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A Second-Order Information-Based Gradient and Function Sampling Method for Nonconvex, Nonsmooth Optimization

Elias Salomão Helou(elias***at***icmc.usp.br)
Sandra Augusta Santos(sandra***at***ime.unicamp.br)
Lucas E. A. Simões(simoes.lea***at***gmail.com)

Abstract: This paper has the goal to propose a gradient and function sampling method that under special circumstances moves superlinearly to a minimizer of a general class of nonsmooth and nonconvex functions. We present global and local convergence theory with illustrative examples that corroborate and elucidate the theoretical results obtained along the manuscript.

Keywords: nonsmooth nonconvex optimization; gradient sampling; local superlinear convergence; global convergence; unconstrained minimization

Category 1: Nonlinear Optimization

Category 2: Convex and Nonsmooth Optimization

Citation:

Download: [PDF]

Entry Submitted: 06/24/2016
Entry Accepted: 06/24/2016
Entry Last Modified: 06/24/2016

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