Conflict Ordering Search for Scheduling Problems

Hartert, Renaud; Lecoutre, Christophe; Schaus, Pierre

doi:10.1007/978-3-319-23219-5_10

Cited by 24 publications

(11 citation statements)

References 15 publications

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“…Last Conflict [9] (LC) and Conflict Ordering Search [10] (COS) are two variable selectors that tend to branch on the variables that trigger conflicts the most. They can be seen as a plugin that can be combined with another variable selector heuristic.…”

Section: B Black-box Search Heuristicsmentioning

confidence: 99%

Making the First Solution Good!

Fages¹,

Prud’Homme

2017

2017 IEEE 29th International Conference on Tools With Artificial Intelligence (ICTAI)

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Providing efficient black-box search procedures is one of the major concerns for constraint-programming solvers. Most of the contributions in that area follow the fail-first principle, which is very useful to close the search tree or to solve SAT/UNSAT problems. However, for reallife applications with an optimization criterion, proving optimality is often unrealistic. Instead, it is very important to compute a good solution fast. This paper introduces a value selector heuristic focusing on objective bounds to make the first solution good. Experiments show that it improves former approaches on a wide range of problems.

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Section: B Black-box Search Heuristicsmentioning

confidence: 99%

Making the First Solution Good!

Fages¹,

Prud’Homme

2017

2017 IEEE 29th International Conference on Tools With Artificial Intelligence (ICTAI)

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“…Through this new perspective, we have shown that using the objective as a feature to make decisions can lead to strong results. In addition, further work can be done, for example, with non valued SSs like the ones using a ranking criteria such as COS [5]. Directly applying this work on such SSs is not trivial, and should be a next step.…”

Section: Resultsmentioning

confidence: 99%

“…That is one of the main motivations for the development of black-box constraint solvers, where the only user's concern is to build an efficient model. Blackbox solvers need robust and efficient SSs, and many researches have been done [11,18,5,16,28]. Notably, in Constraint Programming, activity-based search (ABS) [14], impact-based search (IBS) [20] and weighted degrees (Wdeg) [1] are well known stateof-the-art search strategies for combinatorial problems.…”

Section: Introductionmentioning

confidence: 99%

Objective as a Feature for Robust Search Strategies

Palmieri

Perez

2018

Lecture Notes in Computer Science

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In constraint programming the search strategy entirely guides the solving process, and drastically affects the running time for solving particular problem instances. Many features have been defined so far for the design of efficient and robust search strategies, such as variables' domains, constraint graph, or even the constraints triggering fails. In this paper, we propose to use the objective functions of constraint optimization problems as a feature to guide search strategies. We define an objective-based function, to monitor the objective bounds modifications and to extract information. This function is the main feature to design a new variable selection heuristic, whose results validate human intuitions about the objective modifications. Finally, we introduce a simple but efficient combination of features, to incorporate the objective in the state-of-the-art search strategies. We illustrate this new method by testing it on several classic optimization problems, showing that the new feature often yields to a better running time and finds better solutions in the given time.

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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%

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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Conflict Ordering Search for Scheduling Problems

Cited by 24 publications

References 15 publications

Making the First Solution Good!

Making the First Solution Good!

Objective as a Feature for Robust Search Strategies

Sequential and Parallel Solution-Biased Search for Subgraph Algorithms

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