Sampling-Based Zero-Order Optimization Algorithms
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866918145792409600 |
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| author | Zhang, Xicheng |
| author_facet | Zhang, Xicheng |
| contents | We propose a novel zeroth-order optimization algorithm based on an efficient sampling strategy. Under mild global regularity conditions on the objective function, we establish non-asymptotic convergence rates for the proposed method. Comprehensive numerical experiments demonstrate the algorithm's effectiveness, highlighting three key attributes: (i) Scalability: consistent performance in high-dimensional settings (exceeding 100 dimensions); (ii) Versatility: robust convergence across a diverse suite of benchmark functions, including Schwefel, Rosenbrock, Ackley, Griewank, Lévy, Rastrigin, and Weierstrass; and (iii) Robustness to discontinuities: reliable performance on non-smooth and discontinuous landscapes. These results illustrate the method's strong potential for black-box optimization in complex, real-world scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_18201 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sampling-Based Zero-Order Optimization Algorithms Zhang, Xicheng Optimization and Control Probability 60H10 We propose a novel zeroth-order optimization algorithm based on an efficient sampling strategy. Under mild global regularity conditions on the objective function, we establish non-asymptotic convergence rates for the proposed method. Comprehensive numerical experiments demonstrate the algorithm's effectiveness, highlighting three key attributes: (i) Scalability: consistent performance in high-dimensional settings (exceeding 100 dimensions); (ii) Versatility: robust convergence across a diverse suite of benchmark functions, including Schwefel, Rosenbrock, Ackley, Griewank, Lévy, Rastrigin, and Weierstrass; and (iii) Robustness to discontinuities: reliable performance on non-smooth and discontinuous landscapes. These results illustrate the method's strong potential for black-box optimization in complex, real-world scenarios. |
| title | Sampling-Based Zero-Order Optimization Algorithms |
| topic | Optimization and Control Probability 60H10 |
| url | https://arxiv.org/abs/2509.18201 |