Consider the random set system (Formula presented.), where (Formula presented.) and Ajselected with probabilityp=pn}. A set H⊆[n] is said to be a hitting set for (Formula presented.). The second moment method is used to exhibit the sharp concentration of the minimal size of H for a variety of values of p.

<p>This thesis explores a few geometric optimization problems that arise</p><p>in robotics and sensor networks. In particular we present efficient</p><p>algorithms for the hitting-set problem and the budgeted hitting-set problem.</p><p>Given a set of objects and a collection of subsets of the objects,</p><p>called ranges, the hitting-set problem asks for a minimum number of </p><p>objects that intersect all the subsets in the collection.</p><p>In geometric settings, objects are </p><p>typically a set of points and ranges are defined by a set of geometric</p><p>regions (e.g., disks or polygons), i.e., the subset of points lying in each </p><p>region forms a range.</p><p>The first result of this thesis is an efficient algorithm for an instance</p><p>of the hitting-set problem in which both the set of points and the set</p><p>of ranges are implicitly defined. Namely, we are given a convex</p><p>polygonal robot and a set of convex polygonal obstacles, and we wish</p><p>to find a small number of congruent copies of the robot that intersect</p><p>all the obstacles.</p><p>Next, motivated by the application of sensor placement in sensor networks,</p><p>we study the so-called ``art-gallery'' problem. Given a polygonal</p><p>environment, we wish to place the minimum number of guards so that</p><p>the every point in the environment is visible from at least one guard.</p><p>This problem can be formulated as a hitting-set problem. We present</p><p>a sampling based algorithm for this problem and study various extensions</p><p>of this problem.</p><p>Next, we study the geometric hitting-set problem in a dynamic setting,</p><p>where the objects and/or the ranges change with time and the goal is</p><p>to maintain a hitting set. We present algorithms </p><p>which maintain a small size hitting set with sub-linear update time.</p><p>Finally, we consider the budgeted hitting-set problem, in which we</p><p>are asked to choose a bounded number of objects that intersect as many</p><p>ranges as possible. Motivated by applications in network vulnerability</p><p>analysis we study this problem in a probabilistic setting.</p> / Dissertation

The minimum set cover problem is, without question, among the most ubiquitous and well-studied problems in computer science. Its theoretical hardness has been fully characterized--logarithmic approximability has been established, and no sublogarithmic approximation exists unless P=NP. However, the gap between real-world instances and the theoretical worst case is often immense--many covering problems of practical relevance admit much better approximations, or even solvability in polynomial time. Simple combinatorial or geometric structure can often be exploited to obtain improved algorithms on a problem-by-problem basis, but there is no general method of determining the extent to which this is possible. In this thesis, we aim to shed light on the relationship between the structure and the hardness of covering problems. We discuss several measures of structural complexity of set cover instances and prove new algorithmic and hardness results linking the approximability of a set cover problem to its underlying structure. In particular, we provide: - An APX-hardness proof for a wide family of problems that encode a simple covering problem known as Special-3SC. - A class of polynomial dynamic programming algorithms for a group of weighted geometric set cover problems having simple structure. - A simplified quasi-uniform sampling algorithm that yields improved approximations for weighted covering problems having low cell complexity or geometric union complexity. - Applications of the above to various capacitated covering problems via linear programming strengthening and rounding. In total, we obtain new results for dozens of covering problems exhibiting geometric or combinatorial structure. We tabulate these problems and classify them according to their approximability.

The minimum set cover problem is, without question, among the most ubiquitous and well-studied problems in computer science. Its theoretical hardness has been fully characterized--logarithmic approximability has been established, and no sublogarithmic approximation exists unless P=NP. However, the gap between real-world instances and the theoretical worst case is often immense--many covering problems of practical relevance admit much better approximations, or even solvability in polynomial time. Simple combinatorial or geometric structure can often be exploited to obtain improved algorithms on a problem-by-problem basis, but there is no general method of determining the extent to which this is possible. In this thesis, we aim to shed light on the relationship between the structure and the hardness of covering problems. We discuss several measures of structural complexity of set cover instances and prove new algorithmic and hardness results linking the approximability of a set cover problem to its underlying structure. In particular, we provide: - An APX-hardness proof for a wide family of problems that encode a simple covering problem known as Special-3SC. - A class of polynomial dynamic programming algorithms for a group of weighted geometric set cover problems having simple structure. - A simplified quasi-uniform sampling algorithm that yields improved approximations for weighted covering problems having low cell complexity or geometric union complexity. - Applications of the above to various capacitated covering problems via linear programming strengthening and rounding. In total, we obtain new results for dozens of covering problems exhibiting geometric or combinatorial structure. We tabulate these problems and classify them according to their approximability.

Orientador: Prof. Dr. Luiz Carlos da Silva Rozante / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Ciência da Computação, 2015. / A inferência de redes de regulação gênica é um dos problemas cruciais no campo de Biologia de Sistemas. É ainda um problema em aberto, principalmente devido à alta dimensionalidade (milhares de genes) com um número limitado de amostras (dezenas), tornando difícil estimar dependências entre genes. Além do problema de estimação, outro obstáculo é a inerente complexidade computacional dos métodos de inferência de GRNs. Este trabalho teve como foco contornar problemas de desempenho de uma técnica baseada em perturbação de sinais para inferir dependências entre genes. Um dos passos principais consiste em resolver o problema da Transversal Mínima (do Inglês Hitting Set, ou HSP), o qual é NPDifícil. Existem diversas propostas para se obter soluções aproximadas ou exatas para esse problema. Uma dessas propostas consiste em um algoritmo baseado em GPU (Graphical Processing Unit) para se obter as soluções exatas do HSP. Entretanto, tal método não é escalável para GRNs de tamanho real. Foi proposto nesse trabalho, portanto, uma extensão desse algoritmo para resolver o HSP, que é capaz de lidar com conjuntos de entrada contendomilhares de variáveis, pela introdução de inovações nas estruturas de dados e um mecanismo de ordenação que permite um descarte eficiente de candidatos que não são solução do HSP. Foi provida uma implementação em CPU multi-core e em clusters de GPU. Os resultados experimentais mostraram que o uso do mecanismo de ordenação fornece speedups de até 3,5 na implementação em CPU. Além disso, utilizando uma única GPU, foi obtido um speedup adicional de até 4,7, em comparação com uma implementação multithreaded em CPU. Porfim, o uso de oito GPUs de um cluster de GPU forneceu um speedup adicional de até 6,6. Combinando todas as técnicas, foram obtidos speedups acima de 60 para a parte paralela do algoritmo. / Gene regulatory networks inference is one of the crucial problems of the Systems Biology field. It is still an open problem, mainly because of its high dimensionality (thousands of genes) with a limited number of samples (dozens), making it difficult to estimate dependenciesamong genes. Besides the estimation problem, another important hindrance is the inherent computational complexity of GRN inference methods. In this work, we focus on circumventing performance issues of a technique based on signal perturbations to infer gene dependencies. One of its main steps consists in solving the Hitting Set problem (HSP), which is NP-Hard. There are many proposals to obtain approximate or exact solutions to this problem. One of these proposals consists of a Graphical Processing Unit (GPU) based algorithm to obtain exact solutions to the HSP. However, such method is not scalable for real size GRNs. We propose an extension of the HSP algorithm to deal with input sets containing thousands of variables by introducing innovations in the data structures and a sorting scheme to allow efficient discarding of Hitting Set non-solution candidates. We provide an implementation for multi-core CPUs and GPU clusters. Our experimental results show that the usage of the sorting scheme brings speedups of up to 3.5 in the CPU implementation. Moreover, using a single GPU, we could obtain an additional speedup of up to 4.7, in comparison with the multithreaded CPU implementation. Finally, usage of eight GPUs from a GPU cluster brought an additional speedup of up to 6.6. Combining all techniques, speedups above 60 were obtained for the parallel part of the algorithm.

Interval graphs are well studied structures. Intervals can represent resources like jobs to be sched-uled. Finding maximum independent set in interval graphs would correspond to scheduling maximum number of non-conflicting jobs on the computer. Most optimization problems on interval graphs like independent set, vertex cover, dominating set, maximum clique, etc can be solved efficiently using combinatorial algorithms in polynomial time. Hitting, Covering and Packing problems have been ex-tensively studied in the last few decades and have applications in diverse areas. While they are NP-hard for most settings, they are polynomial solvable for intervals. In this thesis, we consider the generaliza-tions of hitting, covering and packing problems for intervals. We model these problems as min-cost flow problems using non-trivial reduction and solve it using standard flow algorithms. Demand-hitting problem which is a generalization of hitting problem is defined as follows: Given N intervals, a positive integer demand for every interval, M points, a real weight for every point, select a subset of points H, such that every interval contains at least as many points in H as its demand and sum of weight of the points in H is minimized. Note that if the demand is one for all intervals, we get the standard hitting set problem. In this case, we give a dynamic programming based O(M + N) time algorithm assuming that intervals and points are sorted. A special case of the demand-hitting set is the K-hitting set problem where the demand of all the intervals is K. For the K-hitting set problem, we give a O(M2N) time flow based algorithm. For the demand-hitting problem, we make an assumption that no interval is contained in another interval. Under this assumption, we give a O(M2N) time flow based algorithm. Demand-covering problem which is a generalization of covering problem is defined as follows: Given N intervals, a real weight for every interval, M points, a positive integer demand for every point, select a subset of intervals C, such that every point is contained in at least as many intervals in C as its demand and sum of weight of the intervals in C is minimized. Note that if the demand of points are one, we get the standard covering set problem. In this case, we give a dynamic programming based O(M + N log N) time algorithm assuming that points are sorted. A special case of the demand-covering set is the K-covering set problem where the demand of all the points is K. For the K-covering set problem, we give a O(MN2) time flow based algorithm. For the demand-covering problem, we give a O(MN2) time flow based algorithm. K-pack points problem which is a generalization of packing problem is defined as follows: Given N intervals, an integer K, M points, a real weight for every point, select a subset of points Y , such that every interval contains at most K points from Y and sum of weight of the points in Y is maximized. Note that if K is one, we get the standard pack points problem. In this case, we give a dynamic pro-gramming based O(M + N) time algorithm assuming that points and intervals are sorted. For K-pack points problem, we give O(M2 log M) time flow based algorithm assuming that intervals and points are sorted.

Dans cette thèse, nous étudions les aspects structurels et algorithmiques de différents problèmes de théorie des graphes. Rappelons qu’un graphe est un ensemble de sommets éventuellement reliés par des arêtes. Deux sommets sont adjacents s’ils sont reliés par une arête.<p>Tout d’abord, nous considérons les deux problèmes suivants :le problème de vertex cover et celui de dominating set, deux cas particuliers du problème de hitting set. Un vertex cover est un ensemble de sommets qui rencontrent toutes les arêtes alors qu’un dominating set est un ensemble X de sommets tel que chaque sommet n’appartenant pas à X est adjacent à un sommet de X. La version connexe de ces problèmes demande que les sommets choisis forment un sous-graphe connexe. Pour les deux problèmes précédents, nous examinons le prix de la connexité, défini comme étant le rapport entre la taille minimum d’un ensemble répondant à la version connexe du problème et celle d’un ensemble du problème originel. Nous prouvons la difficulté du calcul du prix de la connexité d’un graphe. Cependant, lorsqu’on exige que le prix de la connexité d’un graphe ainsi que de tous ses sous-graphes induits soit borné par une constante fixée, la situation change complètement. En effet, pour les problèmes de vertex cover et de dominating set, nous avons pu caractériser ces classes de graphes pour de petites constantes.<p>Ensuite, nous caractérisons en termes de dominating sets connexes les graphes Pk- free, graphes n’ayant pas de sous-graphes induits isomorphes à un chemin sur k sommets. Beaucoup de problèmes sur les graphes sont étudiés lorsqu’ils sont restreints à cette classe de graphes. De plus, nous appliquons cette caractérisation à la 2-coloration dans les hypergraphes. Pour certains hypergraphes, nous prouvons que ce problème peut être résolu en temps polynomial.<p>Finalement, nous travaillons sur le problème de Pk-hitting set. Un Pk-hitting set est un ensemble de sommets qui rencontrent tous les chemins sur k sommets. Nous développons un algorithme d’approximation avec un facteur de performance de 3. Notre algorithme, basé sur la méthode primal-dual, fournit un Pk-hitting set dont la taille est au plus 3 fois la taille minimum d’un Pk-hitting set. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

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Given a set of n points P ⊂ R2, the Delaunay graph of P for a family of geometric objects C is a graph defined as follows: the vertex set is P and two points p, p' ∈ P are connected by an edge if and only if there exists some C ∈ C containing p, p' but no other point of P. Delaunay graph of circle is often called as Delaunay triangulation as each of its inner face is a triangle if no three points are co-linear and no four points are co-circular. The dual of the Delaunay triangulation is the Voronoi diagram, which is a well studied structure. The study of graph theoretic properties on Delaunay graphs was motivated by its application to wireless sensor networks, meshing, computer vision, computer graphics, computational geometry, height interpolation, etc. The problem of finding an optimal vertex cover on a graph is a classical NP-hard problem. In this thesis we focus on the vertex cover problem on Delaunay graphs for geometric objects like axis-parallel slabs and circles(Delaunay triangulation). 1. We consider the vertex cover problem on Delaunay graph of axis-parallel slabs. It turns out that the Delaunay graph of axis-parallel slabs has a very special property — its edge set is the union of two Hamiltonian paths. Thus, our problem reduces to solving vertex cover on the class of graphs whose edge set is simply the union of two Hamiltonian Paths. We refer to such a graph as a braid graph. Despite the appealing structure, we show that deciding k-vertex cover on braid graphs is NP-complete. This involves a rather intricate reduction from the problem of finding a vertex cover on 2-connected cubic planar graphs. 2. Having established the NP-hardness of the vertex cover problem on braid graphs, we pursue the question of improved fixed parameter algorithms on braid graphs. The best-known algorithm for vertex cover on general graphs has a running time of O(1.2738k + kn) [CKX10]. We propose a branching based fixed parameter tractable algorithm with running time O⋆(1.2637k) for graphs with maximum degree bounded by four. This improves the best known algorithm for this class, which surprisingly has been no better than the algorithm for general graphs. Note that this implies faster algorithms for the class of braid graphs (since they have maximum degree at most four). 3. A triangulation is a 2-connected plane graph in which all the faces except possibly the outer face are triangles, we often refer to such graphs as triangulated graphs. A chordless-NST is a triangulation that does not have chords or separating triangles (non-facial triangles). We focus on the computational problem of optimal vertex covers on triangulations, specifically chordless-NST. We call a triangulation Delaunay realizable if it is combinatorially equivalent to some Delaunay triangulation. Characterizations of Delaunay triangulations have been well studied in graph theory. Dillencourt and Smith [DS96] showed that chordless-NSTs are Delaunay realizable. We show that for chordless-NST, deciding the vertex cover problem is NP-complete. We prove this by giving a reduction from vertex cover on 3-connected, triangle free planar graph to an instance of vertex cover on a chordless-NST. 4. If the outer face of a triangulation is also a triangle, then it is called a maximal planar graph. We prove that the vertex cover problem is NP-complete on maximal planar graphs by reducing an instance of vertex cover on a triangulated graph to an instance of vertex cover on a maximal planar graph.