tree a666103955ae35f9a3e6214382eb115030907e2f
parent c2131dea9cabc74fc3ebbfbdb178915be8ecaae1
author Sameer Agarwal <sameeragarwal@google.com> 1405439555 -0700
committer Sameer Agarwal <sameeragarwal@google.com> 1405866935 -0700

Remove SPARSE_CHOLESKY based covariance estimation.

Sparse Cholesky factorization is not rank revealing. Therefore
this algorithm cannot reliably tell when the Jacobian matrix is
rank deficient or so poorly conditioned that the covariance matrix
cannot be estimated.

Making things worse, this algorithm works on the normal equations,
which makes the conditioning problem much worse.

This change, deletes the SPARSE_CHOLESKY algorithm in the covariance
estimation code. Also to make the naming consistent, it renames

SPARSE_QR -> SUITE_SPARSE_QR

so that it parallels EIGEN_SPARSE_QR.

Also, since we now have EIGEN_SPARSE_QR, we can default to using
it when SuiteSparse is not available instead of DENSE_SVD, which
generally speaking should only be used by folks who are dealing
with small rank deficient jacobians.

Change-Id: I8b134c7e8a2e86ca374371f185b19f1c3e74349c