Aras Selvi: Convex Maximization via Adjustable Robust Optimization

Convex Maximization over a convex set is a very hard problem, even if P = NP. Reformulating this problem as an optimization problem under uncertainty allows us to 'transfer' the hardness of the problem from its non-convexity to the uncertainty of the new problem. The equivalent uncertain optimization problem can be relaxed tightly by using Adjustable Robust Optimization techniques. Our numerical experiments show that we can approximate large-scale problems swiftly with tight bounds for these problems. See our paper here: http://www.optimization-online.org/DB_HTML/2020/07/7881.html ~ Connect with the Computational Optimisation Group at Imperial College London online... Subscribe to the CogImperial YouTube channel for more research related content: https://www.youtube.com/channel/UCXRdjQRm9XfZj2c4XW1xpzg/ Reach out to the presenter: https://www.linkedin.com/in/arasselvi/ Follow us on Twitter for news about our research group: https://twitter.com/CogImperial​ Visit our official site for more information: https://optimisation.doc.ic.ac.uk/​