Combining Nogoods in Restart-Based Search

Glorian, Gael; Boussemart, Frédéric; Lagniez, Jean-Marie; Lecoutre, Christophe; Mazure, Bertrand

doi:10.1007/978-3-319-66158-2_9

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…The general idea is to perform a certain amount of search, and then if no solution has been found (and unsatisfiability has not been proven), to abandon search and restart from the beginning. Such an approach can only be beneficial if something changes after restarting-in a constraint programming setting, this is usually the variable-ordering heuristic [28,13,29,16]. In this paper, we instead rely upon randomness in our new value-ordering heuristic, and continue to use smallest domain first with static tiebreaking for variable-ordering.…”

Section: Restarting Search and Nogood Recordingmentioning

confidence: 99%

“…This has two benefits: the propagation complexity does not particularly depend upon the number of stored nogoods, and it does not require any work upon backtracking. Other more sophisticated nogood generation and propagation schemes exist [29,16], but these are not helpful in this setting (our solver does not maintain arc consistency or use a propagation queue).…”

Section: Restarting Search and Nogood Recordingmentioning

confidence: 99%

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Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

Archibald

Dunlop

Hoffmann

et al. 2019

Lecture Notes in Computer Science

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The current state of the art in subgraph isomorphism solving involves using degree as a value-ordering heuristic to direct backtracking search. Such a search makes a heavy commitment to the first branching choice, which is often incorrect. To mitigate this, we introduce and evaluate a new approach, which we call "solution-biased search". By combining a slightly-random value-ordering heuristic, rapid restarts, and nogood recording, we design an algorithm which instead uses degree to direct the proportion of search effort spent in different subproblems. This increases performance by two orders of magnitude on satisfiable instances, whilst not affecting performance on unsatisfiable instances. This algorithm can also be parallelised in a very simple but effective way: across both satisfiable and unsatisfiable instances, we get a further speedup of over thirty from thirty-six cores, and over one hundred from ten distributed-memory hosts. Finally, we show that solution-biased search is also suitable for optimisation problems, by using it to improve two maximum common induced subgraph algorithms.

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Section: Restarting Search and Nogood Recordingmentioning

confidence: 99%

Section: Restarting Search and Nogood Recordingmentioning

confidence: 99%

Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

Archibald

Dunlop

Hoffmann

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…We choose to fully propagate the constraints (line 3 to 10) before the clauses (line 12 to 18). Indeed, playing with clauses and nogoods can lure an adaptive heuristic like dom/wdeg [15]. Triggering in priority conflicts from the CSP core avoids this kind of behavior.…”

Section: Nogood Recordingmentioning

confidence: 99%

NACRE - A Nogood And Clause Reasoning Engine

Glorian¹,

Lagniez²,

Lecoutre³

EPiC Series in Computing

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NACRE, for Nogood And Clause Reasoning Engine, is a constraint solver written in C++. It is based on a modular architecture designed to work with generic constraints while implementing several state-of-the-art search methods and heuristics. Interestingly, its data structures have been carefully designed to play around nogoods and clauses, making it suit- able for implementing learning strategies. NACRE was submitted to the CSP MiniTrack of the 2018 and 2019 XCSP3 [8] competitions where it took the first place. This paper gives a general description of NACRE as a framework. We present its kernel, the available search algorithms, and the default settings (notably, used for XCSP3 competitions), which makes NACRE efficient in practice when used as a black-box solver.

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Combining Nogoods in Restart-Based Search

Cited by 2 publications

References 10 publications

Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

NACRE - A Nogood And Clause Reasoning Engine

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