تقرير
A Mixing-Accelerated Primal-Dual Proximal Algorithm for Distributed Nonconvex Optimization
| العنوان: | A Mixing-Accelerated Primal-Dual Proximal Algorithm for Distributed Nonconvex Optimization |
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| المؤلفون: | Ou, Zichong, Qiu, Chenyang, Wang, Dandan, Lu, Jie |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Mathematics - Optimization and Control, Electrical Engineering and Systems Science - Systems and Control |
| الوصف: | In this paper, we develop a distributed mixing-accelerated primal-dual proximal algorithm, referred to as MAP-Pro, which enables nodes in multi-agent networks to cooperatively minimize the sum of their nonconvex, smooth local cost functions in a decentralized fashion. The proposed algorithm is constructed upon minimizing a computationally inexpensive augmented-Lagrangian-like function and incorporating a time-varying mixing polynomial to expedite information fusion across the network. The convergence results derived for MAP-Pro include a sublinear rate of convergence to a stationary solution and, under the Polyak-{\L}ojasiewics (P-{\L}) condition, a linear rate of convergence to the global optimal solution. Additionally, we may embed the well-noted Chebyshev acceleration scheme in MAP-Pro, which generates a specific sequence of mixing polynomials with given degrees and enhances the convergence performance based on MAP-Pro. Finally, we illustrate the competitive convergence speed and communication efficiency of MAP-Pro via a numerical example. Comment: 8 pages, 2 figures, accepted by ACC2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2304.02830 |
| رقم الأكسشن: | edsarx.2304.02830 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |