1. Convert a CompressedRowSparseMatrix constructor which
takes a TripletSparseMatrix as input into a factory method
which allows the input to be transposed.
2. Move the random matrix creation routine for CompressedRowSparseMatrix
from being a standalone function to a static method.
3. Add a corresponding random matrix generation static method to
TripletSparseMatrix.
4. Add a new constructor to TripletSparseMatrix, which takes as input
the row, col and values arrays.
Change-Id: Iec7b184646818f432a5e6822bea3b2f3128a82aa
SolveLowerTriangularInPlace
SolveLowerTriangularTransposeInPlace
were unused functions which can be removed.
Change-Id: I0fd29c1efae2a0a74666f6e3541473bebc22ae82
By adding an enum to CompressedRowSparseMatrix, which indicates
whether the matrix is unsymmetric, upper or lower triangular
we are able to improve the readability and fix some minor
bugs in the way some matrix manipulation code was being
called.
Thank to William Rucklidge for this suggestion.
Change-Id: I355c90d11cd5d31f5a25741b0bda4fc4583e9095
Move it to compressed_row_sparse_matrix.h/cc for upcoming re-use.
Also clean up the tests for ComputeOuterProduct so that they do
not depend on CXSparse anymore and use Eigen instead. This also
makes the test simpler and shorter.
Change-Id: I06bbeb3b0c6a07fb1f3da354ef0abd17d246be9a
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
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
The standard sparse normal Cholesky solver assumes a fixed
sparsity pattern which is useful for a large number of problems
presented to Ceres. However, some problems are symbolically dense
but numerically sparse i.e. each residual is a function of a
large number of parameters but at any given state the residual
only depends on a sparse subset of them. For these class of
problems it is faster to re-analyse the sparsity pattern of the
jacobian at each iteration of the non-linear optimisation instead
of including all of the zero entries in the step computation.
The proposed solution adds the dynamic_sparsity option which can
be used with SPARSE_NORMAL_CHOLESKY. A
DynamicCompressedRowSparseMatrix type (which extends
CompressedRowSparseMatrix) has been introduced which allows
dynamic addition and removal of elements. A Finalize method is
provided which then consolidates the matrix so that it can be
used in place of a regular CompressedRowSparseMatrix. An
associated jacobian writer has also been provided.
Changes that were required to make this extension were adding the
SetMaxNumNonZeros method to CompressedRowSparseMatrix and adding
a JacobianFinalizer template parameter to the ProgramEvaluator.
Change-Id: Ia5a8a9523fdae8d5b027bc35e70b4611ec2a8d01
When using sparse cholesky factorization to solve the linear
least squares problem:
Ax = b
There are two sources of computational complexity.
1. Computing H = A'A
2. Computing the sparse Cholesky factorization of H.
Doing 1. using CX_SPARSE is particularly expensive, as it uses
a generic cs_multiply function which computes the structure of
the matrix H everytime, reallocates memory and does not take
advantage of the fact that the matrix being computed is a symmetric
outer product.
This change adds a custom symmetric outer product algorithm for
CompressedRowSparseMatrix.
It has a symbolic phase, where it computes the sparsity structure
of the output matrix and a "program" which allows the actual
multiplication routine to determine exactly which entry in the
values array each term in the product contributes to.
With these two bits of information, the outer product H = A'A
can be computed extremely fast without any reasoning about
the structure of H.
Further gains in efficiency are made by exploiting the block
structure of A.
With this change, SPARSE_NORMAL_CHOLESKY with CX_SPARSE as the
backend results in > 300% speedup for some problems.
The symbolic analysis phase of the solver is a bit more expensive
now but the increased cost is made up in 3-4 iterations.
Change-Id: I5e4a72b4d03ba41b378a2634330bc22b299c0f12
1. When protocol buffers support is enabled CompressedRowSparseMatrix
has a missing virtual method.
2. When SuiteSparse is missing, covariance_test tries to run the
large scale covariance computation test.
Thanks to Alex Stewart for reporting #1.
Change-Id: I4238c966036362175e31749595ea8bb6f12a696c
Drop support for protocol buffers.
Add CompressedRowSparseMatrix::CreateBlockDiagonalMatrix.
Add CompressedRowSparseMatrix::SolveLowerTriangularInPlace.
Add CompressedRowSparseMatrix::SolveLowerTriangularTranposeInPlace.
Add CompressedRowSparseMatrix::Transpose.
Change-Id: I2328afca9fac632685eac72ebb00998bd3510187
1. Added CRSMatrix object which will store the initial
and final jacobians if requested by the user.
2. Conversion routine and test for converting a
CompressedRowSparseMatrix to CRSMatrix.
3. New Evaluator::Evaluate function to do the actual evaluation.
4. Changes to Program::StateVectorToParmeterBlocks and
Program::SetParameterBlockStatePtrstoUserStatePtrs so that
they do not try to set the state of constant parameter blocks.
5. Tests for Evaluator::Evaluate.
6. Minor cleanups in SolverImpl.
7. Minor cpplint cleanups triggered by this CL.
Change-Id: I3ac446484692f943c28f2723b719676f8c83ca3d
By virtue of the modeling layer in Ceres being block oriented,
all the matrices used by Ceres are also block oriented.
When doing sparse direct factorization of these matrices, the
fill-reducing ordering algorithms can either be run on the
block or the scalar form of these matrices. Running it on the
block form exposes more of the super-nodal structure of the
matrix to the Cholesky factorization routines. This leads to
substantial gains in factorization performance.
This changelist adds support for approximate minimium degree
orderings to be computed on the block structure of the
Schur complement matrix. This affects, SchurComplementSolver
and VisibilityBasedPreconditioner and SparseNormalCholesky
when using SuiteSparse.
A bool, use_block_amd has been added to Solver::Options and
bundle_adjuster.cc has been updated to allow testing with it.
When combined with a multithreaded Schur elimination, speed ups
can be seen quite uniformly across the board. For some problems
this can be dramatic, reducing the factorization time from 70
seconds down to 17 seconds.
Change-Id: I15ebb0afcbc85ada032ec8d179ee3a2f7c8d3e46
1. Make the mechanism for writing problems to disk, generic and
controllable using an enum DumpType visible in the API.
2. Instead of single file containing protocol buffers, now matrices can
be written in a matlab/octave friendly format. This is now the default.
3. The support for writing problems to disk is moved into
linear_least_squares_problem.cc/h
4. SparseMatrix now has a ToTextFile virtual method which is
implemented by each of its subclasses to write a (i,j,s) triplets.
5. Minor changes to simple_bundle_adjuster to enable logging at startup.
Sameer Agarwal