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finally, the good approach was two-sided Jacobi. Indeed, it allows

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to guarantee the precision of the output, which is very valuable.
Here, we guarantee that the diagonal matrix returned by the SVD is
actually diagonal, to machine precision.

Performance isn't bad at all at 50% of the current householder SVD
performance for a 200x200 matrix (no vectorization) and we have
lots of room for improvement.
This commit is contained in:
Benoit Jacob
2009-08-12 02:35:07 -04:00
parent ce033ebdfe
commit 22d65d47d0
4 changed files with 63 additions and 52 deletions
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74
Eigen/src/SVD/JacobiSquareSVD.h
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@@ -102,69 +102,65 @@ 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);
RealScalar max_coeff = work_matrix.cwise().abs().maxCoeff();
for(int k = 1; k < 40; ++k) {
while(true)
{
bool finished = true;
for(int p = 1; p < size; ++p)
{
for(int q = 0; q < p; ++q)
{
Scalar c, s;
finished &= work_matrix.makeJacobiForAtA(p,q,max_coeff,&c,&s);
work_matrix.applyJacobiOnTheRight(p,q,c,s);
if(ComputeV) m_matrixV.applyJacobiOnTheRight(p,q,c,s);
if(work_matrix.makeJacobiForAtA(p,q,&c,&s))
{
work_matrix.applyJacobiOnTheRight(p,q,c,s);
if(ComputeV) m_matrixV.applyJacobiOnTheRight(p,q,c,s);
}
if(work_matrix.makeJacobiForAAt(p,q,&c,&s))
{
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)) ))
{
work_matrix.row(p).swap(work_matrix.row(q));
if(ComputeU) m_matrixU.col(p).swap(m_matrixU.col(q));
}
}
}
}
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;
}
m_singularValues = work_matrix.diagonal().cwise().abs();
RealScalar biggestSingularValue = m_singularValues.maxCoeff();
for(int i = 0; i < size; ++i)
{
m_singularValues.coeffRef(i) = work_matrix.col(i).norm();
RealScalar a = ei_abs(work_matrix.coeff(i,i));
m_singularValues.coeffRef(i) = a;
if(ComputeU && !ei_isMuchSmallerThan(a, biggestSingularValue)) m_matrixU.col(i) *= work_matrix.coeff(i,i)/a;
}
int first_zero = size;
RealScalar biggest = m_singularValues.maxCoeff();
for(int i = 0; i < size; i++)
{
int pos;
RealScalar biggest_remaining = m_singularValues.end(size-i).maxCoeff(&pos);
if(first_zero == size && ei_isMuchSmallerThan(biggest_remaining, biggest)) first_zero = pos + i;
m_singularValues.end(size-i).maxCoeff(&pos);
if(pos)
{
pos += i;
std::swap(m_singularValues.coeffRef(i), m_singularValues.coeffRef(pos));
if(ComputeU) work_matrix.col(pos).swap(work_matrix.col(i));
if(ComputeU) m_matrixU.col(pos).swap(m_matrixU.col(i));
if(ComputeV) m_matrixV.col(pos).swap(m_matrixV.col(i));
}
}
if(ComputeU)
{
for(int i = 0; i < first_zero; ++i)
{
m_matrixU.col(i) = work_matrix.col(i) / m_singularValues.coeff(i);
}
if(first_zero < size)
{
for(int i = first_zero; i < size; ++i)
{
for(int j = 0; j < size; ++j)
{
m_matrixU.col(i).setZero();
m_matrixU.coeffRef(j,i) = Scalar(1);
for(int k = 0; k < first_zero; ++k)
m_matrixU.col(i) -= m_matrixU.col(i).dot(m_matrixU.col(k)) * m_matrixU.col(k);
RealScalar n = m_matrixU.col(i).norm();
if(!ei_isMuchSmallerThan(n, biggest))
{
m_matrixU.col(i) /= n;
break;
}
}
}
}
}
m_isInitialized = true;
}
#endif // EIGEN_JACOBISQUARESVD_H