A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP

In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy. Previous work using imitation learning indicates the feasibility of acquiring a node selection policy...

Learning Efficient Search Approximation in Mixed Integer Branch and Bound

In line with the growing trend of using machine learning to improve solving of combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming branch-and-bound tree by using a learned policy. In contrast to previous work using imitation learning, our policy is focused on learning which of a...

Node Selection Technique for Distributed Beamforming in Green Cognitive Radio Networks

The wide deployment of wireless sensor nodes for varieties of applications calls the need for distributed beamforming as a cooperative scheme to efficiently use spectrum, to improve communication range and to save precious battery power during transmission. Given that there are a large set of cooperative cognitive radio users, ensuring the...