Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/11218
Title: | A globally convergent primal-dual interior-point filter method for nonlinear programming: new filter optimality measures and computational results | Authors: | Silva, Renata Ulbrich, Michael Ulbrich, Stefan Vicente, Luís Nunes |
Keywords: | Interior-point methods; Primal-dual; Filter; Global convergence; Largescale NLP | Issue Date: | 2008 | Publisher: | Centro de Matemática da Universidade de Coimbra | Citation: | Pré-Publicações DMUC. 08-49 (2008) | Abstract: | In this paper we prove global convergence for first and second-order stationarity points of a class of derivative-free trust-region methods for unconstrained optimization. These methods are based on the sequential minimization of linear or quadratic models built from evaluating the objective function at sample sets. The derivative-free models are required to satisfy Taylor-type bounds but, apart from that, the analysis is independent of the sampling techniques. A number of new issues are addressed, including global convergence when acceptance of iterates is based on simple decrease of the objective function, trust-region radius maintenance at the criticality step, and global convergence for second-order critical points. | URI: | https://hdl.handle.net/10316/11218 | Rights: | openAccess |
Appears in Collections: | FCTUC Matemática - Vários |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
A globally convergent primal-dual interior-point filter method.pdf | 321.5 kB | Adobe PDF | View/Open |
Page view(s) 50
426
checked on Oct 8, 2024
Download(s) 50
305
checked on Oct 8, 2024
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.