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Title: Stochastic derivative-free optimization using a trust region framework

Abstract

This study presents a trust region algorithm to minimize a function f when one has access only to noise-corrupted function values f¯. The model-based algorithm dynamically adjusts its step length, taking larger steps when the model and function agree and smaller steps when the model is less accurate. The method does not require the user to specify a fixed pattern of points used to build local models and does not repeatedly sample points. If f is sufficiently smooth and the noise is independent and identically distributed with mean zero and finite variance, we prove that our algorithm produces iterates such that the corresponding function gradients converge in probability to zero. As a result, we present a prototype of our algorithm that, while simplistic in its management of previously evaluated points, solves benchmark problems in fewer function evaluations than do existing stochastic approximation methods.

Authors:
 [1];  [2]
+ Show Author Affiliations
  1. Argonne National Lab. (ANL), Lemont, IL (United States)
  2. Univ. of Colorado, Denver, CO (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
OSTI Identifier:
1366470
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
Computational Optimization and Applications
Additional Journal Information:
Journal Volume: 64; Journal Issue: 3; Journal ID: ISSN 0926-6003
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; derivative-free optimization; model-based trust region methods; stochastic optimization

Citation Formats

Larson, Jeffrey, and Billups, Stephen C. Stochastic derivative-free optimization using a trust region framework. United States: N. p., 2016. Web. doi:10.1007/s10589-016-9827-z.
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Larson, Jeffrey, & Billups, Stephen C. Stochastic derivative-free optimization using a trust region framework. United States. doi:10.1007/s10589-016-9827-z.
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Larson, Jeffrey, and Billups, Stephen C. Wed . "Stochastic derivative-free optimization using a trust region framework". United States. doi:10.1007/s10589-016-9827-z. https://www.osti.gov/servlets/purl/1366470.
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@article{osti_1366470,
title = {Stochastic derivative-free optimization using a trust region framework},
author = {Larson, Jeffrey and Billups, Stephen C.},
abstractNote = {This study presents a trust region algorithm to minimize a function f when one has access only to noise-corrupted function values f¯. The model-based algorithm dynamically adjusts its step length, taking larger steps when the model and function agree and smaller steps when the model is less accurate. The method does not require the user to specify a fixed pattern of points used to build local models and does not repeatedly sample points. If f is sufficiently smooth and the noise is independent and identically distributed with mean zero and finite variance, we prove that our algorithm produces iterates such that the corresponding function gradients converge in probability to zero. As a result, we present a prototype of our algorithm that, while simplistic in its management of previously evaluated points, solves benchmark problems in fewer function evaluations than do existing stochastic approximation methods.},
doi = {10.1007/s10589-016-9827-z},
journal = {Computational Optimization and Applications},
number = 3,
volume = 64,
place = {United States},
year = {2016},
month = {2}
}
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DOI:  10.1007/s10589-016-9827-z
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