Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Journal of Global Optimization

New bounds on the unconstrained quadratic integer programming problem

George D. Halikias, Imad M. Jaimoukha, U. Malik, S. K. Gungah

Published Apr 24, 2007• 11 citations
Publisher
DOI
10.1007/s10898-007-9155-z

Related papers

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

Alain Billionnet, Sourour Elloumi

Mathematical Programming•Jun 2, 2006•181 citations

An Effective Branch-and-Bound Algorithm for Convex Quadratic Integer Programming

Christoph Buchheim, Alberto Caprara, Andrea Lodi

Lecture notes in computer science•Jan 1, 2010•10 citations

An effective branch-and-bound algorithm for convex quadratic integer programming

Christoph Buchheim, Alberto Caprara, Andrea Lodi

Mathematical Programming•Jul 14, 2011•55 citations

On the gap between the quadratic integer programming problem and its semidefinite relaxation

U. Malik, Imad M. Jaimoukha, George D. Halikias +1 more

Mathematical Programming•Dec 30, 2005•34 citations

Globally solving nonconvex quadratic programming problems with box constraints via integer programming methods

Pierre Bonami, Oktay Günlük, Jeff Linderoth

Mathematical Programming Computation•Feb 24, 2018•38 citations

Bounds for Binary Integer Programming via Unconstrained Quadratic Pseudo-Boolean Optimization

Sirilak Phonin, Chulin Likasiri

This research proposes a method for finding bounds in binary integer programming (BIP) and quadratic binary integer programming (QBIP). Our approach utilizes a max-flow technique within the framework of quadratic pseudo-Boolean optimization. We apply Lagrange multipliers to transform these problems into a quadratic pseudo-Boolean format, enabling us to create a corresponding network. We develop sufficient conditions under which a flow can or cannot be established in this network. The upper bound of the original problem is obtained through a feasible flow within the network. Additionally, we provide the optimality conditions for the solutions discussed in this paper. We have derived the closed forms for the various functions and values commonly used in our proposed method, as well as a lower bound for both BIP and QBIP, which can assist in solving these problems. This paper includes examples that illustrate both networks containing flows and those that do not, as well as examples that demonstrate lower and upper bound solutions as identified by our method. Furthermore, since our approach can also be applied to Quadratic Unconstrained Binary Optimization (QUBO), we provide the closed forms for the frequently used functions and values in that context, along with an example showcasing its application to a quadratic unconstrained optimization problem.

Mathematics•Aug 17, 2026•1 citation
This research proposes a method for finding bounds in binary integer programming (BIP) and quadratic binary integer programming (QBIP). Our approach utilizes a max-flow technique within the framework of quadratic pseudo-Boolean optimization. We apply Lagrange multipliers to transform these problems into a quadratic pseudo-Boolean format, enabling us to create a corresponding network. We develop sufficient conditions under which a flow can or cannot be established in this network. The upper bound of the original problem is obtained through a feasible flow within the network. Additionally, we provide the optimality conditions for the solutions discussed in this paper. We have derived the closed forms for the various functions and values commonly used in our proposed method, as well as a lower bound for both BIP and QBIP, which can assist in solving these problems. This paper includes examples that illustrate both networks containing flows and those that do not, as well as examples that demonstrate lower and upper bound solutions as identified by our method. Furthermore, since our approach can also be applied to Quadratic Unconstrained Binary Optimization (QUBO), we provide the closed forms for the frequently used functions and values in that context, along with an example showcasing its application to a quadratic unconstrained optimization problem.