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
1. Add a line search based minimization loop.
2. Currently this loop supports steepest descent and three
kinds of non-linear conjugate gradient algorithms.
3. Update SolverImpl to talk to LineSearchMinimizer.
4. Update IterationCallback to carry information about
line search.
5. Update LineSearch to take the initial point as input,
saving on one function evaluation.
6. Updates to the external API.
Change-Id: I901a0e89fc948451ab34c743e70f3dec57c9405e
Add automatic recursive independent set decomposition.
Clean up the naming and the API for inner iterations.
Change-Id: I3d7d6babb9756842d7367e14b7279d2df98fb724
A non-linear generalization of Ruhe & Wedin's algorithm
for separable non-linear least squares problem. It is implemented
as coordinate descent on an independent subset of the parameter
blocks at the end of every successful Newton step. The resulting
algorithm has much improved convergence at the cost of some
execution time.
Change-Id: I8fdc5edbd0ba1e702c9658b98041b2c2ae705402
Remove the old ordering API, and modify solver_impl.cc
to use the new API everywhere.
In the process also clean up the linear solver instantion
logic in solver_impl.cc a bit too.
Change-Id: Ia66898abc7f622070b184b21fce8cc6140c4cebf
Non-monotonic trust region algorithm based on the work of Phil Toint, as
described in
Non-monotone trust region algorithms for nonlinear
optimization subject to convex constraints.
Philippe L. Toint
Mathematical Programming 77 (1997), 69-94.
Change-Id: I199ecc644e8d1a8cb43666052aef66fb93e15569
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
1. Document the use of dogleg and a general discussion of
trust region methods.
2. Added a TBD section on compiler/linker flags.
3. Summary::FullReport now prints out sparse_linear_algebra_library
and trust_region_strategy_type.
Change-Id: I01f680070d510715900f345364855689005d54bb
1. A new dogleg trust region strategy.
2. Consistent naming of all variables taking and reporting
time. Also all are doubles now.
3. Enum to stringification routines.
4. bundle_adjuster.cc accepts max solver time and trust_region_strategy.
5. Time accounting is pushed into solver_impl.cc and there is now
postprocessing time accounted for explicitly.
6. IterationCallback now has cumulative time.
7. LoggingCallback logs per iteration and cumulative time.
8. TrustRegionStrategy now allows for Invalid steps to be indicated
explicitly.
9. Trust region minimizer actually terminates on max_solver_time.
Change-Id: I7e3b82c8beebc17b6b355ea46ddd280754a2d8b2
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. New TrustRegionMinimizer and basic tests for it.
2. New TrustRegionStrategy interface.
3. New LevenbergMarquardtStrategy and tests for it.
4. Updates to SolverImpl to reflect this.
5. Changes to Solver::Options and IterationSummary related to this.
6. Deleted levenberg_marquardt.cc/h/_test.cc
Change-Id: I6c1d1a7c774f014856f9f26263a830aa886e1400
1. Added support for CXSparse - SparseNormalCholesky and
SchurComplementSolver support SuiteSparse and CXSparse now.
I am not sure I will add suport for visibility based
preconditioning using CXSparse. Its not a high priority.
2. New enum SparseLinearAlgebraLibraryType which allows the user
to indicate which sparse linear algebra library should be used.
3. Updated tests for SolverImpl and system_test.
4. Build system changes to automatically detect CXSparse and
link to it by default -- just like SuiteSparse.
5. Minor bug fixes dealing in the cmake files and VBP.
6. Changed the order of the system test.
7. Deduped the unsymmetric linear solver test.
Change-Id: I33252a103c87b722ecb7ed7b5f0ae7fd91249244
In some cases the Levenberg-Marquardt can oscillate between,
two values of the regularizer mu. A small value which causes
the linear solver to fail and a higher value at which the solver
makes progress. This can cause significant wastage of solver
effort, and mu should just be clamped to some value.
This CL provides this setting as Solver::Options::min_mu,
and updates the documentation to reflect this.
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