{"ID":2864978,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21814","arxiv_id":"2509.21814","title":"Distributed Time-Varying Optimization via Unbiased Extremum Seeking","abstract":"This paper proposes a novel distributed optimization framework that addresses time-varying optimization problems without requiring explicit derivative information of the objective functions. Traditional distributed methods often rely on derivative computations, limiting their applicability when only real-time objective function measurements are available. Leveraging unbiased extremum seeking, we develop continuous-time algorithms that utilize local measurements and neighbor-shared data to collaboratively track time-varying optima. Key advancements include compatibility with directed communication graphs, customizable convergence rates (asymptotic, exponential, or prescribed-time), and the ability to handle dynamically evolving objectives. By integrating chirpy probing signals with time-varying frequencies, our unified framework achieves accelerated convergence while maintaining stability under mild assumptions. Theoretical guarantees are established through Lie bracket averaging and Lyapunov-based analysis, with linear matrix inequality conditions ensuring rigorous convergence. Numerical simulations validate the effectiveness of the algorithms.","short_abstract":"This paper proposes a novel distributed optimization framework that addresses time-varying optimization problems without requiring explicit derivative information of the objective functions. Traditional distributed methods often rely on derivative computations, limiting their applicability when only real-time objective...","url_abs":"https://arxiv.org/abs/2509.21814","url_pdf":"https://arxiv.org/pdf/2509.21814v1","authors":"[\"Xuebin Li\",\"Xuefei Yang\",\"Emilia Fridman\",\"Mamadou Diagne\",\"Jiebao Sun\"]","published":"2025-09-26T03:20:49Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"eess.SY\"]","methods":"[]","has_code":false}