Task and motion planning (TAMP) integrates high-level task planning and low-level motion
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …

Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …

Abstract Integer Linear Programs (ILPs) are powerful tools for modeling and solving a large
number of combinatorial optimization problems. Recently, it has been shown that Large …

Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …

Abstract Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free
paths for a team of agents in a common environment. MAPF is NP-hard to solve optimally …

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 …

Abstract Mixed Integer Linear Programming (MILP) is a pillar of mathematical optimization
that offers a powerful modeling language for a wide range of applications. The main engine …

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 …

The latest two-stage optimization framework based on graph neural network (GNN) and
large neighborhood search (LNS) is the most popular framework in solving large-scale …

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 …