Abstract Machine learning techniques have attracted increasing attention in learning Branch-
and-Bound (B&B) variable selection policies, but most of the existing methods lack …

Traditional expert-designed branching rules in branch-and-bound (B&B) are static, often
failing to adapt to diverse and evolving problem instances. Crafting these rules is labor …

The branch-and-bound algorithm for combinatorial optimization typically relies on a plethora
of handcraft expert heuristics, and a research direction, so-called learn-to-branch, proposes …

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 …

Combinatorial optimisation problems framed as mixed integer linear programmes (MILPs)
are ubiquitous across a range of real-world applications. The canonical branch-and-bound …

Most combinatorial optimization problems can be formulated as mixed integer linear
programming (MILP), in which branch-and-bound (B\&B) is a general and widely used …

Finding high-quality solutions to mixed-integer linear programming problems (MILPs) is of
great importance for many practical applications. In this respect, the refinement heuristic …

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

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

In many operational applications, it is necessary to routinely find, within a very limited time
window, provably good solutions to challenging mixed-integer linear programming (MILP) …