Using a Mixed Integer Quadratic Programming Solver for the Unconstrained Quadratic 0-1 Problem

In this paper, we consider problem (P) of minimizing a quadratic function q(x)= xtQx + ctx of binary variables. Our main idea is to use the recent Mixed Integer Quadratic Programming (MIQP) solvers. But, for this, we have to first convexify the objective function q(x). A classical trick is to raise...

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Bibliographic Details
Published in:	Mathematical programming Vol. 109; no. 1; pp. 55 - 68
Main Authors:	Billionnet, Alain, Elloumi, Sourour
Format:	Journal Article
Language:	English
Published:	Heidelberg Springer 01.01.2007 Springer Nature B.V
Subjects:	Applied sciences Eigenvalues Exact sciences and technology Integer programming Mathematical programming Methods Operational research and scientific management Operational research. Management science Optimization Quadratic programming Studies Semidefinite relaxation Quadratic 0-1 optimization Eigenvalue Minimization Mixed integer programming Semi definite programming Vector method Quadratic programming Modeling Convex programming Experiments Optimization Semidefinite positive relaxation Exact solution Max-cut Integer programming Diagonal matrix Convex function Eigenvalue problem Objective function Convex quadratic relaxation Quadratic function Mathematical programming
ISSN:	0025-5610, 1436-4646
Online Access:	Get full text
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