A review of piecewise linearization methods

N/ACitations
Citations of this article
161Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Various optimization problems in engineering and management are formulated as nonlinear programming problems. Because of the nonconvexity nature of this kind of problems, no efficient approach is available to derive the global optimum of the problems. How to locate a global optimal solution of a nonlinear programming problem is an important issue in optimization theory. In the last few decades, piecewise linearization methods have been widely applied to convert a nonlinear programming problem into a linear programming problem or a mixed-integer convex programming problem for obtaining an approximated global optimal solution. In the transformation process, extra binary variables, continuous variables, and constraints are introduced to reformulate the original problem. These extra variables and constraints mainly determine the solution efficiency of the converted problem. This study therefore provides a review of piecewise linearization methods and analyzes the computational efficiency of various piecewise linearization methods. © 2013 Ming-Hua Lin et al.

Cite

CITATION STYLE

APA

Lin, M. H., Carlsson, J. G., Ge, D., Shi, J., & Tsai, J. F. (2013). A review of piecewise linearization methods. Mathematical Problems in Engineering. https://doi.org/10.1155/2013/101376

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free