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
- TripletSparseMatrix in BlockRandomAccessSparseMatrix is replaced with
BlockSparseMatrix
- BlockSparseMatrix::ToCompressedRowSparseMatrix is performed in a
direct sort-less way
Change-Id: Ib951fda1b9394050e2c47a9721172c5e3c674801
1. Add threading to all three subclasses of BlockRandomAccessMatrix.
i.e. BlockRandomAccessDenseMatrix, BlockRandomAccessSparseMatrix
and BlockRandomAccessDenseMatrix.
For BlockRandomAccessDenseMatrix and BlockRandomAccessSparseMatrix
this just means SetZero is parallelized. Which by itself is no
big deal, but by doing so, the constructor for all three subclasses
become uniform.
BlockRandomAccessSparseMatrix::SymmetricRightMultiplyAndAccumulate
maybe threaded in the future if needed.
BlockRandomAccessDiagonalMatrix is the biggest beneficiary. SetZero
Invert and RightMultiplyAndAccumulate are all threaded now.
2. Change the storage in BlockRandomAccessDiagonalMatrix from
TripletSparseMatrix to CompressedRowSparseMatrix. This has no
performance implications since we do not really use the capabilities
of the underlying matrix indexing representation. This is a forward
looking change when we decide to transfer this matrix to the GPU,
a CompressedRowSparseMatrix will save on a data conversion.
3. Use std::unique_ptr as needed and eliminate the need for custom
destructors.
4. Modify CompressedRowSparseMatrix::CreateBlockDiagonalMatrix to
take a nullptr as the data vector.
Fixes https://github.com/ceres-solver/ceres-solver/issues/936
Fixes https://github.com/ceres-solver/ceres-solver/issues/935
Change-Id: Ia6487f2d924fbe669835bdcc38abf2b451bda4ee
Previously some matrices used Block to keep track of
row/column block sizes and some would just use ints, and
then compute the position of each row and column from it.
By uniformly using Block everywhere, we reduce duplicate
computation and data copies.
I also cleaned up a bunch of c++17 related stuff as I edited
these files.
Change-Id: I4c86b1593fd4c91f9057fbb38314f62f303e0477
These methods were historically poorly named and every time I read code
I get confused whether they are just multiplying or multiplying and
adding. Clarifying them also gives us the changce to introduce
RightMultiply and LeftMultiply methods in the base class which will
simplify a number call sites in a subsequent CL.
Fixes https://github.com/ceres-solver/ceres-solver/issues/855
Change-Id: Ice4fb483f1acd02527a6dd753ef0c5a66037f4b0
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
Applied changes correspond to clang-tidy fixes
stemming from the modernize-use-equals-default check.
Change-Id: I254b0908a76d464131564b637cd0e42a6b03fb5a
A number of algorithms like the SchurEliminator do not need
access to the full BlockSparseMatrix interface. They only
need read only access to the values array and the block structure.
This change introduces, BlockSparseDataMatrix a struct that carries
these two bits of information and modifies the Schur type algorithms
to use it.
What this change will allow us to do, in a subsequent CL is to
take the values array of a BlockSparseMatrix and pair it with
a different blocks structure for subset preconditioning.
Change-Id: I1808f12531b586c9ff4d6a70b3d390c7b0d9f441
When using the SchurEliminator to compute a preconditioner, there
is no rhs. This CL removes that limitation and simplifies the
call sites in the two preconditioners.
https://github.com/ceres-solver/ceres-solver/issues/271
Change-Id: I05b8518fdc9f0d1a6d88ae76d3a7e8e838e7204a
1. Add Solver::Options::use_mixed_precision_solves,
and Solver::Options::max_num_refinement_iterations.
2. Make SparseCholesky::Create return a unique_ptr.
3. SparseCholesky::Create now takes LinearSolver::Options
as an argument.
4. IterativeRefiner's constructor does not require num_cols
as an argument.
5. SparseNormalCholeskySolver now uses a separate rhs vector.
This basic implementation results in a 10% reduction in solver time
and 30% reduction in linear solver memory usage.
Change-Id: I6830f32cae2febf082d2733262eb2c9f0482b0ea
1. Replace HashMap and HashSet with std::unordered_map and
std::unordered_set respectively.
2. Extract the pair hasher into a struct pair_hash.
3. Delete collections_port.h
4. Convert explicit iterator based loops to auto based
loops where sensible.
Change-Id: Ib88bcd13a7463d18435639d3b771abaa52080efb
A Ceres Context holds common global state that can be re-used within
Ceres. The Context current contains a thread pool if compiling with
C++11 threading support. Threads are expensive to create and destroy so
it is good to maintain across multiple Ceres solves.
Tested by compiling with and without TBB support and ran unit tests. Ran
bazel as well.
Change-Id: I82f598dfae642aa0e81a6039dc174608a5e8dbfb
SparseCholesky is an interface to sparse cholesky factorization
routines across sparse linear algebra libraries. Each sparse
linear algebra library is responsible for implementing its own
instance of this interface.
As a result the various places - SparseNormalCholeskySolver,
SparseSchurComplementSolver and VisibilityBasedPreconditioner
are significantly simplified.
Change-Id: I8b465705eae83bba9e1adfffcc741a05c70faf2e
SchurEliminator::Init now takes a bool that tells it whether
it can assume that the diagonal blocks it is inverting can
be assumed to be full rank or not.
This information is then passed onto InvertPSDMatrix.
Change-Id: I26037b6233f2aad5584fed245f631c3959928afe
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
For historical reasons we had a "using namespace std;" in port.h. This
is generally a bad idea. So removing it and along the way doing a bunch
of cpplint cleanup.
Change-Id: Ia125601a55ae62695e247fb0250df4c6f86c46c6
Rename Graph -> WeightedGraph.
Add a new Graph class, which is cheaper to construct and
work with if the weights are not needed.
This cuts down the cost of building the Hessian graph
significantly.
Change-Id: Id0cfc81dd2c0bb5ff8f63a1b55aa133c53c0c869
- Previously we passed all compile options to Ceres via add_definitions
in CMake. This was fine for private definitions (used only by Ceres)
but required additional work for public definitions to ensure they
were correctly propagated to clients via CMake using
target_compile_definitions() (>= 2.8.11) or add_definitions().
- A drawback to these approaches is that they did not work for chained
dependencies on Ceres, as in if in the users project B <- A <- Ceres,
then although the required Ceres public compile definitions would
be used when compiling A, they would not be propagated to B.
- This patch replaces the addition of compile definitions via
add_definitions() with an autogenerated config.h header which
is installed with Ceres and defines all of the enabled Ceres compile
options.
- This removes the need for the user to propagate any compile
definitions in their projects, and additionally allows post-install
inspect of the options with which Ceres was compiled.
Change-Id: Idbdb6abdad0eb31e7540370e301afe87a07f2260
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. Move LinearSolverTerminationType to ceres::internal.
2. Add FATAL_ERROR as a new enum to LinearSolverTerminationType.
3. Pipe SuiteSparse errors via a LinearSolverTerminationType so
to distinguish between fatal and non-fatal errors.
4. Update levenberg marquardt and dogleg strategies to deal
with FATAL_ERROR.
5. Update trust_region_minimizer to terminate when FATAL_ERROR
is encountered.
6. Remove SuiteSparse::SolveCholesky as it screws up the error
handling.
7. Fix all clients calling SuiteSparse to handle the result of
SuiteSparse::Cholesky correctly.
8. Remove fatal failures in SuiteSparse when symbolic factorization
fails.
9. Fix all clients of SuiteSparse to deal with null symbolic factors.
This is a temporary fix to deal with some production problems. A more
extensive cleanup and testing regime will be put in place in a
subsequent CL.
Change-Id: I1f60d539799dd95db7ecc340911e261fa4824f92
The original visibility based preconditioning paper and
implementation only used the canonical views algorithm.
This algorithm for large dense graphs can be particularly
expensive. As its worst case complexity is cubic in size
of the graph.
Further, for many uses the SCHUR_JACOBI preconditioner
was both effective enough while being cheap. It however
suffers from a fatal flaw. If the camera parameter blocks
are split between two or more parameter blocks, e.g,
extrinsics and intrinsics. The preconditioner because
it is block diagonal will not capture the interactions
between them.
Using CLUSTER_JACOBI or CLUSTER_TRIDIAGONAL will fix
this problem but as mentioned above this can be quite
expensive depending on the problem.
This change extends the visibility based preconditioner
to allow for multiple clustering algorithms. And adds
a simple thresholded single linkage clustering algorithm
which allows you to construct versions of CLUSTER_JACOBI
and CLUSTER_TRIDIAGONAL preconditioners that are cheap
to construct and are more effective than SCHUR_JACOBI.
Currently the constants controlling the threshold above
which edges are considered in the single linkage algorithm
are not exposed. This would be done in a future change.
Change-Id: I7ddc36790943f24b19c7f08b10694ae9a822f5c9
This sets the stage of preconditioners that can utilize
different kinds of matrix layouts, just like the LinearSolver
class hierarchy.
Change-Id: I3579cf344bcd2eeeecb1ae621cab02a3c9a0f920
Sparse Cholesky factorization algorithms use a fill-reducing
ordering to permute the columns of the Jacobian matrix. There
are two ways of doing this.
1. Compute the Jacobian matrix in some order and then have the
factorization algorithm permute the columns of the Jacobian.
2. Compute the Jacobian with its columns already permuted.
The first option incurs a significant memory penalty. The
factorization algorithm has to make a copy of the permuted
Jacobian matrix.
Starting with this change Ceres pre-permutes the columns of the
Jacobian matrix and generally speaking, there is no performance
penalty for doing so.
In some rare cases, it is worth using a more complicated
reordering algorithm which has slightly better runtime
performance at the expense of an extra copy of the Jacobian
matrix. Setting Solver::Options::use_postordering to true
enables this tradeoff.
This change also removes Solver::Options::use_block_amd
as an option. All matrices are ordered using their block
structure. The ability to order them by their scalar
sparsity structure has been removed.
Here is what performance on looks like on some BAL problems.
Memory
======
HEAD pre-ordering
16-22106 137957376.0 113516544.0
49-7776 56688640.0 46628864.0
245-198739 1718005760.0 1383550976.0
257-65132 387715072.0 319512576.0
356-226730 2014826496.0 1626087424.0
744-543562 4903358464.0 3957878784.0
1024-110968 968626176.0 822071296.0
Time
====
HEAD pre-ordering
16-22106 3.8 3.7
49-7776 1.9 1.8
245-198739 82.6 81.9
257-65132 14.0 13.4
356-226730 98.8 95.8
744-543562 325.2 301.6
1024-110968 42.1 37.1
Change-Id: I6b2e25f3fed7310f88905386a7898ac94d37467e
1. Silence CHOLMOD's indefiniteness warnings.
2. Add a comment about how the error handling in suitesparse.cc
needs to be improved.
3. Move the analysis logging into suitesparse.cc and out of the
three callsites.
Change-Id: Idd396b8ea4bf59fc1ffc7f9fcbbc7b38ed71643c
1. Add -DLINE_SEARCH_MINIMIZER to CMake to make the line search
minimizer optional.
2. Better handling of -DSUITESPARSE/-DCXSPARSE in top level cmake
file.
3. Disable code which will never be used if SuiteSparse and/or
CXSparse is not available.
4. Update build docs.
5. Update jni/Android.mk
6. Minor lint cleanup from William Rucklidge.
Change-Id: If60460a858000df82faed7a6bb056dd2bfdde562
1. Added a Preconditioner interface.
2. SCHUR_JACOBI is now its own class and is independent of
SuiteSparse.
Change-Id: Id912ab19cf3736e61d1b90ddaf5bfba33e877ec4
Sameer Agarwal