Saturday, October 7, 2017

Modern Distributed Optimization

Matt Adereth talks about the Black-box optimization techniques, what’s actually going on inside of these black-boxes and discusses an idea of how they can be used to solve problems today. He deep dives into a few of the most popular ones, such as Distributed Nelder-Mead and Bayesian Optimization, and discusses their trade-offs.
The video of this presentation can be found in [1]. The title is a bit misleading: the talk is really only about two algorithms for unconstrained derivative-free optimization (a small subset of the total optimization field).

For a reference, see the excellent review paper [2].
  1. Modern Distributed Optimization,
  2. Rios, L.M. & Sahinidis, N.V., Derivative-free optimization: a review of algorithms and comparison of software implementations, J Glob Optim (2013) 56: 1247,

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