Randomized Algorithms for the Loop Cutset Problem

We show how to find a minimum weight loop cutset in a Bayesian network with high probability. Finding such a loop cutset is the first step in the method of conditioning for inference. Our randomized algorithm for finding a loop cutset outputs a minimum loop cutset after O(c 6^k kn) steps with probability at least 1 - (1 - 1/(6^k))^c6^k, where c > 1 is a constant specified by the user, k is the minimal size of a minimum weight loop cutset, and n is the number of vertices. We also show empirically that a variant of this algorithm often finds a loop cutset that is closer to the minimum weight loop cutset than the ones found by the best deterministic algorithms known

Markov-Chain-Based Heuristics for the Feedback Vertex Set Problem for Digraphs

A feedback vertex set (FVS) of an undirected or directed graph G=(V, A) is a set F such that G-F is acyclic. The minimum feedback vertex set problem asks for a FVS of G of minimum cardinality whereas the weighted minimum feedback vertex set problem consists of determining a FVS F of minimum weight w(F) given a real-valued weight function w. Both problems are NP-hard [Karp72]. Nethertheless, they have been found to have applications in many fields. So one is naturally interested in approximation algorithms. While most of the existing approximation algorithms for feedback vertex set problems rely on local properties of G only, this thesis explores strategies that use global information about G in order to determine good solutions. The pioneering work in this direction has been initiated by Speckenmeyer [Speckenmeyer89]. He demonstrated the use of Markov chains for determining low cardinality FVSs. Based on his ideas, new approximation algorithms are developed for both the unweighted and the weighted minimum feedback vertex set problem for digraphs. According to the experimental results presented in this thesis, these new algorithms outperform all other existing approximation algorithms. An additional contribution, not related to Markov chains, is the identification of a new class of digraphs G=(V, A) which permit the determination of an optimum FVS in time O(|V|^4). This class strictly encompasses the completely contractible graphs [Levy/Low88]

The minimum length corridor problem : exact, approximative and heuristic algorithms

Orientador: Cid Carvalho de SouzaDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Esta dissertação tem como foco a investigação experimental de algoritmos exatos, aproximativos e heurísticos aplicados na resolução do chamado problema do corredor de comprimento mínimo (PCCM). No PCCM recebemos um polígono retilinear P e um conjunto de polígonos retilineares menores formando uma subdivisão S planar conexa de P. Uma solução para este problema, também chamada de corredor, é formada por um conjunto conexo de arestas de S, e tal que cada face interna em S possui pelo menos um ponto em sua borda que pertence a alguma aresta deste conjunto. O objetivo então é encontrar um corredor tal que a soma total dos comprimentos das arestas seja a menor possível. Trata-se de um problema NP-difícil com aplicações em áreas diversas, tais como telecomunicações, engenharia civil e projeto de circuitos VLSI. O PCCM pode ser reduzido polinomialmente a um problema em grafos denominado problema da árvore de Steiner com grupos (PASG). Considerando esta transformação, estudamos e implementamos dois métodos aproximativos, um método exato de branch-and-cut, e um método heurístico baseado na metaheurística GRASP combinada com um evolutionary path relinking (GRASP+EPR). Além disso, propomos três heurísticas de busca local que visam melhorar a qualidade de soluções do PASG. Instâncias do PCCM foram geradas aleatoriamente, nas quais aplicamos os métodos implementados. Analisamos os resultados, e apresentamos as situações onde é interessante utilizar cada método. Verificamos que o método branch-and-cut foi capaz de encontrar soluções ótimas para instâncias que julgamos ser de grande porte em tempos computacionalmente aceitáveis. O melhor algoritmo aproximativo obteve corredores que na média têm comprimento 17% maior que o comprimento ótimo. Se combinarmos este algoritmo com as heurísticas de melhoria propostas este percentual cai para a média de 3,5%. Finalmente, o GRASP+EPR consome mais tempo que este algoritmo aproximativo, entretanto, o comprimento dos corredores obtidos por ele é em média 0,9% maior que o comprimento ótimoAbstract: This dissertation focuses on the experimental investigation of exact, approximation and heuristic algorithms applied to solve the so-called minimum length corridor problem (MLCP). In the MLCP we receive a rectilinear polygon P and a set of minor rectilinear polygons forming a connected planar subdivision S of P. A solution for this problem, also called corridor, is formed by a set of connected edges of S, and such that each inner face of S has at least one point on its your border which belongs to an edge in this set. The goal is to find a corridor such that the sum of lengths of the edges is as small as possible. This is an NP-hard problem with applications in several areas such as telecommunications, civil engineering and design of VLSI circuits. The MLCP can be polynomially reduced to a graph problem known as group Steiner tree problem (GSTP). Based on this transformation, we studied and implemented two approximation methods, an exact branch-and-cut method, and a heuristic method based on the metaheuristic GRASP combined with an evolutionary path relinking (GRASP+EPR). Furthermore, we propose three local search heuristics to improve the quality of GSTP solutions. MLCP instances were randomly generated, in which we apply the methods implemented. We analyzed the results, and present situations where it is interesting to use each method. We found that the branch-and-cut has been able to find optimal solutions for instances that we consider to be large in acceptable computational times. The best approximation algorithm obtained corridors having average length 17% higher than the optimum length. If we combine this algorithm with the improvement heuristics proposed this percentage drops to an average of 3.5%. Finally, the GRASP+EPR spent more time than this approximation algorithm, however, the length of the corridors obtained by the method is, on average, 0.9% higher than the optimum lengthMestradoCiência da ComputaçãoMestre em Ciência da Computaçã