Package esys :: Package escript :: Module minimize
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Module minimize


Version: 0.3.1

Copyright: Copyright (c) 2003-2009 by University of Queensland Earth Systems Science Computational Center (ESSCC) http://www.uq.edu.au/esscc Primary Business: Queensland, Australia

License: Licensed under the Open Software License version 3.0 http://www.opensource.org/licenses/osl-3.0.php

Functions [hide private]
 
approx_fhess_p(x0, p, fprime, *args)
 
approx_fprime(xk, f, *args)
 
fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, fulloutput=0, printmessg=1)
xopt,{fval,warnflag} = fmin(function, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=200*len(x0), maxfun=200*len(x0), fulloutput=0, printmessg=0)
 
fminBFGS(f, x0, fprime=None, args=(), avegtol=1e-05, maxiter=None, fulloutput=0, printmessg=1)
xopt = fminBFGS(f, x0, fprime=None, args=(), avegtol=1e-5, maxiter=None, fulloutput=0, printmessg=1)
 
fminNCG(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-05, maxiter=None, fulloutput=0, printmessg=1)
xopt = fminNCG(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, maxiter=None, fulloutput=0, printmessg=1)
 
line_search(f, fprime, xk, pk, gfk, args=(), c1=0.0001, c2=0.9, amax=50)
alpha, fc, gc = line_search(f, xk, pk, gfk, args=(), c1=1e-4, c2=0.9, amax=1)
 
line_search_BFGS(f, xk, pk, gfk, args=(), c1=0.0001, alpha0=1)
alpha, fc, gc = line_search(f, xk, pk, gfk, args=(), c1=1e-4, alpha0=1)
 
para(x)
 
para_der(x)
 
para_hess(x)
 
para_hess_p(x, p)
 
rosen(x)
 
rosen3_hess(x)
 
rosen3_hess_p(x, p)
 
rosen_der(x)
 
zoom(a_lo, a_hi)
Variables [hide private]
  __url__ = 'https://launchpad.net/escript-finley'
  epsilon = 1e-08
Function Details [hide private]

fmin(func, x0, args=(), xtol=0.0001, ftol=0.0001, maxiter=None, maxfun=None, fulloutput=0, printmessg=1)

 

xopt,{fval,warnflag} = fmin(function, x0, args=(), xtol=1e-4, ftol=1e-4, maxiter=200*len(x0), maxfun=200*len(x0), fulloutput=0, printmessg=0)

Uses a Nelder-Mead Simplex algorithm to find the minimum of function of one or more variables.

fminBFGS(f, x0, fprime=None, args=(), avegtol=1e-05, maxiter=None, fulloutput=0, printmessg=1)

 

xopt = fminBFGS(f, x0, fprime=None, args=(), avegtol=1e-5, maxiter=None, fulloutput=0, printmessg=1)

Optimize the function, f, whose gradient is given by fprime using the quasi-Newton method of Broyden, Fletcher, Goldfarb, and Shanno (BFGS) See Wright, and Nocedal 'numarrayerical Optimization', 1999, pg. 198.

fminNCG(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-05, maxiter=None, fulloutput=0, printmessg=1)

 

xopt = fminNCG(f, x0, fprime, fhess_p=None, fhess=None, args=(), avextol=1e-5, maxiter=None, fulloutput=0, printmessg=1)

Optimize the function, f, whose gradient is given by fprime using the Newton-CG method. fhess_p must compute the hessian times an arbitrary vector. If it is not given, finite-differences on fprime are used to compute it. See Wright, and Nocedal 'numarrayerical Optimization', 1999, pg. 140.

line_search(f, fprime, xk, pk, gfk, args=(), c1=0.0001, c2=0.9, amax=50)

 

alpha, fc, gc = line_search(f, xk, pk, gfk, args=(), c1=1e-4, c2=0.9, amax=1)

minimize the function f(xk+alpha pk) using the line search algorithm of Wright and Nocedal in 'numarrayerical Optimization', 1999, pg. 59-60

line_search_BFGS(f, xk, pk, gfk, args=(), c1=0.0001, alpha0=1)

 

alpha, fc, gc = line_search(f, xk, pk, gfk, args=(), c1=1e-4, alpha0=1)

minimize over alpha, the function f(xk+alpha pk) using the interpolation algorithm (Armiijo backtracking) as suggested by Wright and Nocedal in 'numarrayerical Optimization', 1999, pg. 56-57