Data-driven algorithm design using neural networks with applications to branch-and-cut

Data-driven algorithm design is a paradigm that uses statistical and machine learning techniques to select from a class of algorithms for a computational problem an algorithm that has the best expected performance with respect to some (unknown) distribution on the instances of the problem. We build upon recent work in this line of research by introducing the idea where, instead of selecting a single algorithm that has the best performance, we allow the possibility of selecting an algorithm based on …

algorithm algorithm design algorithms applications build class computational cs.lg data data-driven design distribution instances machine machine learning machine learning techniques math.oc networks neural networks paradigm performance statistical work