{"ID":2826053,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.18980","arxiv_id":"2512.18980","title":"OPBO: Order-Preserving Bayesian Optimization","abstract":"Bayesian optimization is an effective method for solving expensive black-box optimization problems. Most existing methods use Gaussian processes (GP) as the surrogate model for approximating the black-box objective function, it is well-known that it can fail in high-dimensional space (e.g., dimension over 500). We argue that the reliance of GP on precise numerical fitting is fundamentally ill-suited in high-dimensional space, where it leads to prohibitive computational complexity. In order to address this, we propose a simple order-preserving Bayesian optimization (OPBO) method, where the surrogate model preserves the order, instead of the value, of the black-box objective function. Then we can use a simple but effective OP neural network (NN) to replace GP as the surrogate model. Moreover, instead of searching for the best solution from the acquisition model, we select good-enough solutions in the ordinal set to reduce computational cost. The experimental results show that for high-dimensional (over 500) black-box optimization problems, the proposed OPBO significantly outperforms traditional BO methods based on regression NN and GP. The source code is available at https://github.com/pengwei222/OPBO.","short_abstract":"Bayesian optimization is an effective method for solving expensive black-box optimization problems. Most existing methods use Gaussian processes (GP) as the surrogate model for approximating the black-box objective function, it is well-known that it can fail in high-dimensional space (e.g., dimension over 500). We argu...","url_abs":"https://arxiv.org/abs/2512.18980","url_pdf":"https://arxiv.org/pdf/2512.18980v1","authors":"[\"Wei Peng\",\"Jianchen Hu\",\"Kang Liu\",\"Qiaozhu Zhai\"]","published":"2025-12-22T02:45:41Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"eess.SY\"]","methods":"[]","has_code":false,"code_links":[{"ID":605717,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2826053,"paper_url":"https://arxiv.org/abs/2512.18980","paper_title":"OPBO: Order-Preserving Bayesian Optimization","repo_url":"https://github.com/pengwei222/OPBO","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}