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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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5
Eigen/src/Core/MatrixBase.h
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@ -782,8 +782,9 @@ template<typename Derived> class MatrixBase
void applyJacobiOnTheLeft(int p, int q, Scalar c, Scalar s);
void applyJacobiOnTheRight(int p, int q, Scalar c, Scalar s);
bool makeJacobi(int p, int q, Scalar max_coeff, Scalar *c, Scalar *s);
bool makeJacobiForAtA(int p, int q, Scalar max_coeff, Scalar *c, Scalar *s);
bool makeJacobi(int p, int q, Scalar *c, Scalar *s) const;
bool makeJacobiForAtA(int p, int q, Scalar *c, Scalar *s) const;
bool makeJacobiForAAt(int p, int q, Scalar *c, Scalar *s) const;
#ifdef EIGEN_MATRIXBASE_PLUGIN
#include EIGEN_MATRIXBASE_PLUGIN

6
Eigen/src/Jacobi/CMakeLists.txt Normal file
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@ -0,0 +1,6 @@
FILE(GLOB Eigen_Jacobi_SRCS "*.h")
INSTALL(FILES
${Eigen_Jacobi_SRCS}
DESTINATION ${INCLUDE_INSTALL_DIR}/Eigen/src/Jacobi COMPONENT Devel
)

30
Eigen/src/Jacobi/Jacobi.h
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@ -48,13 +48,13 @@ 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 max_coeff, Scalar *c, Scalar *s)
bool ei_makeJacobi(Scalar x, Scalar y, Scalar z, Scalar *c, Scalar *s)
{
if(ei_abs(y) < max_coeff * 0.5 * machine_epsilon<Scalar>())
if(ei_abs(y) < ei_abs(z-x) * 0.5 * machine_epsilon<Scalar>())
{
*c = Scalar(1);
*s = Scalar(0);
return true;
return false;
}
else
{
@ -67,23 +67,31 @@ bool ei_makeJacobi(Scalar x, Scalar y, Scalar z, Scalar max_coeff, Scalar *c, Sc
t = Scalar(1) / (tau - w);
*c = Scalar(1) / ei_sqrt(1 + ei_abs2(t));
*s = *c * t;
return false;
return true;
}
}
template<typename Derived>
inline bool MatrixBase<Derived>::makeJacobi(int p, int q, Scalar max_coeff, Scalar *c, Scalar *s)
inline bool MatrixBase<Derived>::makeJacobi(int p, int q, Scalar *c, Scalar *s) const
{
return ei_makeJacobi(coeff(p,p), coeff(p,q), coeff(q,q), max_coeff, c, s);
return ei_makeJacobi(coeff(p,p), coeff(p,q), coeff(q,q), c, s);
}
template<typename Derived>
inline bool MatrixBase<Derived>::makeJacobiForAtA(int p, int q, Scalar max_coeff, Scalar *c, Scalar *s)
inline bool MatrixBase<Derived>::makeJacobiForAtA(int p, int q, Scalar *c, Scalar *s) const
{
return ei_makeJacobi(col(p).squaredNorm(),
col(p).dot(col(q)),
col(q).squaredNorm(),
max_coeff,
return ei_makeJacobi(ei_abs2(coeff(p,p)) + ei_abs2(coeff(q,p)),
ei_conj(coeff(p,p))*coeff(p,q) + ei_conj(coeff(q,p))*coeff(q,q),
ei_abs2(coeff(p,q)) + ei_abs2(coeff(q,q)),
c,s);
}
template<typename Derived>
inline bool MatrixBase<Derived>::makeJacobiForAAt(int p, int q, Scalar *c, Scalar *s) const
{
return ei_makeJacobi(ei_abs2(coeff(p,p)) + ei_abs2(coeff(p,q)),
ei_conj(coeff(q,p))*coeff(p,p) + ei_conj(coeff(q,q))*coeff(p,q),
ei_abs2(coeff(q,p)) + ei_abs2(coeff(q,q)),
c,s);
}

74
Eigen/src/SVD/JacobiSquareSVD.h
View File

@ -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