{"ID":2868391,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18201","arxiv_id":"2509.18201","title":"Sampling-Based Zero-Order Optimization Algorithms","abstract":"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.","short_abstract":"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, highlig...","url_abs":"https://arxiv.org/abs/2509.18201","url_pdf":"https://arxiv.org/pdf/2509.18201v1","authors":"[\"Xicheng Zhang\"]","published":"2025-09-20T10:26:20Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"math.PR\"]","methods":"[]","has_code":false}