From 3d8e179aa26772e219c697101d6ce2c45ff0247e Mon Sep 17 00:00:00 2001
From: Gael Guennebaud <g.gael@free.fr>
Date: Thu, 27 Jan 2011 18:02:49 +0100
Subject: 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.

---
 test/nomalloc.cpp | 9 ++-------
 1 file changed, 2 insertions(+), 7 deletions(-)

(limited to 'test/nomalloc.cpp')

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);
-- 
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