** Gauss-Newton and Conjugate-Gradient optimization **

This code implements a Gauss-Newton 
optimization of objective functions that can
be iteratively approximated by quadratics.
This approach is particularly appropriate
for least-squares inversions of moderately
non-linear transforms.  You will also find
code for conjugate-gradient and line-search
optimizations.  

Get documentation of the algorithm here:
[[../../papers/inv/inv.html]] [[../../papers/inv.pdf]] [[../../papers/inv.ps.gz]] 

Several papers describe ways to use this code:
  [[../../papers/regularization.pdf]] [[../../papers/regularization/]]
  [[../../papers/neural.pdf]] [[../../papers/neural/]]
  [[../../papers/rmsinv.pdf]] [[../../papers/rmsinv/]]

See an older C++ version [[../conjugate_gradients/]]

See the java documentation in the 
documentation subdirectory [[documentation]].

The current version of this code is now a part
of the Mines Java Toolkit at 
http://www.mines.edu/~dhale/jtk/ 
in the edu.mines.jtk.opt package,
with code here 
http://boole.mines.edu/jtk/trunk/src/edu/mines/jtk/opt/
and documentation here 
http://boole.mines.edu/jtk/trunk/doc/opt_package/

An older public version is available from
http://code.google.com/p/optimal/