The data presented in this thesis is a comprehensive study on the nature of peptide structure and how subtle and systematic changes in sequence and sidechain affect the basicity, ion stability, and conformation of a peptide. The peptides characterized were acetylated polyalanine di-, tri-, and tetra- peptides containing a proton-accepting probe: lysine and or the non-proteinogenic lysine-homologs: ornithine, 2,4-diaminobutyric acid, and 2,3-diaminopropionic acid. Peptides were studied in isomeric pairs for which the basic amino acid was placed closest to the N-terminus or the C-terminus of each peptide family (A n Probe vs. ProbeA n ). Using a variety of mass spectrometry based techniques and infrared multiphoton dissociation ion spectroscopy, the isomeric families of polyalanine peptides were characterized. Quantum chemical techniques were employed in parallel to provide theoretical predictions of three-dimensional structure, physical properties (dipole moment, polarizability, and accessible surface area), thermochemical values, and vibrational IR spectra, to gain further understanding of the peptides studied and to push the limits of current theoretical models. Overall it was found that the AnProbe peptide was more basic than their ProbeAn isomer. For the dipeptide systems, the greater basicity of AProbe peptides was due to efficiently charge-solvated ions which formed more compact structures compared to their ProbeA counterpart. For the tri- and tetra- peptide systems, greater basicity of the A 2,3 Probe peptides was likely due to formation of α or 3 10 helix-like structures in the protonated forms., introducing the macrodipolar effect, which cooperatively encouraged helical formation while stabilizing the charged site. On the other hand, ProbeA 2,3 peptides formed charge-solvated coils which do not exhibit any kind of dipole effect, resulting in lower basicity than their A2,3Probe counterpart.

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In dieser Arbeit werden die zu einem m-sektoriellen Operator assoziierten Halbgruppen betrachtet, die die Lösungen des Anfangswertproblems der zugehörigen Evolutionsgleichung beschreiben. Es wird eine 1987 von Davies veröffentlichte Methode zur Abschätzung dieser Halbgruppen verallgemeinert. Einen Schwerpunkt bilden die zu Dirichlet-Formen assoziierten Markov-Halbgruppen. Für diese werden die Resultate spezialisiert und der Zusammenhang zur intrinsischen Metrik dargelegt. Die Arbeit schließt mit verschiedenen Beispielen, die zeigen, wie mit diesen Verallgemeinerungen von Davies Methode neue Anwendungsgebiete erschlossen werden können.:Einleitung Funktionalanalytische Grundlagen Spezielle Halbgruppeneigenschaften Symmetrische Dirichlet-Formen Obere Schranken für die Halbgruppe Anwendungen Ausblick Komplexe Maße Anhang / This thesis is about m-sectorial operators and their associated semigroups describing the solutions of the initial value problem of the corresponding evolution equation. We generalize a method published by Davies 1987 to estimate these semigroups. A focus is set on Markov semigroups associated with Dirchlet forms. The results are applied to them and the connection to the intrinsic metric is presented. The thesis ends with different examples showing how this generalization of Davies method can be applied into new fields of application.:Einleitung Funktionalanalytische Grundlagen Spezielle Halbgruppeneigenschaften Symmetrische Dirichlet-Formen Obere Schranken für die Halbgruppe Anwendungen Ausblick Komplexe Maße Anhang

Un système couvrant est un ensemble fini de progressions arithmétiques avec la propriété que chaque entier appartient à au moins une des progressions. L’étude des systèmes couvrants a été initié par Erdős dans les années 1950, et il posa dans les années qui suivirent plusieurs questions sur ces objets mathématiques. Une de ses questions les plus célèbres est celle du plus petit module : est-ce que le plus petit module de tous les systèmes couvrants avec modules distinct est borné uniformément? En 2015, Hough a montré que la réponse était affirmative, et qu’une borne admissible est 1016. En se basant sur son travail, mais en simplifiant la méthode, Balister, Bollobás, Morris, Sahasrabudhe et Tiba on réduit cette borne a 616, 000. Leur méthode a menée a plusieurs applications supplémentaires. Entre autres, ils ont compté le nombre de système couvrant avec un nombre fixe de module. La première partie de ce mémoire vise a étudier une question similaire. Nous allons essayer de compter le nombre de système couvrant avec un ensemble de module fixé. La technique que nous utiliserons nous mènera vers l’étude des symmétries de système couvrant. Dans la seconde partie, nous répondrons à des variantes du problème du plus petit module. Nous regarderons des bornes sur le plus petit module d’un système couvrant de multiplicité s, c’est-à-dire un système couvrant dans lequel chaque module apparait au plus s fois. Nous utiliserons ensuite ce résultat afin montrer que le plus petit module d’un système couvrant de multiplicité 1 d’une progression arithmétique est borné, ainsi que pour montrer que le n-eme plus petit module dans un système couvrant de multiplicité 1 est borné. / A covering system is a finite set of arithmetic progressions with the property that every integer belongs to at least one of them. The study of covering systems was started by Erdős in the 1950’s, and he asked many questions about them in the following years. One of the most famous questions he asked was if the minimum modulus of a covering system with distinct moduli is bounded uniformly. In 2015, Hough showed that it is at most 1016. Following on his work, but simplifying the method, Balister, Bollobás, Morris, Sahasrabudhe and Tiba showed that it is at most 616, 000. Their method led them to many further applications. Notably, they counted the number of covering systems with a fixed number of moduli. The first part of this thesis seeks to study a related question, that is to count the number of covering systems with a given set of moduli. The technique developped to do this for some sets will lead us to look at symmetries of covering systems. The second part of this thesis will look at variants of the minimum modulus problem. Notably, we will be looking at bounds on the minimum modulus of a covering system of multiplicity s, that is a covering system in which each moduli appears at most s times, as well as bounds on the minimum modulus of a covering system of multiplicity 1 of an arithmetic progression, and finally look at bounds for the n-th smallest modulus in a covering system.

En informatique, la résolution de problèmes NP-difficiles en un temps raisonnable est d’une grande importance : optimisation de la chaîne d’approvisionnement, planification, routage, alignement de séquences biologiques multiples, inference dans les modèles graphiques pro- babilistes, et même certains problèmes de cryptographie sont tous des examples de la classe NP-complet. En pratique, nous modélisons beaucoup d’entre eux comme un problème d’op- timisation en nombre entier, que nous résolvons à l’aide de la méthodologie séparation et évaluation. Un algorithme de ce style divise un espace de recherche pour l’explorer récursi- vement (séparation), et obtient des bornes d’optimalité en résolvant des relaxations linéaires sur les sous-espaces (évaluation). Pour spécifier un algorithme, il faut définir plusieurs pa- ramètres, tel que la manière d’explorer les espaces de recherche, de diviser une recherche l’espace une fois exploré, ou de renforcer les relaxations linéaires. Ces politiques peuvent influencer considérablement la performance de résolution. Ce travail se concentre sur une nouvelle manière de dériver politique de recherche, c’est à dire le choix du prochain sous-espace à séparer étant donné une partition en cours, en nous servant de l’apprentissage automatique profond. Premièrement, nous collectons des données résumant, sur une collection de problèmes donnés, quels sous-espaces contiennent l’optimum et quels ne le contiennent pas. En représentant ces sous-espaces sous forme de graphes bipartis qui capturent leurs caractéristiques, nous entraînons un réseau de neurones graphiques à déterminer la probabilité qu’un sous-espace contienne la solution optimale par apprentissage supervisé. Le choix d’un tel modèle est particulièrement utile car il peut s’adapter à des problèmes de différente taille sans modifications. Nous montrons que notre approche bat celle de nos concurrents, consistant à des modèles d’apprentissage automatique plus simples entraînés à partir des statistiques du solveur, ainsi que la politique par défaut de SCIP, un solveur open-source compétitif, sur trois familles NP-dures: des problèmes de recherche de stables de taille maximum, de flots de réseau multicommodité à charge fixe, et de satisfiabilité maximum. / In computer science, solving NP-hard problems in a reasonable time is of great importance, such as in supply chain optimization, scheduling, routing, multiple biological sequence align- ment, inference in probabilistic graphical models, and even some problems in cryptography. In practice, we model many of them as a mixed integer linear optimization problem, which we solve using the branch and bound framework. An algorithm of this style divides a search space to explore it recursively (branch) and obtains optimality bounds by solving linear relaxations in such sub-spaces (bound). To specify an algorithm, one must set several pa- rameters, such as how to explore search spaces, how to divide a search space once it has been explored, or how to tighten these linear relaxations. These policies can significantly influence resolution performance. This work focuses on a novel method for deriving a search policy, that is, a rule for select- ing the next sub-space to explore given a current partitioning, using deep machine learning. First, we collect data summarizing which subspaces contain the optimum, and which do not. By representing these sub-spaces as bipartite graphs encoding their characteristics, we train a graph neural network to determine the probability that a subspace contains the optimal so- lution by supervised learning. The choice of such design is particularly useful as the machine learning model can automatically adapt to problems of different sizes without modifications. We show that our approach beats the one of our competitors, consisting of simpler machine learning models trained from solver statistics, as well as the default policy of SCIP, a state- of-the-art open-source solver, on three NP-hard benchmarks: generalized independent set, fixed-charge multicommodity network flow, and maximum satisfiability problems.

[pt] A programação estocástica dois estágios é uma abordagem matemática amplamente usada em aplicações da vida real, como planejamento da operação de sistemas de energia, cadeias de suprimentos, logística, gerenciamento de inventário e planejamento financeiro. Como a maior parte desses problemas não pode ser resolvida analiticamente, os tomadores de decisão utilizam métodos numéricos para obter uma solução quase ótima. Em algumas aplicações, soluções não convergidas e, portanto, sub-ótimas terminam sendo implementadas devido a limitações de tempo ou esforço computacional. Nesse contexto, os métodos existentes fornecem uma solução otimista sempre que a convergência não é atingida. As soluções otimistas geralmente geram altos níveis de arrependimento porque subestimam os custos reais na função objetivo aproximada. Para resolver esse problema, temos desenvolvido duas metodologias de solução conservadora para problemas de programação linear estocástica dois estágios com incerteza do lado direito e suporte retangular: Quando a verdadeira distribuição de probabilidade da incerteza é conhecida, propomos um problema DRO (Distributionally Robust Optimization) baseado em esperanças condicionais adaptadas à uma partição do suporte cuja complexidade cresce exponencialmente com a dimensionalidade da incerteza; Quando apenas observações históricas da incerteza estão disponíveis, propomos um problema de DRO baseado na métrica de Wasserstein a fim de incorporar ambiguidade sobre a real distribuição de probabilidade da incerteza. Para esta última abordagem, os métodos existentes dependem da enumeração dos vértices duais do problema de segundo estágio, tornando o problema DRO intratável em aplicações práticas. Nesse contexto, propomos esquemas algorítmicos para lidar com a complexidade computacional de ambas abordagens. Experimentos computacionais são apresentados para o problema do fazendeiro, o problema de alocação de aviões, e o problema do planejamento da operação do sistema elétrico (unit ommitmnet problem). / [en] Two-stage stochastic programming is a mathematical framework widely used in real-life applications such as power system operation planning, supply chains, logistics, inventory management, and financial planning. Since most of these problems cannot be solved analytically, decision-makers make use of numerical methods to obtain a near-optimal solution. Some applications rely on the implementation of non-converged and therefore sub-optimal solutions because of computational time or power limitations. In this context, the existing methods provide an optimistic solution whenever convergence is not attained. Optimistic solutions often generate high disappointment levels because they consistently underestimate the actual costs in the approximate objective function. To address this issue, we have developed two conservative-solution methodologies for two-stage stochastic linear programming problems with right-hand-side uncertainty and rectangular support: When the actual data-generating probability distribution is known, we propose a DRO problem based on partition-adapted conditional expectations whose complexity grows exponentially with the uncertainty dimensionality; When only historical observations of the uncertainty are available, we propose a DRO problem based on the Wasserstein metric to incorporate ambiguity over the actual data-generating probability distribution. For this latter approach, existing methods rely on dual vertex enumeration of the second-stage problem rendering the DRO problem intractable in practical applications. In this context, we propose algorithmic schemes to address the computational complexity of both approaches. Computational experiments are presented for the farmer problem, aircraft allocation problem, and the stochastic unit commitment problem.

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