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Replace local variables by member variables in compute() methods.

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This is to avoid dynamic memory allocations in the compute() methods of
ComplexEigenSolver, EigenSolver, and SelfAdjointEigenSolver where possible.
As a result, Tridiagonalization::decomposeInPlace() is no longer used.
Biggest remaining issue is the allocation in HouseholderSequence::evalTo().
This commit is contained in:
Jitse Niesen
2010-05-24 17:43:06 +01:00
parent 68820fd4e8
commit 7a43a4408b
4 changed files with 101 additions and 81 deletions
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23
Eigen/src/Eigenvalues/ComplexEigenSolver.h
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@@ -3,6 +3,7 @@
//
// Copyright (C) 2009 Claire Maurice
// Copyright (C) 2009 Gael Guennebaud <g.gael@free.fr>
// Copyright (C) 2010 Jitse Niesen <jitse@maths.leeds.ac.uk>
//
// Eigen is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
@@ -99,7 +100,8 @@ template<typename _MatrixType> class ComplexEigenSolver
: m_eivec(),
m_eivalues(),
m_schur(),
m_isInitialized(false)
m_isInitialized(false),
m_matX()
{}
/** \brief Default Constructor with memory preallocation
@@ -112,7 +114,8 @@ template<typename _MatrixType> class ComplexEigenSolver
: m_eivec(size, size),
m_eivalues(size),
m_schur(size),
m_isInitialized(false)
m_isInitialized(false),
m_matX(size, size)
{}
/** \brief Constructor; computes eigendecomposition of given matrix.
@@ -125,7 +128,8 @@ template<typename _MatrixType> class ComplexEigenSolver
: m_eivec(matrix.rows(),matrix.cols()),
m_eivalues(matrix.cols()),
m_schur(matrix.rows()),
m_isInitialized(false)
m_isInitialized(false),
m_matX(matrix.rows(),matrix.cols())
{
compute(matrix);
}
@@ -199,6 +203,7 @@ template<typename _MatrixType> class ComplexEigenSolver
EigenvalueType m_eivalues;
ComplexSchur<MatrixType> m_schur;
bool m_isInitialized;
EigenvectorType m_matX;
};
@@ -217,16 +222,16 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
// Step 2: Compute X such that T = X D X^(-1), where D is the diagonal of T.
// The matrix X is unit triangular.
EigenvectorType X = EigenvectorType::Zero(n, n);
m_matX = EigenvectorType::Zero(n, n);
for(int k=n-1 ; k>=0 ; k--)
{
X.coeffRef(k,k) = ComplexScalar(1.0,0.0);
m_matX.coeffRef(k,k) = ComplexScalar(1.0,0.0);
// 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) = -m_schur.matrixT().coeff(i,k);
m_matX.coeffRef(i,k) = -m_schur.matrixT().coeff(i,k);
if(k-i-1>0)
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();
m_matX.coeffRef(i,k) -= (m_schur.matrixT().row(i).segment(i+1,k-i-1) * m_matX.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))
{
@@ -234,12 +239,12 @@ void ComplexEigenSolver<MatrixType>::compute(const MatrixType& matrix)
// Use a small value instead, to prevent division by zero.
ei_real_ref(z) = NumTraits<RealScalar>::epsilon() * matrixnorm;
}
X.coeffRef(i,k) = X.coeff(i,k) / z;
m_matX.coeffRef(i,k) = m_matX.coeff(i,k) / z;
}
}
// 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 = m_schur.matrixU() * X;
m_eivec.noalias() = m_schur.matrixU() * m_matX;
// .. and normalize the eigenvectors
for(int k=0 ; k<n ; k++)
{