Adaptive numeric differentiation using Ridders' method.

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
14 files changed
tree: ea7ee93afe4d8d51b23d1f509bbcb6ae07359d9c
  1. cmake/
  2. config/
  3. data/
  4. docs/
  5. examples/
  6. include/
  7. internal/
  8. jni/
  9. scripts/
  10. .gitignore
  11. CMakeLists.txt
  12. LICENSE
  13. README.md
README.md

Ceres Solver - A non-linear least squares minimizer

Please see ceres-solver.org for more information.

WARNING - Do not make GitHub pull requests!

Ceres development happens on Gerrit, including both repository hosting and code reviews. The GitHub Repository is a continuously updated mirror which is primarily meant for issue tracking. Please see our Contributing to Ceres Guide for more details.

The upstream Gerrit repository is

https://ceres-solver.googlesource.com/ceres-solver