Since Ceres is moving to using GitHub for issues, and the Google
Code URL in the current copyright header will soon become invalid,
update all the headers.
Change-Id: I1fce70375d1bcf098591f07b4d8f01a5c1e0789c
For historical reasons we had a "using namespace std;" in port.h. This
is generally a bad idea. So removing it and along the way doing a bunch
of cpplint cleanup.
Change-Id: Ia125601a55ae62695e247fb0250df4c6f86c46c6
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
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. polynomial_solver* -> polynomial*.
2. Added support for differentiating polynomials.
2. Added support for interpolating polynomials from function
values and gradients.
3. Added support for minimizing polynomials by solving
for the roots of their derivatives in an interval.
4. Added support for finding the minimum of a polynomial
that interpolates function values and gradients in
an interval.
Change-Id: Id7e6764ad4db09c3edd60f1378c7f50f20dd08dc
This way, setting the lower and upper bound both to 1.0, one can disable
the automatic trust region scaling.
Change-Id: Ifa317a6911b813a89c1cf7fdfde25af603705319
In the TrustRegionMinimizer, the step is currently implicitly negated.
This is done so that the linearized residual is |r - J*step|^2, which
corresponds to J*step = r, so neither J nor r have to be modified.
However, it leads to the rather unintuitive situation that the strategy
returns a step in positive gradient direction, which you would expect to
increase the function value. One way is to rename the "step" parameter in
the strategy to "negative_step" and document it.
This patch instead moves the negation inside the strategy, just around
the linear solver call, so that it is done in a local context and easier
to document.
Change-Id: Idb258149a01f61c64e22128ea221c5a30cd89c89
In StepAccepted, reuse_ was not cleared if the improvement was not good
enough. This is obviously wrong as the step was taken nevertheless and
the computed entities may be completely different at the new iterate.
Change-Id: I4bf3441f71550a0595dd8c5565aedd38d03e8b2f
In the Levenberg-Marquardt algorithm, the diagonal of J^T J is used to
regularize the problem. This corresponds to an elliptical trust region
|| D step || <= r, where D = sqrt(diag(J^T J)).
This commit adds the same elliptical trust region to the dogleg
strategy. The trust region problem becomes
min. x^T H x + g^T x
s.t. || D x || <= r
By substituting y = D x, it becomes
min. y^T D^-1 H D^-1 y + g^T D^-1 y
s.t. || y || <= r
which is the traditional spherical trust region problem.
This commit changes the DoglegStrategy so that the Gauss-Newton point,
the gradient, and the Cauchy point are scaled correctly (without
modifying the Jacobian directly). Then the dogleg step is computed the
same way as before, and finally the step is rescaled to obtain
x = D^-1 y.
Change-Id: Iea25a9113ecba911b746e269bc6e6fe51cb59003
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
Sameer Agarwal