[Scheduling seminar] Nicole Megow (Universität Bremen) | Learning-Augmented Online Algorithms... скачать в хорошем качестве

[Scheduling seminar] Nicole Megow (Universität Bremen) | Learning-Augmented Online Algorithms...

Keywords: Scheduling under uncertainty, Algorithms with predictions, Learning-augmented algorithms, Non-clairvoyant scheduling, Online TSP Online optimization refers to solving problems where an initially unknown input is revealed incrementally, and irrevocable decisions must be made not knowing future requests. The assumption of not having any prior knowledge about future requests seems overly pessimistic. Given the success of machine-learning methods and data-driven applications, one may expect to have access to predictions about future requests. However, simply trusting them might lead to very poor solutions, as these predictions come with no quality guarantee. In this talk, we present recent developments in the young line of research that integrates such error-prone predictions into algorithm design to break through worst-case barriers. We discuss different prediction models and algorithmic challenges with a focus on online scheduling and routing and give an outlook on network design problems. Organized by Zdenek Hanzalek (CTU in Prague), Michael Pinedo (New York University), and Guohua Wan (Shanghai Jiao Tong). Seminar's webpage: https://schedulingseminar.com/