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Distributed Adaptive Optimization With Weight-Balancing
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DOI：10.1109/TAC.2021.3071651.png)
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Abstract
En 中文
This article addresses the continuous-time distributed optimization of a strictly convex summation-separable cost function with possibly nonconvex local functions over strongly connected digraphs. Distributed optimization methods in the literature require convexity of local functions, or balanced weights, or vanishing step sizes, or algebraic information (eigenvalues or eigenvectors) of the Laplacian matrix. The solution proposed here covers both weight-balanced and unbalanced digraphs in a unified way, without any of the aforementioned requirements.
Keywords:
Optimization
Eigenvalues and eigenfunctions
Laplace equations
Couplings
Cost function
Radio frequency
Standards
Directed graphs
distributed optimization
multiagent systems
weight balancing
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
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