Abstract: In this talk, we consider a modification of the usual Branching Random Walk (BRW), where at the last step we give certain displacements which may be different from the increments.
Abstract: In 1971, Graham and Pollak showed that if $D_T$ is the distance matrix of a tree $T$ on $n$ nodes, then $\det(D_T)$ depends only on $n$, not $T$.
This course will start with gradient based methods in convex optimization starting with gradient descent, proximal point methods and the use of momentum and randomness.
Abstract: This talk will comprise of two parts. In the first half, I shall discuss about Reed-Muller Codes and Reed-Solomon Codes. Basically, we shall prove that Reed-Muller Codes are a subset of Reed-Solomon Codes.
Abstract: In this talk, we will explore a surprising connection between graph theory and convex geometry. We look at graphs whose edge weights are linear forms in $d$ variables.
Abstract: The advent of big data necessitated design of algorithms that could cope with it. Streaming algorithms are viable for big data because they process their input sequentially and use a small amount of working memory. In
Abstract: Given n jobs with release dates, deadlines and processing times we consider the problem of scheduling them on m parallel machines so as to minimize the total energy consumed.
Abstract: Reachability and strong-connectivity are some of the fundamental graph problems. In the static setting, both problems can be solved in O(m+n) time for any directed graph G with n vertices and m edges.