Seminars and Colloquia by Series

Topics in Ergodic Theory V: Oseledets Theorem.

Series
Dynamical Systems Working Seminar
Time
Friday, April 4, 2014 - 13:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Mikel J. de VianaGeorgia Tech
We begin the proof of Oseledets Theorem. This is part of a reading seminar geared towards understanding of Smooth Ergodic Theory. (The study of dynamical systems using at the same time tools from measure theory and from differential geometry)It should be accesible to graduate students and the presentation is informal. The first goal will be a proof of the Oseledets multiplicative ergodic theorem for random matrices. Then, we will try to cover the Pesin entropy formula, invariant manifolds, etc.

A Cubic Algorithm for Computing Gaussian Volume

Series
ACO Student Seminar
Time
Friday, April 4, 2014 - 12:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Ben CousinsGeorgia Tech
We present randomized algorithms for sampling the standard Gaussian distribution restricted to a convex set and for estimating the Gaussian measure of a convex set, in the general membership oracle model. The complexity of the integration algorithm is O*(n^3) while the complexity of the sampling algorithm is O*(n^3) for the first sample and O*(n^2) for every subsequent sample. These bounds improve on the corresponding state-of-the-art by a factor of n. Our improvement comes from several aspects: better isoperimetry, smoother annealing, avoiding transformation to isotropic position and the use of the ``speedy walk" in the analysis.

John-Nirenberg Theorem

Series
Analysis Working Seminar
Time
Friday, April 4, 2014 - 10:00 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Sergio MayorgaSchool of Mathematics
Sergio will be leading discussion and presenting 6.2: The John-Nirenberg Theorem. Stop by, we will be havIng a good Time.

Concentration Inequalities with Bounded Couplings

Series
Stochastics Seminar
Time
Thursday, April 3, 2014 - 15:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Umit IslakUniversity of Southern California
Let $Y$ be a nonnegative random variable with mean $\mu$, and let $Y^s$, defined on the same space as $Y$, have the $Y$ size biased distribution, that is, the distribution characterized by $\mathbb{E}[Yf(Y)]=\mu \mathbb{E}[f(Y^s)]$ for all functions $f$ for which these expectations exist. Under bounded coupling conditions, such as $Y^s-Y \leq C$ for some $C>0$, we show that $Y$ satisfies certain concentration inequalities around $\mu$. Examples will focus on occupancy models with log-concave marginal distributions.

Feedbackless Information Gathering on Trees

Series
Graph Theory Seminar
Time
Thursday, April 3, 2014 - 12:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Kevin CostelloUniversity of California, Riverside, CA
Suppose that each node of a rooted tree has a message that it wants to pass up the tree to the root. How can we design a protocol that guarantees all messages (eventually) reach there without being interfered with by other messages, if the nodes themselves do not know the underlying structure of the tree, or even whether their previous messages were successfully transmitted or not? I will describe (near optimal) answers to several variations of this problem, based on joint work with Marek Chrobak (UCR), Laszek Gasieniec (Liverpool) and Dariusz Kowalski (Liverpool).

Linear Algebra as a Natural Language for Special Relativity and Its Paradoxes

Series
Other Talks
Time
Wednesday, April 2, 2014 - 19:00 for 1 hour (actually 50 minutes)
Location
Clough Undergraduate Learning Center Room 144
Speaker
John de PillisUniversity of California, Riverside
Using basic linear algebra as a natural language of special relativity, and assuming very little knowledge of physics, we present a novel linear-algebraic derivation of the Lorentz transformation. Through the geometry of Minkowski diagrams, we analyze properties and paradoxes of special relativity including the Twin paradox and the bug-rivet paradox.Dr. de Pillis is a renowned cartoonist and animator, and his new book entitled Illustrated Special Relativity Through its Paradoxes is a fusion of Linear Algebra, Graphics, and Reality.

Variational Model and Imaging Applications

Series
Research Horizons Seminar
Time
Wednesday, April 2, 2014 - 12:05 for 1 hour (actually 50 minutes)
Location
Skiles 005
Speaker
Dr. KangSchool of Mathematics
This talk is an introduction to mathematical approaches to image processing: using variational approaches and PDE based method. Various problems and a few different approaches will be introduced.

Short Paths on the Voronoi Graph and the Closest Vector Problem with Preprocessing

Series
ACO Seminar
Time
Monday, March 31, 2014 - 16:05 for 1 hour (actually 50 minutes)
Location
Skiles 006
Speaker
Daniel DadushNew York University
The closest vector problem (CVP) on lattices (i.e. given an n dimensional lattice L with basis B and target point t, find a closest lattice point in L to t) is fundamental NP-hard problem with applications in coding, cryptography & optimization. In this talk, we will consider the preprocessing version of CVP (CVPP), an important variant of CVP, where we allow arbitrary time to preprocess the lattice before answering CVP queries. In breakthrough work, Micciancio & Voulgaris (STOC 2010) gave the first single exponential time algorithm for CVP under the l_2 norm based on Voronoi cell computations. More precisely, after a preprocessing step on L requiring tilde{O}(2^{2n}) time, during which they compute the Voronoi cell of L (the set of points closer to the origin than to any other point in L), they show that additional CVP queries on L (i.e. CVPP) can be solved in tilde{O}(2^{2n}) time. For our main result, we show that given the Voronoi cell V of L as preprocessing, CVP on any target t can be solved in expected tilde{O}(2^n) time. As our main technical contribution, we give a new randomized procedure that starting from any close enough lattice point to the target t, follows a path in the Voronoi graph of L (i.e. x,y in L are adjacent if x+V and y+V share a facet) to the closest lattice vector to t of expected polynomial size. In contrast, the path used by MV algorithm is only known to have length bounded by tilde{O}(2^n). Furthermore, for points x,y in L, we show that the distance between x and y in the Voronoi graph is within a factor n of ||x-y||_V (norm induced by the Voronoi cell), which is best possible. For our analysis, we rely on tools from high dimensional convex geometry. No background in convex geometry or lattices will be assumed. Time permitting, I will describe related results & open questions about paths on more general lattice Cayley graphs. Joint work with Nicolas Bonifas (Ecole Polytechnique).

TBA by Jun Lu

Series
Dissertation Defense
Time
Monday, March 31, 2014 - 15:00 for 1 hour (actually 50 minutes)
Location
Skiles 269
Speaker
Jun LuSchool of Mathematics, Georgia Tech
This thesis proposes a novel and efficient method (Method of Evolving Junctions) for solving optimal control problems with path constraints, and whose optimal paths are separable. A path is separable if it is the concatenation of finite number of subarcs that are optimal and either entirely constraint active or entirely constraint inactive. In the case when the subarcs can be computed efficiently, the search for the optimal path boils down to determining the junctions that connect those subarcs. In this way, the original infinite dimensional problem of finding the entire path is converted into a finite dimensional problem of determining the optimal junctions. The finite dimensional optimization problem is then solved by a recently developed global optimization strategy, intermittent diffusion. The idea is to add perturbations (noise) to the gradient flow intermittently, which essentially converts the ODE's (gradient descent) into a SDE's problem. It can be shown that the probability of finding the globally optimal path can be arbitrarily close to one. Comparing to existing methods, the method of evolving junctions is fundamentally faster and able to find the globally optimal path as well as a series of locally optimal paths. The efficiency of the algorithm will be demonstrated by solving path planning problems, more specifically, finding the optimal path in cluttered environments with static or dynamic obstacles.

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