Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Li, Xijun Zhu, Fangzhou Zhen, Hui-Ling Luo, Weilin Lu, Meng Huang, Yimin Fan, Zhenan Zhou, Zirui Kuang, Yufei Wang, Zhihai Geng, Zijie Li, Yang Liu, Haoyang An, Zhiwu Yang, Muming Li, Jianshu Wang, Jie Yan, Junchi Sun, Defeng Zhong, Tao Zhang, Yong Zeng, Jia Yuan, Mingxuan Hao, Jianye Yao, Jun Mao, Kun |
| author_facet | Li, Xijun Zhu, Fangzhou Zhen, Hui-Ling Luo, Weilin Lu, Meng Huang, Yimin Fan, Zhenan Zhou, Zirui Kuang, Yufei Wang, Zhihai Geng, Zijie Li, Yang Liu, Haoyang An, Zhiwu Yang, Muming Li, Jianshu Wang, Jie Yan, Junchi Sun, Defeng Zhong, Tao Zhang, Yong Zeng, Jia Yuan, Mingxuan Hao, Jianye Yao, Jun Mao, Kun |
| contents | In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. Furthermore, we introduce a training framework leveraging augmentation policies to maintain solvers' utility in dynamic environments. Besides the data generation and augmentation, our proposed approaches also include novel ML-driven policies for personalized solver strategies, with an emphasis on applications like graph convolutional networks for initial basis selection and reinforcement learning for advanced presolving and cut selection. Additionally, we detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance. Compared with traditional solvers such as Cplex and SCIP, our ML-augmented OptVerse AI Solver demonstrates superior speed and precision across both established benchmarks and real-world scenarios, reinforcing the practical imperative and effectiveness of machine learning techniques in mathematical programming solvers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_05960 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine Learning Insides OptVerse AI Solver: Design Principles and Applications Li, Xijun Zhu, Fangzhou Zhen, Hui-Ling Luo, Weilin Lu, Meng Huang, Yimin Fan, Zhenan Zhou, Zirui Kuang, Yufei Wang, Zhihai Geng, Zijie Li, Yang Liu, Haoyang An, Zhiwu Yang, Muming Li, Jianshu Wang, Jie Yan, Junchi Sun, Defeng Zhong, Tao Zhang, Yong Zeng, Jia Yuan, Mingxuan Hao, Jianye Yao, Jun Mao, Kun Artificial Intelligence In an era of digital ubiquity, efficient resource management and decision-making are paramount across numerous industries. To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. Furthermore, we introduce a training framework leveraging augmentation policies to maintain solvers' utility in dynamic environments. Besides the data generation and augmentation, our proposed approaches also include novel ML-driven policies for personalized solver strategies, with an emphasis on applications like graph convolutional networks for initial basis selection and reinforcement learning for advanced presolving and cut selection. Additionally, we detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance. Compared with traditional solvers such as Cplex and SCIP, our ML-augmented OptVerse AI Solver demonstrates superior speed and precision across both established benchmarks and real-world scenarios, reinforcing the practical imperative and effectiveness of machine learning techniques in mathematical programming solvers. |
| title | Machine Learning Insides OptVerse AI Solver: Design Principles and Applications |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2401.05960 |