Georgia Institute of Technology College of Sciences

Concentration inequalities are fundamental tools in probabilistic combinatorics and theoretical computer science for proving that functions of random variables are typically near their means. Of particular importance is the case where f(X) is a function of independent random variables X=(X_1,...,X_n). Here the well-known bounded differences inequality (also called McDiarmid's or Hoeffding--Azuma inequality) establishes sharp concentration if the function f does not depend too much on any of the variables. One attractive feature is that it relies on a very simple Lipschitz condition (L): it suffices to show that |f(X)-f(X')| \leq c_k whenever X,X' differ only in X_k. While this is easy to check, the main disadvantage is that it considers worst-case changes c_k, which often makes the resulting bounds too weak to be useful. In this talk we discuss a variant of the bounded differences inequality which can be used to establish concentration of functions f(X) where (i) the typical changes are small although (ii) the worst case changes might be very large. One key aspect of this inequality is that it relies on a simple condition that (a) is easy to check and (b) coincides with heuristic considerations as to why concentration should hold. Indeed, given a `good' event G that holds with very high probability, we essentially relax the Lipschitz condition (L) to situations where G occurs. The point is that the resulting typical changes c_k are often much smaller than the worst case ones. If time permits, we shall illustrate its application by considering the reverse H-free process, where H is 2-balanced. We prove that the final number of edges in this process is concentrated, and also determine its likely value up to constant factors. This answers a question of Bollobás and Erdös.

In this talk, I will refine the concept of the symmetry group of a geometric object through its symmetry groupoid, which incorporates both global and local symmetries in a common framework. The symmetry groupoid is related to the weighted differential invariant signature of a submanifold, that is introduced to capture its fine grain equivalence and symmetry properties. The groupoid/signature approach will be connected to recent developments in signature-based recognition and symmetry detection of objects in digital images, including jigsaw puzzle assembly.

Many real-world problems reduce to optimization problems that are solved by iterative methods. In this talk, I focus on recently developed efficient algorithms for solving large-scale optimization problems that arises in medical imaging and image processing. In the first part of my talk, I will introduce the Bregman Operator Splitting with Variable Stepsize (BOSVS) algorithm for solving nonsmooth inverse problems. The proposed algorithm is designed to handle applications where the matrix in the fidelity term is large, dense, and ill-conditioned. Numerical results are provided using test problems from parallel magnetic resonance imaging. In the second part, I will focus on the Euler's Elastica-based model which is non-smooth and non-convex, and involves high-order derivatives. I introduce two efficient alternating minimization methods based on operator splitting and alternating direction method of multipliers, where subproblems can be solved efficiently by Fourier transforms and shrinkage operators. I present the analytical properties of each algorithm, as well as several numerical experiments on image inpainting problems, including comparison with some existing state-of-art methods to show the efficiency and the effectiveness of the proposed methods.

In this talk I am going to present a way to study numerically the complex domains of invariant Tori for the dissipative standar map. The numerical approach is based on a Nash Moser method. This is work in progress jointly with R. Calleja.