Replace WallTimeInSeconds with absl::Now and use
absl::Time and absl::Duration objects instead of doubles.
wall_time.h/cc -> event_logger.h/cc
Change-Id: I41279961368840fbdf6bb3456ffdbdf2f9bfb85b
1. Add a version history
2. Update copyright years across the code base
3. Run format_all.sh
4. Update version strings from 2.1.0 to 2.2.0 in the docs and
elsewhere.
Change-Id: I46d8d479d54bd6002d532785e67342106e73c9ac
1. Add EigenDenseQR & tests.
This implementation now uses an in place decomposition,
which means that we are not allocating, deallocating
memory every call.
2. Add LAPACKDenseQR and tests.
The LAPACK implementation instead of using dgels which is a
routine which does the factorization and solve in one
call, now uses dgeqrf for factorization and then
dormqr and dtrtrs for solving. This allows us to
have a factorize and solve interface like DenseCholesky.
And opens the door to iterative refinement and mixed
precision solves.
3. The refactor also allows us to simplify the interface to
DenseSparseMatrix considerably. The internals of this
class were complicated because we had the AppendDiagonal
and RemoveDiagonal methods and we did not want to allocate
deallocate memory every call. But since we pay the cost
of the copy anyways, we can just hold that buffer
in DenseQRSolver.
4. Delete lapack.cc/h
5. The net result is that everything seems to be a bit faster.
For LAPACK we are not doing some of the scaling work that
dgels was doing. For Eigen I think it maybe the inplace
decomposition.
Benchmark Time CPU Time Old Time New CPU Old CPU New
----------------------------------------------------------------------------------------------------------------------------------------------------------
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/1/1 -0.1154 -0.1159 692 612 691 611
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/2/1 -0.1601 -0.1553 717 603 712 601
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/3/1 -0.1673 -0.1575 733 610 724 610
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/6/2 -0.1008 -0.1003 886 797 884 796
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/10/3 -0.1489 -0.1514 1283 1092 1281 1087
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/12/4 -0.1040 -0.1104 1556 1394 1553 1381
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/20/5 -0.0007 -0.0097 1911 1910 1908 1890
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/40/5 -0.1033 -0.1022 2981 2673 2957 2655
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/100/10 -0.0147 +0.0015 9275 9138 9026 9040
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/200/10 -0.1408 -0.1284 15093 12968 14778 12880
BM_DenseSolver<ceres::EIGEN, ceres::DENSE_QR>/200/20 -0.0310 -0.0355 38973 37765 38837 37460
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/1/1 -0.1228 -0.1256 736 646 731 640
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/2/1 -0.1401 -0.1396 740 636 735 633
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/3/1 -0.1731 -0.1695 744 615 738 613
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/6/2 -0.1399 -0.1408 1121 965 1113 956
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/10/3 -0.1110 -0.1145 1571 1397 1560 1382
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/12/4 -0.1411 -0.1417 2006 1722 1993 1710
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/20/5 -0.1740 -0.1729 2741 2264 2724 2253
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/40/5 -0.0966 -0.1123 3462 3128 3425 3040
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/100/10 -0.0387 -0.0998 10365 9964 10339 9307
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/200/10 -0.2044 -0.2049 16031 12754 15998 12720
BM_DenseSolver<ceres::LAPACK, ceres::DENSE_QR>/200/20 -0.2391 -0.2386 35777 27223 35716 27193
Change-Id: I782f0d7664efe1435eebda92ddf47a0fe66c9c72
- Change formatting standard to Cpp11. Main difference is not having
the space between two closing >> for nested templates. We don't
choose c++14, because older versions of clang-format (version 9
and earlier) don't know this value yet, and it doesn't make a
difference in the formatting.
- Apply clang-format to all (non generated) internal source files.
- Manually fix some code sections (clang-format on/off) and c-strings
- Exclude some embedded external files with very different formatting
(gtest/gmock)
- Add script to format all source files
Change-Id: Ic6cea41575ad6e37c9e136dbce176b0d505dc44d
Since Ceres is moving to using GitHub for issues, and the Google
Code URL in the current copyright header will soon become invalid,
update all the headers.
Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
A lot of error checking cruft has accumulated over the years
in the various linear solvers. This change makes the error reporting
more robust and consistent across the various solvers.
Preconditioners are not covered by this change and will be the
subject of a future change.
Change-Id: Ibeb2572a1e67758953dde8d12e3abc6d1df9052d
1. When a LAPACK implementation is present, then
DENSE_QR, DENSE_NORMAL_CHOLESKY and DENSE_SCHUR
can use it for doing dense linear algebra operations.
2. The user can switch dense linear algebra libraries
by setting Solver::Options::dense_linear_algebra_library_type.
3. Solver::Options::sparse_linear_algebra_library is now
Solver::Options::sparse_linear_algebra_library_type to be consistent
with all the other enums in Solver::Options.
4. Updated documentation as well as Solver::Summary::FullReport
to reflect these changes.
Change-Id: I5ab930bc15e90906b648bc399b551e6bd5d6498f
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
1. Add an ExecutionSummary object to record execution
information about Ceres objects.
2. Add an EventLogger object to log events in a function call.
3. Add a ScopedExecutionTimer object to log times in ExecutionSummary.
4. Instrument ProgramEvaluator and all the linear solvers
to report their timing statistics.
5. Connect the timing statistics to Summary::FullReport.
6. Add high precision timer on unix systems using
gettimeofday() call.
7. Various minor clean ups all around.
Change-Id: I5e09804b730b09535484124be7dbc1c58eccd1d4
For problems with a small number of variables, but a large
number of residuals, it is sometimes beneficial to use the
Cholesky factorization on the normal equations, instead of
the dense QR factorization of the Jacobian, even though it
is numerically the better thing to do.
Change-Id: I3506b006195754018deec964e6e190b7e8c9ac8f
unnecessarily complexity in the structure of linear solvers and preconditioners.
This is the first step towards cleaning up the Preconditioner interface.
2. Minor tweaks and cleanups to the various linear solvers.
Sameer Agarwal