-    "exact_near_singular_3x3": ["det_sign_exact", "det_exact"],
+    "exact_near_singular_3x3": _EXTREME_BENCHES,
+    "exact_large_entries_3x3": _EXTREME_BENCHES,
+    "exact_hilbert_4x4": _EXTREME_BENCHES,
+    "exact_hilbert_5x5": _EXTREME_BENCHES,
 }
 
 
@@ -197,11 +207,16 @@ def _group_by_group(items: list[_T]) -> dict[str, list[_T]]:
 
 def _group_heading(group: str) -> str:
     """Turn a Criterion group name into a readable heading."""
-    # exact_d3 -> "D=3", exact_near_singular_3x3 -> "Near-singular 3x3"
+    # exact_d3 -> "D=3", exact_near_singular_3x3 -> "Near-singular 3x3",
+    # exact_hilbert_4x4 -> "Hilbert 4x4", etc.
     if group.startswith("exact_d"):
         return f"D={group.removeprefix('exact_d')}"
     if group == "exact_near_singular_3x3":
         return "Near-singular 3x3"
+    if group == "exact_large_entries_3x3":
+        return "Large entries 3x3"
+    if group.startswith("exact_hilbert_"):
+        return f"Hilbert {group.removeprefix('exact_hilbert_')}"
     return group
 
 

scripts/tests/test_bench_compare.py

@@ -77,6 +77,15 @@ def test_dimension_group(self) -> None:
     def test_near_singular(self) -> None:
         assert bench_compare._group_heading("exact_near_singular_3x3") == "Near-singular 3x3"
 
+    def test_large_entries(self) -> None:
+        assert bench_compare._group_heading("exact_large_entries_3x3") == "Large entries 3x3"
+
+    def test_hilbert_4x4(self) -> None:
+        assert bench_compare._group_heading("exact_hilbert_4x4") == "Hilbert 4x4"
+
+    def test_hilbert_5x5(self) -> None:
+        assert bench_compare._group_heading("exact_hilbert_5x5") == "Hilbert 5x5"
+
     def test_unknown_passthrough(self) -> None:
         assert bench_compare._group_heading("something_else") == "something_else"