tree c9df4dd6b90863a523d56eac99edaa588e11106d
parent fb465a03b83fad2dceaea091ee3763c3dc6e83d2
author Sameer Agarwal <sameeragarwal@google.com> 1376350117 -0700
committer Keir Mierle <mierle@gmail.com> 1376429275 +0000

Use more performant, less conservative Eigen solvers.

colPivHouseholderQR -> householderQR
ldlt -> llt.

The resulting performance differences are significant enough
to justify switching.

LAPACK's dgels routine used for solving linear least squares
problems does not use pivoting either.

Similarly, we are not actually using the fact that the matrix
being factorized can be indefinite when using LDLT factorization, so
its not clear that the performance hit is worth it.

These two changes result in Eigen being able to use blocking
algorithms, which for Cholesky factorization, brings the performance
closer to hardware optimized LAPACK. Similarly for dense QR
factorization, on intel there is a 2x speedup.

Change-Id: I4459ee0fc8eb87d58e2b299dfaa9e656d539dc5e