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RealSchur: Use Householder module in Francis QR step.

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This commit is contained in:
Jitse Niesen 2010-04-06 16:43:07 +01:00
parent 86df74c765
commit 7dc56b3dfb
1 changed files with 25 additions and 49 deletions
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74
Eigen/src/Eigenvalues/RealSchur.h
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@ -118,6 +118,8 @@ void RealSchur<MatrixType>::compute(const MatrixType& matrix)
template<typename MatrixType>
void RealSchur<MatrixType>::hqr2()
{
typedef Matrix<Scalar, ColsAtCompileTime, 1, Options, MaxColsAtCompileTime, 1> ColumnVectorType;
// This is derived from the Algol procedure hqr2,
// by Martin and Wilkinson, Handbook for Auto. Comp.,
// Vol.ii-Linear Algebra, and the corresponding
@ -126,11 +128,12 @@ void RealSchur<MatrixType>::hqr2()
// Initialize
const int size = m_matU.cols();
int n = size-1;
const int low = 0;
const int high = size-1;
Scalar exshift = 0.0;
Scalar p=0, q=0, r=0;
ColumnVectorType workspaceVector(size);
Scalar* workspace = &workspaceVector.coeffRef(0);
// Compute matrix norm
// FIXME to be efficient the following would requires a triangular reduxion code
// Scalar norm = m_matT.upper().cwiseAbs().sum() + m_matT.corner(BottomLeft,n,n).diagonal().cwiseAbs().sum();
@ -142,11 +145,11 @@ void RealSchur<MatrixType>::hqr2()
// Outer loop over eigenvalue index
int iter = 0;
while (n >= low)
while (n >= 0)
{
// Look for single small sub-diagonal element
int l = n;
while (l > low)
while (l > 0)
{
Scalar s = ei_abs(m_matT.coeff(l-1,l-1)) + ei_abs(m_matT.coeff(l,l));
if (s == 0.0)
@ -216,7 +219,7 @@ void RealSchur<MatrixType>::hqr2()
if (iter == 10)
{
exshift += x;
for (int i = low; i <= n; ++i)
for (int i = 0; i <= n; ++i)
m_matT.coeffRef(i,i) -= x;
Scalar s = ei_abs(m_matT.coeff(n,n-1)) + ei_abs(m_matT.coeff(n-1,n-2));
x = y = Scalar(0.75) * s;
@ -234,7 +237,7 @@ void RealSchur<MatrixType>::hqr2()
if (y < x)
s = -s;
s = Scalar(x - w / ((y - x) / 2.0 + s));
for (int i = low; i <= n; ++i)
for (int i = 0; i <= n; ++i)
m_matT.coeffRef(i,i) -= s;
exshift += s;
x = y = w = Scalar(0.964);
@ -309,54 +312,27 @@ void RealSchur<MatrixType>::hqr2()
m_matT.coeffRef(k,k-1) = -m_matT.coeff(k,k-1);
p = p + s;
x = p / s;
y = q / s;
Scalar z = r / s;
q = q / p;
r = r / p;
// Row modification
for (int j = k; j < size; ++j)
{
p = m_matT.coeff(k,j) + q * m_matT.coeff(k+1,j);
if (notlast)
{
p = p + r * m_matT.coeff(k+2,j);
m_matT.coeffRef(k+2,j) = m_matT.coeff(k+2,j) - p * z;
}
m_matT.coeffRef(k,j) = m_matT.coeff(k,j) - p * x;
m_matT.coeffRef(k+1,j) = m_matT.coeff(k+1,j) - p * y;
}
if (notlast)
{
Matrix<Scalar, 2, 1> ess(q/p, r/p);
m_matT.block(k, k, 3, size-k).applyHouseholderOnTheLeft(ess, p/s, workspace);
m_matT.block(0, k, std::min(n,k+3) + 1, 3).applyHouseholderOnTheRight(ess, p/s, workspace);
m_matU.block(0, k, size, 3).applyHouseholderOnTheRight(ess, p/s, workspace);
}
else
{
Matrix<Scalar, 1, 1> ess;
ess.coeffRef(0) = q/p;
m_matT.block(k, k, 2, size-k).applyHouseholderOnTheLeft(ess, p/s, workspace);
m_matT.block(0, k, std::min(n,k+3) + 1, 2).applyHouseholderOnTheRight(ess, p/s, workspace);
m_matU.block(0, k, size, 2).applyHouseholderOnTheRight(ess, p/s, workspace);
}
// Column modification
for (int i = 0; i <= std::min(n,k+3); ++i)
{
p = x * m_matT.coeff(i,k) + y * m_matT.coeff(i,k+1);
if (notlast)
{
p = p + z * m_matT.coeff(i,k+2);
m_matT.coeffRef(i,k+2) = m_matT.coeff(i,k+2) - p * r;
}
m_matT.coeffRef(i,k) = m_matT.coeff(i,k) - p;
m_matT.coeffRef(i,k+1) = m_matT.coeff(i,k+1) - p * q;
}
// Accumulate transformations
for (int i = low; i <= high; ++i)
{
p = x * m_matU.coeff(i,k) + y * m_matU.coeff(i,k+1);
if (notlast)
{
p = p + z * m_matU.coeff(i,k+2);
m_matU.coeffRef(i,k+2) = m_matU.coeff(i,k+2) - p * r;
}
m_matU.coeffRef(i,k) = m_matU.coeff(i,k) - p;
m_matU.coeffRef(i,k+1) = m_matU.coeff(i,k+1) - p * q;
}
} // (s != 0)
} // k loop
} // check convergence
} // while (n >= low)
} // while (n >= 0)
}
#endif // EIGEN_REAL_SCHUR_H