Fast Solving Complete 2000-Node Optimization Using Stochastic-Computing Simulated Annealing
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866912976337895424 |
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| author | Katsuki, Kota Shin, Duckgyu Onizawa, Naoya Hanyu, Takahiro |
| author_facet | Katsuki, Kota Shin, Duckgyu Onizawa, Naoya Hanyu, Takahiro |
| contents | In this paper, we evaluate stochastic-computing simulated annealing (SC-SA) for solving large-scale combinatorial optimization problems. SC-SA is designed using stochastic computing, where the computatoin is reazlied using random bitstream, resulting in fast converging to the global minimum energy of the problems. The proposed SC-SA is compared with a typical SA and existing simulated-annealing (SA) processors on the maximum cut (MAX-CUT) problems, such as Gset that is a benchmark for SA. The simulation results show that SC-SA realizes a few orders of magnitude faster than a typical SA. In addition, SC-SA achieves better MAX-CUT scores than other existing methods on K2000 that is a complete 2000-node optimization problem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20197 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Fast Solving Complete 2000-Node Optimization Using Stochastic-Computing Simulated Annealing Katsuki, Kota Shin, Duckgyu Onizawa, Naoya Hanyu, Takahiro Optimization and Control In this paper, we evaluate stochastic-computing simulated annealing (SC-SA) for solving large-scale combinatorial optimization problems. SC-SA is designed using stochastic computing, where the computatoin is reazlied using random bitstream, resulting in fast converging to the global minimum energy of the problems. The proposed SC-SA is compared with a typical SA and existing simulated-annealing (SA) processors on the maximum cut (MAX-CUT) problems, such as Gset that is a benchmark for SA. The simulation results show that SC-SA realizes a few orders of magnitude faster than a typical SA. In addition, SC-SA achieves better MAX-CUT scores than other existing methods on K2000 that is a complete 2000-node optimization problem. |
| title | Fast Solving Complete 2000-Node Optimization Using Stochastic-Computing Simulated Annealing |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2603.20197 |