Abstract Large Neighborhood Search (LNS) heuristics are among the most powerful but also expensive for mixed integer programs (MIP). Ideally, a solver adaptively concentrates its limited computational budget by learning which LNS work best MIP problem at hand. To this end, introduces Adaptive (ALNS) MIP, primal heuristic that acts as framework eight popular such Local Branching and Relaxation I...

We introduce a mixed integer program (MIP) for assigning importance scores to each neuron in deep neural network architectures which is guided by the impact of their simultaneous pruning on main learning task network. By carefully devising objective function MIP, we drive solver minimize number critical neurons (i.e., with high score) that need be kept maintaining overall accuracy trained Furth...

Concave Mixed-Integer Quadratic Programming is the problem of minimizing a concave quadratic polynomial over the mixed-integer points in a polyhedral region. In this work we describe two algorithms that find an -approximate solution to a Concave Mixed-Integer Quadratic Programming problem. The running time of the proposed algorithms is polynomial in the size of the problem and in 1/ , provided ...