1. Fix a build breakage in graph_test.
2. Respect Solver::Options::min_num_linear_solver_iterations in
conjugate_gradients_solver.cc
Thanks to Johannes Schönberger for reporting these.
Change-Id: Ib32e3929bf5d92dd576ae5b53d4d88797095136e
SPARSE_NORMAL_CHOLESKY and SPARSE_SCHUR can now be used
with EIGEN_SPARSE as the backend.
The performance is not as good as CXSparse. This needs to be
investigated. Is it because the quality of AMD ordering that
we are computing is not as good as the one for CXSparse? This
could be because we are working with the scalar matrix instead
of the block matrix.
Also, the upper/lower triangular story is not completely clear.
Both of these issues will be benchmarked and tackled in the
near future.
Also included in this change is a bunch of cleanup to the
SparseNormalCholeskySolver and SparseSchurComplementSolver
classes around the use of the of defines used to conditionally
compile out parts of the code.
The system_test has been updated to test EIGEN_SPARSE also.
Change-Id: I46a57e9c4c97782696879e0b15cfc7a93fe5496a
Sparse Cholesky factorization is not rank revealing. Therefore
this algorithm cannot reliably tell when the Jacobian matrix is
rank deficient or so poorly conditioned that the covariance matrix
cannot be estimated.
Making things worse, this algorithm works on the normal equations,
which makes the conditioning problem much worse.
This change, deletes the SPARSE_CHOLESKY algorithm in the covariance
estimation code. Also to make the naming consistent, it renames
SPARSE_QR -> SUITE_SPARSE_QR
so that it parallels EIGEN_SPARSE_QR.
Also, since we now have EIGEN_SPARSE_QR, we can default to using
it when SuiteSparse is not available instead of DENSE_SVD, which
generally speaking should only be used by folks who are dealing
with small rank deficient jacobians.
Change-Id: I8b134c7e8a2e86ca374371f185b19f1c3e74349c
This reworks the Ceres Sphinx documentation such that it can
function as the main Ceres website, now hosted at
ceres-solver.org. This also changes to the theme sphinx_rtd_theme
used by Read The Docs; this theme has strong mobile support and is
well enough designed.
Change-Id: I63232d985859a6dac94ff58f08bf81eb2b9e7f99
1. Reflect the demise of the various FOO_TOLERANCE convergence
types.
2. Update the documentation for Solver::Options::gradient_tolerance.
Change-Id: I6bd11d260d6f8a3222b6a41a62dc4943ec8fcfbf
1. Rename SolverTerminationType to TerminationType.
2. Consolidate the enum as
a. CONVERGENCE - subsumes FUNCTION_TOLERANCE, PARAMETER_TOLERANCE and GRADIENT_TOLERANCE
b. NO_CONVERGENCE
c. FAILURE - captures all kinds of failures including DID_NOT_RUN.
d. USER_SUCCESS
e. USER_FAILURE
3. Solver::Summary::error is renamed to be Solver::Summary::message, to both
reduce confusion as well as capture its true meaning.
Change-Id: I27a382e66e67f5a4750d0ee914d941f6b53c326d
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
1. Update the documentation to be Sphinx friendly.
2. Remove dead fields in Solver::Summary.
a. Solver::Summary::num_eliminate_blocks_given.
b. Solver::Summary::num_eliminate_blocks_used.
Change-Id: I43e0070c88abe3bf285d91e6c7524f3d887deb33
1. Make the documentation build with Sphinx 1.2
2. Update the documentation for IterationSummary to
be Sphinx friendly.
Change-Id: I93c374987bc0597e854fbcb5a4a25dd2058607f5
1. When a LAPACK implementation is present, then
DENSE_QR, DENSE_NORMAL_CHOLESKY and DENSE_SCHUR
can use it for doing dense linear algebra operations.
2. The user can switch dense linear algebra libraries
by setting Solver::Options::dense_linear_algebra_library_type.
3. Solver::Options::sparse_linear_algebra_library is now
Solver::Options::sparse_linear_algebra_library_type to be consistent
with all the other enums in Solver::Options.
4. Updated documentation as well as Solver::Summary::FullReport
to reflect these changes.
Change-Id: I5ab930bc15e90906b648bc399b551e6bd5d6498f
lm_max_diagonal -> max_lm_diagonal
lm_min_diagonal -> min_lm_diagonal
linear_solver_max_num_iterations -> max_linear_solver_iterations
linear_solver_min_num_iterations -> min_linear_solver_iterations
This follows the pattern for the other parameters in Solver::Options
where, the max/min is the first word followed by the name of the
parameter.
Change-Id: I0893610fceb6b7983fdb458a65522ba7079596a7
This support was broken due to the TrustRegionMinimizer refactoring.
It is now enabled again, with the responsibilty for dumping the
problem shifted to the individual TrustRegionStrategy.
There is however one wrinkle, which is perhaps an indication of
poor design to start with. The LinearLeastSquaresProblemProto
carries in it num_eliminate_blocks, something which does not
exist anymore. More importantly, the TrustRegionStrategy does not
have access to this quantity anymore.
Dealing with this will be the subject of a future change.
Change-Id: I358adf6a2e386f4940b617bf950d6c7e87d2635d
1. Further tightening of the Covariance documentation.
2. Documented minimizer progress output.
3. Lint cleanup from William Rucklidge.
4. Updated version history.
Change-Id: I8bc28484675d4edf89a7c050b6379dbac6c39e91
A live version of the doc can be found at
http://homes.cs.washington.edu/~sagarwal/ceres-solver/
As it stands, the documentation has better hyperlinking
and coverage than the latex documentation now.
Change-Id: I7ede3aa83b9b9ef25104caf331e5727b4f5beae5
Sameer Agarwal