Seminars and Colloquia by Series

On graphs decomposable into induced matchings of linear sizes

Series
ACO Student Seminar
Time
Friday, April 1, 2016 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Hao HuangEmory University
A Ruzsa-Szemeredi graph is a graph on n vertices whose edge set can be partitioned into induced matchings of size cn. The study of these graphs goes back more than 35 years and has connections with number theory, combinatorics, complexity theory and information theory. In this talk we will discuss the history and some recent developments in this area. In particular, we show that when c>1/4, there can be only constantly many matchings. On the other hand, for c=1/4, the maximum number of induced matchings is logarithmic in n. This is joint work with Jacob Fox and Benny Sudakov.

Tropical Geometry, Sandpile Groups, and Bijections for Spanning Trees

Series
ACO Student Seminar
Time
Friday, March 18, 2016 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Chi Ho YuenGeorgia Tech
This talk aims to give a glimpse into the theory of divisors on graphs in tropical geometry, and its recent application in bijective combinatorics. I will start by introducing basic notions and results of the subject. Then I will mention some of its connections with other fields in math. Finally I will talk about my own work on how tropical geometry leads to an unexpectedly simple class of bijections between spanning trees of a graph and its sandpile group.

Distributionally Robust Stochastic Programming with Wasserstein Distance

Series
ACO Student Seminar
Time
Friday, March 11, 2016 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 256
Speaker
Rui GaoGeorgia Tech
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.

High dimensional sampling in metabolic networks

Series
ACO Student Seminar
Time
Friday, March 4, 2016 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 256
Speaker
Ben CousinsGeorgia Tech
I will give a tour of high-dimensional sampling algorithms, both from a theoretical and applied perspective, for generating random samples from a convex body. There are many well-studied random walks to choose from, with many of them having rigorous mixing bounds which say when the random walk has converged. We then show that the techniques from theory yield state-of-the-art algorithms in practice, where we analyze various organisms by randomly sampling their metabolic networks.This work is in collaboration with Ronan Fleming, Hulda Haraldsdottir ,and Santosh Vempala.

Strong reductions for extended formulations

Series
ACO Student Seminar
Time
Friday, February 26, 2016 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Aurko RoyGeorgia Tech
We generalize the existing reduction mechanism due to Braun, Pokutta and Zink (2014)for linear programming problems and semidefinite programming problems in two ways 1) relaxing the requirement of affineness2) extending to fractional optimization problems As applications we prove several new LP-hardness and SDP-hardnessresults, e.g., for the (non-uniform) Sparsest Cut problem with bounded treewidth on the supply graph, the Balanced Separator problem with bounded treewidth onthe demand graph, the Max Cut problem and the Matching problem on 3-regular graphs.We also provide a new, very strong Lasserre integrality gapfor the Independent Set problem, which is strictly greater than thebest known LP approximation, showing that the Lasserre hierarchydoes not always provide the tightest SDP relaxation.Joint work with Gabor Braun and Sebastian Pokutta.

On the Widom-Rowlinson occupancy fraction in regular graphs

Series
ACO Student Seminar
Time
Friday, February 5, 2016 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Emma CohenGeorgia Tech
We consider the Widom-Rowlinson model of two types of interacting particles on $d$-regular graphs. We prove a tight upper bound on the occupancy fraction: the expected fraction of vertices occupied by a particle under a random configuration from the model. The upper bound is achieved uniquely by unions of complete graphs on $d+1$ vertices. As a corollary we find that $K_{d+1}$ also maximizes the normalized partition function of the Widom-Rowlinson model over the class of $d$-regular graphs, proving a conjecture of Galvin. Joint work with Will Perkins and Prasad Tetali.

Sparsified Cholesky and Multigrid Solvers for Connection Laplacians

Series
ACO Student Seminar
Time
Friday, January 15, 2016 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Richard PengGeorgia Tech
We introduce the sparsified Cholesky and sparsified multigrid algorithms for solving systems of linear equations. These algorithms accelerate Gaussian elimination by sparsifying the nonzero matrix entries created by the elimination process.We use these new algorithms to derive the first nearly linear time algorithms for solving systems of equations in connection Laplacians, a generalization of Laplacian matrices that arise in many problems inimage and signal processing.We also prove that every connection Laplacian has a linear sized approximate inverse. This is an LU factorization with a linear number of nonzero entries that is a strong approximation of the originalmatrix. Using such a factorization one can solve systems of equations in a connection Laplacian in linear time. Such a factorization was unknown even for ordinary graph Laplacians.Joint work with Rasmus Kyng, Yin Tat Lee, Sushant Sachdeva, and Daniel Spielman. Manuscript at http://arxiv.org/abs/1512.01892.

Sampling weighted perfect matchings on the square octagon lattice

Series
ACO Student Seminar
Time
Friday, December 4, 2015 - 13:00 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Prateek BhaktaGeorgia Tech
We consider perfect matchings of the square-octagon lattice, also known as``fortresses.'' There is a natural local Markov chain on the setof perfect matchings that is known to be ergodic. However, unlike Markov chains for sampling perfect matchings on the square and hexagonallattices, corresponding to domino and lozenge tilings, respectively, the seemingly relatedMarkov chain on the square-octagon lattice appears to converge slowly. Tounderstand why, we consider a weighted version of the problem.As with domino and lozenge tilings, it will be useful to view perfectmatchings on the square-octagon lattice in terms of sets of paths and cycleson a corresponding lattice region; here, the paths and cycles lie on theCartesian lattice and are required to turn left or right at every step. Forinput parameters $\lambda$ and $\mu$, we define the weight of a configurationto be $\lambda^{\abs{E(\sigma)}} \mu^{\abs{V(\sigma)}},$ where $E(\sigma)$ isthe total number of edges on the paths and cycles of $\sigma$ and $V(\sigma)$is the number of vertices that are not incident to any of the paths or cyclesin $\sigma$. Weighted paths already come up in the reduction from perfectmatchings to turning lattice paths, corresponding to the case when $\lambda=1$and $\mu = 2$.First, fixing $\mu=1$, we show that there are choices of~$\lambda$ for whichthe chain converges slowly and another for which it is fast, suggesting a phasechange in the mixing time. More precisely,the chain requires exponential time (in the size of the lattice region) when$\lambda < 1/(2\sqrt{e})$ or $\lambda >2\sqrt{e}$, while it is polynomially mixingat $\lambda = 1$. Further, we show that for $\mu>1$, the Markov chain $\m$ is slowly mixingwhen $\lambda < \sqrt{\mu}/(2\sqrt{e})$ or $\lambda > 2\mu\sqrt{e}$. These arethe first rigorous proofs explaining why the natural local Markov chain can beslow for weighted fortresses or perfect matchings on thesquare-octagon lattice.

A Market for Scheduling, with Applications to Cloud Computing

Series
ACO Student Seminar
Time
Friday, November 20, 2015 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Sadra YazdanbodGeorgia Tech
We present a market for allocating and scheduling resources to agents who have specified budgets and need to complete specific tasks. Two important aspects required in this market are: (1) agents need specific amounts of each resource to complete their tasks, and (2) agents would like to complete their tasks as soon as possible. In incorporating these aspects, we arrive at a model that deviates substantially from market models studied so far in economics and theoretical computer science. Indeed, all known techniques developed to compute equilibria in markets in the last decade and half seem not to apply here.We give a polynomial time algorithm for computing an equilibrium using a new technique that is somewhat reminiscent of the ''ironing" procedure used in the characterization of optimal auctions by Myerson. This is inspite of the fact that the set of equilibrium prices could be non-convex; in fact it could have ''holes''. Our market model is motivated by the cloud computing marketplace. Even though this market is already huge and is projected to grow at a massive rate, it is currently run in an ad hoc manner.Joint work with: Nikhil Devanur, Jugal Garg, Ruta Mehta, Vijay V. Vazirani

On the linear span of lattice points in a parallelepiped

Series
ACO Student Seminar
Time
Friday, November 13, 2015 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Marcel CelayaGeorgia Tech
We find a good characterization for the following problem: Given a rational row vector c and a lattice L in R^n which contains the integer lattice Z^n, do all lattice points of L in the half-open unit cube [0,1)^n lie on the hyperplane {x in R^n : cx = 0}? This work generalizes a theorem due to G. K. White, which provides sufficient and necessary conditions for a tetrahedron in R^3 with integral vertices to have no other integral points. Our approach is based on a novel proof of White's result using number-theoretic techniques due to Morrison and Stevens. In this talk, we illustrate some of the ideas and describe some applications of this problem.

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