I have investigated the properties of the large scale structure of the nearby Universe using data from the Galaxy and Mass Assembly survey (GAMA). I generated complementary halo mass estimates for all groups in the GAMA Galaxy Group Catalogue (G³C) using a modified caustic mass estimation algorithm. On average, the caustic mass estimates agree with dynamical mass estimates within a factor of 2 in 90% of groups. A volume limited sample of these groups and galaxies are used to generate the large scale structure catalogue. An adapted minimal spanning tree algorithm is used to identify and classify structures, detecting 643 filaments that measure up to 200 Mpc/h, each containing 8 groups on average. A secondary population of smaller coherent structures, dubbed `tendrils,' that link filaments together or penetrate into voids are also detected. On average, tendrils measure around 10 Mpc/h and contain 6 galaxies. The so-called line correlation function is used to prove that tendrils are real structures rather than accidental alignments. A population of isolated void galaxies are also identified. The properties of filaments and tendrils in observed and mock GAMA galaxy catalogues agree well. I go on to show that voids from other surveys that overlap with GAMA regions contain a large number of galaxies, primarily belonging to tendrils. This implies that void sizes are strongly dependent on the number density and sensitivity limits of the galaxies observed by a survey. Finally, I examine the properties of galaxies in different environments, finding that galaxies in filaments tend to be early-type, bright, spheroidal, and red whilst those in voids are typically the opposite: blue, late-type, and more faint. I show that group mass does not correlate with the brightness and morphologies of galaxies and that the primary driver of galaxy evolution is stellar mass.

Nous étudions le déterminant du laplacien sur les fibrés vectoriels sur les graphes et l'utilisons, en lien avec des techniques d'analyse complexe discrète, pour comprendre des modèles de physique statistique. Nous calculons certaines constantes de réseaux, construisons des limites d'échelles d'excursions de la marche aléatoire à boucles effacées sur les surfaces, et étudions certains champs gaussiens et processus déterminantaux. / We study the determinant of the Laplacian on vector bundles on graphs and use it, combined with discrete complex analysis, to study models of statistical physics. We compute exact lattice constants, construct scaling limits for excursions of the loop-erased random walk on surfaces, and study some Gaussian fields and determinantal processes.

Spanning tree optimization problems are related to many practical applications. Several of these problems are NP-Hard, which limits the utility of exact methods and can require alternative approaches, like metaheuristics. A common issue for many metaheuristics is the data structure used to represent and manipulate the solutions. A data structure with efficient operations can expand the usefulness of a method by allowing larger instances to be solved in a reasonable amount of time. We propose the 2LETT data structure and uses it to represent spanning trees in two metaheuristics: mutation-based evolutionary algorithms and local search algorithms. The main operation of 2LETT is the exchange of one edge in the represented tree by another one, and it has O(√n) time, where n is the number of vertices in the tree. We conducent qualitative and quantitative evaluations for 2LETT and other structures in the literature. For the main operation of edge exchange in evolutionary algorithms, the computational experiments show that 2LETT has the best performance for trees with more than 10,000 vertices. For local search algorithms, 2LETT is the best option to deal with large trees with large diameters. / Os problemas de otimização de árvores geradoras estão relacionados a muitas aplicações práticas. Vários desses problemas são NP-difícies, o que limita a utilidade de métodos exatos e pode exigir abordagens alternativas, como metaheurísticas. Um questão relevante para muitas metaheurísticas é a estrutura de dados usada para representar e manipular as soluções. Uma estrutura de dados com operações eficientes pode aumentar a utilidade de um método, permitindo que instâncias maiores sejam resolvidas em um período de tempo razoável. Propomos a estrutura de dados 2LETT e a usamos para representar árvores geradoras em duas metaheurísticas: algoritmos evolutivos baseados em mutações e algoritmos de busca local. A operação principal da 2LETT é a troca de uma aresta na árvore representada por outra aresta. Esta operação tem tempo de O(√n), onde n é o número de vértices na árvore. Conduzimos avaliações qualitativas e quantitativas para 2LETT e outras estruturas na literatura. Para a principal operação de troca de arestas em algoritmos evolutivos, os experimentos computacionais mostram que a 2LETT possui o melhor desempenho para árvores com mais de 10.000 vértices. Para algoritmos de busca local, o 2LETT é a melhor opção para lidar com árvores grandes com grandes diâmetros.

Diversos problemas envolvem a classificação de dados em categorias, também denominadas classes. A partir de um conjunto de dados cujas classes são conhecidas, algoritmos de Aprendizado de Máquina (AM) podem ser utilizados na indução de um classificador capaz de predizer a classe de novos dados do mesmo domínio, realizando assim a discriminação desejada. Dentre as diversas técnicas de AM utilizadas em problemas de classificação, as Máquinas de Vetores de Suporte (Support Vector Machines - SVMs) se destacam por sua boa capacidade de generalização. Elas são originalmente concebidas para a solução de problemas com apenas duas classes, também denominados binários. Entretanto, diversos problemas requerem a discriminação dos dados em mais que duas categorias ou classes. Nesta Tese são investigadas e propostas estratégias para a generalização das SVMs para problemas com mais que duas classes, intitulados multiclasses. O foco deste trabalho é em estratégias que decompõem o problema multiclasses original em múltiplos subproblemas binários, cujas saídas são então combinadas na obtenção da classificação final. As estratégias propostas visam investigar a adaptação das decomposições a cada aplicação considerada, a partir de informações do desempenho obtido em sua solução ou extraídas de seus dados. Os algoritmos implementados foram avaliados em conjuntos de dados gerais e em aplicações reais da área de Bioinformática. Os resultados obtidos abrem várias possibilidades de pesquisas futuras. Entre os benefícios verificados tem-se a obtenção de decomposições mais simples, que requerem menos classificadores binários na solução multiclasses. / Several problems involve the classification of data into categories, also called classes. Given a dataset containing data whose classes are known, Machine Learning (ML) algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, thus performing the desired discrimination. Among the several ML techniques applied to classification problems, the Support Vector Machines (SVMs) are known by their high generalization ability. They are originally conceived for the solution of problems with only two classes, also named binary problems. However, several problems require the discrimination of examples into more than two categories or classes. This thesis investigates and proposes strategies for the generalization of SVMs to problems with more than two classes, known as multiclass problems. The focus of this work is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are then combined to obtain the final classification. The proposed strategies aim to investigate the adaptation of the decompositions for each multiclass application considered, using information of the performance obtained for its solution or extracted from its examples. The implemented algorithms were evaluated on general datasets and on real applications from the Bioinformatics domain. The results obtained open possibilities of many future work. Among the benefits observed is the obtainment of simpler decompositions, which require less binary classifiers in the multiclass solution.

Diversos problemas do mundo real estão relacionados ao projeto de redes, tais como projeto de circuitos de energia elétrica, roteamento de veículos, planejamento de redes de telecomunicações e reconstrução filogenética. Em geral, esses problemas podem ser modelados por meio de grafos, que manipulam milhares ou milhões de nós (correspondendo às variáveis de entrada), dificultando a obtenção de soluções em tempo real. O Projeto de uma Rede é um problema combinatório, em que se busca encontrar a rede mais adequada segundo um critério como, por exemplo, menor custo, menor caminho e tempo de percurso. A solução desses problemas é, em geral, computacionalmente complexa. Nesse sentido, metaheurísticas como Algoritmos Evolutivos têm sido amplamente investigadas. Diversas pesquisas mostram que o desempenho de Algoritmos Evolutivos para Problemas de Projetos de Redes pode ser aumentado significativamente por meio de representações mais apropriadas. Este trabalho investiga a paralelização da Representação Nó-Profundidade (RNP) em hardware, com o objetivo de encontrar melhores soluções para Problemas de Projetos de Redes. Para implementar a arquitetura de hardware, denominada de HP-RNP (Hardware Parallelized RNP), foi utilizada a tecnologia de FPGA para explorar o alto grau de paralelismo que essa plataforma pode proporcionar. Os resultados experimentais mostraram que o HP-RNP é capaz de gerar e avaliar novas redes em tempo médio limitado por uma constante (O(1)) / Many problems related to network design can be found in real world applications, such as design of electric circuits, vehicle routing, telecommunication network planning and phylogeny reconstruction. In general, these problems can be modelled using graphs that handle thousands or millions of nodes (input variables), making it hard to obtain solutions in real-time. The Network Design is the combinatorial problem of finding the most suitable network subject to a evaluation criterion as, for example, lower cost, minimal path and time to traverse the network. The solution of those problems is in general computationally complex. Metaheuristics as Evolutionary Algorithms have been widely investigated for such problems. Several researches have shown that the performance of Evolutionary Algorithms for the Network Design Problems can be significantly increased through more appropriated dynamic data structures (encodings). This work investigates the parallelization of Node-Depth Encoding (NDE) in hardware in order to find better solutions for Network Design Problems. To implement the proposed hardware architecture, called HP-NDE (Hardware Parallellized NDE), the FPGA technology was used to explore the high degree of parallelism that such platform can provide. The experimental results have shown that the HP-NDE can generate and evaluate new networks in average time constrained by a constant (O(1))

One of the most important factors on the success of evolutionary algorithms (EAs) about trees is the representation of them. The representation should exhibit efficiency, locality and heritability to enable effective evolutionary computing. Neville proposed three different methods for encoding labeled trees. The first one is similar with Pr&uuml / fer&#039 / s encoding. In 2001, it is reported that, the use of Pr&uuml / fer numbers is a poor representation of spanning trees for evolutionary search, since it has low locality for random trees. In the thesis Neville&#039 / s other two encodings, namely Neville branch numbers and Neville leaf numbers, are studied. For their performance in EA their properties and algorithms for encoding and decoding them are also examined. Optimal algorithms with time and space complexities of O(n) , where n is the number of nodes, for encoding and decoding Neville branch numbers are given. The localities of Neville&#039 / s encodings are investigated. It is shown that, although the localities of Neville branch and leaf numbers are perfect for star type trees, they are low for random trees. Neville branch and Neville leaf numbers are compared with other codings in EAs and SA for four problems: &#039 / onemax tree problem&#039 / , &#039 / degree-constrained minimum spanning tree problem&#039 / , &#039 / all spanning trees problem&#039 / and &#039 / all degree constrained spanning trees problem&#039 / . It is shown that, neither Neville nor Pr&uuml / fer encodings are suitable for EAs. These encodings are suitable for only tree enumeration and degree computation. Algorithms which are timewise and spacewise optimal for &#039 / all spanning trees problem&#039 / (ASTP) for complete graphs, are given by using Neville branch encoding. Computed time and space complexities for solving ASTP of complete graphs are O(nn-2) and O(n) if trees are only enumerated and O(nn-1) and O(n) if all spanning trees are printed , respectively, where n is the number of nodes. Similarly, &#039 / all degree constrained spanning trees problem&#039 / of a complete graph is solvable in O(nn-1) time and O(n) space.

Diversos problemas envolvem a classificação de dados em categorias, também denominadas classes. A partir de um conjunto de dados cujas classes são conhecidas, algoritmos de Aprendizado de Máquina (AM) podem ser utilizados na indução de um classificador capaz de predizer a classe de novos dados do mesmo domínio, realizando assim a discriminação desejada. Dentre as diversas técnicas de AM utilizadas em problemas de classificação, as Máquinas de Vetores de Suporte (Support Vector Machines - SVMs) se destacam por sua boa capacidade de generalização. Elas são originalmente concebidas para a solução de problemas com apenas duas classes, também denominados binários. Entretanto, diversos problemas requerem a discriminação dos dados em mais que duas categorias ou classes. Nesta Tese são investigadas e propostas estratégias para a generalização das SVMs para problemas com mais que duas classes, intitulados multiclasses. O foco deste trabalho é em estratégias que decompõem o problema multiclasses original em múltiplos subproblemas binários, cujas saídas são então combinadas na obtenção da classificação final. As estratégias propostas visam investigar a adaptação das decomposições a cada aplicação considerada, a partir de informações do desempenho obtido em sua solução ou extraídas de seus dados. Os algoritmos implementados foram avaliados em conjuntos de dados gerais e em aplicações reais da área de Bioinformática. Os resultados obtidos abrem várias possibilidades de pesquisas futuras. Entre os benefícios verificados tem-se a obtenção de decomposições mais simples, que requerem menos classificadores binários na solução multiclasses. / Several problems involve the classification of data into categories, also called classes. Given a dataset containing data whose classes are known, Machine Learning (ML) algorithms can be employed for the induction of a classifier able to predict the class of new data from the same domain, thus performing the desired discrimination. Among the several ML techniques applied to classification problems, the Support Vector Machines (SVMs) are known by their high generalization ability. They are originally conceived for the solution of problems with only two classes, also named binary problems. However, several problems require the discrimination of examples into more than two categories or classes. This thesis investigates and proposes strategies for the generalization of SVMs to problems with more than two classes, known as multiclass problems. The focus of this work is on strategies that decompose the original multiclass problem into multiple binary subtasks, whose outputs are then combined to obtain the final classification. The proposed strategies aim to investigate the adaptation of the decompositions for each multiclass application considered, using information of the performance obtained for its solution or extracted from its examples. The implemented algorithms were evaluated on general datasets and on real applications from the Bioinformatics domain. The results obtained open possibilities of many future work. Among the benefits observed is the obtainment of simpler decompositions, which require less binary classifiers in the multiclass solution.

Diversos problemas do mundo real estão relacionados ao projeto de redes, tais como projeto de circuitos de energia elétrica, roteamento de veículos, planejamento de redes de telecomunicações e reconstrução filogenética. Em geral, esses problemas podem ser modelados por meio de grafos, que manipulam milhares ou milhões de nós (correspondendo às variáveis de entrada), dificultando a obtenção de soluções em tempo real. O Projeto de uma Rede é um problema combinatório, em que se busca encontrar a rede mais adequada segundo um critério como, por exemplo, menor custo, menor caminho e tempo de percurso. A solução desses problemas é, em geral, computacionalmente complexa. Nesse sentido, metaheurísticas como Algoritmos Evolutivos têm sido amplamente investigadas. Diversas pesquisas mostram que o desempenho de Algoritmos Evolutivos para Problemas de Projetos de Redes pode ser aumentado significativamente por meio de representações mais apropriadas. Este trabalho investiga a paralelização da Representação Nó-Profundidade (RNP) em hardware, com o objetivo de encontrar melhores soluções para Problemas de Projetos de Redes. Para implementar a arquitetura de hardware, denominada de HP-RNP (Hardware Parallelized RNP), foi utilizada a tecnologia de FPGA para explorar o alto grau de paralelismo que essa plataforma pode proporcionar. Os resultados experimentais mostraram que o HP-RNP é capaz de gerar e avaliar novas redes em tempo médio limitado por uma constante (O(1)) / Many problems related to network design can be found in real world applications, such as design of electric circuits, vehicle routing, telecommunication network planning and phylogeny reconstruction. In general, these problems can be modelled using graphs that handle thousands or millions of nodes (input variables), making it hard to obtain solutions in real-time. The Network Design is the combinatorial problem of finding the most suitable network subject to a evaluation criterion as, for example, lower cost, minimal path and time to traverse the network. The solution of those problems is in general computationally complex. Metaheuristics as Evolutionary Algorithms have been widely investigated for such problems. Several researches have shown that the performance of Evolutionary Algorithms for the Network Design Problems can be significantly increased through more appropriated dynamic data structures (encodings). This work investigates the parallelization of Node-Depth Encoding (NDE) in hardware in order to find better solutions for Network Design Problems. To implement the proposed hardware architecture, called HP-NDE (Hardware Parallellized NDE), the FPGA technology was used to explore the high degree of parallelism that such platform can provide. The experimental results have shown that the HP-NDE can generate and evaluate new networks in average time constrained by a constant (O(1))

Increasing societal demand for automation has led to considerable efforts to control large-scale complex systems, especially in the area of autonomous intelligent control methods. The control system of a large-scale complex system needs to satisfy four system level requirements: robustness, flexibility, reusability, and scalability. Corresponding to the four system level requirements, there arise four major challenges. First, it is difficult to get accurate and complete information. Second, the system may be physically highly distributed. Third, the system evolves very quickly. Fourth, emergent global behaviors of the system can be caused by small disturbances at the component level. The Multi-Agent Based Control (MABC) method as an implementation of distributed intelligent control has been the focus of research since the 1970s, in an effort to solve the above-mentioned problems in controlling large-scale complex systems. However, to the author's best knowledge, all MABC systems for large-scale complex systems with significant uncertainties are problem-specific and thus difficult to extend to other domains or larger systems. This situation is partly due to the control architecture of multiple agents being determined by agent to agent coupling and interaction mechanisms. Therefore, the research objective of this dissertation is to develop a comprehensive, generalized framework for the control system design of general large-scale complex systems with significant uncertainties, with the focus on distributed control architecture design and distributed inference engine design. A Hybrid Multi-Agent Based Control (HyMABC) architecture is proposed by combining hierarchical control architecture and module control architecture with logical replication rings. First, it decomposes a complex system hierarchically; second, it combines the components in the same level as a module, and then designs common interfaces for all of the components in the same module; third, replications are made for critical agents and are organized into logical rings. This architecture maintains clear guidelines for complexity decomposition and also increases the robustness of the whole system. Multiple Sectioned Dynamic Bayesian Networks (MSDBNs) as a distributed dynamic probabilistic inference engine, can be embedded into the control architecture to handle uncertainties of general large-scale complex systems. MSDBNs decomposes a large knowledge-based system into many agents. Each agent holds its partial perspective of a large problem domain by representing its knowledge as a Dynamic Bayesian Network (DBN). Each agent accesses local evidence from its corresponding local sensors and communicates with other agents through finite message passing. If the distributed agents can be organized into a tree structure, satisfying the running intersection property and d-sep set requirements, globally consistent inferences are achievable in a distributed way. By using different frequencies for local DBN agent belief updating and global system belief updating, it balances the communication cost with the global consistency of inferences. In this dissertation, a fully factorized Boyen-Koller (BK) approximation algorithm is used for local DBN agent belief updating, and the static Junction Forest Linkage Tree (JFLT) algorithm is used for global system belief updating. MSDBNs assume a static structure and a stable communication network for the whole system. However, for a real system, sub-Bayesian networks as nodes could be lost, and the communication network could be shut down due to partial damage in the system. Therefore, on-line and automatic MSDBNs structure formation is necessary for making robust state estimations and increasing survivability of the whole system. A Distributed Spanning Tree Optimization (DSTO) algorithm, a Distributed D-Sep Set Satisfaction (DDSSS) algorithm, and a Distributed Running Intersection Satisfaction (DRIS) algorithm are proposed in this dissertation. Combining these three distributed algorithms and a Distributed Belief Propagation (DBP) algorithm in MSDBNs makes state estimations robust to partial damage in the whole system. Combining the distributed control architecture design and the distributed inference engine design leads to a process of control system design for a general large-scale complex system. As applications of the proposed methodology, the control system design of a simplified ship chilled water system and a notional ship chilled water system have been demonstrated step by step. Simulation results not only show that the proposed methodology gives a clear guideline for control system design for general large-scale complex systems with dynamic and uncertain environment, but also indicate that the combination of MSDBNs and HyMABC can provide excellent performance for controlling general large-scale complex systems.

Στην παρούσα διδακτορική διατριβή, ασχολούµαστε µε ζητήµατα που ανακύπτουν σε ασύρµατα δίκτυα επικοινωνίας, δηλ. δίκτυα που βασίζονται σε τηλεπικοινωνιακή υποδοµή όπως τα κυψελικά δίκτυα κινητής τηλεφωνίας, δίκτυα αυτόνοµων ασύρµατων εκποµπών όπως τα ασύρµατα δίκτυα τύπου ad hoc, κτλ. Τα ασύρµατα δίκτυα επικοινωνίας διαφόρων τύπων έχουν εξελιχθεί σηµαντικά τα τελευταία χρόνια. Ειδικότερα, τα ασύρµατα αδόµητα δίκτυα (ή αλλιώς ασύρµατα δίκτυα τύπου ad hoc) έχουν προσελκύσει το έντονο εν­διαφέρον της επιστηµονικής κοινότητας λόγω των πολλών εφαρµογών που έχουν κυρίως σε περιπτώσεις όπου δεν είναι δυνατή ή επιθυµητή η ολική ή µερική κάλυψη µέσω υποδοµής µε βάση την ενσύρµατη δικτύωση (π.χ., επι­κοινωνία σε δυσπρόσιτες ή αποµακρυσµένες περιοχές, φυσικές καταστροφές, στρατιωτικές εφαρµογές, κλπ.). ΄Οπως και στα παραδοσιακά ενσύρµατα δίκτυα, σηµαντικό πρόβληµα αποτελεί η εγκαθίδρυση σχηµάτων επικοινωνίας όπως διάδοση (broadcasting, multicasting), επικοινωνία όλων µε όλους (gossiping, all-to-all communica­tion), και επικοινωνία σε οµάδες (group communication). Για την επικοινω­νία απαιτείται η κατανάλωση ενέργειας στους κόµβους του δικτύου και, λαµβ.άνοντας υπόψη ότι τα αδόµητα ασύρµατα δίκτυα χρησιµοποιούν κόµβους µε περιορισµένα αποθέµατα ενέργειας, είναι απαραίτητη η ορθολογιστική χρήση αυτής της ενέργειας κατά την επικοινωνία. Αυτό µπορεί να σηµαίνει ότι είναι επιθυµητή είτε η ελαχιστοποίηση της συνολικής ενέργειας που κα­ταναλώνεται στους κόµβους του δικτύου για επικοινωνία ή η ελαχιστοποίηση της µέγιστης ενέργειας ώστε να επιτυγχάνεται όσο το δυνατό µεγαλύτερος χρόνος ζωής όλων των κόµβων του δικτύου. Στη διατριβή εξετάζουµε αλγόριθ­µους για την εγκαθίδρυση διαφορετικών σχηµάτων επικοινωνίας σε αδόµητα ασύρµατα δίκτυα όπου βασικό κριτήριο για την εκτίµηση της απόδοσής τους θα είναι η κατανάλωση ενέργειας που επιφέρουν στο δίκτυο. Μοντελοποιούµε τα δίκτυα µε ειδικά γραφήµατα και τα αντίστοιχα προβλήµατα επικοινωνίας σαν προβλήµατα συνδυαστικής βελτιστοποίησης στα γραφήµατα αυτά. Τα αποτελέσµατά µας περιλαµβάνουν νέους αλγόριθµους που βελτιώνουν προηγούµενα γνωστά σχετικά αποτελέσµατα και νέα κάτω φράγµατα. Με κεντρικό στόχο την αποδοτική κατανοµή ενέργειας σε ασύρµατα δίκτυα, η µελέτη µας έχει διττό χαρακτήρα: από τη µια πλευρά, ασχολούµαστε µε µε­λέτη και ανάλυση θεµελιωδών προβληµάτων της Θεωρητικής Επιστήµης των Υπολογιστών (όπως, π.χ., το πρόβληµα Κάλυψης µε Σύνολα). Τέτοια προβλήµατα, και ειδικές περιπτώσεις τους, παρουσιάζουν εξαιρετικό ενδιαφέρον αφού χρησιµοποιούνται (µεταξύ άλλων) συχνά για τη µοντελοποίηση προβλη­µάτων ενεργειακά αποδοτικής επικοινωνίας σε ασύρµατα δίκτυα. Επιπλέον, προτείνουµε και αναλύουµε νέους αλγόριθµους για συγκεκριµένα σενάρια επικοινωνίας σε σύγχρονα ασύρµατα δίκτυα. Από την άλλη πλευρά, µελετάµε και εκτιµούµε πειραµατικά την απόδοση αρκετών αλγορίθµων και τεχνικών (από τη βιβλιογραφία αλλά και νέων) για ενεργειακά αποδοτική επικοινωνία σε ασύρµατα δίκτυα. Ειδικότερα: Μελετάµε το πρόβληµα κάλυψης µε σύνολα και ενδιαφέρουσες παραλ­λαγές του. Παρουσιάζουµε νέους συνδυαστικούς προσεγγιστικούς αλγόριθµους για το πρόβληµα k-κάλυψης συνόλων. Προηγούµενες προσεγγίσεις έχουν βασισθεί σε επεκτάσεις του άπληστου αλγόριθµου µέσω αποδοτικού χειρισµού µικρών συνόλων. Οι νέοι αλγόριθµοι επεκτείνουν περαιτέρω τις προηγούµενες προσεγγίσεις χρησιµοποιώντας την ιδέα του υπολογισµού µεγάλων οµάδων στοιχείων και στη συνέχεια της οµαδοποίησής τους σε σύνολα µεγάλου µεγέθους. Τα αποτελέσµατά µας βελτιώνουν τα καλύτερα γνωστά φράγµατα προσέγγισης για το πρόβληµα k-κάλυψης συνόλων για κάθε τιµή του k >= 6. Η τεχνική που χρησιµοποιούµε για την ανάλυση παρουσιάζει επιπλέον ανεξάρτητα ενδιαφέρον: το πάνω φράγµα για τον παράγοντα προ­σέγγισης επιτυγχάνεται φράσσοντας την αντικειµενική τιµή ενός γραµµικού προγράµµατος η οποία ‘αποκαλύπτει’ το λόγο προσέγγισης του υπό εξέταση αλγορίθµου (factor-revealing). Παρουσιάζουµε έναν απλό αλγόριθµο για το πρόβληµα εύρεσης µέγιστου δάσους γεννητικού αστέρα. Λαµβάνουµε υπόψη το γεγονός ότι το πρόβληµα αποτελεί ειδική περίπτωση του συµπληρωµατικού προβλήµατος κάλυψης συ­νόλου και προσαρµόζουµε έναν αλγόριθµο των Duh και Furer για την επίλυ­σή του. Αποδεικνύουµε ότι ο αλγόριθµος αυτός υπολογίζει 193/240 που είναι περίπου ίσο με 0.804 ­προσεγγιστικά δάση γεννητικών αστέρων. Το αποτέλεσµα αυτό βελτιώνει ένα προηγούµενο άνω φράγµα µε τιµή 0.71 των Chen και άλλων. Αν και ο αλ­γόριθµος είναι καθαρά συνδυαστικός, η ανάλυσή µας ορίζει ένα γραµµικό πρόγραµµα που χρησιµοποιεί µια παράµετρο f το οποίο είναι επιλύσιµο για τιµές της παραµέτρου f που δεν είναι µικρότερες από το λόγο προσέγγισης του αλγορίθµου. Η ανάλυση είναι αυστηρή και, το ενδιαφέρον είναι ότι, µπορεί να εφαρµοστεί και σε συµπληρωµατικές εκδοχές του προβλήµατος κάλυψης συνόλου όπως η εξοικονόµηση χρωµάτων. Δίνει την ίδια εγγύηση προσέγγισης µε τιµή 193/240 που οριακά βελτιώνει το προηγούµενο γνω­στό κάτω φράγµα των Duh και Furer. Αποδεικνύουµε επίσης ότι, γενικά, µια φυσική κλάση αλγορίθµων τοπικής αναζήτησης δε δίνουν καλύτερα από 1/2-προσεγγιστικά δάση γεννητικών αστέρων. Μελετάµε προβλήµατα επικοινωνίας σε ασύρµατα δίκτυα που υποστηρί­ζουν πολλαπλά µέσα ασύρµατης διασύνδεσης. Σε τέτοια δίκτυα, δύο κόµβοι µπορούν να επικοινωνήσουν αν είναι αρκετά κοντά και διαθέτουν κάποιο κοινό µέσο ασύρµατης διασύνδεσης. Η ενεργοποίηση ενός µέσου ασύρµατης διασύνδεσης επιφέρει ένα κόστος που αντανακλά την ενέργεια που καταναλώ­νεται όταν κάποιος κόµβος χρησιµοποιεί το µέσο αυτό. Διακρίνουµε µεταξύ της συµµετρικής και της µη συµµετρικής περίπτωσης, µε βάση το κόστος ενεργοποίησης για κάθε ασύρµατο µέσο διασύνδεσης είναι το ίδιο για όλους τους κόµβους ή όχι. Για τη συµµετρική περίπτωση, παρουσιάζουµε έναν (3/2+ε)–προσεγγιστικό αλγόριθµο για το πρόβληµα πλήρους διασύνδεσης µε ελάχιστο κόστος ενεργοποίησης, βελτιώνοντας ένα προηγούµενο φράγµα µε τιµή 2. Για τη µη συµµετρική περίπτωση, αποδεικνύουµε ότι το πρόβληµα διασύνδεσης δεν είναι προσεγγίσιµο στα πλαίσια ενός παράγοντα υπολογα­ριθµικού ως προς το πλήθος των κόµβων και παρουσιάζουµε ένα λογαριθµι­κό προσεγγιστικό αλγόριθµο για µια γενικότερη περίπτωση που µοντελοποιεί την οµαδική επικοινωνία. Επίσης, µελετάµε αλγόριθµους για τον υπολογισµό αποδοτικών ως προς την ενέργεια δένδρων µετάδοσης (multicasting) σε ασύρµατα αδόµητα δί­κτυα. Τέτοιοι αλγόριθµοι είτε ξεκινούν από µια κενή λύση η οποία σταδιακά επαυξάνεται για να δώσει ένα δένδρο µετάδοσης (επαυξητικοί αλγόριθµοι ­augmentation algorithms) είτε λαµβάνουν σαν είσοδο ένα αρχικό δένδρο µε­τάδοσης και εκτελούν ‘περιπάτους ’ σε διαφορετικά δένδρα µετάδοσης για πεπερασµένο αριθµό βηµάτων µέχρι να επιτευχθεί κάποια αποδεκτή µείωση στην κατανάλωση της ενέργειας (αλγόριθµοι τοπικής αναζήτησης -local search algorithms). Εστιάζουµε τόσο σε επαυξητικούς αλγόριθµους όσο και σε αλγό­ριθµους τοπικής αναζήτησης και συγκεκριµένα έχουµε υλοποιήσει αρκετούς υπάρχοντες αλγόριθµους από τη βιβλιογραφία αλλά και νέους. Συγκρίνου­µε πειραµατικά τους αλγόριθµους αυτούς σε τυχαία γεωµετρικά στιγµιότυπα του προβλήµατος και επιτυγχάνουµε αποτελέσµατα όσον αφορά στην αποδο­τικότητα ως προς την ενέργεια των λύσεων που λαµβάνουµε. Παρουσιάζουµε επίσης αποτελέσµατα σχετικά µε το χρόνο εκτέλεσης των υλοποιήσεών µας. Επίσης διερευνούµε το κατά πόσον οι λύσεις που λαµβάνουµε από επαυ­ξητικούς αλγόριθµους µπορούν να βελτιωθούν µέσω αλγορίθµων τοπικής αναζήτησης. Τα αποτελέσµατά µας αποδεικνύουν ότι ένας από τους νέους αλγόριθµους που προτείνουµε και οι εκδοχές του επιτυγχάνουν τις πιο απο­δοτικές ενεργειακά λύσεις και µάλιστα πολύ γρήγορα και, επιπλέον, υποδεικ­νύουν ιδιότητες γεωµετρικών στιγµιοτύπων του προβλήµατος που συντελούν στη βελτιωµένη απόδοση των επαυξητικών αλγορίθµων. / In this dissertation, we study issues arising in wireless communication networks, i.e., networks based on telecommunication infrastructure like cellular wireless networks, networks of autonomous wireless transmitters like ad hoc wireless networks, and so on. Wireless networks have received significant attention during the recent years. Especially, ad hoc wireless networks for which unlike traditional wired networks or cellular wireless networks, no wired backbone infrastructure is installed emerged due to their potential applications in emergency disaster relief, battlefield, etc. Like in traditional wired networks, an important problem concerns the establishment of communication patterns like broadcasting, multicasting, gossiping, all-to-all communication, and group communication. Communication then requires energy consumption at network nodes, and given that in ad hoc wireless networks energy is a scarce resource, it is of paramount importance to use it efficiently when establishing communication patterns. In such a setting, it is usually pursued that either the total energy consumed at networks nodes or the maximum energy consumed at any network node is minimized so that the network lifetime is prolonged as long as possible. Herein, we present and analyze theoretically and experimentally algorithms for guaranteeing the establishment of various communication patterns in ad hoc wireless networks and evaluate their performance in terms of their energy-efficiency. We represent these networks using graphs and model the corresponding communication problems as combinatorial optimization problems in such graphs. Our results include new algorithms which improve previously known relevant results as well as new lower bounds. Our main objective being the efficient energy allocation in wireless networks, our study is of dual character: on the one hand, we study and analyze fundamental problems of Theoretical Computer Science (like, e.g., Set Cover); such problems, as well as special cases of them, are highly interesting since they usually model energy-efficient communication problems in wireless networks. Furthermore, we propose and analyse new algorithms for particular communication scenaria in modern wireless networks. On the other hand, we experimentally study and evaluate several algorithms and techniques (both from the literature and new ones) for energy-efficient communication in wireless networks.