Iterative rounding has been an increasingly popular approach to solving network design optimization problems ever since Jain introduced the concept in his revolutionary 2-approximation for the Survivable Network Design Problem (SNDP). This paper looks at several important iterative rounding approximation algorithms and makes improvements to some of their proofs. We generalize a matrix restatement of Nagarajan et al.'s token argument, which we can use to simplify the proofs of Jain's 2-approximation for SNDP and Fleischer et al.'s 2-approximation for the Element Connectivity (ELC) problem. Lau et al. show how one can construct a (2,2B + 3)-approximation for the degree bounded ELC problem, and this thesis provides the proof. We provide some structural results for basic feasible solutions of the Prize-Collecting Steiner Tree problem, and introduce a new problem that arises, which we call the Prize-Collecting Generalized Steiner Tree problem.

The subject of this thesis is to study approximation algorithms for some network design problems in face of uncertainty. We consider two widely studied models of handling uncertainties - Robust Optimization and Stochastic Optimization. We study a robust version of the well studied Uncapacitated Facility Location Problem (UFLP). In this version, once the set of facilities to be opened is decided, an adversary may close at most β facilities. The clients must then be assigned to the remaining open facilities. The performance of a solution is measured by the worst possible set of facilities that the adversary may close. We introduce a novel LP for the problem, and provide an LP rounding algorithm when all facilities have same opening costs. We also study the 2-stage Stochastic version of the Steiner Tree Problem. In this version, the set of terminals to be covered is not known in advance. Instead, a probability distribution over the possible sets of terminals is known. One is allowed to build a partial solution in the first stage a low cost, and when the exact scenario to be covered becomes known in the second stage, one is allowed to extend the solution by building a recourse network, albeit at higher cost. The aim is to construct a solution of low cost in expectation. We provide an LP rounding algorithm for this problem that beats the current best known LP rounding based approximation algorithm.

Most combinatorial optimization problems are NP -hard, which imply that under well- believed complexity assumptions, there exist no polynomial time algorithms to solve them. To cope with the NP-hardness, approximation algorithms which return solutions close to the optimal, have become a rich field of study. One successful method for designing approx- imation algorithms has been to model the optimization problem as an integer program and then using its polynomial time solvable linear programming relaxation for the design and analysis of such algorithms. Such a technique is called the LP-based technique. In this thesis, we study the algorithmic aspects of three classes of combinatorial optimization problems using LP-based techniques as our main tool. Connectivity Problems: We study the Steiner tree problem and devise new linear pro- gramming relaxations for the problem. We show an equivalence of our relaxation with the well studied bidirected cut relaxation for the Steiner tree problem. Furthermore, for a class of graphs called quasi-bipartite graphs, we improve the best known upper bound on the integrality gap from 3/2 to 4/3. Algorithmically, we obtain fast and simple approximation algorithms for the Steiner tree problem on quasi-bipartite graphs. Allocation Problems: We study the budgeted al location problem of allocating a set of indivisible items to a set of agents who bid on it but possess a hard budget constraint more than which they are unwilling to pay. This problem is a special case of submodular welfare maximization. We use a natural LP relaxation for the problem and improve the best known approximation factor for the problem from ~ 0.632 to 3/4. We also improve the inapprox- imability factor of the problem to 15/16 and use our techniques to show inapproximability results for many other allocation problems. We also study online allocation problems where the set of items are unknown and appear one at a time. Under some necessary assumptions we provide online algorithms for many problems which attain the (almost) optimal competitive ratio. Both these works have applications in the area of budgeted auctions, the most famous of which are the sponsored search auctions hosted by search engines on the Internet. Design Problems: We formally define and study design problems which asks how the weights of an input instance can be designed, so that the minimum (or maximum) of a certain function of the input can be maximized (respectively, minimized). We show if the function can be approximated to any factor $alpha$, then the optimum design can be approximated to the same factor. We also show that (max-min) design problems are dual to packing problems. We use the framework developed by our study of design problems to obtain results about fraction- ally packing Steiner trees in a "black-box" fashion. Finally, we study integral packing of spanning trees and provide an alternate proof of a theorem of Nash-Williams and Tutte about packing spanning trees.

In this thesis, we do an algorithmic study of optimization problems in budgeted auctions, and some well known covering problems in the multi-agent setting. We give new results for the design of approximation algorithms, online algorithms and hardness of approximation for these problems. Along the way we give new insights for many other related problems. Budgeted Auction. We study the following allocation problem which arises in budgeted auctions (such as advertisement auctions run by Google, Microsoft, Yahoo! etc.) : Given a set of m indivisible items and n agents; agent i is willing to pay b[subscript ij] for item j and has an overall budget of B[subscript i] (i.e. the maximum total amount he is willing to pay). The goal is to allocate items to the agents so as to maximize the total revenue obtained. We study the computation complexity of the above allocation problem, and give improved results for the approximation and the hardness of approximation. We also study the above allocation problem in an online setting. Online version of the problem has motivation in the sponsored search auctions which are run by search engines. Lastly, we propose a new bidding language for the budgeted auctions: decreasing bid curves with budget constraints. We make a case for why this language is better both for the sellers and for the buyers. Multi-agent Covering Problems. To motivate this class of problems, consider the network design problem of constructing a spanning tree of a graph, assuming there are many agents willing to construct different parts of the tree. The cost of each agent for constructing a particular set of edges could be a complex function. For instance, some agents might provide discounts depending on how many edges they construct. The algorithmic question that one would be interested in is: Can we find a spanning tree of minimum cost in polynomial time in these complex settings? Note that such an algorithm will have to find a spanning tree, and partition its edges among the agents. Above are the type of questions that we are trying to answer for various combinatorial problems. We look at the case when the agents' cost functions are submodular. These functions form a rich class and capture the natural properties of economies of scale or the law of diminishing returns.We study the following fundamental problems in this setting- Vertex Cover, Spanning Tree, Perfect Matching, Reverse Auctions. We look at both the single agent and the multi-agent case, and study the approximability of each of these problems.

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Department of Computer Science, 2009. / Includes bibliographical references.

In this thesis, we design and develop new approximation algorithms and complexity results for three guarding and partitioning problems on orthogonal polygons; namely, guarding orthogonal polygons using sliding cameras, partitioning orthogonal polygons so as to minimize the stabbing number and guarding orthogonal terrains using vertex guards. We first study a variant of the well-known art gallery problem in which sliding cameras are used to guard the polygon. We consider two versions of this problem: the Minimum- Cardinality Sliding Cameras (MCSC) problem in which we want to guard P with the minimum number of sliding cameras, and the Minimum-Length Sliding Cameras (MLSC) problem in which the goal is to compute a set S of sliding cameras for guarding P so as to minimize the total length of trajectories along which the cameras in S travel. We answer questions posed by Katz and Morgenstern (2011) by presenting the following results: (i) the MLSC problem is polynomially tractable even for orthogonal polygons with holes, (ii) the MCSC problem is NP-complete when P is allowed to have holes, and (iii) an O(n)-time exact algorithm for the MCSC problem on monotone polygons. We then study a conforming variant of the problem of computing a partition of an orthogonal polygon P into rectangles whose stabbing number is minimum over all such partitions of P. The stabbing number of such a partition is the maximum number of rectangles intersected by any orthogonal line segment inside the polygon. In this thesis, we first give an O(n log n)-time algorithm that solves this problem exactly on histograms. We then show that the problem is NP-hard for orthogonal polygons with holes, providing the first hardness result for this problem. To complement the NP-hardness result, we give a 2-approximation algorithm for the problem on both polygons with and without holes. Finally, we study a variant of the terrain guarding problem on orthogonal terrains in which the objective is to guard the vertices of an orthogonal terrain with the minimum number of vertex guards. We give a linear-time algorithm for this problem under a directed visibility constraint. / February 2016

Orientadores: Flávio Keidi Miyazawa, Maxim Sviridenko / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-25T09:17:37Z (GMT). No. of bitstreams: 1 Pedrosa_LehiltonLelisChaves_D.pdf: 3649302 bytes, checksum: 9f37cca5fca5af1697c2099c8e0f2798 (MD5) Previous issue date: 2014 / Resumo: O resumo poderá ser visualizado no texto completo da tese digital / Abstract: The abstract is available with the full electronic document / Doutorado / Ciência da Computação / Doutor em Ciência da Computação

Orientador: Zanoni Dias / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-21T23:02:27Z (GMT). No. of bitstreams: 1 Galvao_GustavoRodrigues_M.pdf: 1280667 bytes, checksum: 0809ad85a3b7f16ff5d7af5fc4124f0a (MD5) Previous issue date: 2012 / Resumo: Ao longo da evolução, mutações globais podem alterar a ordem dos genes de um genoma. Tais mutações são chamadas de eventos de rearranjo. Em Rearranjo de Genomas, estimamos a distância evolutiva entre dois genomas calculando-se a distância de rearranjo entre eles, que é o tamanho da menor sequência de eventos de rearranjo que transforma um genoma no outro. Representando genomas como permutações, nas quais os genes aparecem como elemento, à distância de rearranjo pode ser obtido resolvendo-se o problema combinatório de ordenar uma permutação utilizando o menor número de eventos de rearranjo. Este problema, que é referido como Problema da Ordenação por Rearranjo, varia de acordo com os tipos de eventos de rearranjo considerados. Nesta dissertação, focamos nosso estudo em dois tipos de eventos: reversões e transposições. Variações do Problema da Ordenação por Rearranjo que consideram esses eventos têm se mostrado difíceis de ser resolvida otimamente, por isso a maior parte dos algoritmos propostos - os quais denominamos genericamente por algoritmos de rearranjo de genomas - são aproximados e é esperado que os próximos avanços ocorram nesse sentido. Em razão disso, desenvolvemos uma ferramenta que avalia as respostas desses algoritmos. Para ilustrar sua aplicação, nós a utilizamos para avaliar as respostas de 16 algoritmos de rearranjo de genomas aproximados relativos a 6 variações do Problema da Ordenação por Rearranjo. Além da ferramenta, este trabalho traz outras contribuições. Desenvolvemos um algoritmo exato para calcular distâncias de rearranjo que é mais eficiente em termos de uso de memória do que qualquer outro algoritmo que encontramos na literatura. Apresentamos conjecturas que dizem respeito à forma como as distâncias de rearranjo se distribuem. Validamos conjecturas referentes ao diâmetro, que é o maior valor alcançável pela distância de rearranjo entre uma permutação qualquer e a identidade considerando-se todas as permutações com o mesmo número de elementos. Apresentamos demonstrações formais para o fator de aproximação de alguns dos algoritmos avaliados. Por fim, mostramos que os fatores de aproximação de 7 dos 16 algoritmos avaliados não podem ser melhorados, o que contradiz algumas hipóteses levantadas na literatura, e conjecturamos que os fatores de aproximação de outros 6 algoritmos também não possam / Abstract: During evolution, global mutations may modify the gene order in a genome and such mutations are called rearrangement events. In Genome Rearrangements, we estimate the evolutionary distance between two genomes by computing the rearrangement distance between them, which is the length of the shortest sequence of rearrangement events that transforms one genome into the other. Representing genomes as permutations, in which genes appear as elements, the rearrangement distance can be obtained by solving the combinatorial problem of sorting a permutation using a minimum number of rearrangement events. This problem is referred to as Rearrangement Sorting Problem and varies accordingly to the types of rearrangement events considered. In this dissertation, we focus on two types of rearrangement events: reversals and transpositions. Variants of Rearrangement Sorting Problem involving these events have been shown to be difficult to solve optimally, therefore most of the proposed algorithms - which we denominate generically as genome rearrangement algorithms - are approximations, which have been the expected direction to follow. For this reason, we developed a tool that evaluates the results of these algorithms. To illustrate its application, we used it to evaluate the results of 16 genome rearrangement algorithms regarding 6 variants of Rearrangement Sorting Problem. Besides this tool, we developed an exact algorithm for computing rearrangement distances that is more efficient in terms of memory than any algorithm we have found in literature. Additionally, we presented conjectures on how the rearrangement distance are distributed and validated them regarding their diameter, which is the greatest value that the rearrangement distance between a permutation and the identity can reach considering all permutations with the same number of elements. Moreover, we presented formal proofs on the approximation ratio of some of the evaluated algorithms and showed that the approximation ratio of 7 out of the 16 evaluated algorithms cannot be improved, which contradicts some hypothesis raised in literature. Lastly, we conjectured that the approximation ratio of another 6 algorithms also cannot be improved / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

[pt] Neste trabalho, apresentamos algoritmos aproximativos para dois problemas de otimização em árvores. Na primeira parte, consideramos o Problema de Atribuição de k-Hotlinks. Seja G= (V,E) um grafo direcionado acíclico representando um web site, onde nós correspondem a páginas e arcos correspondem a hyperlinks. Nesse contexto, hotlink são definidos como atalhos (novos arcos) adicionados às páginas de G de modo a reduzir o tempo gasto pelos usuários para alcançarem as informações desejadas. Neste trabalho consideramos o problema onde G é uma árvore enraizada e o objetivo é minimizar o tempo médio gasto pelos usuários atribuindo no máximo k hotlinks a cada nó. Para a versão mais estudada desse problema onde no máximo um hotlink pode ser atribuído a cada nó, provamos a existência de um FPTAS. Isso representa uma significante melhora em relação ao algoritmo com aproximação constante obtido recentemente em [Jacobs, WADS 2007]. Além disso, desenvolvemos o primeiro algoritmo com aproximação constante para a versão mais geral onde k hotlinks podem ser atribuídos a cada nó. Na segunda parte deste trabalho, consideramos o problema de computar estratégias eficientes para realizar buscas em árvores. Como uma generalização da tradicional busca binária em listas ordenadas, suponha que se deseja encontrar um nó específico (porém desconhecido) de uma árvore realizando consultas em seus arcos, onde cada consulta indica a extremidade do arco mais próxima ao nó desejado. Dada a probabilidade de cada nó ser aquele procurado, o objetivo é computar uma estratégia de busca que minimize o número esperado de consultas. Aplicações práticas desse problema incluem sincronização de file systems e testes de software. Apresentamos um algoritmo linear que obtém a primeira aproximação constante para esse problema. Isso representa uma melhora significativa em relação à O(log n)-aproximação anterior. / [en] Here we present a study on two optimization problems in trees: the k- Hotlink Assignment Problem and the problem of Binary Searching in Trees. As a result, we obtain improved approximation algorithms for both problem. The k-Hotlink Assignment Problem can be defined as follows. Let G = (V,E) be a directed acyclic graph representing a web site, where nodes correspond to pages and arcs to hyperlinks. In this context, hotlinks are defined as shortcuts (new arcs) added to web pages of G in order to reduce the time spent by users to reach their desired information. Here we consider the problem where G is a rooted directed tree and the goal is minimizing the expected time spent by users by assigning at most k hotlinks to each node. For the most studied version of this problem where at most one hotlink can be assigned from each node, we prove the existence of an FPTAS, improving upon the constant factor algorithm recently obtained in [Jacobs, WADS 2007]. In addition, we develop the first constant factor approximation algorithm for the most general version where k hotlinks can be assigned from each node. In the second part of this work, we consider the problem of computing efficient strategies for searching in trees. As a generalization of the classical binary search for ordered lists, suppose one wishes to find a (unknown) specific node of a tree by asking queries to its arcs, where each query indicates the endpoint closer to the desired node. Given the likelihood of each node being the one searched, the objective is to compute a search strategy that minimizes the expected number of queries. Practical applications of this problem include file system synchronization and software testing. Here we present a linear time algorithm which is the first constant factor approximation for this problem. This represents a significant improvement over previous O(log n)-approximation.

Au cours de cette thèse, l'exploration de l'espace de solutions par des métaheuristiques est abordée. Les métaheuristiques sont des méthodes d'optimisation utilisées pour résoudre des problèmes NP-difficile. Elles explorent aléatoirement l'espace de recherche pour trouver les meilleures solutions. Dans un premier temps, l'ensemble des solutions est modélisé par un espace unidimensionnel par une Méthode de Conversion de l'Espace de recherche (MCE). Des métriques sont proposées pour évaluer l'exploration de l'espace de recherche par une métaheuristique en identifiant les zones explorées et inexplorées. Ces métriques sont utilisées pour orienter l'exploration de l'espace de recherche d'une méthode d'optimisation.La convergence est améliorée en accentuant le recherche dans les zones explorées. Pour sortir des minimums locaux, l'exploration est diversifiée en la dirigeant vers les zones inexplorées. En associant l'exploration du voisinage des solutions et ces métriques cartographiques, il est possible d'améliorer les performances des métaheuristiques. Plusieurs algorithmes mono-objectifs et multiobjectifs sont implémentés en version classique, hybridé par la recherche locale et par la MCE. Le Flexible Job Shop Problem (FJSP) est utilisé comme problème de référence. Les expérimentations avec les algorithmes hybridés montrent une amélioration des performances / In this thesis, the solution space exploration by the metaheuristic is developed. The metaheuristics optimization methods are used to solve NP-hard problems. They explore randomly the search space to look for the best solutions. In a first step, the solution set is modeled by a one-dimensional space by a Mapping Method (MaM). Metrics are proposed to evaluate the search space exploration by a metaheuristic, identifying the explored and unexplored zones. These metrics are used to guide the search space exploration of an optimization method. The convergence is improved by emphasizing the research in the zones explored. To get out local minima, the exploration is diversified by pointing it towards the unexplored zones. Combining the neighbour discovery of the solutions and these mapping metrics, it is possible to improve the performance of metaheuristics. Several single-objective and multi-objective algorithms are implemented in the classic version, hybridized with local search and MaM. The Flexible Job Shop Problem (FJSP) is used as a reference problem. The experimentations with hybridized algorithms show performance improved