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- Added problem size constructor to decompositions that did not have one. It preallocates member data structures.

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- Updated unit tests to check above constructor.
- In the compute() method of decompositions: Made temporary matrices/vectors class members to avoid heap allocations during compute() (when dynamic matrices are used, of course).

These  changes can speed up decomposition computation time when a solver instance is used to solve multiple same-sized problems. An added benefit is that the compute() method can now be invoked in contexts were heap allocations are forbidden, such as in real-time control loops.

CAVEAT: Not all of the decompositions in the Eigenvalues module have a heap-allocation-free compute() method. A future patch may address this issue, but some required API changes need to be incorporated first.
This commit is contained in:
Adolfo Rodriguez Tsouroukdissian
2010-04-21 17:15:57 +02:00
parent faf8f7732d
commit 28dde19e40
29 changed files with 396 additions and 121 deletions
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14
Eigen/src/Eigenvalues/ComplexSchur.h
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@@ -96,7 +96,11 @@ template<typename _MatrixType> class ComplexSchur
* \sa compute() for an example.
*/
ComplexSchur(int size = RowsAtCompileTime==Dynamic ? 1 : RowsAtCompileTime)
: m_matT(size,size), m_matU(size,size), m_isInitialized(false), m_matUisUptodate(false)
: m_matT(size,size),
m_matU(size,size),
m_hess(size),
m_isInitialized(false),
m_matUisUptodate(false)
{}
/** \brief Constructor; computes Schur decomposition of given matrix.
@@ -111,6 +115,7 @@ template<typename _MatrixType> class ComplexSchur
ComplexSchur(const MatrixType& matrix, bool skipU = false)
: m_matT(matrix.rows(),matrix.cols()),
m_matU(matrix.rows(),matrix.cols()),
m_hess(matrix.rows()),
m_isInitialized(false),
m_matUisUptodate(false)
{
@@ -182,6 +187,7 @@ template<typename _MatrixType> class ComplexSchur
protected:
ComplexMatrixType m_matT, m_matU;
HessenbergDecomposition<MatrixType> m_hess;
bool m_isInitialized;
bool m_matUisUptodate;
@@ -300,10 +306,10 @@ void ComplexSchur<MatrixType>::compute(const MatrixType& matrix, bool skipU)
// Reduce to Hessenberg form
// TODO skip Q if skipU = true
HessenbergDecomposition<MatrixType> hess(matrix);
m_hess.compute(matrix);
m_matT = hess.matrixH().template cast<ComplexScalar>();
if(!skipU) m_matU = hess.matrixQ().template cast<ComplexScalar>();
m_matT = m_hess.matrixH().template cast<ComplexScalar>();
if(!skipU) m_matU = m_hess.matrixQ().template cast<ComplexScalar>();
// Reduce the Hessenberg matrix m_matT to triangular form by QR iteration.