The minimum vertex cover problem(MVCP)is a well-known combinatorial optimization problem of graph theory.The MVCP is an NP(nondeterministic polynomial)complete problem and it has an exponential growing complexity with...The minimum vertex cover problem(MVCP)is a well-known combinatorial optimization problem of graph theory.The MVCP is an NP(nondeterministic polynomial)complete problem and it has an exponential growing complexity with respect to the size of a graph.No algorithm exits till date that can exactly solve the problem in a deterministic polynomial time scale.However,several algorithms are proposed that solve the problem approximately in a short polynomial time scale.Such algorithms are useful for large size graphs,for which exact solution of MVCP is impossible with current computational resources.The MVCP has a wide range of applications in the fields like bioinformatics,biochemistry,circuit design,electrical engineering,data aggregation,networking,internet traffic monitoring,pattern recognition,marketing and franchising etc.This work aims to solve the MVCP approximately by a novel graph decomposition approach.The decomposition of the graph yields a subgraph that contains edges shared by triangular edge structures.A subgraph is covered to yield a subgraph that forms one or more Hamiltonian cycles or paths.In order to reduce complexity of the algorithm a new strategy is also proposed.The reduction strategy can be used for any algorithm solving MVCP.Based on the graph decomposition and the reduction strategy,two algorithms are formulated to approximately solve the MVCP.These algorithms are tested using well known standard benchmark graphs.The key feature of the results is a good approximate error ratio and improvement in optimum vertex cover values for few graphs.展开更多
二分图受约束最小点覆盖问题作为一个NP-完全问题,无法在多项式时间内得到最优解,除非P=NP。基于此,本文提出了一种基于链暗示技术的二分图受约束最小点覆盖问题的近似算法,具体为:当二分图受约束最小点覆盖问题实例中存在满足约束条件...二分图受约束最小点覆盖问题作为一个NP-完全问题,无法在多项式时间内得到最优解,除非P=NP。基于此,本文提出了一种基于链暗示技术的二分图受约束最小点覆盖问题的近似算法,具体为:当二分图受约束最小点覆盖问题实例中存在满足约束条件的最小点覆盖(ku,kl)时,对任意给定的近似率δ=1+ε>1,一定可以找到一个受约束近似点覆盖(ku,kl),对应的近似率为max{ku/ku,kl/kl}≤1+ε,整个近似算法的运行时间复杂度为O(22/ε)。显然,它是二分图受约束最小点覆盖问题的一个多项式时间近似方案(polynomial time approximation scheme,PTAS算法)。展开更多
文摘The minimum vertex cover problem(MVCP)is a well-known combinatorial optimization problem of graph theory.The MVCP is an NP(nondeterministic polynomial)complete problem and it has an exponential growing complexity with respect to the size of a graph.No algorithm exits till date that can exactly solve the problem in a deterministic polynomial time scale.However,several algorithms are proposed that solve the problem approximately in a short polynomial time scale.Such algorithms are useful for large size graphs,for which exact solution of MVCP is impossible with current computational resources.The MVCP has a wide range of applications in the fields like bioinformatics,biochemistry,circuit design,electrical engineering,data aggregation,networking,internet traffic monitoring,pattern recognition,marketing and franchising etc.This work aims to solve the MVCP approximately by a novel graph decomposition approach.The decomposition of the graph yields a subgraph that contains edges shared by triangular edge structures.A subgraph is covered to yield a subgraph that forms one or more Hamiltonian cycles or paths.In order to reduce complexity of the algorithm a new strategy is also proposed.The reduction strategy can be used for any algorithm solving MVCP.Based on the graph decomposition and the reduction strategy,two algorithms are formulated to approximately solve the MVCP.These algorithms are tested using well known standard benchmark graphs.The key feature of the results is a good approximate error ratio and improvement in optimum vertex cover values for few graphs.
文摘二分图受约束最小点覆盖问题作为一个NP-完全问题,无法在多项式时间内得到最优解,除非P=NP。基于此,本文提出了一种基于链暗示技术的二分图受约束最小点覆盖问题的近似算法,具体为:当二分图受约束最小点覆盖问题实例中存在满足约束条件的最小点覆盖(ku,kl)时,对任意给定的近似率δ=1+ε>1,一定可以找到一个受约束近似点覆盖(ku,kl),对应的近似率为max{ku/ku,kl/kl}≤1+ε,整个近似算法的运行时间复杂度为O(22/ε)。显然,它是二分图受约束最小点覆盖问题的一个多项式时间近似方案(polynomial time approximation scheme,PTAS算法)。
