Learning large neighborhood search policy for integer programming
We propose a deep reinforcement learning (RL) method to learn large neighborhood search
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …
Reinforcement learning for integer programming: Learning to cut
Integer programming is a general optimization framework with a wide variety of applications,
eg, in scheduling, production planning, and graph optimization. As Integer Programs (IPs) …
eg, in scheduling, production planning, and graph optimization. As Integer Programs (IPs) …
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 …
A general large neighborhood search framework for solving integer linear programs
This paper studies how to design abstractions of large-scale combinatorial optimization
problems that can leverage existing state-of-the-art solvers in general-purpose ways, and …
problems that can leverage existing state-of-the-art solvers in general-purpose ways, and …
Learning a large neighborhood search algorithm for mixed integer programs
N Sonnerat, P Wang, I Ktena, S Bartunov… - arXiv preprint arXiv …, 2021 - arxiv.org
Large Neighborhood Search (LNS) is a combinatorial optimization heuristic that starts with
an assignment of values for the variables to be optimized, and iteratively improves it by …
an assignment of values for the variables to be optimized, and iteratively improves it by …
Learning to branch in mixed integer programming
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 …
cycles of parameter tuning and offline experimentation on an extremely heterogeneous …
Graph learning assisted multi-objective integer programming
Objective-space decomposition algorithms (ODAs) are widely studied for solving multi-
objective integer programs. However, they often encounter difficulties in handling scalarized …
objective integer programs. However, they often encounter difficulties in handling scalarized …
Learning primal heuristics for mixed integer programs
This paper proposes a novel primal heuristic for Mixed Integer Programs, by employing
machine learning techniques. Mixed Integer Programming is a general technique for …
machine learning techniques. Mixed Integer Programming is a general technique for …
Solving mixed integer programs using neural networks
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 …
developed with decades of research to solve large-scale MIP instances encountered in …
Learning to schedule heuristics in branch and bound
Primal heuristics play a crucial role in exact solvers for Mixed Integer Programming (MIP).
While solvers are guaranteed to find optimal solutions given sufficient time, real-world …
While solvers are guaranteed to find optimal solutions given sufficient time, real-world …