## Organisers:

## Time:

## Venue:

We present algorithmic applications of an approximate version of Caratheodory's theorem.

Speaker:

Gunjan Kumar, TIFR

Friday, 7 October 2016, 16:00 to 17:30

We present algorithmic applications of an approximate version of Caratheodory's theorem.

Speaker:

Anamay Tengse, TIFR

Friday, 30 September 2016, 16:00 to 17:30

The max-coloring problem is to compute a legal coloring of the vertices of a graph $G=(V,E)$ with vertex weights $w$ such that $sum^k_{i=1} max_{v \in C_i} w(v_i)$ is minimized, where $C_1, \ldots ,C_k$ are the various color classes.

Speaker:

Sagnik Mukhopadhyay, TIFR

Friday, 16 September 2016, 16:00 to 17:30

One of the basic questions in complexity theory is how the complexity of computing $k$ instances of a function relates to the complexity of computing a single instance.

Speaker:

Prerona Chatterjee, TIFR

Friday, 9 September 2016, 16:00 to 17:30

The compactness theorem states that there is a model for an infinite set S of propositional formulas, if and only if, there is a model for every finite subset of S.

Speaker:

Anand Deo, TIFR

Friday, 2 September 2016, 16:00 to 17:30

The Brownian motion is one of the most interesting and useful of all Stochastic Processes. It has an enormous range of applications ranging from physics (Einstein) to finance (starting with Bachelier).

Speaker:

Suhail Sherif, TIFR

Friday, 29 July 2016, 16:00 to 17:30

One of the famous open problems in randomized query complexity is the composition question: Is R(f o g) = Omega(R(f)R(g)) for all boolean functions f and g.

Aditya Potukuchi

Friday, 22 July 2016, 16:00 to 17:30

In this talk, I would like to attempt to give a brief glimpse at and around the recent developments related to the Cap Sets problem:

Speaker:

Anamay Tengse, TIFR

Friday, 15 July 2016, 16:00 to 17:30

In this talk, we will discuss subcubic equivalence between the following problems:

Speaker:

Tulasi mohan Molli, TIFR

Friday, 8 July 2016, 16:00 to 17:30

In this talk we will see an efficient parallel algorithm for the Bipartite Perfect Matching problem (BPM) due to Fenner, Gurjar and Thierauf. The Perfect Matching problem (PM) is to decide whether a given graph has a perfect matching.

Speaker:

Sarat Babu Moka, TIFR

Friday, 24 June 2016, 16:00 to 17:30

Let X_1, X_2, ... ,X_n be, possibly dependent, [0,1]-valued random variables. The following question is important: What is a sharp upper bound on the probability that their sum is significantly larger (or significantly smaller) than their mean?