论文标题
一个通用的大型邻里搜索框架，用于解决整数线性程序
A General Large Neighborhood Search Framework for Solving Integer Linear Programs
论文作者
论文摘要
本文研究了针对大规模组合优化问题的数据驱动算法设计的策略，该问题可以利用一般方式利用现有的最新求解器。目的是达成新方法，这些方法可以在墙上锁定时间内可靠地超过现有的求解器。我们专注于解决整数程序，并在大型邻里搜索（LNS）范式中进行方法，迭代地选择了一部分变量以优化，同时将其余固定固定。 LNS的吸引力是它可以轻松地将任何现有求解器用作子例程，因此可以继承精心设计的启发式或完整方法及其软件实现的好处。我们表明，可以使用模仿和加强学习技术学习一个好的邻里选择者。通过对界限优化的广泛经验验证，我们证明，与最新的商业求解器（如Gurobi）相比，LNS框架的表现可以大大优于。
This paper studies a strategy for data-driven algorithm design for large-scale combinatorial optimization problems that can leverage existing state-of-the-art solvers in general purpose ways. The goal is to arrive at new approaches that can reliably outperform existing solvers in wall-clock time. We focus on solving integer programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic or complete approaches and their software implementations. We show that one can learn a good neighborhood selector using imitation and reinforcement learning techniques. Through an extensive empirical validation in bounded-time optimization, we demonstrate that our LNS framework can significantly outperform compared to state-of-the-art commercial solvers such as Gurobi.
