This method numerically computes function derivatives in different
scales, extrapolating between intermediate results to conserve function
evaluations. Adaptive differentiation is essential to produce accurate
results for functions with noisy derivatives.
Full changelist:
-Created a new type of NumericDiffMethod (RIDDERS).
-Implemented EvaluateRiddersJacobianColumn in NumericDiff.
-Created unit tests with f(x) = x^2 + [random noise] and
f(x) = exp(x).
Change-Id: I2d6e924d7ff686650272f29a8c981351e6f72091
1. Push the boundary handling logic into the underlying array
object. This has two very significant impacts:
a. The interpolation code becomes extremely simple to write
and to test.
b. The user has more flexibility in implementing how out of bounds
values are handled. We provide one default implementation.
Change-Id: Ic2f6cf9257ce7110c62e492688e5a6c8be1e7df2
Often a parameter block is the Cartesian product of a number of
manifolds. For example, a rigid transformation SE(3) = SO(3) x R^3
In such cases, where you have the local parameterization
of the individual manifolds available,
ProductParameterization can be used to construct a local
parameterization of the cartesian product.
Change-Id: I4b5bcbd2407a38739c7725b129789db5c3d65a20
This adds a new wrapper class called DynamicCostFunctionToFunctor
that closes a gap in the current API: the existing
CostFunctionToFunctor can only be used with a SizedCostFunction, where
the number and sizes of all parameter vectors are known at compile-time.
The DynamicCostFunctionToFunctor allows you to wrap a generic
CostFunction into a templated functor which can then be used in a
DynamicAutoDiffCostFunction.
Also updates the existing CostFunctionToFunctor class to internally use
DynamicCostFunctionToFunctor.
Change-Id: I088adc3271c58d2519126c27037c3576965a36d6
- Include example use of new find_dependency() macro in CMake 3.x to
find dependencies in <Project>Config.cmake files.
- Also fix typo in NNLS modeling docs.
Change-Id: Ie9862b69c0451ee8775826f2957f5e182d937439
1. Add documentation for cubic_interpolation.h
2. Remove the list of publications. It is an incomplete list which is
a pain to maintain.
3. Add a note about the interaction between manifolds and
NumericDiffCostFunction.
4. Fix some of the comments in cubic_interpolation.h to better reflect reality.
5. Updated the version history.
Change-Id: I0b4a5a6f3361d3fc85f1b4aec685cd80540934f1
The example code for DynamicNumericDiffCostFunction and
DynamicAutoDiffCostFunction in the documentation was
allocating the cost function objects on the stack rather
than the heap.
Thanks to Rodney Hoskinson for reporting this.
Change-Id: I217aee02d1e2c1e9c25b8197451e9f9e0482915d
1. Complete restructuring of the documentation to account for
GradientProblemSolver.
2. Update the version history to account for changes since 1.9.0.
3. Add links and document the various examples that ship with ceres.
4. Documentation for GradientProblem GradientProblemSolver.
Change-Id: If3a18f2850cbc98be1bc34435e9ea468785b8b27
Tal Ben-Nun