On obtaining Knuth, Morris, and Pratt's string matcher by partial evaluation

Ager, Mads Sig; Danvy, Olivier; Rohde, Henning Korsholm

doi:10.1145/568173.568177

Cited by 11 publications

(6 citation statements)

References 13 publications

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“…some known input data. A classical example is the specialization of a (semi)naive pattern matcher for a fixed pattern into an efficient algorithm-sometimes called "the KMP-test" (Ager et al, 2002;Consel and Danvy, 1989). The considered program is as follows: …”

Section: Program Specializationmentioning

confidence: 99%

Cost-Augmented Partial Evaluation of Functional Logic Programs

Vidal

2004

Higher-Order and Symbolic Computation

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Abstract. We enhance the narrowing-driven partial evaluation scheme for lazy functional logic programs with the computation of symbolic costs. The enhanced scheme allows us to estimate the effects of the program transformer in a precise framework and, moreover, to quantify these effects. The considered costs are "symbolic" in the sense that they measure the number of basic operations performed during a computation rather than actual execution times. Our scheme may serve as a basis to develop speedup analyses and cost-guided transformers. A cost-augmented partial evaluator, which demonstrates the usefulness of our approach, has been implemented in the multi-paradigm language Curry.

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Section: Program Specializationmentioning

confidence: 99%

Cost-Augmented Partial Evaluation of Functional Logic Programs

Vidal

2004

Higher-Order and Symbolic Computation

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“…The existing solutions [1]- [4], [9], [10], [15], [16], [18], [19], [22] generate the pattern p to the suitable data structure such as the automata, the shift table, or the bit parallel to decrease the searching time. Among them, the best one [4] takes O(m) time complexity and O(1) space complexity to process the pattern to its structure.…”

Section: Introductionmentioning

confidence: 99%

Inverted Lists String Matching Algorithms

Khancome¹,

Boonjing²

2010

IJCTE

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This research article presents two algorithms of string pattern matching. These algorithms employ a new data structure called inverted lists structure which is inherited from the inverted index to accommodate a string pattern to be searched. The first solution scans the given text in a single pass for all occurrences of string pattern. The second solution, which improves the first one, takes the comparison times equal to the length of pattern plus the numbers of comparison that lead to be mismatched. For experimental results, these algorithms are efficient in the case of small alphabet sizes. Index Terms-string pattern matching, inverted lists (IVL), inverted index, string algorithm.

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“…This paper shows that some efficient algorithms can be automatically generated from inefficient ones. Automatic generation of an efficient pattern matcher from a naive one, introduced in [7], is a typical problem for partial evaluation [1,2,5,10,14]. Let m be the length of a given pattern and n be the length of a given text.…”

Section: Introductionmentioning

confidence: 99%

“…And is α pm (p) of size O(m)? 1 Apparently, Type 2 problem is more difficult than Type 1 and has never been solved by partial evaluation to the best of authors' knowledge. This paper deals with Type 1 problem and reports that, by Generalized Partial Computation (GPC) [8,9], we can generate (1) a Boyer-Moore (BM) type pattern matcher [3] without the bad-character heuristic (the delta 1 table in [3]; see Appendix 1) from a non-linear backward matcher and (2) a Knuth-Morris-Pratt (KMP) matcher from a non-linear forward matcher.…”

Section: Introductionmentioning

confidence: 99%

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Automatic generation of efficient string matching algorithms by generalized partial computation

Futamura

Konishi

Glück

2002

Proceedings of the ASIAN Symposium on Partial Evaluation and Semantics-Based Program Manipulation

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This paper shows that Generalized Partial Computation (GPC) can automatically generate efficient string matching algorithms. GPC is a program transformation method utilizing partial information about input data and auxiliary functions as well as the logical structure of a source program. GPC uses both a classical partial evaluator and an inference engine such as a theorem prover to optimize programs. First, we show that a Boyer-Moore (BM) type pattern matcher without the bad-character heuristic can be generated from a simple non-linear backward matcher by GPC. This sort of problems has already been discussed in the literature using offline partial evaluators. However, there was no proof that every generated matcher runs in the same way as the BM. In this paper we prove that the problem can be solved starting from a simple non-linear pattern matcher as a source program. We also prove that a Knuth-Morris-Pratt (KMP) type linear string matcher can be generated from a naive non-linear forward matcher by GPC.

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On obtaining Knuth, Morris, and Pratt's string matcher by partial evaluation

Cited by 11 publications

References 13 publications

Cost-Augmented Partial Evaluation of Functional Logic Programs

Cost-Augmented Partial Evaluation of Functional Logic Programs

Inverted Lists String Matching Algorithms

Automatic generation of efficient string matching algorithms by generalized partial computation

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