Module ncg

Line search Newton conjugate gradient optimization.

This file is part of the minfx optimisation library at https://gna.org/projects/minfx. This file is part of the minfx optimisation library.

Classes

Ncg

Functions

ncg(func=None, dfunc=None, d2func=None, args=(), x0=None, min_options=None, func_tol=1e-25, grad_tol=None, maxiter=1000000.0, a0=1.0, mu=0.0001, eta=0.9, full_output=0, print_flag=0, print_prefix='')
Line search Newton conjugate gradient algorithm.

Variables

__package__ = 'minfx'

Imports: dot, float64, sqrt, zeros, Line_search, Min

Function Details

ncg(func=None, dfunc=None, d2func=None, args=(), x0=None, min_options=None, func_tol=1e-25, grad_tol=None, maxiter=1000000.0, a0=1.0, mu=0.0001, eta=0.9, full_output=0, print_flag=0, print_prefix='')

Line search Newton conjugate gradient algorithm.

Page 140 from 'Numerical Optimization' by Jorge Nocedal and Stephen J. Wright, 1999, 2nd ed. The algorithm is:

• Given initial point x0.
• while 1:
  • Compute a search direction pk by applying the CG method to Hk.pk = -gk, starting from x0 = 0. Terminate when ||rk|| <= min(0.5,sqrt(||gk||)), or if negative curvature is encountered.
  • Set xk+1 = xk + ak.pk, where ak satisfies the Wolfe, Goldstein, or Armijo backtracking conditions.