Parallel Combinatorial Optimization with Decision Diagrams

Bergman, David; Ciré, André A.; Sabharwal, Ashish; Samulowitz, Horst; Saraswat, Vijay; Hoeve, Willem‐Jan van

doi:10.1007/978-3-319-07046-9_25

Cited by 13 publications

(14 citation statements)

References 24 publications

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“…We will see that other families of graphs have properties which can interfere with this approach. Bergman et al (2014) used parallel decision diagrams, and showed excellent scalability-however, their sequential runtimes were typically much worse than state of the art algorithms.…”

Section: Parallel Branch and Bound And The Potential For Speedupmentioning

confidence: 99%

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

McCreesh

Prosser

2015

ACM Trans. Parallel Comput.

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Finding a maximum clique in a given graph is one of the fundamental NP-hard problems. We compare two multi-core thread-parallel adaptations of a state-of-the-art branch and bound algorithm for the maximum clique problem, and provide a novel explanation as to why they are successful. We show that load balance is sometimes a problem, but that the interaction of parallel search order and the most likely location of solutions within the search space is often the dominating consideration. We use this explanation to propose a new low-overhead, scalable work splitting mechanism. Our approach uses explicit early diversity to avoid strong commitment to the weakest heuristic advice, and late resplitting for balance. More generally, we argue that for branch and bound, parallel algorithm design should not be performed independently of the underlying sequential algorithm.

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Section: Parallel Branch and Bound And The Potential For Speedupmentioning

confidence: 99%

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

McCreesh

Prosser

2015

ACM Trans. Parallel Comput.

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“…We then teamed up with Andersen and his student P. Tiedemann to propose a concept of relaxed MDDs, which we first applied to constraint programming but later became an essential tool for optimization [3]. Research on MDDs and optimization has moved in several directions since that time, including development of a general-purpose solver for discrete optimization [14,37], applications to nonlinear programming [10], and implementations with parallel processors [9]. Much of this research is described in a recent book [13] and conference [28].…”

Section: Decision Diagramsmentioning

confidence: 99%

A Brief Tour of Logic and Optimization

Hooker

2019

Preprint

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This paper is an informal survey of some of the deep connections between logic and optimization. It covers George Boole's probability logic, decision diagrams, logic and cutting planes, first order predicate logic, default and nonmonotonic logics, logic and duality, and finite-domain constraint programming. There is particular emphasis on practical optimization methods that stem from these connections, including decision-diagram based methods, logic-based Benders decomposition, and integration of CP and optimization technologies. The paper is a slight revision of an invited article for the INFORMS Optimization Society Newsletter in observance of the 2018 Khachian Award.

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“…Much more recently, Bergman et al (2014) worked on decision diagram search trees. When decision diagrams become too wide, usually a number of subproblems are created and evaluated sequentially.…”

Section: Richer Search Treesmentioning

confidence: 99%

A review of literature on parallel constraint solving

Gent¹,

Miguel²,

Nightingale³

et al. 2018

Theory and Practice of Logic Programming

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As multi-core computing is now standard, it seems irresponsible for constraints researchers to ignore the implications of it. Researchers need to address a number of issues to exploit parallelism, such as: investigating which constraint algorithms are amenable to parallelisation; whether to use shared memory or distributed computation; whether to use static or dynamic decomposition; and how to best exploit portfolios and cooperating search. We review the literature, and see that we can sometimes do quite well, some of the time, on some instances, but we are far from a general solution. Yet there seems to be little overall guidance that can be given on how best to exploit multi-core computers to speed up constraint solving. We hope at least that this survey will provide useful pointers to future researchers wishing to correct this situation.

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Parallel Combinatorial Optimization with Decision Diagrams

Cited by 13 publications

References 24 publications

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

The Shape of the Search Tree for the Maximum Clique Problem and the Implications for Parallel Branch and Bound

A Brief Tour of Logic and Optimization

A review of literature on parallel constraint solving

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