標題: Titlebook: Distributed Optimization in Networked Systems; Algorithms and Appli Qingguo Lü,Xiaofeng Liao,Shanfu Gao Book 2023 The Editor(s) (if applica [打印本頁] 作者: 使無罪 時間: 2025-3-21 16:45
書目名稱Distributed Optimization in Networked Systems影響因子(影響力)
書目名稱Distributed Optimization in Networked Systems影響因子(影響力)學(xué)科排名
書目名稱Distributed Optimization in Networked Systems網(wǎng)絡(luò)公開度
書目名稱Distributed Optimization in Networked Systems網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Distributed Optimization in Networked Systems被引頻次
書目名稱Distributed Optimization in Networked Systems被引頻次學(xué)科排名
書目名稱Distributed Optimization in Networked Systems年度引用
書目名稱Distributed Optimization in Networked Systems年度引用學(xué)科排名
書目名稱Distributed Optimization in Networked Systems讀者反饋
書目名稱Distributed Optimization in Networked Systems讀者反饋學(xué)科排名
作者: 閑逛 時間: 2025-3-21 22:32 作者: Magnificent 時間: 2025-3-22 00:33 作者: 變形詞 時間: 2025-3-22 07:29 作者: STIT 時間: 2025-3-22 09:04
https://doi.org/10.1057/9780230253001omentum terms and employs non-uniform step-sizes. This approach can effectively overcome the abovementioned limitations of column-stochastic directed networks in the implementation. The implementation of D-DNGT is straightforward if each node locally chooses a suitable step-size and privately regula作者: 腐蝕 時間: 2025-3-22 16:37
https://doi.org/10.1057/9780230253001ctation when each constituent function (smooth) is strongly convex if the constant step-size is less than an explicitly calculated upper constraint. Regarding the current distributed methods, the suggested technique not only has a low computation cost in terms of the overall number of local gradient作者: 腐蝕 時間: 2025-3-22 19:58 作者: FLING 時間: 2025-3-22 21:38 作者: 排名真古怪 時間: 2025-3-23 02:12 作者: aqueduct 時間: 2025-3-23 06:00
https://doi.org/10.1057/9780230270589orithm, D-DLM, which integrates a distributed gradient tracking method with two momentum terms and non-uniform step-sizes in the update of the Lagrangian multipliers. Next, we give proof that if the maximum step-size and the maximum momentum coefficient are positive and sufficiently small, the D-DLM作者: 陪審團 時間: 2025-3-23 09:57 作者: BUMP 時間: 2025-3-23 13:50
https://doi.org/10.1057/9780230270589cludes Zeno-like behavior, which greatly reduces the interaction cost. ET-DAPDA is investigated on 14-bus and 118-bus systems to evaluate its applicability. Simulation results of convergence rates are further compared with existing techniques to demonstrate the superiority of ET-DAPDA.作者: Observe 時間: 2025-3-23 20:02
https://doi.org/10.1057/9780230270589tical in applications involving sensitive information, such as military affairs or medical treatment. An important feature of DP-DSSP is that it handles distributed online optimization problems in the context of time-varying unbalanced directed networks. Theoretical analysis shows that DP-DSSP can e作者: 消散 時間: 2025-3-24 02:06 作者: 不易燃 時間: 2025-3-24 02:43
Projection Algorithms for Distributed Stochastic Optimization,ctation when each constituent function (smooth) is strongly convex if the constant step-size is less than an explicitly calculated upper constraint. Regarding the current distributed methods, the suggested technique not only has a low computation cost in terms of the overall number of local gradient作者: Gossamer 時間: 2025-3-24 07:56 作者: EWE 時間: 2025-3-24 12:54 作者: 鋼筆尖 時間: 2025-3-24 15:28 作者: PLIC 時間: 2025-3-24 21:46
Accelerated Algorithms for Distributed Economic Dispatch,orithm, D-DLM, which integrates a distributed gradient tracking method with two momentum terms and non-uniform step-sizes in the update of the Lagrangian multipliers. Next, we give proof that if the maximum step-size and the maximum momentum coefficient are positive and sufficiently small, the D-DLM作者: 新鮮 時間: 2025-3-25 00:15
,Primal–Dual Algorithms for Distributed Economic Dispatch,e convex optimization problem only if the step-size does not exceed some upper bound. We also give an explicit analysis of the convergence rate of the proposed optimization algorithm. We perform simulations of economic dispatch problems and demand response problems in power systems to illustrate the作者: labyrinth 時間: 2025-3-25 03:47 作者: 共和國 時間: 2025-3-25 09:15 作者: Nonporous 時間: 2025-3-25 15:32
https://doi.org/10.1007/978-981-19-8559-1Distributed optimization; Networked systems; Acceleration; Communication efficiency; Computational effic作者: 無情 時間: 2025-3-25 16:22
978-981-19-8561-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor作者: FLAG 時間: 2025-3-25 23:44 作者: 尾隨 時間: 2025-3-26 02:56
Wireless Networkshttp://image.papertrans.cn/e/image/281931.jpg作者: 中和 時間: 2025-3-26 07:47 作者: Cardioversion 時間: 2025-3-26 11:13 作者: MAUVE 時間: 2025-3-26 12:59
https://doi.org/10.1057/9780230253001-smooth regularization terms (. norm) subject to locally general constraints. Each of the smooth objective functions is further thought of as the average of several constituent functions, which is motivated by the modern large-scale information processing problems in machine learning (the samples of作者: magenta 時間: 2025-3-26 17:00
https://doi.org/10.1057/9780230253001 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作者: Incommensurate 時間: 2025-3-26 23:49 作者: Atrium 時間: 2025-3-27 01:13
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作者: 善于騙人 時間: 2025-3-27 07:02 作者: 裝飾 時間: 2025-3-27 11:58
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作者: vocation 時間: 2025-3-27 13:56
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作者: 尖 時間: 2025-3-27 20:05
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作者: Mechanics 時間: 2025-3-28 01:50
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 作者: 昏睡中 時間: 2025-3-28 05:00 作者: 嚴厲譴責(zé) 時間: 2025-3-28 07:40
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作者: 打折 時間: 2025-3-28 13:59 作者: 閑聊 時間: 2025-3-28 15:12
Event-Triggered Acceleration Algorithms for Distributed Stochastic Optimization,ems, 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作者: DRILL 時間: 2025-3-28 19:55
Accelerated Algorithms for Distributed Economic Dispatch,(EDP) for smart grids. This application scenario focuses on researching how to allocate the generation power among generators to match the load demand with the minimum total generation cost while observing all constraints on the local generation capacity. Each generator possesses its own local gener作者: 天空 時間: 2025-3-29 02:35
,Primal–Dual Algorithms for Distributed Economic Dispatch,istributed 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作者: 假設(shè) 時間: 2025-3-29 05:31
Event-Triggered Algorithms for Distributed Economic Dispatch,mizing 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作者: Volatile-Oils 時間: 2025-3-29 08:20
Privacy Preserving Algorithms for Distributed Online Learning,ted 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作者: TAP 時間: 2025-3-29 12:33
