An Optimal Filtering Algorithm for Table Constraints

Mairy, Jean-Baptiste; Hentenryck, Pascal Van; Deville, Yves

doi:10.1007/978-3-642-33558-7_37

Cited by 11 publications

(12 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mddc algorithm was made incremental in [3] to deal with this issue. Here, we show that STR2's value-accumulation phase can be made incremental as well, although this does not make STR2w optimal in the worst case (see [6,9] for optimal algorithms). Our experiments show that STR2w outperforms STR2 when solving problems in which a large number of small changes occur during search, whereas STR2 is better at problems where changes are big, while it is also complementary to STR3 at the same time.…”

Section: Discussionmentioning

confidence: 97%

Improving the lower bound of simple tabular reduction

2014

View full text Add to dashboard Cite

Simple Tabular Reduction (STR) is a state-of-the-art filtering technique for enforcing Generalized Arc Consistency (GAC) on positive table constraints. Despite its good performance in practice, the STR2 algorithm suffers from a mandatory lower bound equal to the number of domain values in the current state of the problem, because the presence of each value must be justified every time STR2 is called. We overcome this fixed cost by redesigning STR2 and incorporating watched tuples. Experiments show that the revised algorithm is better at coping with problems that involve a large number of small changes during search and it can be more than twice as fast on certain structured problems.

show abstract

Section: Discussionmentioning

confidence: 97%

Improving the lower bound of simple tabular reduction

2014

View full text Add to dashboard Cite

show abstract

“…A timeout of 1, 000 seconds was used for each instance. The tested GAC algorithms are CT, STR2 [13], STR3 [16], GAC4 [24,26], GAC4R [26], MDDR [26] and AC5TCRecomp [22]. All scripts, codes and benchmarks allowing to reproduce our experiments are available at https://bitbucket.org/pschaus/xp-table.…”

Section: Methodsmentioning

confidence: 99%

“…In [22], AC5TCRecomp was presented as being competitive with STR2. When we analyzed the code 3 of STR2 used in [22], it appeared that STR2 was implemented in Comet using built-in sets (triggering the garbage collection of Comet). We thus believe that the results and conclusions in [22] may over-penalize the performance of STR2.…”

Section: Methodsmentioning

confidence: 99%

“…When we analyzed the code 3 of STR2 used in [22], it appeared that STR2 was implemented in Comet using built-in sets (triggering the garbage collection of Comet). We thus believe that the results and conclusions in [22] may over-penalize the performance of STR2. Our results also somehow contradicts the results in [26] where STR3 and STR2 were dominated by MDD4R and GAC4R.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Compact-Table: Efficiently Filtering Table Constraints with Reversible Sparse Bit-Sets

Demeulenaere

Hartert

Lecoutre

et al. 2016

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper, we describe Compact- Table (CT), a bitwise algorithm to enforce Generalized Arc Consistency (GAC) on table constraints. Although this algorithm is the default propagator for table constraints in or-tools and OscaR, two publicly available CP solvers, it has never been described so far. Importantly, CT has been recently improved further with the introduction of residues, resetting operations and a data-structure called reversible sparse bit-set, used to maintain tables of supports (following the idea of tabular reduction): tuples are invalidated incrementally on value removals by means of bit-set operations. The experimentation that we have conducted with OscaR shows that CT outperforms state-of-the-art algorithms STR2, STR3, GAC4R, MDD4R and AC5-TC on standard benchmarks.

show abstract

“…Classical algorithms iterate over lists of tuples in different ways ; e.g., see [2,19,18]. A recent AC5-based algorithm has been proposed in [20], and has been shown efficient on table constraints of small arity. For tables constraint of large arity, it is recognized that maintaining dynamically the list of supports in constraint tables does pay off: these are the variants of simple tabular reduction (STR) [23,15,16].…”

Section: Introductionmentioning

confidence: 99%

Sliced Table Constraints: Combining Compression and Tabular Reduction

Gharbi

Hémery

Lecoutre

et al. 2014

Integration of AI and OR Techniques in Constraint Programming

View full text Add to dashboard Cite

International audienceMany industrial applications require the use of table constraints (e.g., in configuration problems), sometimes of significant size. During the recent years, researchers have focused on reducing space and time complexities of this type of constraint. Static and dynamic reduction based approaches have been proposed giving new compact representations of table constraints and effective filtering algorithms. In this paper, we study the possibility of combining both static and dynamic reduction techniques by proposing a new compressed form of table constraints based on frequent pattern detection, and exploiting it in STR (Simple Tabular Reduction)

show abstract

An Optimal Filtering Algorithm for Table Constraints

Cited by 11 publications

References 14 publications

Improving the lower bound of simple tabular reduction

Improving the lower bound of simple tabular reduction

Compact-Table: Efficiently Filtering Table Constraints with Reversible Sparse Bit-Sets

Sliced Table Constraints: Combining Compression and Tabular Reduction

Contact Info

Product

Resources

About