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Add condition estimation to Cholesky (LLT) factorization.

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This commit is contained in:
Rasmus Munk Larsen
2016-04-01 16:19:45 -07:00
parent fb8dccc23e
commit f54137606e
4 changed files with 111 additions and 36 deletions
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37
Eigen/src/Core/ConditionEstimator.h
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@@ -13,11 +13,11 @@
namespace Eigen {
namespace internal {
template <typename Decomposition, bool IsComplex>
template <typename Decomposition, bool IsSelfAdjoint, bool IsComplex>
struct EstimateInverseMatrixL1NormImpl {};
} // namespace internal
template <typename Decomposition>
template <typename Decomposition, bool IsSelfAdjoint = false>
class ConditionEstimator {
public:
typedef typename Decomposition::MatrixType MatrixType;
@@ -101,7 +101,8 @@ class ConditionEstimator {
return 0;
}
return internal::EstimateInverseMatrixL1NormImpl<
Decomposition, NumTraits<Scalar>::IsComplex>::compute(dec);
Decomposition, IsSelfAdjoint,
NumTraits<Scalar>::IsComplex != 0>::compute(dec);
}
/**
@@ -116,9 +117,27 @@ class ConditionEstimator {
namespace internal {
template <typename Decomposition, typename Vector, bool IsSelfAdjoint = false>
struct solve_helper {
static inline Vector solve_adjoint(const Decomposition& dec,
const Vector& v) {
return dec.adjoint().solve(v);
}
};
// Partial specialization for self_adjoint matrices.
template <typename Decomposition, typename Vector>
struct solve_helper<Decomposition, Vector, true> {
static inline Vector solve_adjoint(const Decomposition& dec,
const Vector& v) {
return dec.solve(v);
}
};
// Partial specialization for real matrices.
template <typename Decomposition>
struct EstimateInverseMatrixL1NormImpl<Decomposition, 0> {
template <typename Decomposition, bool IsSelfAdjoint>
struct EstimateInverseMatrixL1NormImpl<Decomposition, IsSelfAdjoint, false> {
typedef typename Decomposition::MatrixType MatrixType;
typedef typename internal::traits<MatrixType>::Scalar Scalar;
typedef typename internal::plain_col_type<MatrixType>::type Vector;
@@ -152,7 +171,7 @@ struct EstimateInverseMatrixL1NormImpl<Decomposition, 0> {
int old_v_max_abs_index = v_max_abs_index;
for (int k = 0; k < 4; ++k) {
// argmax |inv(matrix)^T * sign_vector|
v = dec.adjoint().solve(sign_vector);
v = solve_helper<Decomposition, Vector, IsSelfAdjoint>::solve_adjoint(dec, sign_vector);
v.cwiseAbs().maxCoeff(&v_max_abs_index);
if (v_max_abs_index == old_v_max_abs_index) {
// Break if the solution stagnated.
@@ -200,8 +219,8 @@ struct EstimateInverseMatrixL1NormImpl<Decomposition, 0> {
};
// Partial specialization for complex matrices.
template <typename Decomposition>
struct EstimateInverseMatrixL1NormImpl<Decomposition, 1> {
template <typename Decomposition, bool IsSelfAdjoint>
struct EstimateInverseMatrixL1NormImpl<Decomposition, IsSelfAdjoint, true> {
typedef typename Decomposition::MatrixType MatrixType;
typedef typename internal::traits<MatrixType>::Scalar Scalar;
typedef typename NumTraits<Scalar>::Real RealScalar;
@@ -238,7 +257,7 @@ struct EstimateInverseMatrixL1NormImpl<Decomposition, 1> {
RealVector abs_v = v.cwiseAbs();
const Vector psi =
(abs_v.array() == 0).select(v.cwiseQuotient(abs_v), ones);
v = dec.adjoint().solve(psi);
v = solve_helper<Decomposition, Vector, IsSelfAdjoint>::solve_adjoint(dec, psi);
const RealVector z = v.real();
z.cwiseAbs().maxCoeff(&v_max_abs_index);
if (v_max_abs_index == old_v_max_abs_index) {