A Filled Function Method Dominated by Filter for Nonlinearly Global Optimization

  title={A Filled Function Method Dominated by Filter for Nonlinearly Global Optimization},
  author={Wei Wang and Xiaoshan Zhang and Min Li},
  journal={J. Appl. Math.},
This work presents a filled function method based on the filter technique for global optimization. Filled function method is one of the effective methods for nonlinear global optimization, since it can effectively find a better minimizer. Filter technique is applied to local optimization methods for its excellent numerical results. In order to optimize the filled function method, the filter method is employed for global optimizations in this method. A new filled function is proposed first, and… 
1 Citation

Tables from this paper

Increasing the Effects of Auxiliary Function by Multiple Extrema in Global Optimization

A new filled function is proposed in this paper for finding a better minimizer of smooth unconstrained global optimization problems and a new algorithm is proposed depending on the theoretical and numerical properties of the proposed filled function.

A new filled function method for global optimization

A new auxiliary function method for systems of nonlinear equations

In this paper, we present a new global optimization method to solve nonlinear systems of equations. We reformulate given system of nonlinear equations as a global optimization problem and then give

Line Search Filter Methods for Nonlinear Programming: Local Convergence

It is shown that the proposed line search method does not suffer from the Maratos effect, so that fast local convergence to second order sufficient local solutions is achieved.

A class of filled functions for finding global minimizers of a function of several variables

The idea behind constructing a better filled function is given and employed to construct the class of filled functions and a method is explored on how to locate minimizers or saddle points of a filled function through the use of the gradient of a function.

A Generalized Gradient Projection Filter Algorithm for Inequality Constrained Optimization

A generalized gradient projection filter algorithm for inequality constrained optimization is presented and is of global convergence and locally superlinear convergence under some mild conditions.

Nonlinear programming without a penalty function

The aim of the present work is to promote global convergence without the need to use a penalty function, so a new concept of a “filter” is introduced which allows a step to be accepted if it reduces either the objective function or the constraint violation function.