Mathieu Besançon is a researcher at the Zuse Institute Berlin, in the AI in Society, Science, and Technology department, associated with the MODAL-SynLab project and a member of the MATH+ Berlin Mathematics Research Center.
His research interests span solution methods and software in MI(N)LP and convex optimization and in particular the SCIP framework and Frank-Wolfe related approaches.
We present Scylla, a primal heuristic for mixed-integer optimization. It uses matrix-free approximate LP solving with specialized termination criteria and parallelized fix-and-propagate procedures blended with feasibility pump-like objective updates. Besides the presentation of the method and results, we will go over lessons learned on experimentation and implementation tricks including overhead reduction, asynchronous programming, and interfacing with natively-compiled libraries.
In this talk, we will present the JuliaCon proceedings, the purpose, scope, and target audience of this venue. The proceedings are a community-driven initiative to publish articles of interest to the research and developer communities gathered by JuliaCon, they do not require application processing fees nor a paywall on article, making both producing and accessing the articles possible for all. We will then give a quick tour of the reviewing and publication process which happen transparently in