Paper Info
Title
Analysis of Inexact Trust-Region SQP Algorithms
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 system 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, and 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.
Year
DOI
Venue
2002
10.1137/S1052623499361543
SIAM Journal on Optimization
Keywords
Field
DocType
trust-region sqp method,trust-region method,global convergence analysis,accuracy requirement,inexact gradient information,inexact problem information,inexact trust-region sqp algorithms,composite-step trust-region sqp method,known convergence analysis,global convergence theory,sqp method,nonlinear programming,linear system,first order,satisfiability,optimal control,trust region
Convergence (routing),Discrete mathematics,Trust region,Mathematical optimization,Optimal control,Linear system,Nonlinear programming,Symbolic convergence theory,Sequential quadratic programming,Iterated function,Mathematics
Journal
Volume
Issue
ISSN
12
2
1052-6234
Citations 
PageRank 
References 
36
6.47
9
Authors
2
Name
Order
Citations
PageRank
Matthias Heinkenschloss118624.70
Luis N. Vicente28211.72