devtools/eigen
1
0
Fork 0
mirror of https://gitlab.com/libeigen/eigen.git synced 2026-04-10 11:34:33 +08:00
Code Issues Packages Projects Releases Wiki Activity

Big API change in Cholesky module:

Browse Source 
* rename Cholesky to LLT
 * rename CholeskyWithoutSquareRoot to LDLT
 * rename MatrixBase::cholesky() to llt()
 * rename MatrixBase::choleskyNoSqrt() to ldlt()
 * make {LLT,LDLT}::solve() API consistent with other modules

Note that we are going to keep a source compatibility untill the next beta release.
E.g., the "old" Cholesky* classes, etc are still available for some time.
To be clear, Eigen beta2 should be (hopefully) source compatible with beta1,
and so beta2 will contain all the deprecated API of beta1. Those features marked
as deprecated will be removed in beta3 (or in the final 2.0 if there is no beta 3 !).

Also includes various updated in sparse Cholesky.
This commit is contained in:
Gael Guennebaud
2008-10-13 15:53:27 +00:00
parent e2bd8623f8
commit 765219aa51
23 changed files with 608 additions and 183 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
Eigen/src/Sparse/AmbiVector.h
View File

@@ -28,7 +28,7 @@
/** \internal
* Hybrid sparse/dense vector class designed for intensive read-write operations.
*
* See BasicSparseCholesky and SparseProduct for usage examples.
* See BasicSparseLLT and SparseProduct for usage examples.
*/
template<typename _Scalar> class AmbiVector
{
@@ -269,12 +269,14 @@ class AmbiVector<_Scalar>::Iterator
/** Default constructor
* \param vec the vector on which we iterate
* \param nonZeroReferenceValue reference value used to prune zero coefficients.
* In practice, the coefficient are compared to \a nonZeroReferenceValue * precision<Scalar>().
* \param epsilon the minimal value used to prune zero coefficients.
* In practice, all coefficients having a magnitude smaller than \a epsilon
* are skipped.
*/
Iterator(const AmbiVector& vec, RealScalar nonZeroReferenceValue = RealScalar(0.1)) : m_vector(vec)
Iterator(const AmbiVector& vec, RealScalar epsilon = RealScalar(0.1)*precision<RealScalar>())
: m_vector(vec)
{
m_epsilon = nonZeroReferenceValue * precision<Scalar>();
m_epsilon = epsilon;
m_isDense = m_vector.m_mode==IsDense;
if (m_isDense)
{