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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_ldlt.cpp
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@@ -29,15 +29,16 @@
#include <Eigen/CholmodSupport>
#endif
template<typename Scalar> void sparse_ldlt(int rows, int cols)
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;
SparseMatrix<Scalar> m2(rows, cols);
typedef SparseMatrix<Scalar,ColMajor,Index> SparseMatrixType;
SparseMatrixType m2(rows, cols);
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
@@ -45,11 +46,11 @@ template<typename Scalar> void sparse_ldlt(int rows, int cols)
initSparse<Scalar>(density, refMat2, m2, ForceNonZeroDiag|MakeUpperTriangular, 0, 0);
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();
refX = refMat3.template selfadjointView<Upper>().ldlt().solve(b);
typedef SparseMatrix<Scalar,Upper|SelfAdjoint> SparseSelfAdjointMatrix;
typedef SparseMatrix<Scalar,Upper|SelfAdjoint,Index> SparseSelfAdjointMatrix;
x = b;
SparseLDLT<SparseSelfAdjointMatrix> ldlt(m3);
if (ldlt.succeeded())
@@ -84,7 +85,7 @@ template<typename Scalar> void sparse_ldlt(int rows, int cols)
// new API
{
SparseMatrix<Scalar> m2(rows, cols);
SparseMatrixType m2(rows, cols);
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
@@ -98,7 +99,7 @@ template<typename Scalar> void sparse_ldlt(int rows, int cols)
m2.coeffRef(i,i) = refMat2(i,i) = internal::abs(internal::real(refMat2(i,i)));
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);
@@ -107,40 +108,40 @@ template<typename Scalar> void sparse_ldlt(int rows, int cols)
// with a single vector as the rhs
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = SimplicialCholesky<SparseMatrix<Scalar>, Lower>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).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<SparseMatrix<Scalar>, Upper>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(b);
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<SparseMatrix<Scalar>, Lower>(m3_lo).solve(b);
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<SparseMatrix<Scalar>, Upper>(m3_up).solve(b);
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<SparseMatrix<Scalar>, Lower>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).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<SparseMatrix<Scalar>, Upper>().setMode(odd ? SimplicialCholeskyLLt : SimplicialCholeskyLDLt).compute(m3).solve(B);
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
// 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 = SimplicialCholesky<SparseMatrix<Scalar>, Lower>(m3).solve(spB);
// spX = SimplicialCholesky<SparseMatrixType, Lower>(m3).solve(spB);
// VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs");
//
// spX = SimplicialCholesky<SparseMatrix<Scalar>, Upper>(m3).solve(spB);
// spX = SimplicialCholesky<SparseMatrixType, Upper>(m3).solve(spB);
// VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs");
}
@@ -167,9 +168,10 @@ template<typename Scalar> void sparse_ldlt(int rows, int cols)
void test_sparse_ldlt()
{
for(int i = 0; i < g_repeat; i++) {
CALL_SUBTEST_1(sparse_ldlt<double>(8, 8) );
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> >(s,s) );
CALL_SUBTEST_1(sparse_ldlt<double>(s,s) );
CALL_SUBTEST_2( (sparse_ldlt<std::complex<double>,int>(s,s)) );
CALL_SUBTEST_1( (sparse_ldlt<double,int>(s,s)) );
}
}