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
Also
1. Remove an inadvertent LOG(INFO) from trust_region_minimizer.cc
2. Refactor some of the code in FullReport to reduce duplication
across line search and trust region minimizers.
3. Consistent capitalization.
Change-Id: I9078b1704efab23d2858530636f524e60c7d9016
This allows CovarianceImpl to be forward declared without
scoped_ptr freaking out.
Thanks to Nima Keivan for reporting this.
Change-Id: Icd5aa766b3aab70246055225231a4b971c6b7b90
Add a Covariance object to the API.
Given a Problem object and a set of parameter block pairs the
Covariance object computes a sparse covariance matrix corresponding
to those block pairs and provides random access to them.
Constant parameter blocks and parameter blocks with local parameterizations
are correctly handled.
Sparse and dense implementations are provided. With the dense implementation
rank deficient Jacobians can also be handled.
Parts of the code are threaded using OpenMP if available.
Change-Id: I5b49583b3d79579df3e0f334c22567acb23ed4ad
This introduces a simple C API for a subset of Ceres. This opens the door to
using languages like Python to call Ceres, since it is much easier to bind to C
than it is to bind to C++. It will mean giving up the native Ceres autodiff.
The implementation in this patch does not attempt to do everything but is only
just enough to get started. Subsequent patches will increase the surface area
of Ceres that is covered by the C API.
Change-Id: Ic51804bac6865e1a2e476553248aabc91dff3409
1. Bug fix in NumericDiffCostFunction (Thanks to Nicolas Brodu).
2. Minor documentation update in solver.h
3. Version history update.
4. Bump the version and ABI version.
Change-Id: I951574ddd0b2c4c03b9c79ff33eb9bea549071e7
Sparse Cholesky factorization algorithms use a fill-reducing
ordering to permute the columns of the Jacobian matrix. There
are two ways of doing this.
1. Compute the Jacobian matrix in some order and then have the
factorization algorithm permute the columns of the Jacobian.
2. Compute the Jacobian with its columns already permuted.
The first option incurs a significant memory penalty. The
factorization algorithm has to make a copy of the permuted
Jacobian matrix.
Starting with this change Ceres pre-permutes the columns of the
Jacobian matrix and generally speaking, there is no performance
penalty for doing so.
In some rare cases, it is worth using a more complicated
reordering algorithm which has slightly better runtime
performance at the expense of an extra copy of the Jacobian
matrix. Setting Solver::Options::use_postordering to true
enables this tradeoff.
This change also removes Solver::Options::use_block_amd
as an option. All matrices are ordered using their block
structure. The ability to order them by their scalar
sparsity structure has been removed.
Here is what performance on looks like on some BAL problems.
Memory
======
HEAD pre-ordering
16-22106 137957376.0 113516544.0
49-7776 56688640.0 46628864.0
245-198739 1718005760.0 1383550976.0
257-65132 387715072.0 319512576.0
356-226730 2014826496.0 1626087424.0
744-543562 4903358464.0 3957878784.0
1024-110968 968626176.0 822071296.0
Time
====
HEAD pre-ordering
16-22106 3.8 3.7
49-7776 1.9 1.8
245-198739 82.6 81.9
257-65132 14.0 13.4
356-226730 98.8 95.8
744-543562 325.2 301.6
1024-110968 42.1 37.1
Change-Id: I6b2e25f3fed7310f88905386a7898ac94d37467e
This class is used to create local parameterization
with Jacobians computed via automatic differentiation.
To get an auto differentiated local parameterization,
class with a templated operator() (a functor) that
computes
plus_delta = Plus(x, delta);
shall be defined.
Then given such functor, the auto differentiated local
parameterization can be constructed as
LocalParameterization* local_parameterization =
new AutoDiffLocalParameterization<PlusFunctor, 4, 3>;
| |
Global Size ---------------+ |
Local Size -------------------+
See autodiff_local_parameterization.h for more information
and usage example.
Initial implementation by Keir Mierle, finished by self
and integrated into Ceres and covered with unit tests
by Sameer Agarwal.
Change-Id: I1b3e48ae89f81e0cf1f51416c5696e18223f4b21
1. Introduce new typdefs in eigen.h to allow for column
major matrices.
2. Clean up old unused typedefs, and the aligned typedefs
since they do not actually add any real performance.
3. Made eigen.h conform to the google style guide by removing
the using directives. They were polluting the ceres namespace.
4. Made the template specialization generator work again.
Change-Id: Ic2268c784534b737ebd6e1a043e2a327adaeca37
Sameer Agarwal