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make jacobi SVD more robust after experimenting with very nasty matrices...

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it turns out to be better to repeat the jacobi steps on a given (p,q) pair until it
is diagonal to machine precision, before going to the next (p,q) pair. it's also
an optimization as experiments show that in a majority of cases this allows to find out
that the (p,q) pair is already diagonal to machine precision.
This commit is contained in:
Benoit Jacob 2009-08-12 18:23:39 -04:00
parent 309d540d4a
commit 2b618a2c16
2 changed files with 26 additions and 17 deletions
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2
Eigen/src/Jacobi/Jacobi.h
View File

@ -50,7 +50,7 @@ void MatrixBase<Derived>::applyJacobiOnTheRight(int p, int q, Scalar c, Scalar s
template<typename Scalar>
bool ei_makeJacobi(Scalar x, Scalar y, Scalar z, Scalar *c, Scalar *s)
{
if(ei_abs(y) < ei_abs(z-x) * 0.5 * machine_epsilon<Scalar>())
if(ei_abs(y) <= ei_abs(z-x) * 0.5 * machine_epsilon<Scalar>())
{
*c = Scalar(1);
*s = Scalar(0);

41
Eigen/src/SVD/JacobiSquareSVD.h
View File

@ -102,14 +102,16 @@ void JacobiSquareSVD<MatrixType, ComputeU, ComputeV>::compute(const MatrixType&
if(ComputeU) m_matrixU = MatrixUType::Identity(size,size);
if(ComputeV) m_matrixV = MatrixUType::Identity(size,size);
m_singularValues.resize(size);
while(true)
sweep_again:
for(int p = 1; p < size; ++p)
{
bool finished = true;
for(int p = 1; p < size; ++p)
for(int q = 0; q < p; ++q)
{
for(int q = 0; q < p; ++q)
{
Scalar c, s;
Scalar c, s;
while(std::max(ei_abs(work_matrix.coeff(p,q)),ei_abs(work_matrix.coeff(q,p)))
> std::max(ei_abs(work_matrix.coeff(p,p)),ei_abs(work_matrix.coeff(q,q)))*machine_epsilon<Scalar>())
{
if(work_matrix.makeJacobiForAtA(p,q,&c,&s))
{
work_matrix.applyJacobiOnTheRight(p,q,c,s);
@ -117,9 +119,13 @@ void JacobiSquareSVD<MatrixType, ComputeU, ComputeV>::compute(const MatrixType&
}
if(work_matrix.makeJacobiForAAt(p,q,&c,&s))
{
Scalar x = ei_abs2(work_matrix.coeff(p,p)) + ei_abs2(work_matrix.coeff(p,q));
Scalar y = ei_conj(work_matrix.coeff(q,p))*work_matrix.coeff(p,p) + ei_conj(work_matrix.coeff(q,q))*work_matrix.coeff(p,q);
Scalar z = ei_abs2(work_matrix.coeff(q,p)) + ei_abs2(work_matrix.coeff(q,q));
work_matrix.applyJacobiOnTheLeft(p,q,c,s);
if(ComputeU) m_matrixU.applyJacobiOnTheRight(p,q,c,s);
if(std::max(ei_abs(work_matrix.coeff(p,q)), ei_abs(work_matrix.coeff(q,p))) > std::max(ei_abs(work_matrix.coeff(q,q)), ei_abs(work_matrix.coeff(p,p)) ))
if(std::max(ei_abs(work_matrix.coeff(p,q)),ei_abs(work_matrix.coeff(q,p)))
> std::max(ei_abs(work_matrix.coeff(p,p)),ei_abs(work_matrix.coeff(q,q))) )
{
work_matrix.row(p).swap(work_matrix.row(q));
if(ComputeU) m_matrixU.col(p).swap(m_matrixU.col(q));
@ -127,15 +133,18 @@ void JacobiSquareSVD<MatrixType, ComputeU, ComputeV>::compute(const MatrixType&
}
}
}
RealScalar biggest = work_matrix.diagonal().cwise().abs().maxCoeff();
for(int p = 0; p < size; ++p)
{
for(int q = 0; q < size; ++q)
{
if(p!=q && ei_abs(work_matrix.coeff(p,q)) > biggest * machine_epsilon<Scalar>()) finished = false;
}
}
if(finished) break;
}
RealScalar biggestOnDiag = work_matrix.diagonal().cwise().abs().maxCoeff();
RealScalar maxAllowedOffDiag = biggestOnDiag * machine_epsilon<Scalar>();
for(int p = 0; p < size; ++p)
{
for(int q = 0; q < p; ++q)
if(ei_abs(work_matrix.coeff(p,q)) > maxAllowedOffDiag)
goto sweep_again;
for(int q = p+1; q < size; ++q)
if(ei_abs(work_matrix.coeff(p,q)) > maxAllowedOffDiag)
goto sweep_again;
}
m_singularValues = work_matrix.diagonal().cwise().abs();