Combinatorial optimization and reasoning with graph neural networks
Combinatorial optimization is a well-established area in operations research and computer
science. Until recently, its methods have focused on solving problem instances in isolation …
science. Until recently, its methods have focused on solving problem instances in isolation …
A survey of optimization-based task and motion planning: From classical to learning approaches
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 …
planning to equip robots with the autonomy to effectively reason over long-horizon, dynamic …
A survey for solving mixed integer programming via machine learning
Abstract Machine learning (ML) has been recently introduced to solving optimization
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
problems, especially for combinatorial optimization (CO) tasks. In this paper, we survey the …
On representing linear programs by graph neural networks
Learning to optimize is a rapidly growing area that aims to solve optimization problems or
improve existing optimization algorithms using machine learning (ML). In particular, the …
improve existing optimization algorithms using machine learning (ML). In particular, the …
Self-supervised primal-dual learning for constrained optimization
S Park, P Van Hentenryck - Proceedings of the AAAI Conference on …, 2023 - ojs.aaai.org
This paper studies how to train machine-learning models that directly approximate the
optimal solutions of constrained optimization problems. This is an empirical risk minimization …
optimal solutions of constrained optimization problems. This is an empirical risk minimization …
Searching large neighborhoods for integer linear programs with contrastive learning
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 …
number of combinatorial optimization problems. Recently, it has been shown that Large …
Learning to optimize: A tutorial for continuous and mixed-integer optimization
Learning to optimize (L2O) stands at the intersection of traditional optimization and machine
learning, utilizing the capabilities of machine learning to enhance conventional optimization …
learning, utilizing the capabilities of machine learning to enhance conventional optimization …
Machine learning augmented branch and bound for mixed integer linear programming
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 …
that offers a powerful modeling language for a wide range of applications. The main engine …
Taking the human out of decomposition-based optimization via artificial intelligence, Part II: Learning to initialize
I Mitrai, P Daoutidis - Computers & Chemical Engineering, 2024 - Elsevier
The repeated solution of large-scale optimization problems arises frequently in process
systems engineering tasks. Decomposition-based solution methods have been widely used …
systems engineering tasks. Decomposition-based solution methods have been widely used …
Taking the human out of decomposition-based optimization via artificial intelligence, Part I: Learning when to decompose
I Mitrai, P Daoutidis - Computers & Chemical Engineering, 2024 - Elsevier
In this paper, we propose a graph classification approach for automatically determining
whether to use a monolithic or a decomposition-based solution method. In this approach, an …
whether to use a monolithic or a decomposition-based solution method. In this approach, an …