Abstract: A graph is `pseudorandom' if it appears random according to certain statistics. Recently, Conlon, Fox and Zhao proved a counting lemma, counting small graphs in $\varepsilon$-regular subgraphs of sparse pseudorandom graphs.
Abstract: We consider the network RM problem with customer choice, which incorporates customer purchase behavior as a function of the offered products. The optimization problem is a stochastic dynamic program and is intractable.
Abstract: Regularity is a notion of “pseudorandomness” that allows one to decompose a given object into a collection of simpler objects which appear random according to certain statistics.
Abstract: Processor sharing models occur in a wide variety of situations for example in models of internet bottlenecks. They are good models for bandwidth sharing as well as being solutions to NUM for logarithmic utilities.
Abstract: Consider an irreducible continuous time Markov chain with a finite or a countably infinite number of states and admitting a unique stationary probability distribution.
Abstract: We are confronted with very high dimensional data sets. As a result, methods of dealing with high dimensional data have become prominent. One geometrically motivated approach for analyzing data is called manifold learning.
Abstract: A Kakeya set is a subset of [image: F^n], where [image: F] is a finite field of [image: q] elements, that contains a line in every direction. What can we say about the size of this set? How large the size of the set must be?
