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. 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
A live version of the doc can be found at
http://homes.cs.washington.edu/~sagarwal/ceres-solver/
As it stands, the documentation has better hyperlinking
and coverage than the latex documentation now.
Change-Id: I7ede3aa83b9b9ef25104caf331e5727b4f5beae5
Sameer Agarwal