Adds support for computing the covariance using Eigen's sparse QR module.
For smaller problems Eigen is faster than SuiteSparseQR. This has been
tested with Eigen 3.2.1. Below are detailed timings. Problem 1 is the
smallest and problem 3 is the largest. The timings below are:
mean +- standard deviation.
Problem 1:
Eigen 0.0009218 +- 0.0002755
SuiteSparse 0.001406 +- 0.001610
Problem 2:
Eigen 0.002338 +- 0.001005
SuiteSparse 0.001910 +- 0.0004513
Problem 3:
Eigen 0.005455 +- 0.001759
SuiteSparse 0.002411 +- 0.0004974
Detailed problem descriptions:
Problem 1 size:
Original Reduced
Parameter blocks 533 54
Parameters 368 104
Effective parameters 1201 94
Residual blocks 233 77
Residual 1194 258
Problem 2 size:
Original Reduced
Parameter blocks 573 84
Parameters 1458 184
Effective parameters 1281 164
Residual blocks 263 107
Residual 1314 378
Problem 3 size:
Original Reduced
Parameter blocks 613 114
Parameters 1548 264
Effective parameters 1361 234
Residual blocks 293 137
Residual 1434 498
Change-Id: I884a67e2f728fe2992812148d82ccf5f27864fd7
diff --git a/internal/ceres/covariance_impl.h b/internal/ceres/covariance_impl.h
index 0e7e217..ff640b9 100644
--- a/internal/ceres/covariance_impl.h
+++ b/internal/ceres/covariance_impl.h
@@ -67,6 +67,7 @@
bool ComputeCovarianceValuesUsingSparseCholesky();
bool ComputeCovarianceValuesUsingSparseQR();
bool ComputeCovarianceValuesUsingDenseSVD();
+ bool ComputeCovarianceValuesUsingEigenSparseQR();
const CompressedRowSparseMatrix* covariance_matrix() const {
return covariance_matrix_.get();
