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add the possibility to solve for sparse rhs with Cholmod

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This commit is contained in:
Gael Guennebaud
2010-10-27 14:31:23 +02:00
parent 5d4ff3f99c
commit 241e5ee3e7
6 changed files with 171 additions and 17 deletions
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43
unsupported/test/sparse_llt.cpp
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@@ -40,19 +40,21 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
DenseMatrix refMat2(rows, cols);
DenseVector b = DenseVector::Random(cols);
DenseVector refX(cols), x(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)));
refX = refMat2.template selfadjointView<Lower>().llt().solve(b);
ref_x = refMat2.template selfadjointView<Lower>().llt().solve(b);
if (!NumTraits<Scalar>::IsComplex)
{
x = b;
SparseLLT<SparseMatrix<Scalar> > (m2).solveInPlace(x);
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: default");
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: default");
}
#ifdef EIGEN_CHOLMOD_SUPPORT
@@ -62,14 +64,14 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
SparseMatrix<Scalar> m3 = m2.adjoint()*m2;
DenseMatrix refMat3 = refMat2.adjoint()*refMat2;
refX = refMat3.template selfadjointView<Lower>().llt().solve(b);
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = b;
SparseLLT<SparseMatrix<Scalar>, Cholmod>(m3).solveInPlace(x);
VERIFY((m3*x).isApprox(b,test_precision<Scalar>()) && "LLT legacy: cholmod solveInPlace");
x = SparseLLT<SparseMatrix<Scalar>, Cholmod>(m3).solve(b);
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT legacy: cholmod solve");
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT legacy: cholmod solve");
}
// new API
@@ -78,13 +80,38 @@ template<typename Scalar> void sparse_llt(int rows, int cols)
SparseMatrix<Scalar> m3 = m2.adjoint()*m2;
DenseMatrix refMat3 = refMat2.adjoint()*refMat2;
refX = refMat3.template selfadjointView<Lower>().llt().solve(b);
// with a single vector as the rhs
ref_x = refMat3.template selfadjointView<Lower>().llt().solve(b);
x = CholmodDecomposition<SparseMatrix<Scalar>, Lower>(m3).solve(b);
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve");
VERIFY(ref_x.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve, single dense rhs");
x = CholmodDecomposition<SparseMatrix<Scalar>, Upper>(m3).solve(b);
VERIFY(refX.isApprox(x,test_precision<Scalar>()) && "LLT: cholmod solve");
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);
X = CholmodDecomposition<SparseMatrix<Scalar>, 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);
VERIFY(ref_X.isApprox(X,test_precision<Scalar>()) && "LLT: cholmod solve, multiple dense rhs");
// with a sparse rhs
SparseMatrix<Scalar> 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);
VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs");
spX = CholmodDecomposition<SparseMatrix<Scalar>, Upper>(m3).solve(spB);
VERIFY(ref_X.isApprox(spX.toDense(),test_precision<Scalar>()) && "LLT: cholmod solve, multiple sparse rhs");
}
#endif