remove SVD class (was bad code taked from elsewhere)

Use JacobiSVD for now.
We do plan to reintroduce a bidiagonalizing SVD asap.

3 files changed, 1 insertions, 126 deletions

diff --git a/test/CMakeLists.txt b/test/CMakeLists.txt
index 052d4ec13..e774c874d 100644
--- a/test/CMakeLists.txt
+++ b/test/CMakeLists.txt
@@ -90,7 +90,6 @@ ei_add_test(schur_complex)
 ei_add_test(eigensolver_selfadjoint " " "${GSL_LIBRARIES}")
 ei_add_test(eigensolver_generic " " "${GSL_LIBRARIES}")
 ei_add_test(eigensolver_complex)
-ei_add_test(svd)
 ei_add_test(jacobi)
 ei_add_test(jacobisvd)
 ei_add_test(geo_orthomethods)
diff --git a/test/nomalloc.cpp b/test/nomalloc.cpp
index c1965a8ac..b4c0ff741 100644
--- a/test/nomalloc.cpp
+++ b/test/nomalloc.cpp
@@ -125,8 +125,7 @@ void ctms_decompositions()
   Eigen::FullPivHouseholderQR<Matrix> fpQR; fpQR.compute(A);
 
   // SVD module
-  Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A);
-  Eigen::SVD<Matrix>       svd;  svd.compute(A);
+  Eigen::JacobiSVD<Matrix> jSVD; jSVD.compute(A, ComputeFullU | ComputeFullV);
 }
 
 void test_nomalloc()
diff --git a/test/svd.cpp b/test/svd.cpp
deleted file mode 100644
index 1452e39bf..000000000
--- a/test/svd.cpp
+++ /dev/null
@@ -1,123 +0,0 @@
-// This file is part of Eigen, a lightweight C++ template library
-// for linear algebra.
-//
-// Copyright (C) 2008 Gael Guennebaud <gael.guennebaud@inria.fr>
-//
-// Eigen is free software; you can redistribute it and/or
-// modify it under the terms of the GNU Lesser General Public
-// License as published by the Free Software Foundation; either
-// version 3 of the License, or (at your option) any later version.
-//
-// Alternatively, you can redistribute it and/or
-// modify it under the terms of the GNU General Public License as
-// published by the Free Software Foundation; either version 2 of
-// the License, or (at your option) any later version.
-//
-// Eigen is distributed in the hope that it will be useful, but WITHOUT ANY
-// WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
-// FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License or the
-// GNU General Public License for more details.
-//
-// You should have received a copy of the GNU Lesser General Public
-// License and a copy of the GNU General Public License along with
-// Eigen. If not, see <http://www.gnu.org/licenses/>.
-
-#include "main.h"
-#include <Eigen/SVD>
-#include <Eigen/LU>
-
-template<typename MatrixType> void svd(const MatrixType& m)
-{
-  /* this test covers the following files:
-     SVD.h
-  */
-  typename MatrixType::Index rows = m.rows();
-  typename MatrixType::Index cols = m.cols();
-
-  typedef typename MatrixType::Scalar Scalar;
-  typedef typename NumTraits<Scalar>::Real RealScalar;
-  MatrixType a = MatrixType::Random(rows,cols);
-  Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> x(cols,1), x2(cols,1);
-
-  {
-    SVD<MatrixType> svd(a);
-    MatrixType sigma = MatrixType::Zero(rows,cols);
-    MatrixType matU  = MatrixType::Zero(rows,rows);
-    MatrixType matV  = MatrixType::Zero(cols,cols);
-
-    sigma.diagonal() = svd.singularValues();
-    matU = svd.matrixU();
-    VERIFY_IS_UNITARY(matU);
-    matV = svd.matrixV();
-    VERIFY_IS_UNITARY(matV);
-    VERIFY_IS_APPROX(a, matU * sigma * matV.transpose());
-  }
-
-
-  if (rows>=cols)
-  {
-    SVD<MatrixType> svd(a);
-    Matrix<Scalar, MatrixType::ColsAtCompileTime, 1> b = Matrix<Scalar, MatrixType::ColsAtCompileTime, 1>::Random(rows,1);
-    Matrix<Scalar, MatrixType::RowsAtCompileTime, 1> x = svd.solve(b);
-    // evaluate normal equation which works also for least-squares solutions
-    VERIFY_IS_APPROX(a.adjoint()*a*x,a.adjoint()*b);
-  }
-
-
-  if(rows==cols)
-  {
-    SVD<MatrixType> svd(a);
-    MatrixType unitary, positive;
-    svd.computeUnitaryPositive(&unitary, &positive);
-    VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows()));
-    VERIFY_IS_APPROX(positive, positive.adjoint());
-    for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity
-    VERIFY_IS_APPROX(unitary*positive, a);
-
-    svd.computePositiveUnitary(&positive, &unitary);
-    VERIFY_IS_APPROX(unitary * unitary.adjoint(), MatrixType::Identity(unitary.rows(),unitary.rows()));
-    VERIFY_IS_APPROX(positive, positive.adjoint());
-    for(int i = 0; i < rows; i++) VERIFY(positive.diagonal()[i] >= 0); // cheap necessary (not sufficient) condition for positivity
-    VERIFY_IS_APPROX(positive*unitary, a);
-  }
-}
-
-template<typename MatrixType> void svd_verify_assert()
-{
-  MatrixType tmp;
-
-  SVD<MatrixType> svd;
-  VERIFY_RAISES_ASSERT(svd.solve(tmp))
-  VERIFY_RAISES_ASSERT(svd.matrixU())
-  VERIFY_RAISES_ASSERT(svd.singularValues())
-  VERIFY_RAISES_ASSERT(svd.matrixV())
-  VERIFY_RAISES_ASSERT(svd.computeUnitaryPositive(&tmp,&tmp))
-  VERIFY_RAISES_ASSERT(svd.computePositiveUnitary(&tmp,&tmp))
-  VERIFY_RAISES_ASSERT(svd.computeRotationScaling(&tmp,&tmp))
-  VERIFY_RAISES_ASSERT(svd.computeScalingRotation(&tmp,&tmp))
-
-  VERIFY_RAISES_ASSERT(SVD<MatrixXf>(10, 20))
-}
-
-void test_svd()
-{
-  for(int i = 0; i < g_repeat; i++) 
-  {
-    CALL_SUBTEST_1( svd(Matrix3f()) );
-    CALL_SUBTEST_2( svd(Matrix4d()) );
-
-    int cols = ei_random<int>(2,50);
-    int rows = cols + ei_random<int>(0,50);
-    CALL_SUBTEST_3( svd(MatrixXf(rows,cols)) );
-    CALL_SUBTEST_4( svd(MatrixXd(rows,cols)) );
-
-    //complex are not implemented yet
-    //CALL_SUBTEST(svd(MatrixXcd(6,6)) );
-    //CALL_SUBTEST(svd(MatrixXcf(3,3)) );
-  }
-
-  CALL_SUBTEST_1( svd_verify_assert<Matrix3f>() );
-  CALL_SUBTEST_2( svd_verify_assert<Matrix4d>() );
-  CALL_SUBTEST_3( svd_verify_assert<MatrixXf>() );
-  CALL_SUBTEST_4( svd_verify_assert<MatrixXd>() );
-}