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Bug 816 - LDLT for sparse, semi-definite symmetric matrices
Summary: LDLT for sparse, semi-definite symmetric matrices
Status: NEW
Alias: None
Product: Eigen
Classification: Unclassified
Component: Sparse (show other bugs)
Version: 3.2
Hardware: All All
: Normal enhancement
Assignee: Nobody
URL:
Whiteboard:
Keywords:
Depends on:
Blocks:
 
Reported: 2014-06-02 11:22 UTC by matthieu.nesme
Modified: 2014-06-03 09:37 UTC (History)
1 user (show)



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Description matthieu.nesme 2014-06-02 11:22:50 UTC
Hi,

LDLT decomposition works for DENSE semi-definite matrices but not for sparse ones (note the documentation is clear on that point).
Please could you update the implementation for the SPARSE semi-definite matrices?

Thanks!
Comment 1 Christoph Hertzberg 2014-06-02 12:35:07 UTC
That will not be as easy as it may sound. SimplicialLDLT determined its permutation solely on the sparsity pattern with the goal of reducing fill-in, i.e., before doing any numerical operations, whereas dense LDLt makes pivoting on-the-fly to determine the permutation.

You can try using (the slower but rank-revealing) SparseQR if you have non-definite matrices. If your matrix origins from a normal-equation (A.transpose()*A), you can even work on the original A matrix for extra numerical accuracy.

Of course, that feature can be added to Sparse Cholesky decompositions, but don't rely on that happening very soon.
Comment 2 matthieu.nesme 2014-06-03 09:37:36 UTC
Ah ok, what a pity! Thank you anyway.

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