Optimize J' * J in sparse_normal_cholesky_solver.

1. Add stype to the outerproduct computation to control the output
matrix in upper or lower triangular matrix. For SuiteSparse,
upper triangular matrix is generated. SuiteSparse can directly use
this matrix format for cholesky without matrix transpose overhead.

2. Change the outerproduct computation to block multiplication.  This
reduces the computation complexity for the sort in preprocessing, also
allows formulation of the block outerproduct computation as dense Eigen
block matrix multiplication.

3. Solve 32 Tango problems on Qualcomm MSM8994 Cortex-A53 (1.55GHz)
   before change: 140 seconds
   after change: 131 seconds

Change-Id: I8054114cef911de6a303310a448821ca296e4744
8 files changed
tree: 39c892631af07665fed047dd92e3bb6c6efa6ebb
  1. cmake/
  2. config/
  3. data/
  4. docs/
  5. examples/
  6. include/
  7. internal/
  8. jni/
  9. scripts/
  10. .gitignore
  11. CMakeLists.txt
  12. LICENSE
  13. package.xml
  14. README.md
README.md

Ceres Solver

Ceres Solver is an open source C++ library for modeling and solving large, complicated optimization problems. It is a feature rich, mature and performant library which has been used in production at Google since 2010. Ceres Solver can solve two kinds of problems.

  1. Non-linear Least Squares problems with bounds constraints.
  2. General unconstrained optimization problems.

Please see ceres-solver.org for more information.

WARNING - Do not make GitHub pull requests!

Ceres development happens on Gerrit, including both repository hosting and code reviews. The GitHub Repository is a continuously updated mirror which is primarily meant for issue tracking. Please see our Contributing to Ceres Guide for more details.

The upstream Gerrit repository is

https://ceres-solver.googlesource.com/ceres-solver