Kangaroo: An Efficient Constraint-Based Local Search System Using Lazy Propagation

Newton, M. A. Hakim; Pham, Duc Nghia; Sattar, Abdul; Maher, Michael J.

doi:10.1007/978-3-642-23786-7_49

Cited by 29 publications

(17 citation statements)

References 12 publications

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“…For a mandatory difference, we determine the candidate integers to be appended to the series (Lines 15-16). Alternatively, we can consider the mandatory difference as a missing difference (which can be done only once on each path) and go into further recursion (Lines [17][18][19][20]. Lines 22-25 deal with the non-mandatory differences.…”

Section: Required Proceduresmentioning

confidence: 99%

“…Instead of maintaining the number of ways a difference can be obtained, a lazy checking is rather made on demand to determine whether a difference can be obtained from the unused integers. Lazy computation has been using in local search and constraint-based local search for quite long time [14,17,28,10]. Lazy approach compute values only when the values are actually needed.…”

Section: Lazy Checkingmentioning

confidence: 99%

See 1 more Smart Citation

Constraint-directed search for all-interval series

2016

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All-interval series is a standard benchmark problem for constraint satisfaction search. An all-interval series of size n is a permutation of integers [0, n) such that the differences between adjacent integers are a permutation of [1, n). Generating each such all-interval series of size n is an interesting challenge for constraint community. The problem is very difficult in terms of the size of the search space. Different approaches have been used to date to generate all the solutions of AIS but the search space that must be explored still remains huge. In this paper, we present a constraint-directed backtrackingbased tree search algorithm that performs efficient lazy checking rather than immediate constraint propagation. Moreover, we prove several key properties of all-interval series that help prune the search space significantly. The reduced search space essentially results into fewer backtracking. We also present scalable parallel versions of our algorithm that can exploit the advantage of having multi-core processors and even multiple computer systems. Our new algorithm generates all the solutions of size up to 27 while a satisfiability-based state-of-the-art approach generates all solutions up to size 24.

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Section: Required Proceduresmentioning

confidence: 99%

Section: Lazy Checkingmentioning

confidence: 99%

Constraint-directed search for all-interval series

2016

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“…We developed our stochastic local search based (or SLS-based) algorithm on top of Kangaroo, a constraint-based local search system [12]. We define the variables and functions such as constraints and objectives in the Kangaroo system.…”

Section: Stochastic Local Search Based Algorithmmentioning

confidence: 99%

“…We developed our algorithm on top of Kangaroo, a constraint-based local search system [12]. We designed a new constraint to model the interface constraint at each mesh node.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Local Search Based Channel Assignment in Wireless Mesh Networks

Newton

Pham

Tan

et al. 2013

Lecture Notes in Computer Science

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In this paper, we consider the problem of channel assignment in multi-radio, multi-channel wireless mesh networks. We assume a binary interference model and represent the set of interfering links in a network topology as a conflict graph. We then develop a new centralised stochastic local search algorithm to find a channel assignment that minimises the network interference. Our algorithm assigns channels to communication links rather than radio interfaces. By doing so, our algorithm not only does preserve the network topology, but is also independent of the network routing layer. We compare the performance of our algorithm with that of a well-known tabu-based approach (by Subramanian et al.) on randomly generated sparse and dense network topologies. Using graph-theoretic evaluation and ns2 simulations (a widely used discrete event network simulator), we show that our algorithm consistently outperforms the tabu-based approach in terms of both the network interference and the throughput obtained under various offered loads. In particular, for a practical setting of 3 radio interfaces per mesh node in a dense network topology with 12 channels available, our approach achieves 70% lower network interference and thus 15 times higher average throughput than those achieved by the tabu-based approach.

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“…Recent work on the area includes Kangaroo, a CBLS system that appeared only in 2011 [11]. It is presented as a more efficient alternative to the Comet platform [23].…”

Section: Introductionmentioning

confidence: 99%

Constraint Propagation as the Core of Local Search

Pothitos

Kastrinis

Stamatopoulos

2012

Lecture Notes in Computer Science

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Abstract. Constraint programming is a powerful paradigm for solving constraint satisfaction problems, using various techniques. Amongst them, local search is a prominent methodology, particularly for large instances. However, it lacks uniformity, as it includes many variations accompanied by complex data structures, that cannot be easily brought under the same "umbrella." In this work we embrace their wide diversity by adopting propagation algorithms. Our constraint based local search (CBLS) system provides declarative alternative tools to express search methods, by exploiting conflict-sets of constraints and variables. Their maintenance is straightforward as it does not employ queues, unlike the state of the art CBLS systems. Thus, the propagation complexity is kept linear in the number of changes required after each assignment. Experimental results illustrate the capabilities, not only of the already implemented methods, such as hill climbing, simulated annealing, etc., but also the robustness of the underlying propagation engine.

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Kangaroo: An Efficient Constraint-Based Local Search System Using Lazy Propagation

Cited by 29 publications

References 12 publications

Constraint-directed search for all-interval series

Constraint-directed search for all-interval series

Stochastic Local Search Based Channel Assignment in Wireless Mesh Networks

Constraint Propagation as the Core of Local Search

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