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SimplicialCholesky: avoid multiple twisting of the same matrix when calling compute()

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This commit is contained in:
Gael Guennebaud 2012-06-01 15:51:03 +02:00
parent 97cdf6ce9e
commit 7f63169f09
1 changed files with 80 additions and 29 deletions
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105
Eigen/src/SparseCholesky/SimplicialCholesky.h
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@ -104,7 +104,7 @@ class SimplicialCholeskyBase
SimplicialCholeskyBase(const MatrixType& matrix)
: m_info(Success), m_isInitialized(false), m_shiftOffset(0), m_shiftScale(1)
{
compute(matrix);
derived().compute(matrix);
}
~SimplicialCholeskyBase()
@ -128,14 +128,6 @@ class SimplicialCholeskyBase
return m_info;
}
/** Computes the sparse Cholesky decomposition of \a matrix */
Derived& compute(const MatrixType& matrix)
{
derived().analyzePattern(matrix);
derived().factorize(matrix);
return derived();
}
/** \returns the solution x of \f$ A x = b \f$ using the current decomposition of A.
*
* \sa compute()
@ -258,10 +250,42 @@ class SimplicialCholeskyBase
protected:
/** Computes the sparse Cholesky decomposition of \a matrix */
template<bool DoLDLT>
void factorize(const MatrixType& a);
void compute(const MatrixType& matrix)
{
eigen_assert(matrix.rows()==matrix.cols());
Index size = matrix.cols();
CholMatrixType ap(size,size);
ordering(matrix, ap);
analyzePattern_preordered(ap, DoLDLT);
factorize_preordered<DoLDLT>(ap);
}
void analyzePattern(const MatrixType& a, bool doLDLT);
template<bool DoLDLT>
void factorize(const MatrixType& a)
{
eigen_assert(a.rows()==a.cols());
int size = a.cols();
CholMatrixType ap(size,size);
ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_Pinv);
factorize_preordered<DoLDLT>(ap);
}
template<bool DoLDLT>
void factorize_preordered(const CholMatrixType& a);
void analyzePattern(const MatrixType& a, bool doLDLT)
{
eigen_assert(a.rows()==a.cols());
int size = a.cols();
CholMatrixType ap(size,size);
ordering(a, ap);
analyzePattern_preordered(ap,doLDLT);
}
void analyzePattern_preordered(const CholMatrixType& a, bool doLDLT);
void ordering(const MatrixType& a, CholMatrixType& ap);
/** keeps off-diagonal entries; drops diagonal entries */
struct keep_diag {
@ -375,6 +399,13 @@ public:
return Traits::getU(Base::m_matrix);
}
/** Computes the sparse Cholesky decomposition of \a matrix */
SimplicialLLT compute(const MatrixType& matrix)
{
Base::template compute<false>(matrix);
return *this;
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
@ -459,6 +490,13 @@ public:
return Traits::getU(Base::m_matrix);
}
/** Computes the sparse Cholesky decomposition of \a matrix */
SimplicialLDLT compute(const MatrixType& matrix)
{
Base::template compute<true>(matrix);
return *this;
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
@ -515,7 +553,7 @@ public:
SimplicialCholesky(const MatrixType& matrix)
: Base(), m_LDLT(true)
{
Base::compute(matrix);
compute(matrix);
}
SimplicialCholesky& setMode(SimplicialCholeskyMode mode)
@ -544,6 +582,16 @@ public:
return Base::m_matrix;
}
/** Computes the sparse Cholesky decomposition of \a matrix */
SimplicialCholesky compute(const MatrixType& matrix)
{
if(m_LDLT)
Base::template compute<true>(matrix);
else
Base::template compute<false>(matrix);
return *this;
}
/** Performs a symbolic decomposition on the sparcity of \a matrix.
*
* This function is particularly useful when solving for several problems having the same structure.
@ -625,22 +673,17 @@ public:
};
template<typename Derived>
void SimplicialCholeskyBase<Derived>::analyzePattern(const MatrixType& a, bool doLDLT)
void SimplicialCholeskyBase<Derived>::ordering(const MatrixType& a, CholMatrixType& ap)
{
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
m_matrix.resize(size, size);
m_parent.resize(size);
m_nonZerosPerCol.resize(size);
ei_declare_aligned_stack_constructed_variable(Index, tags, size, 0);
// TODO allows to configure the permutation
{
CholMatrixType C;
C = a.template selfadjointView<UpLo>();
// remove diagonal entries:
C.prune(keep_diag());
// seems not to be needed
// C.prune(keep_diag());
internal::minimum_degree_ordering(C, m_P);
}
@ -649,8 +692,19 @@ void SimplicialCholeskyBase<Derived>::analyzePattern(const MatrixType& a, bool d
else
m_Pinv.resize(0);
SparseMatrix<Scalar,ColMajor,Index> ap(size,size);
ap.resize(size,size);
ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_Pinv);
}
template<typename Derived>
void SimplicialCholeskyBase<Derived>::analyzePattern_preordered(const CholMatrixType& ap, bool doLDLT)
{
const Index size = ap.rows();
m_matrix.resize(size, size);
m_parent.resize(size);
m_nonZerosPerCol.resize(size);
ei_declare_aligned_stack_constructed_variable(Index, tags, size, 0);
for(Index k = 0; k < size; ++k)
{
@ -693,11 +747,11 @@ void SimplicialCholeskyBase<Derived>::analyzePattern(const MatrixType& a, bool d
template<typename Derived>
template<bool DoLDLT>
void SimplicialCholeskyBase<Derived>::factorize(const MatrixType& a)
void SimplicialCholeskyBase<Derived>::factorize_preordered(const CholMatrixType& ap)
{
eigen_assert(m_analysisIsOk && "You must first call analyzePattern()");
eigen_assert(a.rows()==a.cols());
const Index size = a.rows();
eigen_assert(ap.rows()==ap.cols());
const Index size = ap.rows();
eigen_assert(m_parent.size()==size);
eigen_assert(m_nonZerosPerCol.size()==size);
@ -709,9 +763,6 @@ void SimplicialCholeskyBase<Derived>::factorize(const MatrixType& a)
ei_declare_aligned_stack_constructed_variable(Index, pattern, size, 0);
ei_declare_aligned_stack_constructed_variable(Index, tags, size, 0);
SparseMatrix<Scalar,ColMajor,Index> ap(size,size);
ap.template selfadjointView<Upper>() = a.template selfadjointView<UpLo>().twistedBy(m_Pinv);
bool ok = true;
m_diag.resize(DoLDLT ? size : 0);