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
Despite its relative size, this is very significant change
to Ceres.
Why
===
Up till now, when the user chose SPARSE_NORMAL_CHOLESKY,
the Jacobian was evaluated in a CompressedRowSparseMatrix,
which was then use to compute the normal equations which were
passed to a sparse linear algebra library for factorization.
The reason to do this was because in the case of SuiteSparse,
we were able to pass the Jacobian matrix directly without
computing the normal equations and SuiteSparse/CHOLMOD did the
normal equation computation.
This turned out to be slow, so Cheng Wang implemented a high
performance version of the matrix-matrix multiply to compute
the normal equations, and all the sparse linear algebra libraries
now are passed the normal equations.
So that raises the question, as to what the best representation
of the Jacobian which is suitable for the normal equation computation.
Turns out BlockSparseMatrix is ideal. It brings two advantages.
1. Jacobian evaluation into a BlockSparseMatrix is considerably
faster when using a BlockSparseMatrix than
CompressedRowSparseMatrix. This is because we save on a bunch
of memory copies.
2. To make the matrix multiplication fast and use the block structure
Cheng Wang had to essentially make the CompressedRowSparseMatrix
carry a bunch of sidecar information about the block sparsity,
essentially making it behave like a BlockSparseMatrix. The resulting
code had fairly complicated indexing and complicated the semantics
of CompressedRowSparseMatrix. The new InnerProductComputer class
does away with all that and once this CL goes in, I will be able to
remove all that code and simplify the semantics of
CompressedRowSparseMatrix.
Changes
=======
1. Use InnerProductComputer in SparseNormalCholeskySolver.
2. Change the evaluator instantiated for SPARSE_NORMAL_CHOLESKY with
static sparsity inside evaluator.cc
3. The former change necessitates that we change ProblemImpl::Evaluate
to create the evaluate it needs on its own, because it was
depending on passing "SPARSE_NORMAL_CHOLESKY" as linear solver type
to the evaluator factor to get an Evaluator which can use
CompressedRowSparseMatrix objects for storing the Jacobian.
4. Update the tests for SparseNormalCholeskySolver.
5. Separate out the tests for DynamicSparseNormalCholeskySolver into its
own file.
Change-Id: I2ef7ef8fbfbb4967d0c1ec2068c1c778248fdf5b
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
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
1. Add the ability to evaluate the problem without loss function.
2. Remove static Evaluator::Evaluate
3. Refactor the common code from problem_test.cc and
evaluator_test.cc into evaluator_test_utils.cc
Change-Id: I1aa841580afe91d288fbb65288b0ffdd1e43e827
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
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
- Rename BlockDiagonalPreconditioner to BlockJacobiPreconditioner
- Include the diagonal in the block jacobi preconditioner.
- Better flag help for eta.
- Enable test for CGNR
- Rename CONJUGATE_GRADIENTS to CGNR.
- etc.
This adds a new LinearOperator which implements symmetric
products of a matrix, and a new CGNR solver to leverage
CG to directly solve the normal equations. This also
includes a block diagonal preconditioner. In experiments
on problem-16, the non-preconditioned version is about
1/5 the speed of SPARSE_SCHUR, and the preconditioned
version using block cholesky is about 20% slower than
SPARSE_SCHUR.
Mike Vitus