Construction of test problems in quadratic bivalent programming
ACM Transactions on Mathematical Software (TOMS)
Convex relaxations of (0, 1)-quadratric programming
Mathematics of Operations Research
Adaptive Memory Tabu Search for Binary Quadratic Programs
Management Science
A scatter search approach to unconstrained quadratic binary programs
New ideas in optimization
Computers and Industrial Engineering
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Greedy and Local Search Heuristics for Unconstrained Binary Quadratic Programming
Journal of Heuristics
Discrete Applied Mathematics
The Cross Entropy Method: A Unified Approach To Combinatorial Optimization, Monte-carlo Simulation (Information Science and Statistics)
Local search heuristics for Quadratic Unconstrained Binary Optimization (QUBO)
Journal of Heuristics
A linearization framework for unconstrained quadratic (0-1) problems
Discrete Applied Mathematics
Improved compact linearizations for the unconstrained quadratic 0-1 minimization problem
Discrete Applied Mathematics
Computation of Multivariate Normal and t Probabilities
Computation of Multivariate Normal and t Probabilities
Monte Carlo Statistical Methods
Monte Carlo Statistical Methods
An adaptive sequential Monte Carlo method for approximate Bayesian computation
Statistics and Computing
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We discuss a unified approach to stochastic optimization of pseudo-Boolean objective functions based on particle methods, including the cross-entropy method and simulated annealing as special cases. We point out the need for auxiliary sampling distributions, meaning parametric families on binary spaces, which are able to reproduce complex dependency structures, and illustrate their usefulness in our numerical experiments. We provide numerical evidence that particle-driven optimization algorithms based on parametric families yield superior results on strongly multimodal optimization problems while local search heuristics outperform them on easier problems.