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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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33
Eigen/src/Eigenvalues/ComplexEigenSolver.h
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@@ -95,7 +95,24 @@ template<typename _MatrixType> class ComplexEigenSolver
* The default constructor is useful in cases in which the user intends to
* perform decompositions via compute().
*/
ComplexEigenSolver() : m_eivec(), m_eivalues(), m_isInitialized(false)
ComplexEigenSolver()
: m_eivec(),
m_eivalues(),
m_schur(),
m_isInitialized(false)
{}
/** \brief Default Constructor with memory preallocation
*
* Like the default constructor but with preallocation of the internal data
* according to the specified problem \a size.
* \sa ComplexEigenSolver()
*/
ComplexEigenSolver(int size)
: m_eivec(size, size),
m_eivalues(size),
m_schur(size),
m_isInitialized(false)
{}
/** \brief Constructor; computes eigendecomposition of given matrix.
@@ -107,6 +124,7 @@ template<typename _MatrixType> class ComplexEigenSolver
ComplexEigenSolver(const MatrixType& matrix)
: m_eivec(matrix.rows(),matrix.cols()),
m_eivalues(matrix.cols()),
m_schur(matrix.rows()),
m_isInitialized(false)
{
compute(matrix);
@@ -179,6 +197,7 @@ template<typename _MatrixType> class ComplexEigenSolver
protected:
EigenvectorType m_eivec;
EigenvalueType m_eivalues;
ComplexSchur<MatrixType> m_schur;
bool m_isInitialized;
};
@@ -193,8 +212,8 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
// Step 1: Do a complex Schur decomposition, A = U T U^*
// The eigenvalues are on the diagonal of T.
ComplexSchur<MatrixType> schur(matrix);
m_eivalues = schur.matrixT().diagonal();
m_schur.compute(matrix);
m_eivalues = m_schur.matrixT().diagonal();
// Step 2: Compute X such that T = X D X^(-1), where D is the diagonal of T.
// The matrix X is unit triangular.
@@ -205,10 +224,10 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
// Compute X(i,k) using the (i,k) entry of the equation X T = D X
for(int i=k-1 ; i>=0 ; i--)
{
X.coeffRef(i,k) = -schur.matrixT().coeff(i,k);
X.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
if(k-i-1>0)
X.coeffRef(i,k) -= (schur.matrixT().row(i).segment(i+1,k-i-1) * X.col(k).segment(i+1,k-i-1)).value();
ComplexScalar z = schur.matrixT().coeff(i,i) - schur.matrixT().coeff(k,k);
X.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * X.col(k).segment(i+1,k-i-1)).value();
ComplexScalar z = m_schur.matrixT().coeff(i,i) - m_schur.matrixT().coeff(k,k);
if(z==ComplexScalar(0))
{
// If the i-th and k-th eigenvalue are equal, then z equals 0.
@@ -220,7 +239,7 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
}
// Step 3: Compute V as V = U X; now A = U T U^* = U X D X^(-1) U^* = V D V^(-1)
m_eivec = schur.matrixU() * X;
m_eivec = m_schur.matrixU() * X;
// .. and normalize the eigenvectors
for(int k=0 ; k<n ; k++)
{