From: Axel Etzold <AEtzold@...>
Date: 2007-07-14T00:34:50+09:00
Subject: More general multidimensional minimization in Rb-GSL ?

Dear all,

in Ruby-Gsl's multidimensional minimization, the FMinimizer function
requires an equal number of variables and parameters, as
illustrated in the example

include GSL::MultiMin

my_f = Proc.new { |v, params|
  x = v[0]; y = v[1]
  p0 = params[0]; p1 = params[1]
  10.0*(x - p0)*(x - p0) + 20.0*(y - p1)*(y - p1) + 30.0
}

my_df = Proc.new { |v, params, df|
  x = v[0]; y = v[1]
  p0 = params[0]; p1 = params[1]
  df[0] = 20.0*(x-p0)
  df[1] = 40.0*(y-p1)
}

my_func = Function_fdf.alloc(my_f, my_df, 2)
my_func.set_params([1.0, 2.0])      # parameters

Can this be generalized somehow ... without tinkering with the
C code ?

More precisely, I'd like to do some minimization of the values
of a vector under some constraints, like:

m*(target-vector)= minimal,

 where m is a matrix,
all entries of vectors target and vector are whole numbers,
but differ in a prescribed number of entries .

(Pseudo-)inverting m is not really an option, as can it be quite big ( several thousand lines / rows ). 
I've tried to implement the constraints in a Proc involving m and
target, called on vector, but can't get it to work with the Ruby-GSL minimization
FMinimizer.

Any ideas?

Thank you very much,

Axel 
  
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