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 to Solve Quadratic Unconstrained Binary Optimization in a Classification Way
Authors: Ming Chen, Jie Chun, Shang Xiang, Luona Wei, Yonghao Du, Qian Wan, Yuning Chen, Yingwu Chen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the effectiveness of the proposed VCM and GST. A well-trained VCM can directly generate near-optimal solutions for QUBO within milliseconds and exhibit remarkable generalization capabilities across both instance sizes and data distributions. For example, a VCM trained on instances of size 10 can produce near-optimal results for instances of size 7,000 in milliseconds. |
| Researcher Affiliation | Academia | Ming Chen1 , Jie Chun 1 , Shang Xiang 2, Luona Wei3, Yonghao Du1, Qian Wan4, Yuning Chen1 , Yingwu Chen1 1College of Systems Engineering, National University of Defense Technology 2School of Public Administration, Xiangtan University 3College of Electronics and Information Engineering, South-Central Minzu University 4National Engineering Research Center of Educational Big Data, Central China Normal University |
| Pseudocode | Yes | Algorithm 1 The Greedy-guided Self Trainer; Algorithm 2 The Batch Greedy Flip (BGF) algorithm |
| Open Source Code | Yes | 3The code is available at https://github.com/cmself100/VCM-QUBO. |
| Open Datasets | Yes | The datasets used in our experiments include generated instances (G), benchmarks (B), and well-known instances (P)... The B set is B2500(10) consisting of ten ORLIB instances of size 2500 [45]. The P set includes 21 very-large instances [46] including P3000(5), P4000(5), P5000(5), P6000(3), and P7000(3). |
| Dataset Splits | Yes | The datasets used in our experiments include generated instances (G), benchmarks (B), and well-known instances (P)... VCM is trained for 100 epochs under four sets of small-size instances (with 10, 20, 50, and 100 variables)... We use the B set and P set to validate the performance of the trained VCMs. |
| Hardware Specification | Yes | Experiments were run on an NVIDIA Ge Force RTX 3090 and an Intel i9-9900K CPU with 64GB RAM and Ubuntu 18.04 using Pytorch 1.90 in a Python 3.7 environment. |
| Software Dependencies | Yes | Ubuntu 18.04 using Pytorch 1.90 in a Python 3.7 environment. |
| Experiment Setup | Yes | Following the Parameters Study (see Appendix F), the default values for VCM parameters are set as h = 128, α = 4, and d = 40. VCM is trained for 100 epochs under four sets of small-size instances (with 10, 20, 50, and 100 variables) with a batch size of 512, resulting in 400 VCMs. Each model is initialized with Xavier initialization [47], and the Adam optimizer is applied with a 10 4 learning rate and 0.975 decay factor. |