Основные понятия
The author presents the Mixing-Accelerated Primal-Dual Proximal Algorithm (MAP-Pro) for decentralized nonconvex optimization, emphasizing convergence rates and communication efficiency.
Аннотация
The paper introduces MAP-Pro, a novel algorithm for distributed nonconvex optimization. It focuses on accelerating information fusion in multi-agent networks while achieving sublinear and linear convergence rates. The integration of Chebyshev acceleration enhances performance compared to existing methods. The numerical example showcases superior convergence speed and communication efficiency of MAP-Pro-CA over other algorithms.
Статистика
The global cost function f(x) is smooth.
The algorithm incorporates a time-varying mixing polynomial.
MAP-Pro requires inner loops in each iteration.
MAP-Pro-CA conducts 3 inner loops per primal update.
Цитаты
"The proposed algorithm enables nodes to cooperatively minimize local cost functions."
"MAP-Pro converges to a stationary solution at a sublinear rate."
"Chebyshev acceleration improves convergence rates."