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Sparse: fix long int as index type in simplicial cholesky and other decompositions

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This commit is contained in:
Gael Guennebaud
2011-06-06 10:17:28 +02:00
parent 7a61a564ef
commit 421ece38e1
7 changed files with 68 additions and 63 deletions
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40
unsupported/test/sparse_llt.cpp
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@@ -29,14 +29,15 @@
#include <Eigen/CholmodSupport>
#endif
template<typename Scalar> void sparse_llt(int rows, int cols)
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)
SparseMatrix<Scalar> m2(rows, cols);
SparseMatrixType m2(rows, cols);
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
@@ -53,7 +54,7 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
if (!NumTraits<Scalar>::IsComplex)
{
x = b;
SparseLLT<SparseMatrix<Scalar> > (m2).solveInPlace(x);
SparseLLT<SparseMatrixType > (m2).solveInPlace(x);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: default");
}
@@ -61,23 +62,23 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
// legacy API
{
// Cholmod, as configured in CholmodSupport.h, only supports self-adjoint matrices
SparseMatrix<Scalar> m3 = m2.adjoint()*m2;
SparseMatrixType m3 = m2.adjoint()*m2;
DenseMatrix refMat3 = refMat2.adjoint()*refMat2;
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = b;
SparseLLT<SparseMatrix<Scalar>, Cholmod>(m3).solveInPlace(x);
SparseLLT<SparseMatrixType, Cholmod>(m3).solveInPlace(x);
VERIFY((m3*x).isApprox(b,test_precision<Scalar>()) && "LLT legacy: cholmod solveInPlace");
x = SparseLLT<SparseMatrix<Scalar>, Cholmod>(m3).solve(b);
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
SparseMatrix<Scalar> m3 = m2 * m2.adjoint(), m3_lo(rows,rows), m3_up(rows,rows);
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);
@@ -86,16 +87,16 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
// with a single vector as the rhs
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = CholmodDecomposition<SparseMatrix<Scalar>, Lower>(m3).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<SparseMatrix<Scalar>, Upper>(m3).solve(b);
x = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
x = CholmodDecomposition<SparseMatrix<Scalar>, Lower>(m3_lo).solve(b);
x = CholmodDecomposition<SparseMatrixType, Lower>(m3_lo).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
x = CholmodDecomposition<SparseMatrix<Scalar>, Upper>(m3_up).solve(b);
x = CholmodDecomposition<SparseMatrixType, Upper>(m3_up).solve(b);
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
@@ -104,25 +105,25 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
#ifndef EIGEN_DEFAULT_TO_ROW_MAJOR
// TODO make sure the API is properly documented about this fact
X = CholmodDecomposition<SparseMatrix<Scalar>, Lower>(m3).solve(B);
X = CholmodDecomposition<SparseMatrixType, Lower>(m3).solve(B);
VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "LLT: cholmod solve, multiple dense rhs");
X = CholmodDecomposition<SparseMatrix<Scalar>, Upper>(m3).solve(B);
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
SparseMatrix<Scalar> spB(rows,cols), spX(rows,cols);
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<SparseMatrix<Scalar>, Lower>(m3).solve(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<SparseMatrix<Scalar>, Upper>(m3).solve(spB);
spX = CholmodDecomposition<SparseMatrixType, Upper>(m3).solve(spB);
VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs");
}
#endif
@@ -132,9 +133,10 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
void test_sparse_llt()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(sparse_llt<double>(8, 8) );
CALL_SUBTEST_1( (sparse_llt<double,int>(8, 8)) );
int s = internal::random<int>(1,300);
CALL_SUBTEST_2(sparse_llt<std::complex<double> >(s,s) );
CALL_SUBTEST_1(sparse_llt<double>(s,s) );
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)) );
}
}