Nonlinear programming without a penalty function or a filter

• 成果类型: Article
• 署名作者: Gould, N. I. M.; Toint, Ph. L.
• 署名单位: University of Namur; University of Oxford
• 刊物名称: MATHEMATICAL PROGRAMMING
• ISSN/ISSBN: 0025-5610
• DOI: 10.1007/s10107-008-0244-7
• 发表日期: 2010
• 页码: 155-196
• 关键词: Constrained optimization; GLOBAL CONVERGENCE; algorithm; EQUALITY

摘要

A new method is introduced for solving equality constrained nonlinear optimization problems. This method does not use a penalty function, nor a filter, and yet can be proved to be globally convergent to first-order stationary points. It uses different trust-regions to cope with the nonlinearities of the objective function and the constraints, and allows inexact SQP steps that do not lie exactly in the nullspace of the local Jacobian. Preliminary numerical experiments on CUTEr problems indicate that the method performs well.