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Timely survey on applying machine learning to solve complex combinatorial optimization problems over networks

Effective use of networked resources requires the ability to solve complex large-scale optimization problems fast while accounting for many input variables and performance requirements, such as end-to-end latency. Advancing beyond heuristic approaches, we begin with surveying the current state of applied machine learning to solve complex combinatorial optimization problems over networks. In our IEEE Access article titled “Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking”, we qualitatively analyse existing learning approaches and applications in the networking domain. Full abstract is as follows:


“Existing approaches to solving combinatorial optimization problems on graphs suffer from the need to engineer each problem algorithmically, with practical problems recurring in many instances. The practical side of theoretical computer science, such as computational complexity, then needs to be addressed. Relevant developments in machine learning research on graphs are surveyed for this purpose. We organize and compare the structures involved with learning to solve combinatorial optimization problems, with a special eye on the telecommunications domain and its continuous development of live and research networks.”


This work was done by Natalia Vesselinova (RISE), Rebecca Steinert (RISE), Daniel Felipe Perez-Ramirez (RISE) and Magnus Boman (KTH).