Function minimzation functions

[Etienne Grossmann]

  Hello,

  just to say that I have put a conjugate gradient and a
Davidon-Fletcher-Powell method for optimization of functions whose
derivatives are available. 

  I am not very happy with the Synopsis, so I might change it in the
  future. 

  Cheers,

  Etienne

  The Synopsis for DFP goes like this : 



======================================================================

      [x,v,niter,h] = dfp_min(func,dfunc,xinit,options)

 Broyden-Fletcher-Goldfarb-Shanno Variable metric method for
 minimizing the function 'func', whose derivative is 'dfunc', starting
 from 'xinit'. 

 TODO : Ease the following restrictions

 - xinit is a N-by-1 column vector. 
 - func  takes a single (column vector) argument.
 - dfunc takes a single (column vector) argument and returns a row
   vector. 

 OPTIONS

 'vinit',v     : Value of the function at the starting xinit.
 'maxiter', m  : At most m iterations are done (default : 200) 
 'gtol',gtol   : Set the threshold for the stopping criterion  

              gtol > max { df(i)*max(|x(i)|,1)/max(v,1) | i in 1..N }

                 where x is the current minimum, v is func(x) and df
                 is dfunc(x).
                 Default value is 10*eps

  'tol', tol    : Set the threshold for the stopping criterion  

              tol > max { dx(i)/max(|x(i)|,1) | i in 1..N }

                 where  dx is the change in the x that occured in the
                 last iteration.
                 Default value is 10*eps



-----------------------------------------------------------------------
Octave is freely available under the terms of the GNU GPL.

Octave's home on the web:  http://www.che.wisc.edu/octave/octave.html
How to fund new projects:  http://www.che.wisc.edu/octave/funding.html
Subscription information:  http://www.che.wisc.edu/octave/archive.html
-----------------------------------------------------------------------