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An Algorithm for Nonsmooth Optimization by Successive Piecewise Linearization

Sabrina Fiege(sfiege***at***math.uni-paderborn.de)
Andreas Griewank(griewank***at***yachaytech.edu.ec)
Andrea Walther(andrea.walther***at***uni-paderborn.de)

Abstract: We present an optimization method for Lipschitz continuous, piecewise smooth (PS) objective functions based on successive piecewise linearization. Since, in many realistic cases, nondifferentiabilities are caused by the occurrence of abs(), max(), and min(), we concentrate on these nonsmooth elemental functions. The method's idea is to locate an optimum of a PS objective function by explicitly handling the kink structure at the level of piecewise linear models. This piecewise linearization can be generated in its abs-normal-form by minor extension of standard algorithmic, or automatic differentiation tools. This paper first presents convergence results for the minimization algorithm developed. Numerical results including comparisons with other nonsmooth optimization methods then illustrate the capabilities of the proposed approach.

Keywords: Piecewise smoothness, Nonsmooth optimization, Algorithmic differentiation, Abs-normal form, Clarke stationary

Category 1: Convex and Nonsmooth Optimization (Nonsmooth Optimization )

Citation: Universitšt Paderborn, December 2016

Download: [PDF]

Entry Submitted: 12/08/2016
Entry Accepted: 12/08/2016
Entry Last Modified: 12/08/2016

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