Compact-MDD: Efficiently Filtering (s)MDD Constraints with Reversible Sparse Bit-sets

Verhaeghe, Hélène; Lecoutre, Christophe; Schaus, Pierre

doi:10.24963/ijcai.2018/192

Cited by 15 publications

(14 citation statements)

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“…From Figure 1, we can see the overall performance of CT and STRbit is quite close and clearly superior to the other algorithms. The principles of CT was also extended to the decision diagram based algorithm Compact-MDD in (Verhaeghe, Lecoutre, and Schaus 2018). Although Compact-MDD is still slower than CT, it reduces the gap between MDD based algorithms and CT.…”

Section: Figure 3: Table Representations For Str3 and Strbitmentioning

confidence: 99%

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Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Yap

Xia

Wang

2020

AAAI

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Constraint Programming is a powerful paradigm to model and solve combinatorial problems. While there are many kinds of constraints, the table constraint (also called a CSP) is perhaps the most significant—being the most well-studied and has the ability to encode any other constraints defined on finite variables. Thus, designing efficient filtering algorithms on table constraints has attracted significant research efforts. In turn, there have been great improvements in efficiency over time with the evolution and development of AC and GAC algorithms. In this paper, we survey the existing filtering algorithms for table constraint focusing on historically important ideas and recent successful techniques shown to be effective.

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Section: Figure 3: Table Representations For Str3 and Strbitmentioning

confidence: 99%

“…A number of MDD based algorithms have been proposed, including Mddc,incremental-MDD (Gange, Stuckey, and Szymanek 2011), MDD4 (Perez and Régin 2014), Compact-MDD (Compact-Diagram) (Verhaeghe, Lecoutre, and Schaus 2018;, and BDDF (Vion and Piechowiak 2018). The Mddc is the first MDD based filtering algorithm.…”

Section: Multi-valued Decision Diagrammentioning

confidence: 99%

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Yap

Xia

Wang

2020

AAAI

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“…Recently, these graphical models have drawn the attention of researchers from the CP and OR communities. The popularity of these decision diagrams (DD) stems from their ability to provide a compact representation of large solution spaces as in the case of the table constraint [29,34]. One of the research streams which emerged from this increased interest about MDDs is decision-diagram-based optimization (DDO) [5].…”

Section: Introductionmentioning

confidence: 99%

Improving the filtering of Branch-And-Bound MDD solver (extended)

Gillard,

Coppé,

Schaus

et al. 2021

Preprint

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This paper presents and evaluates two pruning techniques to reinforce the efficiency of constraint optimization solvers based on multi-valued decision-diagrams (MDD). It adopts the branch-and-bound framework proposed by Bergman et al. in 2016 to solve dynamic programs to optimality. In particular, our paper presents and evaluates the effectiveness of the local-bound (LocB) and rough upper-bound pruning (RUB). LocB is a new and effective rule that leverages the approximate MDD structure to avoid the exploration of non-interesting nodes. RUB is a rule to reduce the search space during the development of boundedwidth-MDDs. The experimental study we conducted on the Maximum Independent Set Problem (MISP), Maximum Cut Problem (MCP), Maximum 2 Satisfiability (MAX2SAT) and the Traveling Salesman Problem with Time Windows (TSPTW) shows evidence indicating that roughupper-bound and local-bound pruning have a high impact on optimization solvers based on branch-and-bound with MDDs. In particular, it shows that RUB delivers excellent results but requires some effort when defining the model. Also, it shows that LocB provides a significant improvement automatically; without necessitating any user-supplied information. Finally, it also shows that rough-upper-bound and local-bound pruning are not mutually exclusive, and their combined benefit supersedes the individual benefit of using each technique.

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“…For decades, many filtering algorithms have been proposed for enforcing Generalized Arc Consistency (GAC) on table constraints [7]- [12]. Among them, STRbit [11], Compact-Table [12] and its extensions [13]- [15] are considered to be the state-of-the-art algorithms. In modern constraint solver, enforcing GAC on a constraint model is a schedule scheme for the filter function of propagators.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Thread Parallel Constraint Propagation Scheme

Zhe

et al. 2019

IEEE Access

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Constraint Programming (CP) is an efficient technique for solving combinatorial (optimization) problems. In modern constraint solver, a CP Model is defined over reversible variables that take values in domains and propagators which filter the domains of the variables. Constraint propagation scheme schedules the propagators. A reasonable constraint propagation algorithm can improve the efficiency of solving CP problems. In this paper, we propose two efficient parallel propagation schemes based on multi-thread technique for table constraint. First, we give the formal definition of the parallel consistency and prove that the parallel propagation scheme is equivalent to the classic serial propagation scheme. Then, we propose two parallel propagation schemes: static submission and dynamic submission, which exploit work stealing thread pool and atomic operations to parallelize the classic propagation of table constraint. Finally, extensive experiments on various types of problems show that the two parallel schemes outperform their original serial version on a large number of instances. The results demonstrate the competitiveness of parallel propagation algorithms on solving extensional constraints. INDEX TERMS Constraint programming, constraint satisfaction problem, parallel constraint propagation, parallel generalized arc consistency, simple tabular reduction.

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Compact-MDD: Efficiently Filtering (s)MDD Constraints with Reversible Sparse Bit-sets

Cited by 15 publications

References 0 publications

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Generalized Arc Consistency Algorithms for Table Constraints: A Summary of Algorithmic Ideas

Improving the filtering of Branch-And-Bound MDD solver (extended)

A Novel Multi-Thread Parallel Constraint Propagation Scheme

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