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A Distributed Optimization Problem Subject to Partial-Impact Cost Functions
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DOI：10.1109/TCYB.2021.3086183.png)
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摘要
En 中文
This article focuses on a distributed optimization problem subject to partial-impact cost functions that relates to two decision variable vectors. To this end, two algorithms are presented with the aim of solving the considered optimization problem in a structure fashion and in a gradient fashion, respectively. Furthermore, a connection between the equilibrium of the induced algorithm and the involved optimization problem is established, with the aid of the tools from nonsmooth analysis and change of coordinate theorem. Two numerical examples with practical significance are given to demonstrate the efficiency of the designed algorithm.
Keyword:
Cost function
Linear programming
Laplace equations
Tools
Minimization methods
Cybernetics
Convergence
Distributed optimization
nonsmooth analysis
partial-impact cost function
subdifferentiation
unified dimension
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
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