Learning large neighborhood search policy for integer programming

• Main Authors: WU, Yaoxin, SONG, Wen, CAO, Zhiguang, ZHANG, Jie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Actor-critic algorithm; Binary decision; Integer programming problems; Neural-networks; Reinforcement learning algorithms; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/8159; https://ink.library.smu.edu.sg/context/sis_research/article/9162/viewcontent/NeurIPS_2021_learning_large_neighborhood_search_policy_for_integer_programming_Paper.pdf
• Institution: Singapore Management University

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 select a subset of variables at each step, which is reoptimized by an IP solver as the repair operator. However, the...