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remove dead code

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This commit is contained in:
Gael Guennebaud
2011-12-08 23:22:28 +01:00
parent e36a4c880a
commit 86bb20c431
8 changed files with 0 additions and 1408 deletions
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100
unsupported/test/cg.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2011 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 "sparse.h"
#include <Eigen/IterativeSolvers>
template<typename Scalar,typename Index> void cg(int size)
{
double density = (std::max)(8./(size*size), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType;
SparseMatrixType m2(size,size);
DenseMatrix refMat2(size,size);
DenseVector b = DenseVector::Random(size);
DenseVector ref_x(size), x(size);
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag, 0, 0);
// for(int i=0; i<rows; ++i)
// m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i)));
SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(size,size), m3_up(size,size);
DenseMatrix refMat3 = refMat2 * refMat2.adjoint();
m3_lo.template selfadjointView<Lower>().rankUpdate(m2,0);
m3_up.template selfadjointView<Upper>().rankUpdate(m2,0);
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = ConjugateGradient<SparseMatrixType, Lower>().compute(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, lower");
x.setRandom();
x = ConjugateGradient<SparseMatrixType, Lower>().compute(m3).solveWithGuess(b,x);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solveWithGuess, full storage, lower");
x = ConjugateGradient<SparseMatrixType, Upper>().compute(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, upper, single dense rhs");
x = ConjugateGradient<SparseMatrixType, Lower>(m3_lo).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, lower only, single dense rhs");
x = ConjugateGradient<SparseMatrixType, Upper>(m3_up).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, upper only, single dense rhs");
x = ConjugateGradient<SparseMatrixType, Lower, IdentityPreconditioner>().compute(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, lower");
x = ConjugateGradient<SparseMatrixType, Upper, IdentityPreconditioner>().compute(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, full storage, upper, single dense rhs");
x = ConjugateGradient<SparseMatrixType, Lower, IdentityPreconditioner>(m3_lo).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, lower only, single dense rhs");
x = ConjugateGradient<SparseMatrixType, Upper, IdentityPreconditioner>(m3_up).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "ConjugateGradient: solve, upper only, single dense rhs");
ref_x = refMat2.lu().solve(b);
x = BiCGSTAB<SparseMatrixType, IdentityPreconditioner>(m2).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "BiCGSTAB: solve, I, single dense rhs");
x = BiCGSTAB<SparseMatrixType>(m2).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "BiCGSTAB: solve, diag, single dense rhs");
}
void test_cg()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( (cg<double,int>(8)) );
CALL_SUBTEST_1( (cg<double,long int>(8)) );
CALL_SUBTEST_2( (cg<std::complex<double>,int>(internal::random<int>(1,300))) );
CALL_SUBTEST_1( (cg<double,int>(internal::random<int>(1,300))) );
}
}

179
unsupported/test/sparse_ldlt.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.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/>.
#define EIGEN_NO_DEPRECATED_WARNING
#include "sparse.h"
#include <Eigen/SparseExtra>
#ifdef EIGEN_CHOLMOD_SUPPORT
#include <Eigen/CholmodSupport>
#endif
template<typename Scalar,typename Index> void sparse_ldlt(int rows, int cols)
{
static bool odd = true;
odd = !odd;
double density = (std::max)(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType;
SparseMatrixType m2(rows, cols);
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
DenseVector refX(cols), x(cols);
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, 0, 0);
SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(rows,rows), m3_up(rows,rows);
DenseMatrix refMat3 = refMat2 * refMat2.adjoint();
refX = refMat3.template selfadjointView<Upper>().ldlt().solve(b);
typedef SparseMatrix<Scalar,Upper|SelfAdjoint,Index> SparseSelfAdjointMatrix;
x = b;
SparseLDLT<SparseSelfAdjointMatrix> ldlt(m3);
if (ldlt.succeeded())
ldlt.solveInPlace(x);
else
std::cerr << "warning LDLT failed\n";
VERIFY_IS_APPROX(refMat3.template selfadjointView<Upper>() * x, b);
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default");
#ifdef EIGEN_CHOLMOD_SUPPORT
{
x = b;
SparseLDLT<SparseSelfAdjointMatrix, Cholmod> ldlt2(m3);
if (ldlt2.succeeded())
{
ldlt2.solveInPlace(x);
VERIFY_IS_APPROX(refMat3.template selfadjointView<Upper>() * x, b);
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: cholmod solveInPlace");
x = ldlt2.solve(b);
VERIFY_IS_APPROX(refMat3.template selfadjointView<Upper>() * x, b);
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: cholmod solve");
}
else
std::cerr << "warning LDLT failed\n";
}
#endif
// new Simplicial LLT
// new API
{
SparseMatrixType m2(rows, cols);
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
DenseVector ref_x(cols), x(cols);
DenseMatrix B = DenseMatrix::Random(rows,cols);
DenseMatrix ref_X(rows,cols), X(rows,cols);
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, 0, 0);
for(int i=0; i<rows; ++i)
m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i)));
SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(rows,rows), m3_up(rows,rows);
DenseMatrix refMat3 = refMat2 * refMat2.adjoint();
m3_lo.template selfadjointView<Lower>().rankUpdate(m2,0);
m3_up.template selfadjointView<Upper>().rankUpdate(m2,0);
// with a single vector as the rhs
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = SimplicialCholesky<SparseMatrixType, Lower>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, lower, single dense rhs");
x = SimplicialCholesky<SparseMatrixType, Upper>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, upper, single dense rhs");
x = SimplicialCholesky<SparseMatrixType, Lower>(m3_lo).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, lower only, single dense rhs");
x = SimplicialCholesky<SparseMatrixType, Upper>(m3_up).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "SimplicialCholesky: solve, upper only, single dense rhs");
// with multiple rhs
ref_X = refMat3.template selfadjointView<Lower>().llt().solve(B);
X = SimplicialCholesky<SparseMatrixType, Lower>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(B);
VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, lower, multiple dense rhs");
X = SimplicialCholesky<SparseMatrixType, Upper>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(B);
VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "SimplicialCholesky: solve, full storage, upper, multiple dense rhs");
// with a sparse rhs
SparseMatrixType spB(rows,cols), spX(rows,cols);
B.diagonal().array() += 1;
spB = B.sparseView(0.5,1);
ref_X = refMat3.template selfadjointView<Lower>().llt().solve(DenseMatrix(spB));
spX = SimplicialCholesky<SparseMatrixType, Lower>(m3).solve(spB);
VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: SimplicialCholesky solve, multiple sparse rhs");
//
spX = SimplicialCholesky<SparseMatrixType, Upper>(m3).solve(spB);
VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: SimplicialCholesky solve, multiple sparse rhs");
}
// for(int i=0; i<rows; ++i)
// m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i)));
//
// refX = refMat2.template selfadjointView<Upper>().ldlt().solve(b);
// typedef SparseMatrix<Scalar,Upper|SelfAdjoint> SparseSelfAdjointMatrix;
// x = b;
// SparseLDLT<SparseSelfAdjointMatrix> ldlt(m2);
// if (ldlt.succeeded())
// ldlt.solveInPlace(x);
// else
// std::cerr << "warning LDLT failed\n";
//
// VERIFY_IS_APPROX(refMat2.template selfadjointView<Upper>() * x, b);
// VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LDLT: default");
}
void test_sparse_ldlt()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( (sparse_ldlt<double,int>(8, 8)) );
CALL_SUBTEST_1( (sparse_ldlt<double,long int>(8, 8)) );
int s = internal::random<int>(1,300);
CALL_SUBTEST_2( (sparse_ldlt<std::complex<double>,int>(s,s)) );
CALL_SUBTEST_1( (sparse_ldlt<double,int>(s,s)) );
}
}

144
unsupported/test/sparse_llt.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.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/>.
#define EIGEN_NO_DEPRECATED_WARNING
#include "sparse.h"
#include <Eigen/SparseExtra>
#ifdef EIGEN_CHOLMOD_SUPPORT
#include <Eigen/CholmodSupport>
#endif
template<typename Scalar,typename Index> void sparse_llt(int rows, int cols)
{
double density = (std::max)(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType;
// TODO fix the issue with complex (see SparseLLT::solveInPlace)
SparseMatrixType m2(rows, cols);
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
DenseVector ref_x(cols), x(cols);
DenseMatrix B = DenseMatrix::Random(rows,cols);
DenseMatrix ref_X(rows,cols), X(rows,cols);
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeLowerTriangular, 0, 0);
for(int i=0; i<rows; ++i)
m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i)));
ref_x = refMat2.template selfadjointView<Lower>().llt().solve(b);
if (!NumTraits<Scalar>::IsComplex)
{
x = b;
SparseLLT<SparseMatrixType > (m2).solveInPlace(x);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: default");
}
#ifdef EIGEN_CHOLMOD_SUPPORT
// legacy API
{
// Cholmod, as configured in CholmodSupport.h, only supports self-adjoint matrices
SparseMatrixType m3 = m2.adjoint()*m2;
DenseMatrix refMat3 = refMat2.adjoint()*refMat2;
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = b;
SparseLLT<SparseMatrixType, Cholmod>(m3).solveInPlace(x);
VERIFY((m3*x).isApprox(b,test_precision<Scalar>()) && "LLT legacy: cholmod solveInPlace");
x = SparseLLT<SparseMatrixType, Cholmod>(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT legacy: cholmod solve");
}
// new API
{
// Cholmod, as configured in CholmodSupport.h, only supports self-adjoint matrices
SparseMatrixType m3 = m2 * m2.adjoint(), m3_lo(rows,rows), m3_up(rows,rows);
DenseMatrix refMat3 = refMat2 * refMat2.adjoint();
m3_lo.template selfadjointView<Lower>().rankUpdate(m2,0);
m3_up.template selfadjointView<Upper>().rankUpdate(m2,0);
// with a single vector as the rhs
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = CholmodDecomposition<SparseMatrixType, Lower>(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
x = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
x = CholmodDecomposition<SparseMatrixType, Lower>(m3_lo).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
x = CholmodDecomposition<SparseMatrixType, Upper>(m3_up).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
// with multiple rhs
ref_X = refMat3.template selfadjointView<Lower>().llt().solve(B);
#ifndef EIGEN_DEFAULT_TO_ROW_MAJOR
// TODO make sure the API is properly documented about this fact
X = CholmodDecomposition<SparseMatrixType, Lower>(m3).solve(B);
VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "LLT: cholmod solve, multiple dense rhs");
X = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(B);
VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "LLT: cholmod solve, multiple dense rhs");
#endif
// with a sparse rhs
SparseMatrixType spB(rows,cols), spX(rows,cols);
B.diagonal().array() += 1;
spB = B.sparseView(0.5,1);
ref_X = refMat3.template selfadjointView<Lower>().llt().solve(DenseMatrix(spB));
spX = CholmodDecomposition<SparseMatrixType, Lower>(m3).solve(spB);
VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs");
spX = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(spB);
VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs");
}
#endif
}
void test_sparse_llt()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1( (sparse_llt<double,int>(8, 8)) );
int s = internal::random<int>(1,300);
CALL_SUBTEST_2( (sparse_llt<std::complex<double>,int>(s,s)) );
CALL_SUBTEST_1( (sparse_llt<double,int>(s,s)) );
CALL_SUBTEST_1( (sparse_llt<double,long int>(s,s)) );
}
}

128
unsupported/test/sparse_lu_legacy.cpp
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// This file is part of Eigen, a lightweight C++ template library
// for linear algebra.
//
// Copyright (C) 2008-2010 Gael Guennebaud <g.gael@free.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/>.
#define EIGEN_NO_DEPRECATED_WARNING
#include "sparse.h"
#include <Eigen/SparseExtra>
#ifdef EIGEN_UMFPACK_SUPPORT
#include <Eigen/UmfPackSupport>
#endif
#ifdef EIGEN_SUPERLU_SUPPORT
#include <Eigen/SuperLUSupport>
#endif
template<typename Scalar> void sparse_lu_legacy(int rows, int cols)
{
double density = (std::max)(8./(rows*cols), 0.01);
typedef Matrix<Scalar,Dynamic,Dynamic> DenseMatrix;
typedef Matrix<Scalar,Dynamic,1> DenseVector;
DenseVector vec1 = DenseVector::Random(rows);
std::vector<Vector2i> zeroCoords;
std::vector<Vector2i> nonzeroCoords;
SparseMatrix<Scalar> m2(rows, cols);
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
DenseVector refX(cols), x(cols);
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag, &zeroCoords, &nonzeroCoords);
FullPivLU<DenseMatrix> refLu(refMat2);
refX = refLu.solve(b);
#if defined(EIGEN_SUPERLU_SUPPORT) || defined(EIGEN_UMFPACK_SUPPORT)
Scalar refDet = refLu.determinant();
#endif
x.setZero();
// // SparseLU<SparseMatrix<Scalar> > (m2).solve(b,&x);
// // VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: default");
#ifdef EIGEN_UMFPACK_SUPPORT
{
// check solve
x.setZero();
SparseLU<SparseMatrix<Scalar>,UmfPack> lu(m2);
VERIFY(lu.succeeded() && "umfpack LU decomposition failed");
VERIFY(lu.solve(b,&x) && "umfpack LU solving failed");
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: umfpack");
VERIFY_IS_APPROX(refDet,lu.determinant());
// TODO check the extracted data
//std::cerr << slu.matrixL() << "\n";
}
#endif
#ifdef EIGEN_SUPERLU_SUPPORT
// legacy, deprecated API
{
x.setZero();
SparseLU<SparseMatrix<Scalar>,SuperLULegacy> slu(m2);
if (slu.succeeded())
{
DenseVector oldb = b;
if (slu.solve(b,&x)) {
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LU: SuperLU");
}
else
std::cerr << "super lu solving failed\n";
VERIFY(oldb.isApprox(b) && "the rhs should not be modified!");
// std::cerr << refDet << " == " << slu.determinant() << "\n";
if (slu.solve(b, &x, SvTranspose)) {
VERIFY(b.isApprox(m2.transpose() * x, test_precision<Scalar>()));
}
else
std::cerr << "super lu solving failed\n";
if (slu.solve(b, &x, SvAdjoint)) {
VERIFY(b.isApprox(m2.adjoint() * x, test_precision<Scalar>()));
}
else
std::cerr << "super lu solving failed\n";
if (!NumTraits<Scalar>::IsComplex) {
VERIFY_IS_APPROX(refDet,slu.determinant()); // FIXME det is not very stable for complex
}
}
else
std::cerr << "super lu factorize failed\n";
}
#endif
}
void test_sparse_lu_legacy()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(sparse_lu_legacy<double>(8, 8) );
int s = internal::random<int>(1,300);
CALL_SUBTEST_1(sparse_lu_legacy<std::complex<double> >(s,s) );
CALL_SUBTEST_1(sparse_lu_legacy<double>(s,s) );
}
}