A nonmonotone filter trust region method for nonlinear constrained optimization

Authors:
Ke Su;Dingguo Pu
Affiliations:
Department of Mathematics, Tongji University, ShangHai, 200092, PR China and College of Mathematics and Computer, Hebei University, Baoding, 071002, PR China;Department of Mathematics, Tongji University, ShangHai, 200092, PR China
Venue:
Journal of Computational and Applied Mathematics
Year:
2009

Citing 10
Cited 0

A nonmonotone line search technique for Newton's method

SIAM Journal on Numerical Analysis
More test examples for nonlinear programming codes

More test examples for nonlinear programming codes
Trust region algorithms for optimization with nonlinear equality and inequality constraints

Trust region algorithms for optimization with nonlinear equality and inequality constraints
A trust region algorithm for equality constrained optimization

Mathematical Programming: Series A and B
On the Global Convergence of a Filter--SQP Algorithm

SIAM Journal on Optimization
A Global Convergence Theory for General Trust-Region-Based Algorithms for Equality Constrained Optimization

SIAM Journal on Optimization
Global Convergence of a Trust-Region SQP-Filter Algorithm for General Nonlinear Programming

SIAM Journal on Optimization
On the superlinear local convergence of a filter-SQP method

Mathematical Programming: Series A and B
A globally convergent primal-dual interior-point filter method for nonlinear programming

Mathematical Programming: Series A and B
A nonmonotone trust-region algorithm with nonmonotone penalty parameters for constrained optimization

Journal of Computational and Applied Mathematics

Quantified Score

Hi-index	7.29

Visualization

Abstract

In this paper, we present a nonmonotone filter trust region algorithm for solving nonlinear equality constrained optimization. Similar to Bryd-Omojokun class of algorithms, each step is composed of a quasi-normal step and a tangential step. This new method has more flexibility for the acceptance of the trial step compared to the filter methods, and requires less computational costs compared with the monotone methods. Under reasonable conditions, we give the globally convergence properties. Numerical tests are presented that confirm the efficiency of the approach.