A rank-two feasible direction algorithm for the binary quadratic programming
type: Item
instance of: scholarly article
title: A rank-two feasible direction algorithm for the binary quadratic programming
zbMATH Open document ID: 1397.90294
DOI: 10.1155/2013/963563
author: Xuewen Mu
author: Ya-Ling Zhang
published in: Journal of Applied Mathematics
publication date: 2018-10-10T00:00:00Z
review text: Summary: Based on the semidefinite programming relaxation of the binary quadratic programming, a rank-two feasible direction algorithm is presented. The proposed algorithm restricts the rank of matrix variable to be two in the semidefinite programming relaxation and yields a quadratic objective function with simple quadratic constraints. A feasible direction algorithm is used to solve the nonlinear programming. The convergent analysis and time complexity of the method is given. Coupled with randomized algorithm, a suboptimal solution is obtained for the binary quadratic programming. At last, we report some numerical examples to compare our algorithm with randomized algorithm based on the interior point method and the feasible direction algorithm on max-cut problem. Simulation results have shown that our method is faster than the other two methods.
Mathematics Subject Classification ID: 90C20
Mathematics Subject Classification ID: 90C22
zbMATH DE Number: 6950963
Wikidata QID: Q59007211
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2013/963563
OpenAlex ID: W2073941858
description: scientific article; zbMATH DE number 6950963