Apr 25

Approximate groups and applications to the growth of groups 
Matthew Tointon 
Given a set A in a group, write $A^n$ for the set of all products $x_1…x_n$ with each $x_i$ belonging to A. Roughly speaking, a set A for which $A^2$ is “not much larger than” A is called an “approximate group”. The “growth” of A, on the other hand, refers to the behaviour of $ 
A^n 
$ as n tends to infinity. Both of these have been fruitful areas of study, with applications in various branches of mathematics. 
Remarkably, understanding the behaviour of approximate groups allows us to convert information $A^k$ for some single fixed k into information about the sequence $A^n$ as n tends to infinity, and in particular about the growth of A.
In this talk I will present various results describing the algebraic structure of approximate groups, and then explain how to use these to prove new results about growth. In particular, I will describe work with Romain Tessera in which we show that if $ 
A^k 
$ is bounded by $Mk^D$ for some given M and D then, provided k is large enough, 
A^n 
is bounded by $M’n^D$ for every n > k, with M’ depending only on M and D. This verifies a conjecture of Itai Benjamini. 

May 23

Quantitative multiple recurrence for two and three transformations. 
Sebastián Donoso (University of O'higgins) 
In this talk I will provide some counterexamples for quantitative multiple recurrence problems for systems with more than one transformation. For instance, I will show that there exists an ergodic system with two commuting transformations such that for every there exists such that
for every $n \in \mathbb{N}$.
The construction of such a system is based on the study of ``big’’ subsets of and satisfying combinatorial properties.
This a joint work with Wenbo Sun.

Jun 13

Mixing Time Estimation in Reversible Markov Chains from a Single Sample Path 
Aryeh Kontorovich (BGU) 
We propose a procedure (the first of its kind) for computing a fully
datadependent interval that traps the mixing time t_mix of a finite
reversible ergodic Markov chain at a prescribed confidence level. The
interval is computed from a single finitelength sample path from the
Markov chain, and does not require the knowledge of any parameters of
the chain. This stands in contrast to previous approaches, which
either only provide point estimates, or require a reset mechanism, or
additional prior knowledge.
The interval is constructed around the relaxation time t_relax, which
is strongly related to the mixing time, and the width of the interval
converges to zero roughly
at a sqrt{n} rate, where n is the length of the sample path. Upper and
lower bounds are given on the number of samples required to achieve
constantfactor multiplicative accuracy. The lower bounds indicate
that, unless further restrictions are placed on the chain, no
procedure can achieve this accuracy level before seeing each state at
least \Omega(t_relax) times on the average. Finally, future
directions of research are identified.
Join work with Daniel Hsu and Csaba Szepesvári

Jun 20

On weak nets 
Amir Yehudayoff (Technion) 
We will discuss an equivalence between the existence of weak nets and the Radon number, in an abstractly convex space. Based on work with Shay Moran.

Jun 27

Furstenbergʼs conjecture on intersections of times 2 and times 3invariant sets 
Meng Wu (HUJI) 
In 1969, H. Furstenberg proposed a conjecture on dimension of intersections of and invariant sets.
We will present some of the steps involved in a recent solution of this conjecture.

Wed, Jul 12, 11:00–12:00

Definition of Chaos In Sense Of Devaney 
Basheer Abu Khalil 
Devaney’s definition of chaos is one of the most popular and widely known. A continuous map f from a compact
metric space (X, d) to itself is chaotic in the sense of Devaney if
(1) f is transitive,
(2) the set of all periodic points of f is dense in X, and
(3) f has sensitive dependence on initial conditions.
we will show that (1) and (2) imply (3) in Devaney’s definition in any metric space. We will show (1) and (3) do not imply (2)
and (2) ; (3) do not imply (1).
We will also show that for continuous maps on an interval in R; transitivity implies that the set of periodic points
is dense. It follows that transitivity implies chaos, and we will give some examples to note that there are no other trivialities in Devaney’s definition when restricted to interval.
