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A Dwindling Filter Line Search Method for Unconstrained Optimization

Wenyu Sun (wysun***at***njnu.edu.cn)
Yannan Chen (cyannan2008***at***163.com)

Abstract: In this paper, we propose a new dwindling multidimensional filter second-order line search method for solving large-scale unconstrained optimization problems. Usually, the multidimensional filter is constructed with a fixed envelope, which is a strict condition for the gradient vectors. A dwindling multidimensional filter technique, which is a modification and improvement of the original multidimensional filter, is presented. Under some reasonable assumptions, the new algorithm is globally convergent to a second-order critical point, when negative curvature direction is exploited. Preliminary numerical experiments on a set of \texttt{CUTEr} test problems indicate that the new algorithm is more competitive than the traditional second-order line search algorithms.

Keywords: filter method, line search, negative curvature direction, second-order critical point, global convergence.

Category 1: Nonlinear Optimization (Unconstrained Optimization )

Citation: Technical Report of Optimization No: 2010-09-01, School of Mathematical Science, Nanjing Normal University, Nanjing, China.

Download: [PDF]

Entry Submitted: 03/08/2011
Entry Accepted: 03/08/2011
Entry Last Modified: 03/08/2011

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