A Bundle of Method for Minimizing a Sum of Convex Functions with Smooth Weights

Kiwiel K (1994). A Bundle of Method for Minimizing a Sum of Convex Functions with Smooth Weights. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-94-013

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Abstract

We give a bundle method for minimizing a (possibly nondifferentiable and nonconvex) function h(z) = sum_{i=1}^m p_i(x) f_i(x) over a closed convex set in R^n, where p_i are nonnegative and smooth and f_i are finite-valued convex. Such functions arise in certain stochastic programming problems and scenario analysis. The method finds search directions via quadratic programming, using a polyhedral model of h that involves current linearizations of p_i and polyhedral models of f_i based on their accumulated subgradients. We show that the method is globally convergent to stationary points of h. The method exploits the structure of h and hence seems more promising than general-purpose bundle methods for nonconvex minimization.

Item Type: Monograph (IIASA Working Paper)
Research Programs: Optimization under Uncertainty (OPT)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:04
Last Modified: 26 Oct 2016 00:58
URI: http://pure.iiasa.ac.at/4196

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