Primal heuristics are important for solving mixed integer linear programs, because they find
feasible solutions that facilitate branch and bound search. A prominent group of primal …

This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing
machine learning techniques. Mixed Integer Programming is a general technique for …

Mixed Integer Programming (MIP) solvers rely on an array of sophisticated heuristics
developed with decades of research to solve large-scale MIP instances encountered in …

The expressive and computationally inexpensive bipartite Graph Neural Networks (GNN)
have been shown to be an important component of deep learning based Mixed-Integer …

Branch-and-bound approaches in integer programming require ordering portions of the
space to explore next, a problem known as node comparison. We propose a new siamese …

Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …

Finding a better feasible solution in a shorter time is an integral part of solving Mixed Integer
Programs. We present a post-hoc method based on Neural Diving to build heuristics more …

The design of strategies for branching in Mixed Integer Programming (MIP) is guided by
cycles of parameter tuning and offline experimentation on an extremely heterogeneous …

Many real-world problems can be efficiently modeled as Mixed Integer Programs (MIPs) and
solved with the Branch-and-Bound method. Prior work has shown the existence of MIP …

Abstract Branch and Bound (B&B) is the exact tree search method typically used to solve
Mixed-Integer Linear Programming problems (MILPs). Learning branching policies for MILP …