Experiments in quadratic 0-1 programming
Mathematical Programming: Series A and B
Construction of test problems in quadratic bivalent programming
ACM Transactions on Mathematical Software (TOMS)
Global Optimality Conditions for Quadratic Optimization Problems with Binary Constraints
SIAM Journal on Optimization
Ellipsoidal Approach to Box-Constrained Quadratic Problems
Journal of Global Optimization
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Introduction to Global Optimization (Nonconvex Optimization and Its Applications)
Box-constrained quadratic programs with fixed charge variables
Journal of Global Optimization
Global equilibrium search applied to the unconstrained binary quadratic optimization problem
Optimization Methods & Software
Global optimality conditions for quadratic 0-1 optimization problems
Journal of Global Optimization
Global optimality conditions and optimization methods for quadratic integer programming problems
Journal of Global Optimization
Improving a Lagrangian decomposition for the unconstrained binary quadratic programming problem
Computers and Operations Research
An exact solution method for unconstrained quadratic 0---1 programming: a geometric approach
Journal of Global Optimization
On duality gap in binary quadratic programming
Journal of Global Optimization
On characterization of maximal independent sets via quadratic optimization
Journal of Heuristics
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In this paper several equivalent formulations for the quadratic binary programming problem are presented. Based on these formulations we describe four different kinds of strategies for estimating lower bounds of the objective function, which can be integrated into a branch and bound algorithm for solving the quadratic binary programming problem. We also give a theoretical explanation for forcing rules used to branch the variables efficiently, and explore several properties related to obtained subproblems. From the viewpoint of the number of subproblems solved, new strategies for estimating lower bounds are better than those used before. A variant of a depth-first branch and bound algorithm is described and its numerical performance is presented.