A derivative-free trust-region algorithm for unconstrained optimization with controlled error

Jun Takaki; Nobuo Yamashita

doi:10.3934/naco.2011.1.117

Article Contents

2011, Volume 1, Issue 1: 117-145. Doi: 10.3934/naco.2011.1.117

This issue Previous Article Filter-based genetic algorithm for mixed variable programming Next Article A nonconvergent example for the iterative water-filling algorithm

A derivative-free trust-region algorithm for unconstrained optimization with controlled error

Jun Takaki¹ and
Nobuo Yamashita^2,

1.
Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501,
2.
Graduate School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501

Received: September 2010

Revised: December 2010

Published: February 2011

Abstract / Introduction Related Papers Cited by

Abstract

In this paper, we consider the unconstrained optimization problem under the following conditions: (S1) The objective function is evaluated with a certain bounded error, (S2) the error is controllable, that is, the objective function can be evaluated to any desired accuracy, and (S3) more accurate evaluation requires a greater computation time. This situation arises in many fields such as engineering and financial problems, where objective function values are obtained from considerable numerical calculation or a simulation. Under (S2) and (S3), it seems reasonable to set the accuracy of the evaluation to be low at points far from a solution, and high at points in the neighborhood of a solution. In this paper, we propose a derivative-free trust-region algorithm based on this idea. For this purpose, we consider (i) how to construct a quadratic model function by exploiting pointwise errors and (ii) how to control the accuracy of function evaluations to reduce the total computation time of the algorithm. For (i), we propose a method based on support vector regression. For (ii), we present an updating formula of the accuracy of which is related to the trust-region radius. We present numerical results for several test problems taken from CUTEr and a financial problem of estimating implied volatilities from option prices.

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Mathematics Subject Classification: Primary: 90C56; Secondary: 90C30.

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