
The author presents the Mixing-Accelerated Primal-Dual Proximal Algorithm (MAP-Pro) for decentralized nonconvex optimization, emphasizing convergence rates and communication efficiency.


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a-mixing-accelerated-primal-dual-proximal-algorithm-for-distributed-nonconvex-optimization


A Mixing-Accelerated Primal-Dual Proximal Algorithm for Distributed Nonconvex Optimization


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The proposed CPCA algorithm can obtain ε-globally optimal solutions for distributed nonconvex optimization problems with univariate objectives, using gradient-free iterations and efficient communication.


distributed-nonconvex-optimization-with-gradient-free-iterations-and-ε-globally-optimal-solutions


Distributed Nonconvex Optimization with Gradient-free Iterations and ε-Globally Optimal Solutions
