**Speaker**: Amardeep Sarkar

**Date: **6th November, 2018

**Time: **9:15pm-10:15pm

**Abstract:**

**Prerequisites:**Basic calculus and definition of a holomorphic function in one variable.

Skip to content
## The great Picard theorem

**Abstract:**
**Prerequisites:** Basic calculus and definition of a holomorphic function in one variable.
## Algebra in Practice

**Speaker**: Sruthi Sekar
**Date:** 23rd October and 30th October, 2018.
**Time:**** **9:15pm-10:15pm
**Abstract**:
**Pre-requisites**: Basic linear algebra, elementary probability theory.
## Characterization of Normed Linear Spaces

**Prerequisites:** Familiarity with vector spaces and a little bit of metric spaces (though not necessary). The talk will be self-contained.
## The Theory of Optimal Control Problems Governed by PDE

**Prerequisites: **Basics of PDE and Functional Analysis.
**Area:** Optimal Control Theory
## Commutator of operators

**Speaker: **Paramita Pramanick
**Date: **2nd october,2018
**Time: **9:15pm-10:15pm
** **
**Abstract:**
** **
**Prerequisites:** Basic Linear Algebra and Introduction to Functional Analysis.
## Fixed point theory and some of its applications

**Date**: 18th September 2018
**Time**: 09:15 pm-10:15 pm
**Venue**: Lecture Hall I, Department of Mathematics
## Closest Unitary Matrix to a given matrix

**Speaker**: Amardeep Sarkar

**Date: **6th November, 2018

**Time: **9:15pm-10:15pm

In complex analysis, one of the well-known theorems is great Picard theorem. Roughly speaking, it says that a holomorphic function near an essential singularity takes each complex number infinitely many times except at most one. There are many ways to prove this result, and I am going to present a proof of this using method of conformal metrics.

Advertisements

Over the years, techniques from Algebra and Computational Number Theory have played an important role in fields of theoretical computer science like Cryptography, Coding Theory and Computational Complexity Theory. The blend of theory and practice is important and extremely motivated.

In this talk, I would begin by giving a quick glimpse of some common algebraic/number theoretic techniques that are used in building computer algebra systems. Then very specifically, I would move on to an application, which lies in the field of my research, Cryptography.

I will start by introducing the study of Lattices and a specific problem in Lattice that is NP-hard. Lattice based Cryptography is an extremely vibrant field, specifically as they help in building what are known as “post-quantum schemes” (i.e., Crypto-systems that would be safe even if quantum computers come into use!). I aim to give an introduction to Lattices and then describe the RSA encryption scheme, as is, followed by giving an attack to the scheme, that is based on this problem in lattice called the shortest vector problem (SVP).

**This would be a two part talk and is meant to be accessible to a general audience.

**Speaker: **Babhrubahan Bose

**Date: **16th October, 2018

**Time: **9:15pm-10:15pm

**Abstract:**

In functional analysis, we often encounter characterization of Hilbert spaces. Two nice examples are: 1) when parallelogram law holds in a Banach space and 2) when given any two vectors of the same norm in the Banach space, there exists an isometry of the space that takes one of the points to the other. Here we are asking the next natural question, i.e., when does a vector space, equipped with a metric, becomes normed linear. It is elementary to note that if the metric is translation invariant and scaling equivariant, the metric comes from a norm. In the paper that I shall present, Peter Semrl proves that if the metric is translation invariant and every one-dimensional subspace of the vector space has and isometry with the reals, then the vector space is normed linear if the dimension is 2 or more.

**Speaker: **Ramesh Chandra Sau

**Date: **9th October, 2018

**Time: **9:15pm – 10:15pm

**Abstract:** In this talk, I will give a brief introduction to the optimal control problem through real life examples. These optimal control problems play a very important role in the modern scientific world for example in Aerospace Engineering and Medical Sciences. Firstly, I would introduce weak formulation of an elliptic PDE. Then I will proceed to prove the existence and uniqueness of the solution of the optimal control problem for the same. A necessary and sufficient optimality condition will be derived.

The commutator of two operators A and B is defined as the operator AB-BA**.** In this talk we will start by proving a basic theorem about commutator of matrices : a complex matrix has trace zero if and only if it is a commutator. Then we will prove Kleinecke-Shirokov theorem.If time permits we may discuss limit of commutators and distance from a commutator to the identity.

**Speaker: **Nidhi Rathi

**Abstract: **

We will start by looking at the classical theorem in fixed point theory — Brouwer’s Fixed point theorem. Furthermore, I will talk about its combinatorial equivalent — Sperner’s lemma and its set-theoretic equivalent — KKM lemma. We will see proofs of these equivalences. Time permitting, I would love to introduce the celebrated area of ‘Fair Division’ in Game theory and see how wonderfully it is connected to the world of topology.

**Prerequisites:**

Basic Analysis and Introduction to Topology (though not necessary). The talk will be self-contained and thus I highly encourage UG students to attend it.

**Speaker: **Poornendu Kumar

**Date: **11th-September- 2018

**Time: **9:15pm-10:15pm

**Abstract: **

In various physics and chemistry experiments we get the measurements as vectors. And, theoretical considerations often suggest that these vectors should be orthogonal/orthonormal. One way to achieve this is to apply Gram orthogonalisation to obtain a new set of orthogonal vectors. But, often the orthogonal set of vectors obtained via Gram process is not close to the original set of vectors. This led to the problem of ‘orthonormalising’ a set of vectors without changing the vectors much. Put more precisely, we can see the set of vectors as a matrix (square) A and we may ask for the unitary matrix U which is closest to A (in operator norm sense).

Interestingly, the solution of this problem goes through the polar decomposition of the matrix A, which gives an answer but conceals much of the geometry. In this talk, I will present a proof of the fact that the closest unitary matrix to given matrix A is the one which is obtained in it’s polar decomposition. There are natural generalizations of this problem which I shall discuss if time permits.

**Prerequisites: **

Basic Linear Algebra

(Knowing the statement of polar decomposition would be enough.)