gnu/packages/patches/eigen-stabilise-sparseqr-test.patch

   1 From: Tobias Geerinckx-Rice <me@tobias.gr>
   2 Date: Mon, 16 Mar 2020 22:51:37 +0000
   3 Subject: gnu: eigen: Stabilise sparseqr test.
   4
   5 Taken verbatim from this[0] upstream commit.
   6
   7 [0]: https://gitlab.com/libeigen/eigen/-/commit/3b5deeb546d4017b24846f5b0dc3296a50a039fe
   8
   9 From 3b5deeb546d4017b24846f5b0dc3296a50a039fe Mon Sep 17 00:00:00 2001
  10 From: Gael Guennebaud <g.gael@free.fr>
  11 Date: Tue, 19 Feb 2019 22:57:51 +0100
  12 Subject: [PATCH] bug #899: make sparseqr unit test more stable by 1) trying
  13  with larger threshold and 2) relax rank computation for rank-deficient
  14  problems.
  15
  16 ---
  17  test/sparseqr.cpp | 31 ++++++++++++++++++++++++++-----
  18  1 file changed, 26 insertions(+), 5 deletions(-)
  19
  20 diff --git a/test/sparseqr.cpp b/test/sparseqr.cpp
  21 index 3ffe62314..3576cc626 100644
  22 --- a/test/sparseqr.cpp
  23 +++ b/test/sparseqr.cpp
  24 @@ -43,6 +43,7 @@ int generate_sparse_rectangular_problem(MatrixType& A, DenseMat& dA, int maxRows
  25
  26  template<typename Scalar> void test_sparseqr_scalar()
  27  {
  28 +  typedef typename NumTraits<Scalar>::Real RealScalar;
  29    typedef SparseMatrix<Scalar,ColMajor> MatrixType;
  30    typedef Matrix<Scalar,Dynamic,Dynamic> DenseMat;
  31    typedef Matrix<Scalar,Dynamic,1> DenseVector;
  32 @@ -91,14 +92,34 @@ template<typename Scalar> void test_sparseqr_scalar()
  33      exit(0);
  34      return;
  35    }
  36 -
  37 -  VERIFY_IS_APPROX(A * x, b);
  38 -
  39 -  //Compare with a dense QR solver
  40 +
  41 +  // Compare with a dense QR solver
  42    ColPivHouseholderQR<DenseMat> dqr(dA);
  43    refX = dqr.solve(b);
  44
  45 -  VERIFY_IS_EQUAL(dqr.rank(), solver.rank());
  46 +  bool rank_deficient = A.cols()>A.rows() || dqr.rank()<A.cols();
  47 +  if(rank_deficient)
  48 +  {
  49 +    // rank deficient problem -> we might have to increase the threshold
  50 +    // to get a correct solution.
  51 +    RealScalar th = RealScalar(20)*dA.colwise().norm().maxCoeff()*(A.rows()+A.cols()) * NumTraits<RealScalar>::epsilon();
  52 +    for(Index k=0; (k<16) && !test_isApprox(A*x,b); ++k)
  53 +    {
  54 +      th *= RealScalar(10);
  55 +      solver.setPivotThreshold(th);
  56 +      solver.compute(A);
  57 +      x = solver.solve(b);
  58 +    }
  59 +  }
  60 +
  61 +  VERIFY_IS_APPROX(A * x, b);
  62 +
  63 +  // For rank deficient problem, the estimated rank might
  64 +  // be slightly off, so let's only raise a warning in such cases.
  65 +  if(rank_deficient) ++g_test_level;
  66 +  VERIFY_IS_EQUAL(solver.rank(), dqr.rank());
  67 +  if(rank_deficient) --g_test_level;
  68 +
  69    if(solver.rank()==A.cols()) // full rank
  70      VERIFY_IS_APPROX(x, refX);
  71  //   else
  72 --
  73 2.24.1
  74