Optimization Seminar

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26 September 2016
16:00-17:00

Dr. David Adjiashvili
Institute for Operations Research of ETH Zurich

Event Details

Title	Improved Approximation for Weighted Tree Augmentation with Bounded Costs (discussion seminar)
Speaker, Affiliation	Dr. David Adjiashvili, Institute for Operations Research of ETH Zurich
Date, Time	26 September 2016, 16:00-17:00
Location	HG G 19.1

Improved Approximation for Weighted Tree Augmentation with Bounded Costs (discussion seminar)

17 October 2016
16:00-17:00

Dr. Stefan Weltge
Institute for Operations Research of ETH Zurich

Event Details

Title	Extended formulations: Constructions (discussion seminar)
Speaker, Affiliation	Dr. Stefan Weltge, Institute for Operations Research of ETH Zurich
Date, Time	17 October 2016, 16:00-17:00
Location	HG G 19.1

Extended formulations: Constructions (discussion seminar)

24 October 2016
16:00-17:00

Silvia Di Gregorio
Università degli Studi di Padova, Padova, Italia

Event Details

Title	Structure of extreme functions with discrete domain
Speaker, Affiliation	Silvia Di Gregorio, Università degli Studi di Padova, Padova, Italia
Date, Time	24 October 2016, 16:00-17:00
Location	HG G 19.1

Structure of extreme functions with discrete domain

31 October 2016
16:00-17:00

Dr. Stephen Chestnut
Institute for Operations Research of ETH Zurich

Event Details

Title	Concentration (discussion seminar)
Speaker, Affiliation	Dr. Stephen Chestnut, Institute for Operations Research of ETH Zurich
Date, Time	31 October 2016, 16:00-17:00
Location	HG F 26.3
Abstract	In probability theory, a concentration inequality bounds how a random variable X deviates from its mean. These inequalities are extremely important for the study of randomized algorithms. When X is a sum of independent random variables, we can apply our old favorites like Chebyshev's and Chernoff's inequalities to bound the deviation of the sum from its mean. But, what if X is more complicated than a simple sum? Can we still prove that it concentrates? Very often, the answer is yes. We'll start the talk with some variants of the famous Johnson-Lindenstrauss Lemma and I'll show you how one concentration inequality plays a role in a fast randomized algorithm for linear least squares. Next I'll introduce some more general methods for proving concentration inequalities, with a focus on the Efron-Stein Inequality and the "Entropy Method". We'll see how these work by applying them to prove concentration for a monotone submodular function evaluated on a random set.

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