commit b22d063075ec545a59a25abd5d83e4642dc329c2
author Sameer Agarwal <sameeragarwal@google.com> Thu Aug 15 22:55:23 2013 -0700
committer Sameer Agarwal <sameeragarwal@google.com> Fri Aug 16 10:48:54 2013 -0700
tree 0529893c06e6ef5837468563314d80340053c8cc
parent f258e4624f5bd86105ea28b9b92dd70a3f4a3a44

Reduce memory usage in covariance estimation.

When using the SPARSE_QR algorithm, now a Q-less
factorization is used. This results in significantly
less memory usage.

The inversion of the semi-normal equations is now
threaded using openmp. Indeed if one has SuiteSparse
compiled with TBB, then both the factorization
and the inversion are completely threaded.

Change-Id: Ia07591e48e7958d427ef91ff9e67662f6e982c21

internal/ceres/covariance_test.cc

diff --git a/internal/ceres/covariance_test.cc b/internal/ceres/covariance_test.cc
index e7d25a1..f3a5051 100644
--- a/internal/ceres/covariance_test.cc
+++ b/internal/ceres/covariance_test.cc
@@ -499,6 +499,9 @@
 #ifndef CERES_NO_SUITESPARSE
   options.algorithm_type = SPARSE_CHOLESKY;
   ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
 #endif
 
   options.algorithm_type = DENSE_SVD;
@@ -552,6 +555,9 @@
 #ifndef CERES_NO_SUITESPARSE
   options.algorithm_type = SPARSE_CHOLESKY;
   ComputeAndCompareCovarianceBlocks(options, expected_covariance);
+
+  options.algorithm_type = SPARSE_QR;
+  ComputeAndCompareCovarianceBlocks(options, expected_covariance);
 #endif
 
   options.algorithm_type = DENSE_SVD;
@@ -776,6 +782,7 @@
 
 TEST_F(LargeScaleCovarianceTest, Parallel) {
   ComputeAndCompare(SPARSE_CHOLESKY, 4);
+  ComputeAndCompare(SPARSE_QR, 4);
 }
 
 #endif  // !defined(CERES_NO_SUITESPARSE) && defined(CERES_USE_OPENMP)