Module hestenes_stiefel_cg

Hestenes-Stiefel conjugate gradient optimization.

This file is part of the minfx optimisation library.

Classes

Hestenes_stiefel

Functions

hestenes_stiefel(func=None, dfunc=None, args=(), x0=None, min_options=None, func_tol=1e-25, grad_tol=None, maxiter=1000000.0, a0=1.0, mu=0.0001, eta=0.1, full_output=0, print_flag=0, print_prefix='')
Hestenes-Stiefel conjugate gradient algorithm.

Variables

__package__ = 'minfx'

Imports: dot, Conjugate_gradient, Line_search, Min

Function Details

hestenes_stiefel(func=None, dfunc=None, args=(), x0=None, min_options=None, func_tol=1e-25, grad_tol=None, maxiter=1000000.0, a0=1.0, mu=0.0001, eta=0.1, full_output=0, print_flag=0, print_prefix='')

Hestenes-Stiefel conjugate gradient algorithm.

Page 122 from 'Numerical Optimization' by Jorge Nocedal and Stephen J. Wright, 1999, 2nd ed. The algorithm is:

• Given x0
• Evaluate f0 = f(x0), g0 = g(x0)
• Set p0 = -g0, k = 0
• while g0 != 0:
  • Compute ak and set xk+1 = xk + ak.pk
  • Evaluate gk+1
  • bk+1 = dot(gk+1, (gk+1 - gk)) / dot((gk+1 - gk), pk)
  • pk+1 = -gk+1 + bk+1.pk
  • k = k + 1