Many quantum computation algorithms, and processes like measurement based quantum computing, require the initial state of the quantum computer to be an eigenstate of a specific unitary operator. Here we study how quantum states that are eigenstates of finite dimensional irreducible representations of the special unitary (SU(d)) and the permutation (S_n) groups can be efficiently constructed in the computational basis formed by tensor products of the qudit states. The procedure is a unitary transform, which first uses Schur-Weyl duality to map every eigenstate to a unique Schur basis state, and then recursively uses the Clebsch - Gordan transform to rotate the Schur basis state to the computational basis. We explicitly provide an efficient quantum algorithm, and the corresponding quantum logic circuit, to generate any desired eigenstate of SU(2) and SU(3) irreducible representations in the computational basis.

Nos recherches traitent de coloration de graphes avec des contraintes de distance (coloration de packing) ou des contraintes sur le voisinage (coloration de Grundy). Soit S={si| i in N*} une série croissante d’entiers. Une S -coloration de packing est une coloration propre de sommets telle que tout ensemble coloré i est un si-packing (un ensemble où tous les sommets sont à distance mutuelle supérieure à si). Un graphe G est (s1,... ,sk)-colorable si il existe une S -coloration de packing de G avec les couleurs 1, ...,,k. Une coloration de Grundy est une coloration propre de sommets telle que pour tout sommet u coloré i, u est adjacent à un sommet coloré j, pour chaque j<i.Dans cette exposé, nous présentons des résultats connus à propos de la S-coloration de packing. Nous apportons de nouveaux résultats à propos de la S-coloration de packing, pour des classes de graphes telles que les chemins, les cycles et les arbres. Nous étudions en détail la complexité du problème de complexité associé à la S-coloration de packing, noté S -COL. Pour certaines instances de S -COL, nous caractérisons des dichotomies entre problèmes NP-complets et problèmes résolubles en tempspolynomial. Nous nous intéressons aux différentes grilles infinies, les grilles hexagonale, carrée, triangulaire et du roi et nous déterminons des propriétés de subdivisions d’un i-packing en plusieurs j-packings, avec j>i. Ces résultats nous permettent de déterminer des S-colorations de packings de ces grilles pour plusieurs séries d’entiers. Nous examinons une classe de graphe jamais étudiée en ce qui concerne la S -coloration de packing: les graphes subcubiques. Nous déterminons que tous les graphes subcubiques sont (1,2,2,2,2,2,2)-colorables et (1,1,2,2,3)-colorables. Un certain nombre de résultats sont prouvés pour certaines sous-classes des graphes subcubiques. Pour finir, nous nous intéressons au nombre de Grundy des graphes réguliers. Nous déterminons une caractérisation des graphes cubiques avec un nombre de Grundy de 4. De plus, nous prouvons que tous les graphes r-réguliers sans carré induit ont pour nombre de Grundy de r+1, pour r<5. / Our research are about graph coloring with distance constraints (packing coloring) or neighborhood constraints (Grundy coloring). Let S={si| i in N*} be a non decreasing sequence of integers. An S-packing coloring is a proper coloring such that every set of color i is an si-packing (a set of vertices at pairwise distance greater than si). A graph G is (s1,... ,sk)-colorable if there exists a packing coloring of G with colors 1,... ,k. A Grundy coloring is a proper vertex coloring such that for every vertex of color i, u is adjacent to a vertex of color j, for each j<i.In this presentation, we present results about S-packing coloring. We prove new results about the S-coloring of graphs including paths, cycles and trees. We study the complexity problem associated to the S-packing coloring, this problem is denoted S-COL. For some instances of S-COL, we characterize dichotomy between NP-complete problems and problems solved by a polynomial time algorithm. We study also different lattices, the hexagonal, square, triangular and king lattices. We determine properties on the subdivision of an i-packing in several j-packings, for j>i. These results allow us to determine S-packing coloring of these lattices for several sequences of integers. We examine a class of graph that has never been studied for S-packing coloring: the subcubic graphs. We determine that every subcubic graph is (1,2,2,2,2,2,2)-colorable and (1,1,2,2,3)-colorable. Few results are proven about some subclasses. Finally, we study the Grundy number of regular graphs. We determine a characterization of the cubic graphs with Grundy number 4. Moreover, we prove that every r-regular graph without induced square has Grundy number r+1, for r<5.

In many real world situations, especially involving multiagent systems and artificial intelligence, participating agents often need to agree upon a common alternative even if they have differing preferences over the available alternatives. Voting is one of the tools of choice in these situations. Common and classic applications of voting in modern applications include collaborative filtering and recommender systems, metasearch engines, coordination and planning among multiple automated agents etc. Agents in these applications usually have computational power at their disposal. This makes the study of computational aspects of voting crucial. This thesis is devoted to a study of computational complexity of several fundamental algorithmic and complexity-theoretic problems arising in the context of voting theory. The typical setting for our work is an “election”; an election consists of a set of voters or agents, a set of alternatives, and a voting rule. The vote of any agent can be thought of as a ranking (more precisely, a complete order) of the set of alternatives. A voting profile comprises a collection of votes of all the agents. Finally, a voting rule is a mapping that takes as input a voting profile and outputs an alternative, which is called the “winner” or “outcome” of the election. Our contributions in this thesis can be categorized into three parts and are described below. Part I: Preference Elicitation. In the first part of the thesis, we study the problem of eliciting the preferences of a set of voters by asking a small number of comparison queries (such as who a voter prefers between two given alternatives) for various interesting domains of preferences. We commence with considering the domain of single peaked preferences on trees in Chapter 3. This domain is a significant generalization of the classical well studied domain of single peaked preferences. The domain of single peaked preferences and its generalizations are hugely popular among political and social scientists. We show tight dependencies between query complexity of preference elicitation and various parameters of the single peaked tree, for example, number of leaves, diameter, path width, maximum degree of a node etc. We next consider preference elicitation for the domain of single crossing preference profiles in Chapter 4. This domain has also been studied extensively by political scientists, social choice theorists, and computer scientists. We establish that the query complexity of preference elicitation in this domain crucially depends on how the votes are accessed and on whether or not any single crossing ordering is a priori known. Part II: Winner Determination. In the second part of the thesis, we undertake a study of the computational complexity of several important problems related to determining winner of an election. We begin with a study of the following problem: Given an election, predict the winners of the election under some fixed voting rule by sampling as few votes as possible. We establish optimal or almost optimal bounds on the number of votes that one needs to sample for many commonly used voting rules when the margin of victory is at least n (n is the number of voters and is a parameter). We next study efficient sampling based algorithms for estimating the margin of victory of a given election for many common voting rules. The margin of victory of an election is a useful measure that captures the robustness of an election outcome. The above two works are presented in Chapter 5. In Chapter 6, we design an optimal algorithm for determining the plurality winner of an election when the votes are arriving one-by-one in a streaming fashion. This resolves an intriguing question on finding heavy hitters in a stream of items, that has remained open for more than 35 years in the data stream literature. We also provide near optimal algorithms for determining the winner of a stream of votes for other popular voting rules, for example, veto, Borda, maximin etc. Voters’ preferences are often partial orders instead of complete orders. This is known as the incomplete information setting in computational social choice theory. In an incomplete information setting, an extension of the winner determination problem which has been studied extensively is the problem of determining possible winners. We study the kernelization complexity (under the complexity-theoretic framework of parameterized complexity) of the possible winner problem in Chapter 7. We show that there do not exist kernels of size that is polynomial in the number of alternatives for this problem for commonly used voting rules under a plausible complexity theoretic assumption. However, we also show that the problem of coalitional manipulation which is an important special case of the possible winner problem admits a kernel whose size is polynomial bounded in the number of alternatives for common voting rules. \Part III: Election Control. In the final part of the thesis, we study the computational complexity of various interesting aspects of strategic behaviour in voting. First, we consider the impact of partial information in the context of strategic manipulation in Chapter 8. We show that lack of complete information makes the computational problem of manipulation intractable for many commonly used voting rules. In Chapter 9, we initiate the study of the computational problem of detecting possible instances of election manipulation. We show that detecting manipulation may be computationally easy under certain scenarios even when manipulation is intractable. The computational problem of bribery is an extensively studied problem in computational social choice theory. We study computational complexity of bribery when the briber is “frugal” in nature. We show for many common voting rules that the bribery problem remains intractable even when the briber’s behaviour is restricted to be frugal, thereby strengthening the intractability results from the literature. This forms the subject of Chapter 10.

High resolution surveillance video cameras are invaluable resources for effective crime prevention and forensic investigations. However, increasing communication bandwidth requirements of high definition surveillance videos are severely limiting the number of cameras that can be deployed. Higher bitrate also increases operating expenses due to higher data communication and storage costs. Hence, it is essential to develop low complexity algorithms which reduce data rate of the compressed video stream without affecting the image fidelity. In this thesis, a computer vision aided H.264 surveillance video encoder and four associated algorithms are proposed to reduce the bitrate. The proposed techniques are (I) Speeded up foreground segmentation, (II) Skip decision, (III) Reference frame selection and (IV) Face Region-of-Interest (ROI) coding. In the first part of the thesis, a modification to the adaptive Gaussian Mixture Model (GMM) based foreground segmentation algorithm is proposed to reduce computational complexity. This is achieved by replacing expensive floating point computations with low cost integer operations. To maintain accuracy, we compute periodic floating point updates for the GMM weight parameter using the value of an integer counter. Experiments show speedups in the range of 1.33 - 1.44 on standard video datasets where a large fraction of pixels are multimodal. In the second part, we propose a skip decision technique that uses a spatial sampler to sample pixels. The sampled pixels are segmented using the speeded up GMM algorithm. The storage pattern of the GMM parameters in memory is also modified to improve cache performance. Skip selection is performed using the segmentation results of the sampled pixels. In the third part, a reference frame selection algorithm is proposed to maximize the number of background Macroblocks (MB’s) (i.e. MB’s that contain background image content) in the Decoded Picture Buffer. This reduces the cost of coding uncovered background regions. Distortion over foreground pixels is measured to quantify the performance of skip decision and reference frame selection techniques. Experimental results show bit rate savings of up to 94.5% over methods proposed in literature on video surveillance data sets. The proposed techniques also provide up to 74.5% reduction in compression complexity without increasing the distortion over the foreground regions in the video sequence. In the final part of the thesis, face and shadow region detection is combined with the skip decision algorithm to perform ROI coding for pedestrian surveillance videos. Since person identification requires high quality face images, MB’s containing face image content are encoded with a low Quantization Parameter setting (i.e. high quality). Other regions of the body in the image are considered as RORI (Regions of reduced interest) and are encoded at low quality. The shadow regions are marked as Skip. Techniques that use only facial features to detect faces (e.g. Viola Jones face detector) are not robust in real world scenarios. Hence, we propose to initially detect pedestrians using deformable part models. The face region is determined using the deformed part locations. Detected pedestrians are tracked using an optical flow based tracker combined with a Kalman filter. The tracker improves the accuracy and also avoids the need to run the object detector on already detected pedestrians. Shadow and skin detector scores are computed over super pixels. Bilattice based logic inference is used to combine multiple likelihood scores and classify the super pixels as ROI, RORI or RONI. The coding mode and QP values of the MB’s are determined using the super pixel labels. The proposed techniques provide a further reduction in bitrate of up to 50.2%.

Les problèmes d’ordonnancement de rendez-vous en tête-à-tête sont des problèmes dans lesquels des personnes souhaitent se rencontrer par deux lors de courts rendez-vous qui se déroulent lors d’une session unique. Dans cette thèse, nous référençons plusieurs applications de ce type de problèmes et proposons des notations qui généralisent les notations standards de problèmes d’ordonnancement α|β|γ. Nous nous intéressons en particulier à un cas dans lequel deux populations distinctes se rencontrent, des participants peuvent arriver en retard et des rencontres sont interdites. L’objectif est de minimiser le nombre maximal d’attentes des participants. Nous étudions dans un premier temps la complexité de ces problèmes : nous démontrons que plusieurs cas sans rencontre interdite sont polynomiaux et que le cas général est NP-complet au sens fort. Nous proposons ensuite des bornes inférieures. Puis nous développons plusieurs méthodes de résolution. Des modèles de programmation linéaire en nombres entiers et un modèle de programmation par contraintes sont tout d’abord proposés. Des règles de dominance permettant de limiter les symétries sont intégrées à ces modèles dans le but de limiter l’espace des solutions. Enfin, nous proposons une recherche à divergence limitée (limited discrepancy search) qui est une méthode approchée basée sur l’exploration d’un arbre de recherche tronqué. Dans cette méthode, nous exploitons le plus possible les propriétés de symétrie du problème pour faciliter la convergence vers une bonne solution. Toutes ces méthodes sont testées et comparées sur un ensemble de 300 instances générées aléatoirement d’après des paramètres réalistes. / One-to-one meeting scheduling problems are problems where a population of actors want to meet each other during short time slots that take place in a single session. In this thesis, we reference several applications of this type of problems found in the literature and introduce a notation extending the well-known scheduling notation α|β|γ. We are particularly interested in a case in which two distinct populations meet, participants may arrive late and some meetings are forbidden. The objective is to minimize the maximum number of participants waiting slots. First, we study the complexity of these problems: we show that several cases with no forbidden meeting are polynomial and that the general case is NP-complete in the strong sense. We then propose lower bounds. After that, we develop several resolution methods. Integer linear programming models and a constraint programming model are developed. To limit the solution space, we add dominance rules based on symmetries to these methods. Finally, we present a limited discrepancy search (i.e. an approximate method based on the exploration of a truncated tree search). In this method, we use as much as possible the symmetry properties of the problem to facilitate the convergence to a good solution. All these methods are tested and compared on a set of 300 randomly generated instances from realistic parameters.

The vertex coloring problem is a classic problem in graph theory that asks for a partition of the vertex set into a minimum number of stable sets. This thesis presents our studies on three vertex (re)coloring problems on graphs and on a problem related to a long-standing conjecture on subdivision of digraphs. Firstly, we address the convex recoloring problem in which an arbitrarily colored graph G is given and one wishes to find a minimum weight recoloring such that each color class induces a connected subgraph of G. We show inapproximability results, introduce an integer linear programming (ILP) formulation that models the problem and present some computational experiments using a column generation approach. The k-fold coloring problem is a generalization of the classic vertex coloring problem and consists in covering the vertex set of a graph by a minimum number of stable sets in such a way that every vertex is covered by at least k (possibly identical) stable sets. We present an ILP formulation for this problem and show a detailed polyhedral study of the polytope associated with this formulation. The last coloring problem studied in this thesis is the proper orientation problem. It consists in orienting the edge set of a given graph so that adjacent vertices have different in-degrees and the maximum in-degree is minimized. Clearly, the in-degrees induce a partition of the vertex set into stable sets, that is, a coloring (in the conventional sense) of the vertices. Our contributions in this problem are on hardness and upper bounds for bipartite graphs. Finally, we study a problem related to a conjecture of Mader from the eighties on subdivision of digraphs. This conjecture states that, for every acyclic digraph H, there exists an integer f(H) such that every digraph with minimum out-degree at least f(H) contains a subdivision of H as a subdigraph. We show evidences for this conjecture by proving that it holds for some particular classes of acyclic digraphs. / O problema de coloração de grafos é um problema clássico em teoria dos grafos cujo objetivo é particionar o conjunto de vértices em um número mínimo de conjuntos estáveis. Nesta tese apresentamos nossas contribuições sobre três problemas de coloração de grafos e um problema relacionado a uma antiga conjectura sobre subdivisão de digrafos. Primeiramente, abordamos o problema de recoloração convexa no qual é dado um grafo arbitrariamente colorido G e deseja-se encontrar uma recoloração de peso mínimo tal que cada classe de cor induza um subgrafo conexo de G. Mostramos resultados sobre inaproximabilidade, introduzimos uma formulação linear inteira que modela esse problema, e apresentamos alguns resultados computacionais usando uma abordagem de geração de colunas. O problema de k-upla coloração é uma generalização do problema clássico de coloração de vértices e consiste em cobrir o conjunto de vértices de um grafo com uma quantidade mínima de conjuntos estáveis de tal forma que cada vértice seja coberto por pelo menos k conjuntos estáveis (possivelmente idênticos). Apresentamos uma formulação linear inteira para esse problema e fazemos um estudo detalhado do politopo associado a essa formulação. O último problema de coloração estudado nesta tese é o problema de orientação própria. Ele consiste em orientar o conjunto de arestas de um dado grafo de tal forma que vértices adjacentes possuam graus de entrada distintos e o maior grau de entrada seja minimizado. Claramente, os graus de entrada induzem uma partição do conjunto de vértices em conjuntos estáveis, ou seja, induzem uma coloração (no sentido convencional) dos vértices. Nossas contribuições nesse problema são em complexidade computacional e limitantes superiores para grafos bipartidos. Finalmente, estudamos um problema relacionado a uma conjectura de Mader, dos anos oitenta, sobre subdivisão de digrafos. Esta conjectura afirma que, para cada digrafo acíclico H, existe um inteiro f(H) tal que todo digrafo com grau mínimo de saída pelo menos f(H) contém uma subdivisão de H como subdigrafo. Damos evidências para essa conjectura mostrando que ela é válida para classes particulares de digrafos acíclicos.

Let G be a connected graph and k be a positive integer. A vertex subset D of G is a k-hop connected dominating set if the subgraph of G induced by D is connected, and for every vertex v in G, there is a vertex u in D such that the distance between v and u in G is at most k. We study the problem of finding a minimum k-hop connected dominating set of a graph (Mink-CDS). We prove that Mink-CDS is NP-hard on planar bipartite graphs of maximum degree 4. We also prove that Mink-CDS is APX-complete on bipartite graphs of maximum degree 4. We present inapproximability thresholds for Mink-CDS on bipar- tite and on (1, 2)-split graphs. Interestingly, one of these thresholds is a parameter of the input graph which is not a function of its number of vertices. We also discuss the complex- ity of computing this graph parameter. On the positive side, we show an approximation algorithm for Mink-CDS. When k = 1, we present two new approximation algorithms for the weighted version of the problem, one of them restricted to graphs with a poly- nomially bounded number of minimal separators. Finally, also for the weighted variant of the problem where k = 1, we discuss an integer linear programming formulation and conduct a polyhedral study of its associated polytope. / Seja G um grafo conexo e k um inteiro positivo. Um subconjunto D de vértices de G é um conjunto dominante conexo de k-saltos se o subgrafo de G induzido por D é conexo e se, para todo vértice v em G, existe um vértice u em D a uma distância não maior do que k de v. Estudamos neste trabalho o problema de se encontrar um conjunto dominante conexo de k-saltos com cardinalidade mínima (Mink-CDS). Provamos que Mink-CDS é NP-difícil em grafos planares bipartidos com grau máximo 4. Mostramos que Mink-CDS é APX-completo em grafos bipartidos com grau máximo 4. Apresentamos limiares de inaproximabilidade para Mink-CDS para grafos bipartidos e (1, 2)-split, sendo que um desses é expresso em função de um parâmetro independente da ordem do grafo. Também discutimos a complexidade computacional do problema de se computar tal parâmetro. No lado positivo, propomos um algoritmo de aproximação para Mink-CDS cuja razão de aproximação é melhor do que a que se conhecia para esse problema. Finalmente, quando k = 1, apresentamos dois novos algoritmos de aproximação para a versão do problema com pesos nos vértices, sendo que um deles restrito a classes de grafos com um número polinomial de separadores minimais. Além disso, discutimos uma formulação de programação linear inteira para essa versão do problema e provamos resultados poliédricos a respeito de algumas das desigualdades que constituem o politopo associado à formulação.

<div>Using sensor measurements to estimate the states and parameters of a system is a fundamental task in understanding the behavior of the system. Moreover, as modern systems grow rapidly in scale and complexity, it is not always possible to deploy sensors to measure all of the states and parameters of the system, due to cost and physical constraints. Therefore, selecting an optimal subset of all the candidate sensors to deploy and gather measurements of the system is an important and challenging problem. In addition, the systems may be targeted by external attackers who attempt to remove or destroy the deployed sensors. This further motivates the formulation of resilient sensor selection strategies. In this thesis, we address the sensor selection problem under different settings as follows. </div><div><br></div><div>First, we consider the optimal sensor selection problem for linear dynamical systems with stochastic inputs, where the Kalman filter is applied based on the sensor measurements to give an estimate of the system states. The goal is to select a subset of sensors under certain budget constraints such that the trace of the steady-state error covariance of the Kalman filter with the selected sensors is minimized. We characterize the complexity of this problem by showing that the Kalman filtering sensor selection problem is NP-hard and cannot be approximated within any constant factor in polynomial time for general systems. We then consider the optimal sensor attack problem for Kalman filtering. The Kalman filtering sensor attack problem is to attack a subset of selected sensors under certain budget constraints in order to maximize the trace of the steady-state error covariance of the Kalman filter with sensors after the attack. We show that the same results as the Kalman filtering sensor selection problem also hold for the Kalman filtering sensor attack problem. Having shown that the general sensor selection and sensor attack problems for Kalman filtering are hard to solve, our next step is to consider special classes of the general problems. Specifically, we consider the underlying directed network corresponding to a linear dynamical system and investigate the case when there is a single node of the network that is affected by a stochastic input. In this setting, we show that the corresponding sensor selection and sensor attack problems for Kalman filtering can be solved in polynomial time. We further study the resilient sensor selection problem for Kalman filtering, where the problem is to find a sensor selection strategy under sensor selection budget constraints such that the trace of the steady-state error covariance of the Kalman filter is minimized after an adversary removes some of the deployed sensors. We show that the resilient sensor selection problem for Kalman filtering is NP-hard, and provide a pseudo-polynomial-time algorithm to solve it optimally.</div><div> </div><div> Next, we consider the sensor selection problem for binary hypothesis testing. The problem is to select a subset of sensors under certain budget constraints such that a certain metric of the Neyman-Pearson (resp., Bayesian) detector corresponding to the selected sensors is optimized. We show that this problem is NP-hard if the objective is to minimize the miss probability (resp., error probability) of the Neyman-Pearson (resp., Bayesian) detector. We then consider three optimization objectives based on the Kullback-Leibler distance, J-Divergence and Bhattacharyya distance, respectively, in the hypothesis testing sensor selection problem, and provide performance bounds on greedy algorithms when applied to the sensor selection problem associated with these optimization objectives.</div><div> </div><div> Moving beyond the binary hypothesis setting, we also consider the setting where the true state of the world comes from a set that can have cardinality greater than two. A Bayesian approach is then used to learn the true state of the world based on the data streams provided by the data sources. We formulate the Bayesian learning data source selection problem under this setting, where the goal is to minimize the cost spent on the data sources such that the learning error is within a certain range. We show that the Bayesian learning data source selection is also NP-hard, and provide greedy algorithms with performance guarantees.</div><div> </div><div> Finally, in light of the COVID-19 pandemic, we study the parameter estimation measurement selection problem for epidemics spreading in networks. Here, the measurements (with certain costs) are collected by conducting virus and antibody tests on the individuals in the epidemic spread network. The goal of the problem is then to optimally estimate the parameters (i.e., the infection rate and the recovery rate of the virus) in the epidemic spread network, while satisfying the budget constraint on collecting the measurements. Again, we show that the measurement selection problem is NP-hard, and provide approximation algorithms with performance guarantees.</div>

First, basic aspects of interval analysis, roles of intervals and their applications are addressed. Then, various classes of interval matrices are described and their relations are depicted. This material forms a prelude to the unifying theme of the rest of the work - solving interval linear systems. Several methods for enclosing the solution set of square and overdetermined interval linear systems are covered and compared. For square systems the new shaving method is introduced, for overdetermined systems the new subsquares approach is introduced. Detecting unsolvability and solvability of such systems is discussed and several polynomial conditions are compared. Two strongest condi- tions are proved to be equivalent under certain assumption. Solving of interval linear systems is used to approach other problems in the rest of the work. Computing enclosures of determinants of interval matrices is addressed. NP- hardness of both relative and absolute approximation is proved. New method based on solving square interval linear systems and Cramer's rule is designed. Various classes of matrices with polynomially computable bounds on determinant are characterized. Solving of interval linear systems is also used to compute the least squares linear and nonlinear interval regression. It is then applied to real...

Bibliography: pages 192-195 / The avoidability, or unavoidability of patterns in words over finite alphabets has been studied extensively. The word α over a finite set A is said to be unavoidable for an infinite set B+ of nonempty words over a finite set B if, for all but finitely many elements w of B+, there exists a semigroup morphism φ ∶ A+ → B+ such that φ(α) is a factor of w. In this treatise, we start by presenting a historical background of results that are related to unavoidability. We present and discuss the most important theorems surrounding unavoidability in detail. We present various complexity-related properties of unavoidable words. For words that are unavoidable, we provide a constructive upper bound to the lengths of words that avoid them. In particular, for a pattern α of length n over an alphabet of size r, we give a concrete function N(n, r) such that no word of length N(n, r) over the alphabet of size r avoids α. A natural subsequent question is how many unavoidable words there are. We show that the fraction of words that are unavoidable drops exponentially fast in the length of the word. This allows us to calculate an upper bound on the number of unavoidable patterns for any given finite alphabet. Subsequently, we investigate computational aspects of unavoidable words. In particular, we exhibit concrete algorithms for determining whether a word is unavoidable. We also prove results on the computational complexity of the problem of determining whether a given word is unavoidable. Specifically, the NP-completeness of the aforementioned problem is established. / Decision Sciences / D. Phil. (Operations Research)