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.
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by Zichong Ou,C... às arxiv.org 03-12-2024
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