There was a bug in the way RemoveFixedBlocksFromProgram was working.
It only removed the constant parameter blocks from the
linear_solver_ordering, it was not even aware of the
inner_iteration_ordering.
This change fixes this bug. The code for RemoveFixedBlocksFromProgram
is also cleaned up and made more readable and the test have been updated.
Thanks to Mikael Persson for reporting this.
Change-Id: I454fa89f9b6f4f6320b02d5235e6f322cc15ff51
1. Move LinearSolverTerminationType to ceres::internal.
2. Add FATAL_ERROR as a new enum to LinearSolverTerminationType.
3. Pipe SuiteSparse errors via a LinearSolverTerminationType so
to distinguish between fatal and non-fatal errors.
4. Update levenberg marquardt and dogleg strategies to deal
with FATAL_ERROR.
5. Update trust_region_minimizer to terminate when FATAL_ERROR
is encountered.
6. Remove SuiteSparse::SolveCholesky as it screws up the error
handling.
7. Fix all clients calling SuiteSparse to handle the result of
SuiteSparse::Cholesky correctly.
8. Remove fatal failures in SuiteSparse when symbolic factorization
fails.
9. Fix all clients of SuiteSparse to deal with null symbolic factors.
This is a temporary fix to deal with some production problems. A more
extensive cleanup and testing regime will be put in place in a
subsequent CL.
Change-Id: I1f60d539799dd95db7ecc340911e261fa4824f92
Since we added special handling for the case for rho[2] < 0,
the bulk of CorrectJacobian is pointless in the common case.
So add a simple one dimensional loop which rescales the Jacobian.
This speeds up this method immensely.
The robustification of a Jacobian gets speeded up by > 50%.
Change-Id: I97c4e897ccbb5521c053e1fb931c5d0d32f542c7
This triggers -Wtype-limits warnings on comparisons
which are always true, since the test being done is
n >= 0, where n is of type size_t, which is always
true.
This causes problems when compiling Ceres on linux
with miniglog.
Change-Id: Ia1d1d1483e03469c71fde029b62ca6d84e9b27e0
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
This adds three new public methods to ceres::Problem:
Problem::GetResidualBlocks()
Problem::GetParameterBlocksForResidualBlock()
Problem::GetResidualBlocksForParameterBlock()
These permit access to the underlying graph structure of the problem.
Change-Id: I55a4c7f0e5f325f140cb4830e7a7070554594650
The Taylor series approximation had its sign flipped and the
tests did not catch it since we were switching exactly at zero,
which was not getting triggered.
This changes modifies the tolerance, adds a test that triggers
and fixes the bug.
Thanks to Michael Samples for reporting this.
Change-Id: I6f92f6348e5d4421ffe194fba92c04285449484c
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
Move the GradientCheckingCostFunction to DynamicNumericDiffCostFunction.
Also fix a const correctness issue with DynamicNumericDiffCostFunction.
Change-Id: Id446810f43374e7b7db7fe4dd01a891e3c54abb9
- Earlier versions of Clang (up to at least v3.0) throw an ambiguous
operator= error in this assignment. Variations on this error have
cropped up occassionally in some other projects (e.g. PCL).
Change-Id: I73a632c43528eb69840ce697c55d9afc5f3d8e59
This brings the ability to have numerically differentiated
cost functions to be added with its structure decided on
runtime rather than compile time.
And some minor cleanups.
Two things still need to be done.
a. Update the modeling docs.
b. Remove RuntimeNumericDiffCostFunction in ceres::internal
and replace its usage with DynamicNumericDiffCostFunction.
Change-Id: Ib771f093f29236c95a99df31c584d579b8e36615
1. Update AutoDiffCostFunction template parameters to be consistent
with NumericDiffCostFunction.
2. Update the documentation for NumericDiffCostFunction and
AutoDiffCostFunction.
Change-Id: I113038abb5bedebb0f6f326f2a4ac31480d785fc
directly from eigen expressions, instead of creating a temporary). Also
changed several variable names from temp to tmp to be consistent with
the code base.
Change-Id: I3f7b834cedca9c4af8e5e086237b40301dbde619
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
1. Typo in c_api.h
2. The stream operator for FunctionSample is now in the ceres::internal namespace.
Change-Id: Id927a7a49c47d8903505535749ecca78cd2e83b3
This extends the C API to support loss functions. Both
user-supplied cost functions as well as the stock Ceres cost
functions (Cauchy, Huber, etc) are supported. In addition, this
adds a simple unit test for the C API.
Supporting loss functions required changing the signature of the
ceres_add_residual_block() function to also take a thunk for the
loss function.
Change-Id: Iefa58cf709adbb8f24588e5eb6aed9aef46b6d73
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
Changed DynamicAutoDiffCostFunction to handle multiple derivative
sections as opposed to just a single contiguous block.
In the previous implementation it was assumed that non-constant
parameters occur in a single contiguous block so that constant
parameters could NOT lie between non-constant parameters. Previously,
start_derivative_section was first set as soon as the first
non-constant parameter block (marked by jacobians[i] != NULL) was
encountered. After this, entries in input_jets[parameter_cursor].v were
accessed with `parameter_cursor - start_derivative_section`. For
contiguous non-constant parameter blocks this is fine, but if constant
parameter blocks fall between then this indexing is incorrect because
`parameter_cursor - start_derivative_section` can go out of bounds.
For a concrete example, take a cost function with three parameter
blocks, each of size 1 and with the center block fixed. Assume that
Stride=1 so that two passes are required. On the first pass
start_derivative_section=0, and the first variable block is handled
correctly. At the end of the first pass end_derivative_section=1, so
for the second pass start_derivative_section=1. Now comes the problem.
When parameter_cursor=1, parameter_cursor >= start_derivative_section
so jacobian[1] is checked to be NULL. Since it is NULL (second
parameter block is constant) then nothing is done and
active_parameter_count is NOT incremented. Next, when
parameter_cursor=2, parameter_cursor >= start_derivative_section and
jacobian[2] is checked. Since it is not NULL then
input_jets[parameter_cursor].v[parameter_cursor -
start_derivative_section] is set to 1.0, BUT parameter_cursor -
start_derivative_section = 2 - 1 = 1 which is out of bounds
(input_jets[parameter_cursor].v is only of size Stride=1).
The proposed solution records the start of each contiguous block of
non-constant parameters and indexing into
input_jets[parameter_cursor].v is independent of parameter_cursor.
Change-Id: I388ab6a0bafa35d317491135ec6fe980453ff888
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. Multithread the inversion of J'J.
2. Simplify the dense rank truncation loop.
3. Minor correction to building documentation.
Change-Id: Ide932811c0f28dc6c253809339fb2caa083865b5
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
1. Sparse covariance estimation now uses cholmod_rcond to
detect singular Jacobians.
2. Dense covariance estimation now uses relative magnitude
of singular/eigen values to compute the pseudoinverse.
3. Truncation logic is now unified with Solver::Options::null_space_rank.
Change-Id: I095bd737510c836b4251255926190a7f31d64bce
Sameer Agarwal