sparse linear solvers.
* Add methods to convert TripletSparseMatrix and BlockSparseMatrix to
CRSMatrix structure.
* Added tests for conversion of TripletSparseMatrix and BlockSparseMatrix
to CRSMatrix structure.
* Added documentation on the BlockSparseMatrix structure.
Change-Id: I020cfa91c301567ceeb39ff2064183c5d88c9ed5
On problem-744-543562-pre.txt
The time spent in linear solver on my M1 Pro is
eigen 81.550970
eigen+mixed 54.107383
LAPACK 47.078127
LAPACK+mixed 28.639868
Solution quality is unaffected.
The implementation of RefinedDenseCholesky and DenseIterativeRefiner
are straightforward ports of RefinedSparseCholesky and
SparseIterativeRefiner (formerly IterativeRefiner).
It maybe possible to refactor the SparseCholesky and DenseCholesky
interfaces so that this code duplication can be removed in the
future.
Change-Id: I921334224cb97629a60390f2add822de207f7923
Since Eigen does not allow to have a RowMajor column vector (see
https://gitlab.com/libeigen/eigen/-/issues/416), the storage order
must be set to ColMajor in that case. This fix adds that special
case when generating 2D sphere manifolds.
Change-Id: I594932e0dafc878e0b348f72524478588e61b34d
CERES_METIS_VERSION needs to be set if either Eigen or
SuiteSparse are using it. Previously, we were conditioning it
only on EIGENMETIS being ON.
Change-Id: I380a3b138b79903aa142b560ed46f438ff549a82
Because we store the matrix as row major matrix, the
obvious Eigen expression performs rather poorly. A straight
c++ loop speeds things up considerably.
Also replace use of matrix() with direct use of m_.
Change-Id: I3d6166df4765ad8400ab9602a54b65fd21b1d50f
MSVC rightfully issues warning C4305: 'if': truncation from 'size_t' to
'bool' in a static_assert condition that implicitly converts sizeof
result to a boolean.
Change-Id: Ie3b913288bfeaa7a4b362ef7f83d2505ed368641
- These are non-standard C++, and whilst they are accepted by GCC and
Clang on *NIX and macOS, they are rejected by MSVC.
Change-Id: Ie627d74bb02ebdce3dc5e13c2010616c26cb5dea
- Also fixes behaviour of EIGENMETIS option to match that of the other
CMake dependency options, and ensure that its value aligns exactly
with whether Eigen support for METIS will be compiled into Ceres.
Change-Id: Ifbf6f5d82b9ba89a156673eb6042519a985e6b04
* Split `CERES_NO_METIS` into two defines: `CERES_NO_PARTITION` and
`CERES_NO_METIS`. The former refers to METIS support in SuiteSparse,
the latter to the Eigen's MetisSupport module. This enables the use of
sparse matrix reordering independent from SuiteSparse.
* Run Linux, macOS, and macOS Github workflows with METIS enabled
SuiteSparse.
Fixes#808
Change-Id: I5076b7e1268d32cc3e7e56650edcbaf7fb3b59ce
- Prior to 48cb54d1, Ceres' fmin/fmax() for Jets followed the convention
of std::min/max(), and always returned the first argument on equality,
irrespective of whether this argument was natively a scalar or a Jet.
- After 48cb54d1, Ceres' fmin/fmax() instead returned the second
argument on equality, again irrespective of whether this argument was
natively a scalar or a Jet.
- Now on equality we average the arguments as Jets, which ensures that
a consistent answer is produced irrespective of the ordering or type
(Jet or scalar) of the input arguments. This also ensures that we
preserve a non-zero derivative where it exists, excluding the edge
case of two Jet inputs with equal but oppositely signed infinitesimal
components.
- We retain the behaviour introduced in 48cb54d1 whereby NaNs are
treated as missing values, following the convention of
std::fmin/fmax().
- Raised as issue #816.
Change-Id: I01217c0e32c1be83be440e4515b57c79dd290923
* Unfortunately on some systems such as the Nvidia Jetson, while the
compiler supports C++17, the STL implementations are incomplete.
One such missing implementation is std::exclusive_scan, so this
patch reverts to the old way of manually computing prefix sums.
Change-Id: I4192257519b0083560a4b44e2659ee44d7421105
1. The platform specific threads library is only needed if we actually
use threads. In this case, the library is not optional opposed to
previous logic.
2. Do not hide the find module output to allow the user to understand
what happens in case of a CMake failure to locate Threads.
3. Finally, Threads is private dependency that does need to be
propagated to consumers unless Ceres was compiled as a static
library.
Change-Id: I8d9d9cd42930e1ed234f69a2dba70d0ee2755b4e
Depending on the compiler in use, linking against OpenMP may require
passing specific compiler flags instead of linking against a library.
Use the CMake OpenMP find module to abstract OpenMP activation.
Change-Id: Ib43f576ac12e2c5e9598e9586df3dfa018e9c08b
* Previously the Cuda memcheck tests relied on the Cuda binaries being
on the environment PATH. This has been changed instead to use the
path discovered by CMake when searching for Cuda. This has the added
benefit that the memcheck tool will be sure to be from the same Cuda
version install as the version being compiled against.
Change-Id: I650d1bb7e14064ca98a01e3c13eb1bcb772b51cc
Eigen provides all the functionality that we need from CXSparse
with a more liberal license.
I will update the documentation in a follow up CL.
Change-Id: I0b9fd8be3c27754cc2986cc0e06595c8b3fdec0b
Previously, the tests did not run because the CMake version shipped with
Ubuntu 20.04 does not understand the `--test-dir` option and silently
fails.
Change-Id: I335e1d9e3890aa56e66a9dfd0fccd4594a84a08c
Previously when using a natural ordering, we had postordering
turned off. This is not a good idea. Enabling postordering will
also has the possibility of improving the size of the supernodes.
Change-Id: I8c270e54751b8bed53b38a0b461f647f5c8f5640
This was an ill-advised and complicated to interpret option
which offers nothing particularly useful.
Change-Id: Ia7741ed62ef977c96fa52299a884e404bee659ac
Thanks to nate-thirdwave@ for pointing this out and offering
a fix.
Also add a TODO about an odd loop in covariance_impl.cc which was
revealed as I was testing the bazel build
https: //github.com/ceres-solver/ceres-solver/issues/800
Change-Id: I87d17155ee43ea2a52b8031177d6b3ac5ae1460a
With this change, the user can now choose between Approximate Minimum
Degree and Nested Dissection as a fill reducing algorithm when using
a sparse direct factorization based linear solver like SPARSE_NORMAL_CHOLESKY
or SPARSE_SCHUR.
Currenly only SUITE_SPARSE is supported. It requires that
SuiteSparse be compiled with Metis support enabled.
On most problems AMD is still the better choice, but in some cases
like the grid3D dataset from https://lucacarlone.mit.edu/datasets/
the solution time with AMD is 57s and with NESDIS 38 on my M1 Mac.
On some other problems at Google we have observed speedups of 10x,
there is also a corresponding decrease in the total amount of memory
used.
This patch is based on the original work done by NeroBurner in
https://ceres-solver-review.googlesource.com/c/ceres-solver/+/20580
1. Add a new enum to the public api LinearSolverOrderingType and
a setting Solver::Options::linear_solver_ordering_type.
2. TrustRegionPreprocessor had some complicated logic which determined
when linear solvers should reorder their matrices on their own and not
this has been refactored into a more readable function that lives
inside reorder_program.h/cc.
3. Plumbing in reorder_program.cc and trust_region_processor.cc to use
nested dissection.
4. Update bundle_adjuster.cc to use nested dissection.
Change-Id: I388b027934f86c58b4da2b65a4fa5204ea73bf40
1. Generalize SuiteSparse::AnalyzeCholesky and
SuiteSparse::BlockAnalyzeCholesky from just doing AMD to taking
OrderingType as an argument and using that to determine whether
AMD & Nested Dissection algorithms are used for computing the
fill-reducing ordering or a natural ordering when computing
the symbolic factorization.
2. Remove AnalyzeCholeskyWithNaturalOrdering.
3. Replace and generalize SuiteSparse::BlockAMDOrdering with
SuiteSparse::BlockOrdering which also takes OrderingType as an
argument. Same for SuiteSparse::ApproximateMinimumDegreeOrdering
and SuiteSparse::NestedDissectionOrdering by
SuiteSparse::Ordering.
4. Remove LinearSolver::Options::use_postordering and replace it
with LinearSolver::Options::ordering_type.
5. Replace Preconditioner::Options::use_postordering and replace it
with Preconditioner::Options::ordering_type.
6. Add NESDIS to OrderingType. With the above changes, the linear
solvers can now use Nested Dissection once this information
is piped through the nonlinear solver.
Change-Id: Ib8e93fbf34ae2981bf2ac54dcda9e25c7c213790
With this change we can drop the complicated/conditional handling
around CAMD and assume that it is always available.
Change-Id: I93e1da676fb75817f79824b8b2b6549d03f278b0
Joydeep Biswas