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Add dedicated unit tests and benchmark for ConditionEstimator

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libeigen/eigen!2223

Co-authored-by: Rasmus Munk Larsen <rmlarsen@gmail.com>
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Rasmus Munk Larsen
2026-02-26 18:26:38 -08:00
parent e730b1fe33
commit 8525491eb1
4 changed files with 346 additions and 0 deletions
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1
benchmarks/LU/CMakeLists.txt
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eigen_add_benchmark(bench_lu bench_lu.cpp)
eigen_add_benchmark(bench_rcond bench_rcond.cpp)

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benchmarks/LU/bench_rcond.cpp Normal file
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2026 Rasmus Munk Larsen (rmlarsen@gmail.com)
//
// This Source Code Form is subject to the terms of the Mozilla
// Public License v. 2.0. If a copy of the MPL was not distributed
// with this file, You can obtain one at http://mozilla.org/MPL/2.0/.
// Benchmarks for reciprocal condition number estimation.
//
// Times the rcond() call on pre-computed factorizations.
// The rcond estimator is O(n^2), so this isolates its cost from the O(n^3) factorization.
#include <benchmark/benchmark.h>
#include <Eigen/Dense>
using namespace Eigen;
// --- PartialPivLU ---
template <typename Scalar>
static void BM_PartialPivLU_Rcond(benchmark::State& state) {
const Index n = state.range(0);
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
Mat A = Mat::Random(n, n);
A.diagonal().array() += Scalar(2 * n);
PartialPivLU<Mat> lu(A);
for (auto _ : state) {
auto rc = lu.rcond();
benchmark::DoNotOptimize(rc);
}
state.SetItemsProcessed(state.iterations());
}
// --- FullPivLU ---
template <typename Scalar>
static void BM_FullPivLU_Rcond(benchmark::State& state) {
const Index n = state.range(0);
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
Mat A = Mat::Random(n, n);
A.diagonal().array() += Scalar(2 * n);
FullPivLU<Mat> lu(A);
for (auto _ : state) {
auto rc = lu.rcond();
benchmark::DoNotOptimize(rc);
}
state.SetItemsProcessed(state.iterations());
}
// --- LLT ---
template <typename Scalar>
static void BM_LLT_Rcond(benchmark::State& state) {
const Index n = state.range(0);
using Mat = Matrix<Scalar, Dynamic, Dynamic>;
Mat A = Mat::Random(n, n);
Mat SPD = A.adjoint() * A + Mat::Identity(n, n);
LLT<Mat> llt(SPD);
for (auto _ : state) {
auto rc = llt.rcond();
benchmark::DoNotOptimize(rc);
}
state.SetItemsProcessed(state.iterations());
}
// --- Size configurations ---
static void SquareSizes(::benchmark::Benchmark* b) {
for (int n : {8, 32, 64, 128, 256, 512, 1024}) b->Arg(n);
}
BENCHMARK(BM_PartialPivLU_Rcond<float>)->Apply(SquareSizes)->Name("PartialPivLU_Rcond_float");
BENCHMARK(BM_PartialPivLU_Rcond<double>)->Apply(SquareSizes)->Name("PartialPivLU_Rcond_double");
BENCHMARK(BM_FullPivLU_Rcond<float>)->Apply(SquareSizes)->Name("FullPivLU_Rcond_float");
BENCHMARK(BM_FullPivLU_Rcond<double>)->Apply(SquareSizes)->Name("FullPivLU_Rcond_double");
BENCHMARK(BM_LLT_Rcond<float>)->Apply(SquareSizes)->Name("LLT_Rcond_float");
BENCHMARK(BM_LLT_Rcond<double>)->Apply(SquareSizes)->Name("LLT_Rcond_double");