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authorGravatar Gael Guennebaud <g.gael@free.fr>2011-01-27 18:02:49 +0100
committerGravatar Gael Guennebaud <g.gael@free.fr>2011-01-27 18:02:49 +0100
commit3d8e179aa26772e219c697101d6ce2c45ff0247e (patch)
treef4ed774ea172ae98c72105b1bf93fb3fcc8df236 /test/nomalloc.cpp
parent32124bc64ace50f40c7e2f79b39dd14e284fb9a3 (diff)
fix MaxCols in ComplexEigenSolver which was causing memory allocation instead of static allocation in the nomalloc test. Uncomment commenetd parts of the nomalloc test since now matrix-matrix products are safe.
Diffstat (limited to 'test/nomalloc.cpp')
-rw-r--r--test/nomalloc.cpp9
1 files changed, 2 insertions, 7 deletions
diff --git a/test/nomalloc.cpp b/test/nomalloc.cpp
index 0e3caa4ae..c6f24c035 100644
--- a/test/nomalloc.cpp
+++ b/test/nomalloc.cpp
@@ -70,12 +70,7 @@ template<typename MatrixType> void nomalloc(const MatrixType& m)
VERIFY_IS_APPROX((m1+m2)*s1, s1*m1+s1*m2);
VERIFY_IS_APPROX((m1+m2)(r,c), (m1(r,c))+(m2(r,c)));
VERIFY_IS_APPROX(m1.cwiseProduct(m1.block(0,0,rows,cols)), (m1.array()*m1.array()).matrix());
- if (MatrixType::RowsAtCompileTime<EIGEN_CACHEFRIENDLY_PRODUCT_THRESHOLD) {
- // If the matrices are too large, we have better to use the optimized GEMM
- // routines which allocates temporaries. However, on some platforms
- // these temporaries are allocated on the stack using alloca.
- VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
- }
+ VERIFY_IS_APPROX((m1*m1.transpose())*m2, m1*(m1.transpose()*m2));
}
template<typename Scalar>
@@ -110,7 +105,7 @@ void ctms_decompositions()
// Eigenvalues module
Eigen::HessenbergDecomposition<ComplexMatrix> hessDecomp; hessDecomp.compute(complexA);
Eigen::ComplexSchur<ComplexMatrix> cSchur(size); cSchur.compute(complexA);
- Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; //cEigSolver.compute(complexA); // NOTE: Commented-out because makes test fail (L135 of ComplexEigenSolver.h has a product that allocates on the stack)
+ Eigen::ComplexEigenSolver<ComplexMatrix> cEigSolver; cEigSolver.compute(complexA);
Eigen::EigenSolver<Matrix> eigSolver; eigSolver.compute(A);
Eigen::SelfAdjointEigenSolver<Matrix> saEigSolver(size); saEigSolver.compute(saA);
Eigen::Tridiagonalization<Matrix> tridiag; tridiag.compute(saA);