Georgia Institute of Technology College of Sciences

Hurwitz correspondences are certain multivalued self-maps of the moduli space M0,N parametrizing marked genus zero curves. We study the dynamics of these correspondences via numerical invariants called dynamical degrees. We compare a given Hurwitz correspondence H on various compactifications of M0,N to show that, for k ≥ ( dim M0,N )/2, the k-th dynamical degree of H is the largest eigenvalue of the pushforward map induced by H on a comparatively small quotient of H2k(M0,N). We also show that this is the optimal result of this form.

We present an algebraic framework which simultaneously generalizes the notion of linear subspaces, matroids, valuated matroids, and oriented matroids. We call the resulting objects matroids over hyperfields. We give "cryptomorphic" axiom systems for such matroids in terms of circuits, Grassmann-Plucker functions, and dual pairs, and establish some basic duality theorems.

Stochastic programming is a powerful approach for decision-making under uncertainty. Unfortunately, the solution may be misleading if the underlying distribution of the involved random parameters is not known exactly. In this talk, we study distributionally robust stochastic programming (DRSP), in which the decision hedges against the worst possible distribution that belongs to an ambiguity set. More specifically, we consider the DRSP with the ambiguity set comprising all distributions that are close to some reference distribution in terms of Wasserstein distance. We derive a tractable reformulation of the DRSP problem by constructing the worst-case distribution explicitly via the first-order optimality condition of the dual problem. Our approach has several theoretical and computational implications. First, using the precise characterization of the worst-case distribution, we show that the DRSP can be approximated by robust programs to arbitrary accuracy, and thus many DRSP problems become tractable with tools from robust optimization. Second, when the objective is concave in the uncertainty, the robust-program approximation is exact and equivalent to a saddle-point problem, which can be solved by a Mirror-Prox algorithm. Third, our framework can also be applied to problems other than stochastic programming, such as a class of distributionally robust transportation problems. Furthermore, we perform sensitivity analysis with respect to the radius of the Wasserstein ball, and apply our results to the newsvendor problem, two-stage linear program with uncertainty-affected recourse, and worst-case Value-at-risk analysis.

Decomposition techniques such as atomic, molecular, wavelet and wave-packet expansions provide a multi-scale refinement of Fourier analysis and exploit a rather simple concept: “waves with very different frequencies are almost invisible to each other”. Starting with the classical Calderon-Zygmund and Littlewood-Paley decompositions, many of these useful techniques have been developed around the study of singular integral operators. By breaking an operator or splitting the functions on which it acts into non-interacting almost orthogonal pieces, these tools capture subtle cancelations and quantify properties of an operator in terms of norm estimates in function spaces. This type of analysis has been used to study linear operators with tremendous success. More recently, similar decomposition techniques have been pushed to the analysis of new multilinear operators that arise in the study of (para) product-like operations, commutators, null-forms and other nonlinear functional expressions. In this talk we will present some of our contributions in the study of multilinear singular integrals, function spaces, and the analysis of nanostructure in biological tissues, not all immediately connected topics, yet all centered on some notion of almost orthogonality.