Please use this identifier to cite or link to this item:
Title: Analysis of Inexact Trust-Region SQP Algorithms
Authors: Heinkenschloss, Matthias 
Vicente, Luís N. 
Keywords: Nonlinear programming; Trust–region methods; Inexact linear systems solvers; Krylov subspace methods; Optimal control
Issue Date: 2002
Publisher: Society for Industrial and Applied Mathematics
Citation: SIAM Journal on Optimization. 12:2 (2002) 283-302
Serial title, monograph or event: SIAM Journal on Optimization. 12:2 (2002) 283-302
Issue: 2
Abstract: In this paper we extend the design of a class of composite–step trust–region SQP methods and their global convergence analysis to allow inexact problem information. The inexact problem information can result from iterative linear systems solves within the trust–region SQP method or from approximations of first–order derivatives. Accuracy requirements in our trust–region SQP methods are adjusted based on feasibility and optimality of the iterates. Our accuracy requirements are stated in general terms, but we show how they can be enforced using information that is already available in matrix–free implementations of SQP methods. In the absence of inexactness our global convergence theory is equal to that of Dennis, El–Alem, Maciel (SIAM J. Optim., 7 (1997), pp. 177–207). If all iterates are feasible, i.e., if all iterates satisfy the equality constraints, then our results are related to the known convergence analyses for trust–region methods with inexact gradient information for unconstrained optimization
ISSN: 1052-6234
Rights: openAccess
Appears in Collections:FCTUC Matemática - Artigos em Revistas Internacionais

Files in This Item:
File Description SizeFormat
Analysis of Inexact Trust-Region SQP Algorithms.pdf195 kBAdobe PDFView/Open
Show full item record

Page view(s)

checked on Sep 22, 2020


checked on Sep 22, 2020

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.