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
The GradientChecker is a utility class written by
William Rucklidge that can be used to check that the
derivatives returned by a cost function match those
returned by numerically differentiating the residuals
returned by the same cost function.
This is useful when developing CostFunction objects
and testing them before plugging them into an optimization
problem.
Change-Id: Ic60f859b48b6246406448555d25556784e097b81
Following the last commit, which extends the number of parameters blocks autodiff can accept, the interface of Problem::AddResidualBlock is extended to accept up to 10 parameter blocks.
Change-Id: I162c3d1b1868fdda32c1522d57e9a211a9c02f90
Supporting only 6 parameters in autodiff was enough for most
cases, but 6 was not always sufficient. This extends the
current implementation to work with up to 10 parameters.
This also increases the number of parameters supported in
SizedCostFunction to 10.
Change-Id: Ic783602f93e6ddf4af24fa34eff37c0a4b775dc1
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
The warnings got disabled at some point; this re-enables some of them, and
fixes some of the warnings.
Change-Id: I290a4fdfad18cea85e9177ba57744d97b6856bb2
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
This will make it possible to write code which detects Ceres
versions and does different things with different versions.
Ideally this wouldn't be necessary, but in practice it is
sometimes useful.
Change-Id: I8d9f56d664ef75706e87c9bd7954e709dd7c0278
More flexible testing.
Read and parse the certified cost value from the data file.
Remove the ugly hack for computing the certified cost.
Refactored the flags parsing logic
Change-Id: I8f2e6be183b758b2453302fcdc6696bfa0db5eb8
Eigen3 does not allow column vectors to be stored in row-major
format. NumericDiffCostFunction by default stores its Jacobian
matrices in row-major format. This works fine if the residual
contains more than one variable. But if the residual block
depends on one variable and has more than one residuals, the
resulting Jacobian matrix is a column matrix in row-major format
resulting in a compile time error.
The fix is to check the template parameters and switch to column-major
storage as needed.
Thanks to Lena Gieseke for reporting this.
Change-Id: Icc51c5b38e1f3609e0e1ecb3c4e4a02aecd72c3b
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
This is a workaround for anyone building Ceres in an environment
where there is a non-standard string implementation in the global
namespace. Due to the way the standard is written, a "using
namespace X" import is not high enough precedence to resolve a
naked reference to "string". Instead, by explicitly importing
string, the lookup becomes unambiguous.
Change-Id: I8d70463de01c482796c5bc09da05b37d21e7af96
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
This is a preliminary, but full, port of Ceres to Windows.
Currently all tests compile and run, with only system_test
failing to work correctly due to a path issue.
Change-Id: I4152c1588bf51ffd7f4d9401ef9759f5d28c299c
Ceres has traditionally battled with portability issues
when trying to classify floating point values as one
type or another. For example, in C99 'isnan' is a
macro. Since it is a macro, it is impossible to
override the name in other namespaces.
Instead of trying to use preprocessor hacks to work
around the issue, define our own set of camel-case
names for use internally and by Ceres clients. For
example do this:
template<typename T>
void MyFunction(T x, T y) {
if (ceres::IsNaN(x)) {
...
}
}
instead of using "isnan" or "std::isnan". Note that
while GCC and Apple GCC both import 'isnan' into
the std namespace, it is not standard until C++11
which Ceres will not require for some years.
Change-Id: Ibcc96a8bb4ba63aa67cbbc58658b2e5671cd5824
Added EIGEN_MAKE_ALIGNED_OPERATOR_NEW to the struct so that
the Jet members are aligned. This fixes an eigen assert in
autodiff_test reported by multiple Ceres users.
Thanks Koichi Akabe & Stephan Kassemeyer
Change-Id: Id3574e926deffa57d205dddaa9d08389b5dc33a8
This fixes the bug introduced in a previous commit,
and adds a test to check that constant parameter
blocks work as expected.
This also refactors the Solver/SolverImpl split so
that SolverImpl is no longer a friend of Problem;
instead, Solver is. This makes it possible to
verify the invariant on parameter block states in
the unit test, and is a more symmetric design
anyway.
Bug: 51
Change-Id: Id503f5b526cfb8bc24aae3aaad2e414b14063d78
1. Test that reproduces the failure on macos.
2. Move the alignment macros from manual_constructor.h
to macros.h and rename them to prevent conflicts.
3. The inline array used by FixedArray is now aligned.
4. Jet has been modified to be eigen friendly.
Change-Id: I4563847a767a92156dabab1ab420f0cdddb8ba77
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
Sameer Agarwal