
FOLLOWUS
1.DACI, Universidad Autnoma del Carmen, Cd. del Carmen 24180, Mexico
2.Computer Science, Centro Nacional de Investigacin y Desarrollo Tecnolgico, Cuernavaca 62490, Mexico
3.Systems and Computer Science, Instituto Tecnolgico de Ciudad Madero, Ciudad Madero 89440, Mexico
4.Informatics, Universidad Politcnica del Estado de Morelos, Jiutepec 62560, Mexico
5.Computer Science, Instituto Tecnolgico de Cuautla, Cuautla 62745, Mexico
[ "Jorge A. RUIZ-VANOYE, jorge@ruizvanoye.com" ]
收稿:2012-12-03,
修回:2013-;7-12,
纸质出版:2013-08
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Application of formal languages in polynomial transformations of instances between NP-complete problems[J]. 信息与电子工程前沿(英文), 2013,14(8):623-633.
Jorge A. RUIZ-VANOYE, Joaqun PREZ-ORTEGA, Rodolfo A. PAZOS RANGEL, et al. Application of formal languages in polynomial transformations of instances between NP-complete problems[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(8): 623-633.
Application of formal languages in polynomial transformations of instances between NP-complete problems[J]. 信息与电子工程前沿(英文), 2013,14(8):623-633. DOI: 10.1631/jzus.C1200349.
Jorge A. RUIZ-VANOYE, Joaqun PREZ-ORTEGA, Rodolfo A. PAZOS RANGEL, et al. Application of formal languages in polynomial transformations of instances between NP-complete problems[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(8): 623-633. DOI: 10.1631/jzus.C1200349.
We propose the usage of formal languages for expressing instances of NP-complete problems for their application in polynomial transformations. The proposed approach
which consists of using formal language theory for polynomial transformations
is more robust
more practical
and faster to apply to real problems than the theory of polynomial transformations. In this paper we propose a methodology for transforming instances between NP-complete problems
which differs from Garey and Johnson's. Unlike most transformations which are used for proving that a problem is NP-complete based on the NP-completeness of another problem
the proposed approach is intended for extrapolating some known characteristics
phenomena
or behaviors from a problem
A
to another problem
B
. This extrapolation could be useful for predicting the performance of an algorithm for solving
B
based on its known performance for problem
A
or for taking an algorithm that solves
A
and adapting it to solve
B
.
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