A hierarchy of relaxation between the continuous and convex hull representations
SIAM Journal on Discrete Mathematics
A linearization procedure for quadratic and cubic mixed-integer problems
Operations Research - Supplement
Persistency in quadratic 0–1 optimization
Mathematical Programming: Series A and B
Discrete Applied Mathematics
Discrete Applied Mathematics
A linearization method for mixed 0-1 polynomial programs
Computers and Operations Research
Discrete Applied Mathematics
Using a Mixed Integer Programming Tool for Solving the 0-1 Quadratic Knapsack Problem
INFORMS Journal on Computing
Seizure warning algorithm based on optimization and nonlinear dynamics
Mathematical Programming: Series A and B
Block linear majorants in quadratic 0-1 optimization
Discrete Applied Mathematics - The fourth international colloquium on graphs and optimisation (GO-IV)
Comparisons and enhancement strategies for linearizing mixed 0-1 quadratic programs
Discrete Optimization
A new linearization technique for multi-quadratic 0-1 programming problems
Operations Research Letters
A simple recipe for concise mixed 0-1 linearizations
Operations Research Letters
A computational study on the quadratic knapsack problem with multiple constraints
Computers and Operations Research
Improving a Lagrangian decomposition for the unconstrained binary quadratic programming problem
Computers and Operations Research
Easy distributions for combinatorial optimization problems with probabilistic constraints
Operations Research Letters
Particle Algorithms for Optimization on Binary Spaces
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
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We present and compare three new compact linearizations for the quadratic 0-1 minimization problem, two of which achieve the same lower bound as does the ''standard linearization''. Two of the linearizations require the same number of constraints with respect to Glover's one, while the last one requires n additional constraints where n is the number of variables in the quadratic 0-1 problem. All three linearizations require the same number of additional variables as does Glover's linearization. This is an improvement on the linearization of Adams, Forrester and Glover (2004) which requires n additional variables and 2n additional constraints to reach the same lower bound as does the standard linearization. Computational results show however that linearizations achieving a weaker lower bound at the root node have better global performances than stronger linearizations when solved by Cplex.