Georgia Institute of Technology College of Sciences

Chudnovsky and Seymour's structure theorem for quasi-line graphs has led to a multitude of recent results that exploit two structural operations: compositions of strips and thickenings. In this paper we prove that compositions of linear interval strips have a unique optimal strip decomposition in the absence of a specific degeneracy, and that every claw-free graph has a unique optimal antithickening, where our two definitions of optimal are chosen carefully to respect the structural foundation of the graph. Furthermore, we give algorithms to find the optimal strip decomposition in O(nm) time and find the optimal antithickening in O(m2) time. For the sake of both completeness and ease of proof, we prove stronger results in the more general setting of trigraphs. This gives a comprehensive "black box" for decomposing quasi-line graphs that is not only useful for future work but also improves the complexity of some previous algorithmic results. Joint work with Maria Chudnovsky.

Optimization problems involving sparse vectors or low-rank matrices are of great importance in applied mathematics and engineering. They provide a rich and fruitful interaction between algebraic-geometric concepts and convex optimization, with strong synergies with popular techniques like L1 and nuclear norm minimization. In this lecture we will provide a gentle introduction to this exciting research area, highlighting key algebraic-geometric ideas as well as a survey of recent developments, including extensions to very general families of parsimonious models such as sums of a few permutations matrices, low-rank tensors, orthogonal matrices, and atomic measures, as well as the corresponding structure-inducing norms.Based on joint work with Venkat Chandrasekaran, Maryam Fazel, Ben Recht, Sujay Sanghavi, and Alan Willsky.

We propose a new fast algorithm for finding the global shortest path connecting two points while avoiding obstacles in a region by solving an initial value problem of ordinary differential equations (ODE's). The idea is based on the factthat the global shortest path possesses a simple geometric structure. This enables us to restrict the search in a set of feasible paths that share the same structure. The resulting search space is reduced to a finite dimensional set. We use a gradient descent strategy based on the intermittent diffusion (ID) in conjunction with the level set framework to obtain the global shortest path by solving a randomly perturbed ODE's with initial conditions.Compared to the existing methods, such as the combinatorial methods or partial differential equation(PDE) methods, our algorithm is faster and easier to implement. We can also handle cases in which obstacles shape are arbitrary and/or the dimension of the base space is three or higher.

A discussion of the Allali and Sagot (2005) paper "A New Distance for High Level RNA Secondary Structure Comparison."