On the Global Convergence of a Filter--SQP Algorithm

Authors:
Roger Fletcher;Sven Leyffer;Philippe L. Toint
Affiliations:
-;-;-
Venue:
SIAM Journal on Optimization
Year:
2002

Citing 0
Cited 28

A Trust Region SQP Algorithm for Equality Constrained Parameter Estimation with Simple Parameter Bounds

Computational Optimization and Applications
Interior-Point Algorithms, Penalty Methods and Equilibrium Problems

Computational Optimization and Applications
FILTRANE, a Fortran 95 filter-trust-region package for solving nonlinear least-squares and nonlinear feasibility problems

ACM Transactions on Mathematical Software (TOMS)
A line search filter approach for the system of nonlinear equations

Computers & Mathematics with Applications
A globally and superlinearly convergent modified SQP-filter method

Journal of Global Optimization
Projected filter trust region methods for a semismooth least squares formulation of mixed complementarity problems

Optimization Methods & Software
A filter-trust-region method for simple-bound constrained optimization

Optimization Methods & Software
Global and local convergence of a filter line search method for nonlinear programming

Optimization Methods & Software
Two derivative-free algorithms for nonlinear equations

Optimization Methods & Software
Classification model selection via bilevel programming

Optimization Methods & Software - Mathematical programming in data mining and machine learning
Optimization and dynamical systems algorithms for finding equilibria of stochastic games

Optimization Methods & Software
A nonmonotone filter trust region method for nonlinear constrained optimization

Journal of Computational and Applied Mathematics
Global convergence of a tri-dimensional filter SQP algorithm based on the line search method

Applied Numerical Mathematics
A modified SQP method with nonmonotone technique and its global convergence

Computers & Mathematics with Applications
A penalty-function-free line search SQP method for nonlinear programming

Journal of Computational and Applied Mathematics
A Complementarity Constraint Formulation of Convex Multiobjective Optimization Problems

INFORMS Journal on Computing
Brief paper: Two nonlinear optimization methods for black box identification compared

Automatica (Journal of IFAC)
FilMINT: An Outer Approximation-Based Solver for Convex Mixed-Integer Nonlinear Programs

INFORMS Journal on Computing
A filter method to solve nonlinear bilevel programming problems

ICICA'10 Proceedings of the First international conference on Information computing and applications
A non-monotone line search multidimensional filter-SQP method for general nonlinear programming

Numerical Algorithms
Implementation of an interior point multidimensional filter line search method for constrained optimization

ICOSSE'06 Proceedings of the 5th WSEAS international conference on System science and simulation in engineering
A modified SLP algorithm and its global convergence

Journal of Computational and Applied Mathematics
A line search filter algorithm with inexact step computations for equality constrained optimization

Applied Numerical Mathematics
A trust region SQP-filter method for nonlinear second-order cone programming

Computers & Mathematics with Applications
Fletcher's filter methodology as a soft selector in evolutionary algorithms for constrained optimization

SIDE'12 Proceedings of the 2012 international conference on Swarm and Evolutionary Computation
Handling infeasibility in a large-scale nonlinear optimization algorithm

Numerical Algorithms
Global and local convergence of a penalty-free method for nonlinear programming

Computers & Mathematics with Applications
An alternating structured trust region algorithm for separable optimization problems with nonconvex constraints

Computational Optimization and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

A mechanism for proving global convergence in SQP--filter methods for nonlinear programming (NLP) is described. Such methods are characterized by their use of the dominance concept of multiobjective optimization, instead of a penalty parameter whose adjustment can be problematic. The main point of interest is to demonstrate how convergence for NLP can be induced without forcing sufficient descent in a penalty-type merit function.The proof relates to a prototypical algorithm, within which is allowed a range of specific algorithm choices associated with the Hessian matrix representation, updating the trust region radius, and feasibility restoration.