Currently, the logic for exporting symbols is rather complicated: when
tests are enabled internal symbols are exported in addition to the
public symbols. Such logic causes several problems. (1) Test binaries
link against a Ceres build that is different from the final release
since fewer optimizations are applied if more symbols are exported. (2)
Also, some toolchains hide symbols by default breaking the existing
logic eventually causing linker errors.
Since internal symbols are not intended to be used outside of the
project, we can compile them into object files and use exactly the same
binary code both for the final build and the tests without relying on
conditionals.
By default, all symbols are now hidden unless annotated as public.
Internal symbols are explicitly marked as not being exported in case
users chose not to hide symbols by default.
Change-Id: I589dd10be2f6f438508783cf99d141af0120057b
This change is needed because compiler attributes and c++
annotations like [[deprecated]] do not mix well and we need that
for our public API.
https://github.com/ceres-solver/ceres-solver/issues/749
Change-Id: I79eda795f2912f9af9ab36ee24b8428b47104743
Add [[deprecate]] notices to everything LocalParameterization
related.
Make sure that Ceres can be compiled without triggering
deprecation warnings.
Update the documentation:
a. Add deprecation notices.
b. Document interaction between LocalParameterization and Manifold
coexisting in the Problem.
c. Add documentation for Manifold(s)
Change-Id: Ie4ad48963c83fded86e533c8c60561af402fbaff
Manifolds are now part of the public API and co-exist
with LocalParameterizations.
1. Add Manifolds to the Problem API.
a. AddParameterBlock(double*, int, Manifold*)
b. SetParameterization(double*, Manifold*)
b. GetManifold(const double*)
c. HasManifold(const double*)
2. Internally Ceres now only uses Manifolds. When the user uses
a LocalParameterization, it is wrapped in a ManifoldAdapter.
3. To preserve the API semantics while keeping the internals clean
we need a new map in ProblemImpl which stores the association
between parameter blocks and local parameterizations. This
is temporary, it will go away once this transition is complete.
4. There are NO algorithmic changes, as in we are not using
any of the expanded interface of the Manifold objects yet.
That will come later.
5. All tests that use LocalParameterization have been duplicated
to use Manifolds, and when this transition is complete the
LocalParameterization based tests will be deleted.
6. Public documentation for the API has been updated. Deprecation
notices to the documentation as well as C++ annotations will come
later.
7. Similar changes have been made to GradientProblem.
Change-Id: I8e03c8ced6e141876ef3eca5740c113afa788f0c
This is to FirstOrderFunction, what AutoDiffCostFunction is to CostFunction.
This allows users of GradientSolver to be able to define objective functions
without requiring them to define the derivatives.
The implementation uses the same Jet objects for computing the gradient as
is used by AutoDiffCostFunction.
Change-Id: Ide6e60532a3adab9be9899ba9b368dc267fd2dbb
1. Replace CERES_DISALLOW_* with explicitly deleted constructors.
2. Replace use of CERES_ARRAY_SIZE and stack allocated arrays
with std::vector.
3. Move CERES_ALIGN_* macros into manual_constructor.h, which is
the one place they are used and will be deprecated along with that
file.
4. Introduce isnan,isnormal,isinf and isfinite for Jets.
5. Replace IsNormal,IsFinite,IsNaN and IsInfinite with corresponding
c++11 function calls.
Change-Id: I04f33a221aae77d247602150988b6d4aa4efeeab
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
The line search minimizer in Ceres does not require that the
problems that is solving is a sum of squares. Over the past
year there have been multiple requests to expose this algorithm
on its own so that it can be used to solve unconstrained
non-linear minimization problems on its own.
With this change, a new optimization problem called
GradientProblem is introduced which is basically a thin
wrapper around a user defined functor that evaluates cost
and gradients (FirstOrderFunction) and an optional LocalParameterization.
Corresponding to it, a GradientProblemSolver and its associated
options and summary structs are introduced too.
An example that uses the new API to find the minimum of Rosenbrock's
function is also added.
Change-Id: I42bf687540da25de991e9bdb00e321239244e8b4
Sergiu Deitsch