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* remove EIGEN_DONT_INLINE that harm performance for small sizes

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* normalize left Jacobi rotations to avoid having to swap rows
* set precision to 2*machine_epsilon instead of machine_epsilon, we lose 1 bit of precision
  but gain between 10% and 100% speed, plus reduce the risk that some day we hit a bad matrix
  where it's impossible to approach machine precision
This commit is contained in:
Benoit Jacob
2009-08-13 14:56:39 -04:00
parent 76a3089a43
commit f2536416da
3 changed files with 20 additions and 15 deletions
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15
Eigen/src/SVD/JacobiSquareSVD.h
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@@ -102,6 +102,7 @@ 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);
const RealScalar precision = 2 * machine_epsilon<Scalar>();
sweep_again:
for(int p = 1; p < size; ++p)
@@ -110,7 +111,7 @@ sweep_again:
{
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>())
> std::max(ei_abs(work_matrix.coeff(p,p)),ei_abs(work_matrix.coeff(q,q)))*precision)
{
if(work_matrix.makeJacobiForAtA(p,q,&c,&s))
{
@@ -119,24 +120,16 @@ sweep_again:
}
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));
ei_normalizeJacobi(&c, &s, work_matrix.coeff(p,p), work_matrix.coeff(q,p)),
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(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));
}
}
}
}
}
RealScalar biggestOnDiag = work_matrix.diagonal().cwise().abs().maxCoeff();
RealScalar maxAllowedOffDiag = biggestOnDiag * machine_epsilon<Scalar>();
RealScalar maxAllowedOffDiag = biggestOnDiag * precision;
for(int p = 0; p < size; ++p)
{
for(int q = 0; q < p; ++q)