Titlebook: Distributed Optimization in Networked Systems; Algorithms and Appli Qingguo Lü,Xiaofeng Liao,Shanfu Gao Book 2023 The Editor(s) (if applica

Incommensurate

Atrium

https://doi.org/10.1057/9780230270589ems, and the problem under study remains the problem of distributed optimization to minimize a finite sum of convex cost functions over the nodes of a network where each cost function is further considered as the average of several constituent functions. Reviewing the existing work, no method can im

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裝飾

https://doi.org/10.1057/9780230270589istributed economic dispatch problem for smart grids where each node can only obtain its own locally convex objective function and the estimation of each node is restricted to coupled linear constraints and single-box constraints. In this algorithm, we assume that the communication network between t

vocation

https://doi.org/10.1057/9780230270589mizing a sum of local convex cost functions subjected to both local interval constraints and coupling linear constraint over an undirected network. We propose a new event-triggered distributed accelerated primal–dual algorithm, ET-DAPDA, that achieves a reduction in computation and interaction to so

尖

https://doi.org/10.1057/9780230270589ted network, while considering the problem of how to preserve the privacy of their local cost functions. The main goal of this set of nodes is to cooperatively minimize the sum of all locally known convex cost functions (global cost function). We propose a differentially private distributed stochast

Mechanics

Accelerated Algorithms for Distributed Convex Optimization, obeying the network connectivity structure, and the principal target of these problems is to minimize the global cost function (formulated by the average of all local cost functions). Most of the existing methods, such as the push-sum strategy, have eliminated the unbalancedness caused by directed

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嚴(yán)厲譴責(zé)

Proximal Algorithms for Distributed Coupled Optimization, of several constituent functions, and the network aims to minimize a finite sum of all local functions plus a coupling function (possibly non-smooth). Due to its benefits in scalability, robustness, and flexibility, distributed optimization has been a significant focus in engineering research to ta

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