No description available.

A Theory of Algorithms course is essential to any Computer Science curriculum at both the undergraduate and graduate levels. It is also considered to be difficult material to teach or to learn. In particular the topics of Computational Complexity Theory, reductions, and the NP-Complete class of problems are considered difficult by students. Numerous algorithm visualizations (AVs) have been developed over the years to portray the dynamic nature of known algorithms commonly taught in undergraduate classes. However, to the best of our knowledge, the instructional material available for NP-Completeness is mostly static and textual, which does little to alleviate the complexity of the topic. Our aim is to improve the pedagogy of NP-Completeness by providing intuitive, interactive, and easy-to-understand visualizations for standard NP Complete problems, reductions, and proofs. In this thesis, we present a set of visualizations that we developed using the OpenDSA framework for certain NP-Complete problems. Our paradigm is a three step process. We first use an AV to illustrate a particular NP-Complete problem. Then we present an exercise to provide a first-hand experience with attempting to solve a problem instance. Finally, we present a visualization of a reduction as a part of the proof for NP-Completeness. Our work has been delivered as a collection of modules in OpenDSA, an interactive eTextbook system developed at Virginia Tech. The tutorial has been introduced as a teaching supplement in both a senior undergraduate and a graduate class. We present an analysis of the system use based on records of online interactions by students who used the tutorial. We also present results from a survey of the students. / Master of Science

Dans cette thèse, nous considérons plusieurs paramètres des hypergraphes et nous étudions si les restrictions aux sous-classes des hypergraphes permettent d’obtenir des propriétés combinatoires et algorithmiques souhaitables. La plupart des paramètres que nous prenons en compte sont des instances spéciales des packings et transversals des hypergraphes.Dans la première partie, nous allons nous concentrer sur les line graphs des graphes subcubiques sans triangle et nous allons démontrer que pour tous ces graphes il y a un independent set de taille au moins 3|V(G)|/10 et cette borne est optimale. Conséquence immédiate: nous obtenons une borne inférieure optimale pour la taille d’un couplage maximum dans les graphes subcubiques sans triangle. De plus, nous montrons plusieurs résultats algorithmiques liés au FEEDBACK VERTEX SET, HAMILTONIAN CYCLE et HAMILTONIAN PATH quand restreints aux line graphs des graphes subcubiques sans triangle.Puis nous examinons trois hypergraphes ayant la propriété d’Erdős-Pósa et nous cherchons à déterminer les fonctions limites optimales. Tout d’abord, nous apportons une fonction theta-bounding pour la classe des graphes subcubiques et nous étudions CLIQUE COVER: en répondant à une question de Cerioli et al., nous montrons qu’il admet un PTAS pour les graphes planaires. Par la suite, nous nous intéressons à la Conjecture de Tuza et nous montrons que la constante 2 peut être améliorée pour les graphes avec arêtes contenues dans au maximum quatre triangles et pour les graphes sans certains odd-wheels. Enfin, nous nous concentrons sur la Conjecture de Jones: nous la démontrons dans le cas des graphes sans griffes avec degré maximal 4 et nous faisons quelques observations dans le cas des graphes subcubiques.Nous étudions ensuite la VC-dimension de certains hypergraphes résultants des graphes. En particulier, nous considérons l’hypergraphe sur l’ensemble des sommets d’un certain graphe qui est induit par la famille de ses sous-graphes k-connexes. En généralisant les résultats de Kranakis et al., nous fournissons des bornes supérieures et inférieures optimales pour la VC-dimension et nous montrons que son calcul est NP-complet, pour chacun k > 0. Enfin, nous démontrons que ce problème (dans le cas k = 1) et le problème étroitement lié CONNECTED DOMINATING SET sont soit solvables en temps polynomial ou NP-complet, quand restreints aux classes de graphes obtenues en interdisant un seul sous-graphe induit.Dans la partie finale de cette thèse, nous nous attaquons aux meta-questions suivantes: Quand est-ce qu’un certain problème “difficile” de graphe devient “facile”?; Existe-t-il des frontières séparant des instances “faciles” et “difficiles”? Afin de répondre à ces questions, dans le cas des classes héréditaires, Alekseev a introduit la notion de boundary class pour un problème NP-difficile et a montré qu’un problème Pi est NP-difficile pour une classe héréditaire X finiment défini si et seulement si X contient un boundary class pour Pi. Nouscontinuons la recherche des boundary classes pour les problèmes suivants: HAMILTONIAN CYCLE THROUGH SPECIFIED EDGE, HAMILTONIAN PATH, FEEDBACK VERTEX SET, CONNECTED DOMINATING SET and CONNECTED VERTEX COVER. / In this thesis, we consider several hypergraph parameters and study whether restrictions to subclasses of hypergraphs allow to obtain desirable combinatorial or algorithmic properties. Most of the parameters we consider are special instances of packings and transversals of hypergraphs.In the first part, we focus on line graphs of subcubic triangle-free graphs and show that any such graph G has an independent set of size at least 3|V(G)|/10, the bound being sharp. As an immediate consequence, we obtain a tight lower bound for the matching number of subcubic triangle-free graphs. Moreover, we prove several algorithmic results related to FEEDBACK VERTEX SET, HAMILTONIAN CYCLE and HAMILTONIAN PATH when restricted to line graphs of subcubic triangle-free graphs.Then we consider three hypergraphs having the Erdős-Pósa Property and we seek to determine the optimal bounding functions. First, we provide an optimal theta-bounding function for the class of subcubic graphs and we study CLIQUE COVER: answering a question by Cerioli et al., we show it admits a PTAS for planar graphs. Then we focus on Tuza’s Conjecture and show that the constant 2 in the statement can be improved for graphs whose edges are contained in at most four triangles and graphs obtained by forbidding certain odd-wheels. Finally, we concentrate on Jones’ Conjecture: we prove it in the case of claw-free graphs with maximum degree at most 4 and we make some observations in the case of subcubic graphs.Then we study the VC-dimension of certain set systems arising from graphs. In particular, we consider the set system on the vertex set of some graph which is induced by the family of its k-connected subgraphs. Generalizing results by Kranakis et al., we provide tight upper and lower bounds for the VC-dimension and we show that its computation is NP-complete, for each k > 0. Finally, we show that this problem (in the case k = 1) and the closely related CONNECTED DOMINATING SET are either NP-complete or polynomial-time solvable when restricted to classes of graphs obtained by forbidding a single induced subgraph.In the final part of the thesis, we consider the following meta-questions: When does a certain “hard” graph problem become “easy”?; Is there any “boundary” separating “easy” and “hard” instances? In order to answer these questions in the case of hereditary classes, Alekseev introduced the notion of a boundary class for an NP-hard problem and showed that a problem Pi is NP-hard for a finitely defined (hereditary) class X if and only if X contains a boundary class for Pi. We continue the search of boundary classes for the following problems: HAMILTONIAN CYCLE THROUGH SPECIFIED EDGE, HAMILTONIAN PATH, FEEDBACK VERTEX SET, CONNECTED DOMINATING SET and CONNECTED VERTEX COVER.

abstract: We live in a networked world with a multitude of networks, such as communication networks, electric power grid, transportation networks and water distribution networks, all around us. In addition to such physical (infrastructure) networks, recent years have seen tremendous proliferation of social networks, such as Facebook, Twitter, LinkedIn, Instagram, Google+ and others. These powerful social networks are not only used for harnessing revenue from the infrastructure networks, but are also increasingly being used as “non-conventional sensors” for monitoring the infrastructure networks. Accordingly, nowadays, analyses of social and infrastructure networks go hand-in-hand. This dissertation studies resource allocation problems encountered in this set of diverse, heterogeneous, and interdependent networks. Three problems studied in this dissertation are encountered in the physical network domain while the three other problems studied are encountered in the social network domain. The first problem from the infrastructure network domain relates to distributed files storage scheme with a goal of enhancing robustness of data storage by making it tolerant against large scale geographically-correlated failures. The second problem relates to placement of relay nodes in a deployment area with multiple sensor nodes with a goal of augmenting connectivity of the resulting network, while staying within the budget specifying the maximum number of relay nodes that can be deployed. The third problem studied in this dissertation relates to complex interdependencies that exist between infrastructure networks, such as power grid and communication network. The progressive recovery problem in an interdependent network is studied whose goal is to maximize system utility over the time when recovery process of failed entities takes place in a sequential manner. The three problems studied from the social network domain relate to influence propagation in adversarial environment and political sentiment assessment in various states in a country with a goal of creation of a “political heat map” of the country. In the first problem of the influence propagation domain, the goal of the second player is to restrict the influence of the first player, while in the second problem the goal of the second player is to have a larger market share with least amount of initial investment. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016

Un code identifiant est un ensemble de sommets d'un graphe tel que, d'une part, chaque sommet hors du code a un voisin dans le code (propriété de domination) et, d'autre part, tous les sommets ont un voisinage distinct à l'intérieur du code (propriété de séparation). Dans cette thèse, nous nous intéressons à des aspects combinatoires et algorithmiques relatifs aux codes identifiants.Pour la partie combinatoire, nous étudions tout d'abord des questions extrémales en donnant une caractérisation complète des graphes non-orientés finis ayant comme taille minimum de code identifiant leur ordre moins un. Nous caractérisons également les graphes dirigés finis, les graphes non-orientés infinis et les graphes orientés infinis ayant pour seul code identifiant leur ensemble de sommets. Ces résultats répondent à des questions ouvertes précédemment étudiées dans la littérature.Puis, nous étudions la relation entre la taille minimum d'un code identifiant et le degré maximum d'un graphe, en particulier en donnant divers majorants pour ce paramètre en fonction de l'ordre et du degré maximum. Ces majorants sont obtenus via deux techniques. L'une est basée sur la construction d'ensembles indépendants satisfaisant certaines propriétés, et l'autre utilise la combinaison de deux outils de la méthode probabiliste : le lemme local de Lovasz et une borne de Chernoff. Nous donnons également des constructions de familles de graphes en relation avec ce type de majorants, et nous conjecturons que ces constructions sont optimales à une constante additive près.Nous présentons également de nouveaux minorants et majorants pour la cardinalité minimum d'un code identifiant dans des classes de graphes particulières. Nous étudions les graphes de maille au moins 5 et de degré minimum donné en montrant que la combinaison de ces deux paramètres influe fortement sur la taille minimum d'un code identifiant. Nous appliquons ensuite ces résultats aux graphes réguliers aléatoires. Puis, nous donnons des minorants pour la taille d'un code identifiant des graphes d'intervalles et des graphes d'intervalles unitaires. Enfin, nous donnons divers minorants et majorants pour cette quantité lorsque l'on se restreint aux graphes adjoints. Cette dernière question est abordée via la notion nouvelle de codes arête-identifiants.Pour la partie algorithmique, il est connu que le problème de décision associés à la notion de code identifiant est NP-complet même pour des classes de graphes restreintes. Nous étendons ces résultats à d'autres classes de graphes telles que celles des graphes split, des co-bipartis, des adjoints ou d'intervalles. Pour cela nous proposons des réductions polynomiales depuis divers problèmes algorithmiques classiques. Ces résultats montrent que dans beaucoup de classes de graphes, le problème des codes identifiants est algorithmiquement plus difficile que des problèms liés (tel que le problème des ensembles dominants).Par ailleurs, nous complétons les connaissances relatives à l'approximabilité du problème d'optimisation associé aux codes identifiants. Nous étendons le résultat connu de NP-difficulté pour l'approximation de ce problème avec un facteur sous-logarithmique (en fonction de la taille du graphe instance) aux graphes bipartis, split et co-bipartis, respectivement. Nous étendons également le résultat connu d'APX-complétude pour les graphes de degré maximum donné à une sous-classe des graphes split, aux graphes bipartis de degré maximum 4 et aux graphes adjoints. Enfin, nous montrons l'existence d'un algorithme de type PTAS pour les graphes d'intervalles unitaires. / An identifying code is a set of vertices of a graph such that, on the one hand, each vertex out of the code has a neighbour in the code (domination property), and, on the other hand, all vertices have a distinct neighbourhood within the code (separation property). In this thesis, we investigate combinatorial and algorithmic aspects of identifying codes.For the combinatorial part, we first study extremal questions by giving a complete characterization of all finite undirected graphs having their order minus one as minimum size of an identifying code. We also characterize finite directed graphs, infinite undirected graphs and infinite oriented graphs having their whole vertex set as unique identifying code. These results answer open questions that were previously studied in the literature.We then study the relationship between the minimum size of an identifying code and the maximum degree of a graph. In particular, we give several upper bounds for this parameter as a function of the order and the maximum degree. These bounds are obtained using two techniques. The first one consists in the construction of independent sets satisfying certain properties, and the second one is the combination of two tools from the probabilistic method: the Lovasz local lemma and a Chernoff bound. We also provide constructions of graph families related to this type of upper bounds, and we conjecture that they are optimal up to an additive constant.We also present new lower and upper bounds for the minimum cardinality of an identifying code in specific graph classes. We study graphs of girth at least 5 and of given minimum degree by showing that the combination of these two parameters has a strong influence on the minimum size of an identifying code. We apply these results to random regular graphs. Then, we give lower bounds on the size of a minimum identifying code of interval and unit interval graphs. Finally, we prove several lower and upper bounds for this parameter when considering line graphs. The latter question is tackled using the new notion of an edge-identifying code.For the algorithmic part, it is known that the decision problem associated to the notion of an identifying code is NP-complete, even for restricted graph classes. We extend the known results to other classes such as split graphs, co-bipartite graphs, line graphs or interval graphs. To this end, we propose polynomial-time reductions from several classical hard algorithmic problems. These results show that in many graph classes, the identifying code problem is computationally more difficult than related problems (such as the dominating set problem).Furthermore, we extend the knowledge of the approximability of the optimization problem associated to identifying codes. We extend the known result of NP-hardness of approximating this problem within a sub-logarithmic factor (as a function of the instance graph) to bipartite, split and co-bipartite graphs, respectively. We also extendthe known result of its APX-hardness for graphs of given maximum degree to a subclass of split graphs, bipartite graphs of maximum degree 4 and line graphs. Finally, we show the existence of a PTAS algorithm for unit interval graphs.

Un hypercube latin (LHD) maximin est un ensemble de points contenus dans un hypercube tel que les points ne partagent de coordonnées sur aucune dimension et tel que la distance minimale entre deux points est maximale. Les LHDs maximin sont particulièrement utilisés pour la construction de métamodèles en raison de leurs bonnes propriétés pour l’échantillonnage. Comme la plus grande partie des travaux concernant les LHD se sont concentrés sur leur construction par des algorithmes heuristiques, nous avons décidé de produire une étude détaillée du problème, et en particulier de sa complexité et de son approximabilité en plus des algorithmes heuristiques permettant de le résoudre en pratique.Nous avons généralisé le problème de construction d’un LHD maximin en définissant le problème de compléter un LHD entamé en respectant la contrainte maximin. Le sous-problème dans lequel le LHD partiel est vide correspond au problème de construction de LHD classique. Nous avons étudié la complexité du problème de complétion et avons prouvé qu’il est NP-complet dans de nombreux cas. N’ayant pas déterminé la complexité du sous-problème, nous avons cherché des garanties de performances pour les algorithmes résolvant les deux problèmes.D’un côté, nous avons prouvé que le problème de complétion n’est approximable pour aucune norme en dimensions k ≥ 3. Nous avons également prouvé un résultat d’inapproximabilité plus faible pour la norme L1 en dimension k = 2. D’un autre côté, nous avons proposé un algorithme d’approximation pour le problème de construction, et avons calculé le rapport d’approximation grâce à deux bornes supérieures que nous avons établies. En plus de l’aspect théorique de cette étude, nous avons travaillé sur les algorithmes heuristiques, et en particulier sur la méta-heuristique du recuit simulé. Nous avons proposé une nouvelle fonction d’évaluation pour le problème de construction et de nouvelles mutations pour les deux problèmes, permettant d’améliorer les résultats rapportés dans la littérature. / A maximin Latin Hypercube Design (LHD) is a set of point in a hypercube which do not share a coordinate on any dimension and such that the minimal distance between two points, is maximal. Maximin LHDs are widely used in metamodeling thanks to their good properties for sampling. As most work concerning LHDs focused on heuristic algorithms to produce them, we decided to make a detailed study of this problem, including its complexity, approximability, and the design of practical heuristic algorithms.We generalized the maximin LHD construction problem by defining the problem of completing a partial LHD while respecting the maximin constraint. The subproblem where the partial LHD is initially empty corresponds to the classical LHD construction problem. We studied the complexity of the completion problem and proved its NP-completeness for many cases. As we did not determine the complexity of the subproblem, we searched for performance guarantees of algorithms which may be designed for both problems. On the one hand, we found that the completion problem is inapproximable for all norms in dimensions k ≥ 3. We also gave a weaker inapproximation result for norm L1 in dimension k = 2. On the other hand, we designed an approximation algorithm for the construction problem which we proved using two new upper bounds we introduced.Besides the theoretical aspect of this study, we worked on heuristic algorithms adapted for these problems, focusing on the Simulated Annealing metaheuristic. We proposed a new evaluation function for the construction problem and new mutations for both the construction and completion problems, improving the results found in the literature.

<p>The thesis studies two communication<br />channels and corresponding error-correcting<br />codes. Multiset codes are introduced and<br />their applications described. Properties of<br />entropy and relative entropy are investigated.</p> / <p>U tezi su analizirana dva tipa komunikacionih<br />kanala i odgovarajući za&scaron;titni kodovi.<br />Uveden je pojam multiskupovnog koda i<br />opisane njegove primene. Proučavane su<br />osobine entropije i relativne entropije.</p>

Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-16T12:52:36Z No. of bitstreams: 2 Dissertação EnioPerez.pdf: 2190954 bytes, checksum: 8abd6c2cd31279e28971c632f6ed378b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2014-09-16T14:10:30Z (GMT) No. of bitstreams: 2 Dissertação EnioPerez.pdf: 2190954 bytes, checksum: 8abd6c2cd31279e28971c632f6ed378b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2014-09-16T14:10:30Z (GMT). No. of bitstreams: 2 Dissertação EnioPerez.pdf: 2190954 bytes, checksum: 8abd6c2cd31279e28971c632f6ed378b (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2011-08-26 / Intuitively, the basic idea of Design Theory consists of a way to select subsets, also called blocks, of a finite set, so that some properties are satisfied. The more general case are the blocks designs. A PBD is an ordered pair (S;B), where S is a finite set of symbols, and B is a collection of subsets of S called blocks, such that each pair of distinct elements of S occur together in exactly one block of B. A Steiner Triple System is a particular case of a PBD, where every block has size only 3, being called triples. The main focus is in building technology systems. By resolvability is discussed as a Steiner Triple Systems is resolvable, and when it is not resolvable. This theory has several applications, eg, embeddings and even problems related to computational complexity. / Intuitivamente, a idéia básica de um Planejamento Combinatório consiste em uma maneira de selecionar subconjuntos, também chamados de blocos, de um conjunto finito, de modo que algumas propriedades especificadas sejam satisfeitas. O caso mais geral são os planejamentos balanceados. Um PBD é um par ordenado (S;B), onde S é um conjunto finito de símbolos, e B é uma coleção de subconjuntos de S chamados blocos, tais que cada par de elementos distintos de S ocorrem juntos em exatamente um bloco de B. Um Sistema Triplo de Steiner é um caso particular de um PBD, em que todos os blocos tem tamanho único 3, sendo chamados de triplas. O foco principal está nas técnicas de construção dos sistemas. Por meio da resolubilidade se discute quando um Sistema Triplo de Steiner é resolvível e quando não é resolvível. Esta teoria possui várias aplicações, por exemplo: imersões e até mesmo problemas relacionados à complexidade computacional.

Conselho Nacional de Desenvolvimento CientÃfico e TecnolÃgico / In this work, we study some parameters of monophonic convexity in some classes of graphs and we present our results about this subject. We prove that decide if the $m$-interval number is at most 2 and decide if the $m$-percolation time is at most 1 are NP-complete problems even on bipartite graphs. We also prove that the $m$-convexity number is as hard to approximate as the maximum clique problem, which is, $O(n^{1-varepsilon})$-unapproachable in polynomial-time, unless P=NP, for each $varepsilon>0$. Finally, we obtain polynomial time algorithms to compute the $m$-convexity number on hereditary graph classes such that the computation of the clique number is polynomial-time solvable (e.g. perfect graphs and planar graphs). / Neste trabalho, estudamos alguns parÃmetros para a convexidade monofÃnica em algumas classes de grafos e apresentamos nossos resultados acerca do assunto. Provamos que decidir se o nÃmero de $m$-intervalo à no mÃximo 2 e decidir se o tempo de $m$-percolaÃÃo à no mÃximo 1 sÃo problemas NP-completos mesmo em grafos bipartidos. TambÃm provamos que o nÃmero de $m$-convexidade à tÃo difÃcil de aproximar quanto o problema da Clique MÃxima, que Ã, $O(n^{1-varepsilon})$-inaproximÃvel em tempo polinomial, a menos que P=NP, para cada $varepsilon>0$. Finalmente, apresentamos um algoritmo de tempo polinomial para determinar o nÃmero de $m$-convexidade em classes hereditÃrias de grafos onde a computaÃÃo do tamanho da clique mÃxima à em tempo polinomial (como grafos perfeitos e grafos planares).

This thesis examines so called folding neural networks as a mechanism for machine learning. Folding networks form a generalization of partial recurrent neural networks such that they are able to deal with tree structured inputs instead of simple linear lists. In particular, they can handle classical formulas - they were proposed originally for this purpose. After a short explanation of the neural architecture we show that folding networks are well suited as a learning mechanism in principle. This includes three parts: the proof of their universal approximation ability, the aspect of information theoretical learnability, and the examination of the complexity of training. Approximation ability: It is shown that any measurable function can be approximated in probability. Explicit bounds on the number of neurons result if only a finite number of points is dealt with. These bounds are new results in the case of simple recurrent networks, too. Several restrictions occur if a function is to be approximated in the maximum norm. Afterwards, we consider briefly the topic of computability. It is shown that a sigmoidal recurrent neural network can compute any mapping in exponential time. However, if the computation is subject to noise almost the capability of tree automata arises. Information theoretical learnability: This part contains several contributions to distribution dependent learnability: The notation of PAC and PUAC learnability, consistent PAC/ PUAC learnability, and scale sensitive versions are considered. We find equivalent characterizations of these terms and examine their respective relation answering in particular an open question posed by Vidyasagar. It is shown at which level learnability only because of an encoding trick is possible. Two approaches from the literature which can guarantee distribution dependent learnability if the VC dimension of the concept class is infinite are generalized to function classes: The function class is stratified according to the input space or according to a so-called luckiness function which depends on the output of the learning algorithm and the concrete training data. Afterwards, the VC, pseudo-, and fat shattering dimension of folding networks are estimated: We improve some lower bounds for recurrent networks and derive new lower bounds for the pseudodimension and lower and upper bounds for folding networks in general. As a consequence, folding architectures are not distribution independent learnable. Distribution dependent learnability can be guaranteed. Explicit bounds on the number of examples which guarantee valid generalization can be derived using the two approaches mentioned above. We examine in which cases these bounds are polynomial. Furthermore, we construct an explicit example for a learning scenario where an exponential number of examples is necessary. Complexity: It is shown that training a fixed folding architecture with perceptron activation function is polynomial. Afterwards, a decision problem, the so-called loading problem, which is correlated to neural network training is examined. For standard multilayer feed-forward networks the following situations turn out to be NP-hard: Concerning the perceptron activation function, a classical result from the literature, the NP-hardness for varying input dimension, is generalized to arbitrary multilayer architectures. Additionally, NP-hardness can be found if the input dimension is fixed but the number of neurons may vary in at least two hidden layers. Furthermore, the NP-hardness is examined if the number of patterns and number of hidden neurons are correlated. We finish with a generalization of the classical NP result as mentioned above to the sigmoidal activation function which is used in practical applications.