Αρχικά εισάγονται οι ανισότητες Sobolev για τις οποίες ο ρόλος της διάστασης είναι θεμελιώδης και στην συνέχεια δείχνουμε τον τρόπο με τιν οποίο αυτές εξασθενίζουν. Ακόμα θα δούμε πως με τη βοήθεια των ανισοτήτων εντροπίας ενέργειας γίνεται κατανοητός ο απειροδιάστατος χαρακτήρας της λογαριθμικής ανισότητας Sobolev. / Firstly we introduce the Sobolev inequalities that the role of the dimension is vital. Furthermore, we will show the way that they have been weakened. We see also with the help of the inequalities of Entropy-Energy how we can clarify the infinite dimensional character of the logarithmic Sobolev inequalities.

Molecular simulation is an essential tool in understanding complex chemical and biochemical processes as real-life experiments prove increasingly costly or infeasible in practice . This thesis is devoted to methodological aspects of molecular simulation, with a particular focus on computing transition paths and their associated free energy profiles. The first part is dedicated to computational methods for reaction path and transition state searches on a potential energy surface. In Chapter 3 we propose an improvement to a widely-used transition state search method, the Activation Relaxation Technique (ART). We also present a local convergence study of a prototypical algorithm. The second part is dedicated to free energy computations. We focus in particular on an adaptive importance sampling technique, the Adaptive Biasing Force (ABF) method. The first contribution to this field, presented in Chapter 5, consists in showing the applicability to a large molecular system of a new parallel implementation, named multiple-walker ABF (MW-ABF). Numerical experiments demonstrated the robustness of MW-ABF against artefacts arising due to poorly chosen or oversimplified reaction coordinates. These numerical findings inspired a new study of the longtime convergence of the ABF method, as presented in Chapter 6. By studying a slightly modified model, we back our numerical results by showing a faster theoretical rate of convergence of ABF than was previously shown

La simulation moléculaire est un outil indispensable pour comprendre le comportement de systèmes complexes pour lesquels les expériences s'avèrent coûteuses ou irréalisables à l'heure actuelle. Cette thèse est dédiée aux aspects méthodologiques de la simulation moléculaire et comprend deux volets. Le premier volet porte sur la recherche de chemins de réaction et de points col d'une surface d'énergie potentielle. Nous proposons, dans le chaptire 3, une amélioration d'une des méthodes de cette classe, appelée '"Activation Relaxation Technique"(ART). Nous donnons également une preuve de convergence pour un algorithme prototype. Le deuxieme volet porte sur le calcul d'énergie libre pour les transitions caractérisées par une coordonnée de réaction. Nous nous plaçons dans le cadre d'une méthode d'échantillonnage d'importance adaptative, appelée 'Adaptive Biasing Force' (ABF). Ce volet comprend en soi deux sous-parties. La première partie (chapitre 5) s'attache à montrer l'applicabilité à un système biomoléculaire, d'une nouvelle mise en oeuvre parallèle d'ABF, nommée 'multiple-walker ABF' (MW-ABF), consistant à utiliser plusieurs répliques. Cette mise en oeuvre s'est avérée utile pour surmonter des problèmes liés à un mauvais choix de coordonnée de réaction. Nous confirmons ensuite ces résultats numériques en étudiant la convergence théorique d'un algorithme d'ABF adapté. Le chapitre 6 comprend une étude de convergence en temps long utilisant les méthodes d'entropie relative et les inégalités de Sobolev logarithmiques / Molecular simulation is an essential tool in understanding complex chemical and biochemical processes as real-life experiments prove increasingly costly or infeasible in practice . This thesis is devoted to methodological aspects of molecular simulation, with a particular focus on computing transition paths and their associated free energy profiles. The first part is dedicated to computational methods for reaction path and transition state searches on a potential energy surface. In Chapter 3 we propose an improvement to a widely-used transition state search method, the Activation Relaxation Technique (ART). We also present a local convergence study of a prototypical algorithm. The second part is dedicated to free energy computations. We focus in particular on an adaptive importance sampling technique, the Adaptive Biasing Force (ABF) method. The first contribution to this field, presented in Chapter 5, consists in showing the applicability to a large molecular system of a new parallel implementation, named multiple-walker ABF (MW-ABF). Numerical experiments demonstrated the robustness of MW-ABF against artefacts arising due to poorly chosen or oversimplified reaction coordinates. These numerical findings inspired a new study of the longtime convergence of the ABF method, as presented in Chapter 6. By studying a slightly modified model, we back our numerical results by showing a faster theoretical rate of convergence of ABF than was previously shown

Η παρούσα διατριβή εντάσσεται ερευνητικά στην περιοχή της μη γραμμικής ανάλυσης και ειδικότερα στην εύρεση βέλτιστων σταθερών για ανισότητες Sobolev στο χώρο Rn με ανώτερης τάξης δεκαδικές παραγώγους. Επίσης, δίνονται οι αντίστοιχες βέλτιστες σταθερές αυτών των ανισοτήτων πάνω στη σφαίρα Sn με τη χρησιμοποίηση ως βασικού εργαλείου την στερεογραφική προβολή. Τέλος, σαν μια εφαρμογή των ευρεθέντων ανισοτήτων έχουμε ένα θεώρημα σχετικό με αυτό των Rellich-Kondrashov και το οποίο είναι εξαιρετικής σημασίας, ιδιαίτερα στο λογισμό των μεταβολών.

In this thesis, new methods for proving concentration inequalities for Poisson functionals are developed. The focus is on techniques that are based on logarithmic Sobolev inequalities, but also results that are based on the convex distance for Poisson processes are presented. The general methods are applied to a variety of functionals associated with random geometric graphs. In particular, concentration inequalities for subgraph and component counts are proved. Finally, the established concentration results are used to derive strong laws of large numbers for subgraph and component counts associated with random geometric graphs.

In dieser Arbeit untersuchen wir einige Möglichkeiten, financial und soziale Netzwerke zu analysieren, ein Thema, das in letzter Zeit in der ökonometrischen Literatur große Beachtung gefunden hat. Kapitel 2 untersucht den Risiko-Spillover-Effekt über das in White et al. (2015) eingeführte multivariate bedingtes autoregressives Value-at-Risk-Modell. Wir sind an der Anwendung auf nicht stationäre Zeitreihen interessiert und entwickeln einen sequentiellen statistischen Test, welcher das größte verfügbare Homogenitätsintervall auswählt. Unser Ansatz basiert auf der Changepoint-Teststatistik und wir verwenden einen neuartigen Multiplier Bootstrap Ansatz zur Bewertung der kritischen Werte. In Kapitel 3 konzentrieren wir uns auf soziale Netzwerke. Wir modellieren Interaktionen zwischen Benutzern durch ein Vektor-Autoregressivmodell, das Zhu et al. (2017) folgt. Um für die hohe Dimensionalität kontrollieren, betrachten wir ein Netzwerk, das einerseits von Influencers und Andererseits von Communities gesteuert wird, was uns hilft, den autoregressiven Operator selbst dann abzuschätzen, wenn die Anzahl der aktiven Parameter kleiner als die Stichprobegröße ist. Kapitel 4 befasst sich mit technischen Tools für die Schätzung des Kovarianzmatrix und Kreuzkovarianzmatrix. Wir entwickeln eine neue Version von der Hanson-Wright- Ungleichung für einen Zufallsvektor mit subgaußschen Komponenten. Ausgehend von unseren Ergebnissen zeigen wir eine Version der dimensionslosen Bernstein-Ungleichung, die für Zufallsmatrizen mit einer subexponentiellen Spektralnorm gilt. Wir wenden diese Ungleichung auf das Problem der Schätzung der Kovarianzmatrix mit fehlenden Beobachtungen an und beweisen eine verbesserte Version des früheren Ergebnisses von (Lounici 2014). / In this work we explore some ways of studying financial and social networks, a topic that has recently received tremendous amount of attention in the Econometric literature. Chapter 2 studies risk spillover effect via Multivariate Conditional Autoregressive Value at Risk model introduced in White et al. (2015). We are particularly interested in application to non-stationary time series and develop a sequential test procedure that chooses the largest available interval of homogeneity. Our approach is based on change point test statistics and we use a novel Multiplier Bootstrap approach for the evaluation of critical values. In Chapter 3 we aim at social networks. We model interactions between users through a vector autoregressive model, following Zhu et al. (2017). To cope with high dimensionality we consider a network that is driven by influencers on one side, and communities on the other, which helps us to estimate the autoregressive operator even when the number of active parameters is smaller than the sample size. Chapter 4 is devoted to technical tools related to covariance cross-covariance estimation. We derive uniform versions of the Hanson-Wright inequality for a random vector with independent subgaussian components. The core technique is based on the entropy method combined with truncations of both gradients of functions of interest and of the coordinates itself. We provide several applications of our techniques: we establish a version of the standard Hanson-Wright inequality, which is tighter in some regimes. Extending our results we show a version of the dimension-free matrix Bernstein inequality that holds for random matrices with a subexponential spectral norm. We apply the derived inequality to the problem of covariance estimation with missing observations and prove an improved high probability version of the recent result of Lounici (2014).