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Add an info() method to the SVDBase class to make it possible to tell the user that the computation failed, possibly due to invalid input.

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Make Jacobi and divide-and-conquer fail fast and return info() == InvalidInput if the matrix contains NaN or +/-Inf.
This commit is contained in:
Rasmus Munk Larsen
2021-03-31 21:09:19 +00:00
parent b5a926a0f6
commit 5bbc9cea93
4 changed files with 72 additions and 19 deletions
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32
Eigen/src/SVD/BDCSVD.h
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@@ -208,6 +208,7 @@ protected:
using Base::m_computeThinV;
using Base::m_matrixU;
using Base::m_matrixV;
using Base::m_info;
using Base::m_isInitialized;
using Base::m_nonzeroSingularValues;
@@ -256,16 +257,25 @@ BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsign
{
// FIXME this line involves temporaries
JacobiSVD<MatrixType> jsvd(matrix,computationOptions);
if(computeU()) m_matrixU = jsvd.matrixU();
if(computeV()) m_matrixV = jsvd.matrixV();
m_singularValues = jsvd.singularValues();
m_nonzeroSingularValues = jsvd.nonzeroSingularValues();
m_isInitialized = true;
m_info = jsvd.info();
if (m_info == Success || m_info == NoConvergence) {
if(computeU()) m_matrixU = jsvd.matrixU();
if(computeV()) m_matrixV = jsvd.matrixV();
m_singularValues = jsvd.singularValues();
m_nonzeroSingularValues = jsvd.nonzeroSingularValues();
}
return *this;
}
//**** step 0 - Copy the input matrix and apply scaling to reduce over/under-flows
RealScalar scale = matrix.cwiseAbs().maxCoeff();
RealScalar scale = matrix.cwiseAbs().template maxCoeff<PropagateNaN>();
if (!(numext::isfinite)(scale)) {
m_isInitialized = true;
m_info = InvalidInput;
return *this;
}
if(scale==Literal(0)) scale = Literal(1);
MatrixX copy;
if (m_isTranspose) copy = matrix.adjoint()/scale;
@@ -282,7 +292,11 @@ BDCSVD<MatrixType>& BDCSVD<MatrixType>::compute(const MatrixType& matrix, unsign
m_computed.topRows(m_diagSize) = bid.bidiagonal().toDenseMatrix().transpose();
m_computed.template bottomRows<1>().setZero();
divide(0, m_diagSize - 1, 0, 0, 0);
if (m_info != Success && m_info != NoConvergence) {
m_isInitialized = true;
return *this;
}
//**** step 3 - Copy singular values and vectors
for (int i=0; i<m_diagSize; i++)
{
@@ -394,7 +408,7 @@ void BDCSVD<MatrixType>::structured_update(Block<MatrixXr,Dynamic,Dynamic> A, co
//@param shift : Each time one takes the left submatrix, one must add 1 to the shift. Why? Because! We actually want the last column of the U submatrix
// to become the first column (*coeff) and to shift all the other columns to the right. There are more details on the reference paper.
template<typename MatrixType>
void BDCSVD<MatrixType>::divide (Eigen::Index firstCol, Eigen::Index lastCol, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index shift)
void BDCSVD<MatrixType>::divide(Eigen::Index firstCol, Eigen::Index lastCol, Eigen::Index firstRowW, Eigen::Index firstColW, Eigen::Index shift)
{
// requires rows = cols + 1;
using std::pow;
@@ -414,6 +428,8 @@ void BDCSVD<MatrixType>::divide (Eigen::Index firstCol, Eigen::Index lastCol, Ei
{
// FIXME this line involves temporaries
JacobiSVD<MatrixXr> b(m_computed.block(firstCol, firstCol, n + 1, n), ComputeFullU | (m_compV ? ComputeFullV : 0));
m_info = b.info();
if (m_info != Success && m_info != NoConvergence) return;
if (m_compU)
m_naiveU.block(firstCol, firstCol, n + 1, n + 1).real() = b.matrixU();
else
@@ -433,7 +449,9 @@ void BDCSVD<MatrixType>::divide (Eigen::Index firstCol, Eigen::Index lastCol, Ei
// and the divide of the right submatrice reads one column of the left submatrice. That's why we need to treat the
// right submatrix before the left one.
divide(k + 1 + firstCol, lastCol, k + 1 + firstRowW, k + 1 + firstColW, shift);
if (m_info != Success && m_info != NoConvergence) return;
divide(firstCol, k - 1 + firstCol, firstRowW, firstColW + 1, shift + 1);
if (m_info != Success && m_info != NoConvergence) return;
if (m_compU)
{