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guix/gnu/packages/patches/eigen-stabilise-sparseqr-test.patch
Tobias Geerinckx-Rice 5144e31492
gnu: eigen: Update to 3.3.7.
* gnu/packages/algebra.scm (eigen): Update to 3.3.7.
[source]: Add a patch to fix a test failure.
* gnu/packages/patches/eigen-stabilise-sparseqr-test.patch: New file.
* gnu/local.mk (dist_patch_DATA): Add it.
2020-03-17 03:00:18 +01:00

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From: Tobias Geerinckx-Rice <me@tobias.gr>
Date: Mon, 16 Mar 2020 22:51:37 +0000
Subject: gnu: eigen: Stabilise sparseqr test.
Taken verbatim from this[0] upstream commit.
[0]: https://gitlab.com/libeigen/eigen/-/commit/3b5deeb546d4017b24846f5b0dc3296a50a039fe
From 3b5deeb546d4017b24846f5b0dc3296a50a039fe Mon Sep 17 00:00:00 2001
From: Gael Guennebaud <g.gael@free.fr>
Date: Tue, 19 Feb 2019 22:57:51 +0100
Subject: [PATCH] bug #899: make sparseqr unit test more stable by 1) trying
with larger threshold and 2) relax rank computation for rank-deficient
problems.
---
test/sparseqr.cpp | 31 ++++++++++++++++++++++++++-----
1 file changed, 26 insertions(+), 5 deletions(-)
diff --git a/test/sparseqr.cpp b/test/sparseqr.cpp
index 3ffe62314..3576cc626 100644
--- a/test/sparseqr.cpp
+++ b/test/sparseqr.cpp
@@ -43,6 +43,7 @@ int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows
template<typename Scalar> void test_sparseqr_scalar()
{
+ typedef typename NumTraits<Scalar>::Real RealScalar;
typedef SparseMatrix<Scalar,ColMajor> MatrixType;
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMat;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
@@ -91,14 +92,34 @@ template<typename Scalar> void test_sparseqr_scalar()
exit(0);
return;
}
-
- VERIFY_IS_APPROX(A * x, b);
-
- //Compare with a dense QR solver
+
+ // Compare with a dense QR solver
ColPivHouseholderQR<DenseMat> dqr(dA);
refX = dqr.solve(b);
- VERIFY_IS_EQUAL(dqr.rank(), solver.rank());
+ bool rank_deficient = A.cols()>A.rows() || dqr.rank()<A.cols();
+ if(rank_deficient)
+ {
+ // rank deficient problem -> we might have to increase the threshold
+ // to get a correct solution.
+ RealScalar th = RealScalar(20)*dA.colwise().norm().maxCoeff()*(A.rows()+A.cols()) * NumTraits<RealScalar>::epsilon();
+ for(Index k=0; (k<16) && !test_isApprox(A*x,b); ++k)
+ {
+ th *= RealScalar(10);
+ solver.setPivotThreshold(th);
+ solver.compute(A);
+ x = solver.solve(b);
+ }
+ }
+
+ VERIFY_IS_APPROX(A * x, b);
+
+ // For rank deficient problem, the estimated rank might
+ // be slightly off, so let's only raise a warning in such cases.
+ if(rank_deficient) ++g_test_level;
+ VERIFY_IS_EQUAL(solver.rank(), dqr.rank());
+ if(rank_deficient) --g_test_level;
+
if(solver.rank()==A.cols()) // full rank
VERIFY_IS_APPROX(x, refX);
// else
--
2.24.1