A new global optimization algorithm for solving a class of nonconvex programming problems
type: Item
instance of: scholarly article
title: A new global optimization algorithm for solving a class of nonconvex programming problems
zbMATH Open document ID: 1406.90100
DOI: 10.1155/2014/697321
author: Xue-Gang Zhou
author: Bingyuan Cao
published in: Journal of Applied Mathematics
publication date: 2019-02-01T00:00:00Z
review text: Summary: A new two-part parametric linearization technique is proposed globally to a class of nonconvex programming problems (NPP). Firstly, a two-part parametric linearization method is adopted to construct the underestimator of objective and constraint functions, by utilizing a transformation and a parametric linear upper bounding function (LUBF) and a linear lower bounding function (LLBF) of a natural logarithm function and an exponential function with \(e\) as the base, respectively. Then, a sequence of relaxation lower linear programming problems, which are embedded in a branch-and-bound algorithm, are derived in an initial nonconvex programming problem. The proposed algorithm is converged to global optimal solution by means of a subsequent solution to a series of linear programming problems. Finally, some examples are given to illustrate the feasibility of the presented algorithm.
Mathematics Subject Classification ID: 90C26
zbMATH DE Number: 7010716
Wikidata QID: Q59054094
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/697321
OpenAlex ID: W2007317081
DBLP publication ID: journals/jam/ZhouC14
description: scientific article; zbMATH DE number 7010716