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A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

Published: 08 July 2020 Publication History

Abstract

The location problem is of great interest in order to establish different location demands in the state or private sector. The model of this problem is usually reduced to a mathematical optimization problem. In this paper we present a proximal method to solve location problems where the objective function is quasi-convex and non-differentiable. We prove that the iterations given by the method are well defined and under some assumptions on the objective function we prove the convergence of the method.

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F. Clarke, Optimization and Nonsmooth Analysis (New York, Wiley (1990))
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F. Navarro, Algunas aplicaciones y extensión del método del subgradiente (These: Universidad Nacional Mayor de San Marcos, 2013).

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  1. A Proximal Method to Solve Quasiconvex Non-differentiable Location Problems

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    ICMSSP '20: Proceedings of the 2020 5th International Conference on Multimedia Systems and Signal Processing
    May 2020
    112 pages
    ISBN:9781450377485
    DOI:10.1145/3404716
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Shenzhen University: Shenzhen University

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 08 July 2020

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    Author Tags

    1. Global convergence
    2. Location theory
    3. Proximal point method
    4. Quasiconvex function

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