Seminar

Title: Stability Yields a PTAS for k-Median and k-Means Clustering

Abstract: Previously, we have seen a variety of approximation methods for k-median, and last week we saw

Title: Why Random Reshuffling Beats Stochastic Gradient Descent

Abstract:  Over the first few lectures in the seminar we studied the Stochastic Gradient Descent (SGD) method

Title: Solving DNN Relaxations of the Quadratic Assignment Problem with ADMM and Facial Reduction

Abstract:

The quadratic assignment problem, QAP, has many important applications ranging from the planning of building locations of a university, to the positioning of modules on a computer chip (VLSI design), to the design of keyboards.

Title: Approximation Algorithms for Distributionally Robust Stochastic Optimization

Abstract:

Two-stage stochastic optimization is a widely-used framework for modeling uncertainty, where we have a probability distribution over possible realizations of the data, called scenarios, and decisions are taken in two stages: we make first-stage decisions knowing only the underlying distribution and before a scenario is realized, and may take additional second-stage recourse actions after a scenario is realized.

Title: Approximate Clustering without the Approximation

Abstract: Previously in this CombOpt Reading Group series, we have discussed improving approximation factors for particular distance-based objective functions in clustering problems.

Title:  2-universality of random graphs.

Abstract: For a family of graphs F, a graph G is F-universal if G contains every graph in F as a (not necessarily induced) subgraph.

Title: Hamiltonian Descent Methods

Abstract: In this talk, I will present a paper on the Hamiltonian descent methods

Title: Approximating k-Median via Pseudo-Approximation

Abstract: In the last two talks we saw two approaches for approximating k-median.

Title: Vertex Minors

Abstract:

I will give an introduction to vertex-minors of graphs. At first sight vertex minors are a bit peculiar but they are closely related to graph minors and possibly even easier to work with.

Title: Some problems on the size Ramsey numbers

Abstract: For given simple graphs $G_1$ and $G_2$, the size Ramsey number $\hat{R}(G_1,G_2)$ is the smallest positive integer $m$,