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
Sameer Agarwal