Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning Large Neighborhood Search Policy for Integer Programming
Authors: Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments in this section on four NP-hard benchmark problems: Set Covering (SC), Maximal Independent Set (MIS), Combinatorial Auction (CA) and Maximum Cut (MC), which are widely used in existing works. |
| Researcher Affiliation | Collaboration | Yaoxin Wu SCALE@NTU Corp Lab Nanyang Technological University, Singapore EMAIL Wen Song Shandong University Qingdao, China EMAIL Zhiguang Cao Singapore Institute of Manufacturing Technology A*STAR, Singapore EMAIL Jie Zhang Nanyang Technological University Singapore EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | Our code is available.3 [Footnote 3: https://github.com/WXY1427/Learn-LNS-policy] |
| Open Datasets | Yes | We generate SC instances with 1000 columns and 5000 rows following the procedure in [42]. MIS instances are generated following [43], where we use the Erd os-Rényi random graphs with 1500 nodes and set the affinity number to 4. CA instances with 2000 items and 4000 bids are generated according to arbitrary relationships in [44]. MC instances are generated according to Barabasi-Albert random graph models [45], with average degree 4, and we adopt graphs of 500 nodes. |
| Dataset Splits | Yes | For each problem type, we generate 100, 20, 50 instances for training, validation, and testing. |
| Hardware Specification | Yes | We run all experiments on an Intel(R) Xeon(R) E5-2698 v4 2.20GHz CPU. |
| Software Dependencies | Yes | We use the state-of-the-art open source IP solver SCIP (v6.0.1) [46] as the repair operator... Here we evaluate its performance by leveraging Gurobi (v9.0.3) [47] as the repair operator. |
| Experiment Setup | Yes | For each problem, we train 200 iterations, during each we randomly draw M =10 instances. We set the training step limit T=50, 50, 70, 100 for SC, MIS, CA and MC respectively. The time limit for repair at each step is 2 seconds, unless stated otherwise. We use ϵ=0.2 for probability clipping. For the Q-actor-critic algorithm, we set the length of the experience replay TM, the number of updating the network U=4 and the batch size B=TM/U. We set the discount factor γ=0.99 for all problems, and use Adam optimizer with learning rate 1 10 4. |