Class Min

Instance Methods

__init__(self)
Base class containing the main minimisation iterative loop algorithm.

double_test(self, fk_new, fk, gk)
Default base class function for both function and gradient convergence tests.

func_test(self, fk_new, fk, gk=None)
Default base class function for the function convergence test.

grad_test(self, fk_new, fk, gk)
Default base class function for the gradient convergence test.

hessian_type_and_mod(self, min_options, default_type='Newton', default_mod='GMW')
Hessian type and modification options.

minimise(self)
Main minimisation iterative loop algorithm.

setup_conv_tests(self)
Default base class for selecting the convergence tests.

update(self)
Default base class update function.

Method Details

__init__(self)
(Constructor)

Base class containing the main minimisation iterative loop algorithm.

The algorithm is defined in the minimise function. Also supplied are generic setup, convergence tests, and update functions.

double_test(self, fk_new, fk, gk)

Default base class function for both function and gradient convergence tests.

Test if the minimum function tolerance between fk and fk+1 has been reached as well as if the minimum gradient tolerance has been reached.

func_test(self, fk_new, fk, gk=None)

Default base class function for the function convergence test.

Test if the minimum function tolerance between fk and fk+1 has been reached.

grad_test(self, fk_new, fk, gk)

Default base class function for the gradient convergence test.

Test if the minimum gradient tolerance has been reached. Minimisation will also terminate if the function value difference between fk and fk+1 is zero. This modification is essential for the quasi-Newton techniques.

hessian_type_and_mod(self, min_options, default_type='Newton', default_mod='GMW')

Hessian type and modification options.

Function for sorting out the minimisation options when either the Hessian type or Hessian modification can be selected.

minimise(self)

Main minimisation iterative loop algorithm.

This algorithm is designed to be compatible with all iterative minimisers. The outline is:

• k = 0
• while 1:
  • New parameter function
  • Convergence tests
  • Update function
  • k = k + 1

update(self)

Default base class update function.

xk+1 is shifted to xk fk+1 is shifted to fk