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Distributed Nonconvex Optimization: Gradient-free Iterations and ϵ-Globally Optimal Solution
[article]
2022
arXiv
pre-print
Distributed optimization utilizes local computation and communication to realize a global aim of optimizing the sum of local objective functions. It has gained wide attention for a variety of applications in networked systems. This paper addresses a class of constrained distributed nonconvex optimization problems involving univariate objectives, aiming to achieve global optimization without requiring local evaluations of gradients at every iteration. We propose a novel algorithm named CPCA,
arXiv:2008.00252v4
fatcat:m7nw4juntvhargoaajks5we55a