Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/45703
Title: A surrogate management framework using rigorous trust-region steps
Authors: Gratton, S. 
Vicente, Luís Nunes 
Issue Date: 2014
Publisher: Taylor & Francis
Project: PEst-C/MAT/UI0324/2011 
Serial title, monograph or event: Optimization Methods and Software
Volume: 29
Issue: 1
Abstract: Surrogate models are frequently used in the optimization engineering community as convenient approaches to deal with functions for which evaluations are expensive or noisy, or lack convexity. These methodologies do not typically guarantee any type of convergence under reasonable assumptions. In this article, we will show how to incorporate the use of surrogate models, heuristics, or any other process of attempting a function value decrease in trust-region algorithms for unconstrained derivative-free optimization, in a way that global convergence of the latter algorithms to stationary points is retained. Our approach follows the lines of search/poll direct-search methods and corresponding surrogate management frameworks, both in algorithmic design and in the form of organizing the convergence theory.
URI: http://hdl.handle.net/10316/45703
DOI: 10.1080/10556788.2012.719508
Rights: embargoedAccess
Appears in Collections:I&D CMUC - Artigos em Revistas Internacionais

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