Events

Matchings, permanents and their random approximations

Abstract:

The number of matchings in graphs, appears frequently in applications: the number of possible matchings of applicants to open positions, the monomer-dimer model in statistical mechanics.

Linear Programming Relaxations for Steiner Trees

Abstract:

Given a set T of terminal vertices in an undirected weighted graph, the Steiner tree problem is to find the cheapest tree containing T using possibly some other vertices of the graph. 

Symmetry in Integer Programming

Abstract:

Symmetry has long been considered a curse in integer linear programming, and auxiliary (often extended) formulations are sought to reduce the amount of symmetry in an integer linear programming (ILP) formulation.

Quantum property testing for sparse graphs

Abstract:

In property testing, the goal is to determine whether an object has a certain property or is far from having that property.

Forbidden Configurations: a survey

Abstract:

Elliptic Curve Cyptography: The Serpentine Course of a Paradigm Shift

Abstract:

Elliptic curve cryptography (ECC) was invented by Neal Koblitz and Victor Miller in 1985. However, for many years its security was viewed with suspicion by cryptographers.

Multivariate stable polynomials

Abstract:

Univariate polynomials with only real roots -- while special -- do occur often enough that their properties lead to interesting conclusions in diverse areas.

Risk-Averse Stochastic Optimization: Models and Algorithms

Abstract:

Stochastic optimization problems provide a means to model uncertainty in the input data, where the uncertainty is modeled by a probability distribution over the possible realizations of the data.

Positive Polynomials Over Equality Constrained Unbounded Sets

Abstract:

A simple yet powerful algebraic connection between the set of polynomials that are non-negative on a given closed domain and the set of polynomials that are non-negative on the intersection of the same domain and the zero set of a g

Convex relaxation for the clique, biclique and clustering problems

Abstract:

We consider the clique, biclique, and clustering problems in the case that the problem instance consists of a clique, biclique, or perfectly clustered data plus some noisy data.