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Random ordinary differential equations (RODEs) are ordinary differential equations (ODEs) which have a stochastic process in their vector field functions. RODEs have been used in a wide range of applications such as biology, medicine, population dynamics and engineering and play an important role in the theory of random dynamical systems, however, they have been long overshadowed by stochastic differential equations.
Typically, the driving stochastic process has at most Hoelder continuous sample paths and the resulting vector field is, thus, at most Hoelder continuous in time, no matter how smooth the vector function is in its original variables, so the sample paths of the solution are certainly continuously differentiable, but their derivatives are at most Hoelder continuous in time. Consequently, although the classical numerical schemes for ODEs can be applied pathwise to RODEs, they do not achieve their traditional orders.
Recently, Gruene and Kloeden derived the explicit averaged Euler scheme by taking the average of the noise within the vector field. In addition, new forms of higher order Taylor-like schemes for RODEs are derived systematically by Jentzen and Kloeden.
However, it is still important to build higher order numerical schemes and computationally less expensive schemes as well as numerically stable schemes and this is the motivation of this thesis. The schemes by Gruene and Kloeden and Jentzen and Kloeden are very general, so RODEs with special structure, i.e., RODEs with Ito noise and RODEs with affine structure, are focused and numerical schemes which exploit these special structures are investigated.
The developed numerical schemes are applied to several mathematical models in biology and medicine. In order to see the performance of the numerical schemes, trajectories of solutions are illustrated. In addition, the error vs. step sizes as well as the computational costs are compared among newly developed schemes and the schemes in literature.
In the qualitative analysis of solutions of partial differential equations, many interesting questions are related to the shape of solutions. In particular, the symmetries of a given solution are of interest. One of the first more general results in this direction was given in 1979 by Gidas, Ni and Nirenberg... The main tool in proving this symmetry and monotonicity result is the moving plane method. This method, which goes back to Alexandrov’s work on constant mean curvature surfaces in 1962, was introduced in 1971 by Serrin in the context of partial differential equations to analyze an overdetermined problem...
Containment problems belong to the classical problems of (convex) geometry. In the proper sense, a containment problem is the task to decide the set-theoretic inclusion of two given sets, which is hard from both the theoretical and the practical perspective. In a broader sense, this includes, e.g., radii or packing problems, which are even harder. For some classes of convex sets there has been strong interest in containment problems. This includes containment problems of polyhedra and balls, and containment of polyhedra, which have been studied in the late 20th century because of their inherent relevance in linear programming and combinatorics.
Since then, there has only been limited progress in understanding containment problems of that type. In recent years, containment problems for spectrahedra, which naturally generalize the class of polyhedra, have seen great interest. This interest is particularly driven by the intrinsic relevance of spectrahedra and their projections in polynomial optimization and convex algebraic geometry. Except for the treatment of special classes or situations, there has been no overall treatment of that kind of problems, though.
In this thesis, we provide a comprehensive treatment of containment problems concerning polyhedra, spectrahedra, and their projections from the viewpoint of low-degree semialgebraic problems and study algebraic certificates for containment. This leads to a new and systematic access to studying containment problems of (projections of) polyhedra and spectrahedra, and provides several new and partially unexpected results.
The main idea - which is meanwhile common in polynomial optimization, but whose understanding of the particular potential on low-degree geometric problems is still a major challenge - can be explained as follows. One point of view towards linear programming is as an application of Farkas' Lemma which characterizes the (non-)solvability of a system of linear inequalities. The affine form of Farkas' Lemma characterizes linear polynomials which are nonnegative on a given polyhedron. By omitting the linearity condition, one gets a polynomial nonnegativity question on a semialgebraic set, leading to so-called Positivstellensaetze (or, more precisely Nichtnegativstellensaetze). A Positivstellensatz provides a certificate for the positivity of a polynomial function in terms of a polynomial identity. As in the linear case, these Positivstellensaetze are the foundation of polynomial optimization and relaxation methods. The transition from positivity to nonnegativity is still a major challenge in real algebraic geometry and polynomial optimization.
With this in mind, several principal questions arise in the context of containment problems: Can the particular containment problem be formulated as a polynomial nonnegativity (or, feasibility) problem in a sophisticated way? If so, how are positivity and nonnegativity related to the containment question in the sense of their geometric meaning? Is there a sophisticated Positivstellensatz for the particular situation, yielding certificates for containment? Concerning the degree of the semialgebraic certificates, which degree is necessary, which degree is sufficient to decide containment?
Indeed, (almost) all containment problems studied in this thesis can be formulated as polynomial nonnegativity problems allowing the application of semialgebraic relaxations. Other than this general result, the answer to all the other questions (highly) depends on the specific containment problem, particularly with regard to its underlying geometry. An important point is whether the hierarchies coming from increasing the degree in the polynomial relaxations always decide containment in finitely many steps.
We focus on the containment problem of an H-polytope in a V-polytope and of a spectrahedron in a spectrahedron. Moreover, we address containment problems concerning projections of H-polyhedra and spectrahedra. This selection is justified by the fact that the mentioned containment problems are computationally hard and their geometry is not well understood.
This thesis covers the analysis of radix sort, radix select and the path length of digital trees under a stochastic input assumption known as the Markov model.
The main results are asymptotic expansions of mean and variance as well as a central limit theorem for the complexity of radix sort and the path length of tries, PATRICIA tries and digital search trees.
Concerning radix select, a variety of different models for ranks are discussed including a law of large numbers for the worst case behavior, a limit theorem for the grand averages model and the first order asymptotic of the average complexity in the quantile model.
Some of the results are achieved by moment transfer techniques, the limit laws are based on a novel use of the contraction method suited for systems of stochastic recurrences.
This work is concerned with two topics at the intersection of convex algebraic geometry and optimization.
We develop a new method for the optimization of polynomials over polytopes. From the point of view of convex algebraic geometry the most common method for the approximation of polynomial optimization problems is to solve semidefinite programming relaxations coming from the application of Positivstellensätze. In optimization, non-linear programming problems are often solved using branch and bound methods. We propose a fused method that uses Positivstellensatz-relaxations as lower bounding methods in a branch and bound scheme. By deriving a new error bound for Handelman's Positivstellensatz, we show convergence of the resulting branch and bound method. Through the application of Positivstellensätze, semidefinite programming has gained importance in polynomial optimization in recent years. While it arises to be a powerful tool, the underlying geometry of the feasibility regions (spectrahedra) is not yet well understood. In this work, we study polyhedral and spectrahedral containment problems, in particular we classify their complexity and introduce sufficient criteria to certify the containment of one spectrahedron in another one.
The cones of nonnegative polynomials and sums of squares arise as central objects in convex algebraic geometry and have their origin in the seminal work of Hilbert ([Hil88]). Depending on the number of variables n and the degree d of the polynomials, Hilbert famously characterizes all cases of equality between the cone of nonnegative polynomials and the cone of sums of squares. This equality precisely holds for bivariate forms, quadratic forms and ternary quartics ([Hil88]). Since then, a lot of work has been done in understanding the difference between these two cones, which has major consequences for many practical applications such as for polynomial optimization problems. Roughly speaking, minimizing polynomial functions (constrained as well as unconstrained) can be done efficiently whenever certain nonnegative polynomials can be written as sums of squares (see Section 2.3 for the precise relationship). The underlying reason is the fundamental difference that checking nonnegativity of polynomials is an NP-hard problem whenever the degree is greater or equal than four ([BCSS98]), whereas checking whether a polynomial can be written as a sum of squares is a semidefinite feasibility problem (see Section 2.2). Although the complexity status of the semidefinite feasibility problem is still an open problem, it is polynomial for fixed number of variables. Hence, understanding the difference between nonnegative polynomials and sums of squares is highly desirable both from a theoretical and a practical viewpoint.
We consider a class of nonautonomous nonlinear competitive parabolic systems on bounded radial domains under Neumann or Dirichlet boundary conditions. We show that, if the initial profiles satisfy a reflection inequality with respect to a hyperplane, then bounded positive solutions are asymptotically (in time) foliated Schwarz symmetric with respect to antipodal points. Additionally, a related result for (positive and sign changing solutions) of scalar equations with Neumann or Dirichlet boundary conditions is given. The asymptotic shape of solutions to cooperative systems is also discussed.
A multiple filter test for the detection of rate changes in renewal processes with varying variance
(2014)
The thesis provides novel procedures in the statistical field of change point detection in time series.
Motivated by a variety of neuronal spike train patterns, a broad stochastic point process model is introduced. This model features points in time (change points), where the associated event rate changes. For purposes of change point detection, filtered derivative processes (MOSUM) are studied. Functional limit theorems for the filtered derivative processes are derived. These results are used to support novel procedures for change point detection; in particular, multiple filters (bandwidths) are applied simultaneously in oder to detect change points in different time scales.
The work presented in this thesis is devoted to two classes of mathematical population genetics models, namely the Kingman-coalescent and the Beta-coalescents. Chapters 2, 3 and 4 of the thesis include results concerned with the first model, whereas Chapter 5 presents contributions to the second class of models.
We study the price-setting problem of market makers under perfect competition in continuous time. Thereby we follow the classic Glosten-Milgrom model that defines bid and ask prices as the expectation of a true value of the asset given the market makers partial information that includes the customers trading decisions. The true value is modeled as a Markov process that can be observed by the customers with some noise at Poisson times.
We analyze the price-setting problem by solving a non-standard filtering problem with an endogenous filtration that depends on the bid and ask price process quoted by the market maker. Under some conditions we show existence and uniqueness of the price processes. In a different setting we construct a counterexample to uniqueness. Further, we discuss the behavior of the spread by a convergence result and simulations.