Výsledky vyhledávání - Distributed control subgradient algorithm

Distributed multi-step subgradient optimization for multi-agent system Autor Li, Chaoyong, Chen, Sai, Li, Jianqing, Wang, Feng

“…This paper proposes a novel multi-step subgradient scheme to solve distributed optimization problem with a balanced digraph…”
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Distributed Subgradient Projection Algorithm Over Directed Graphs Autor Chenguang Xi, Khan, Usman A.

“…We propose Directed-Distributed Projected Subgradient (D-DPS) to solve a constrained optimization problem over a multi-agent network, where the goal of agents is to collectively minimize the sum of locally known convex functions…”
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Distributed Saddle-Point Subgradient Algorithms With Laplacian Averaging Autor Mateos-Nunez, David, Cortes, Jorge

“…We present distributed subgradient methods for min-max problems with agreement constraints on a subset of the arguments of both the convex and concave parts…”
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On the Convergence Properties of a Distributed Projected Subgradient Algorithm Autor Li, Weijian, Chen, Zihan, Lou, Youcheng, Hong, Yiguang

“… Different from most of existing works focusing on designing distributed algorithms, we analyze the convergence of a well-known distributed projected subgradient algorithm over time-varying general graph sequences, i.e…”
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Continuous-Time Distributed Subgradient Algorithm for Convex Optimization With General Constraints Autor Zhu, Yanan, Yu, Wenwu, Wen, Guanghui, Chen, Guanrong, Ren, Wei

“… To solve such an optimization problem, a new type of continuous-time distributed subgradient optimization algorithm is proposed based on the Karuch-Kuhn-Tucker condition…”
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Distributed Projection Subgradient Algorithm Over Time-Varying General Unbalanced Directed Graphs Autor Li, Huaqing, Lu, Qingguo, Huang, Tingwen

“…This paper is concerned with a general class of distributed constrained optimization problems over a multiagent network, where the global objective function is represented by the sum of all local objective functions…”
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Quantized Subgradient Algorithm and Data-Rate Analysis for Distributed Optimization Autor Yi, Peng, Hong, Yiguang

“… A distributed quantized subgradient algorithm is presented with quantized information exchange between agents…”
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A Distributed Control Method With Minimum Generation Cost for DC Microgrids Autor Wang, Zhongguan, Wu, Wenchuan, Zhang, Boming

“… minimal communication burden. By combining the equal increment rate criteria and a subgradient algorithm, this distributed control method can efficiently regulate the bus voltage to the nominal value with minimum generation cost…”
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Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization Autor Sundhar Ram, S., Nedić, A., Veeravalli, V. V.

“… The goal of this paper is to explore the effects of stochastic subgradient errors on the convergence of the algorithm…”
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Convergence rate analysis of distributed optimization with projected subgradient algorithm Autor Liu, Shuai, Qiu, Zhirong, Xie, Lihua

“…In this paper, we revisit the consensus-based projected subgradient algorithm proposed for a common set constraint…”
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Privacy Preservation in Distributed Subgradient Optimization Algorithms Autor Lou, Youcheng, Yu, Lean, Wang, Shouyang, Yi, Peng

“…In this paper, some privacy-preserving features for distributed subgradient optimization algorithms are considered…”
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Privacy Masking Stochastic Subgradient-Push Algorithm for Distributed Online Optimization Autor Lu, Qingguo, Liao, Xiaofeng, Xiang, Tao, Li, Huaqing, Huang, Tingwen

“… To address such optimization problems in a collaborative and distributed fashion, a differentially private-distributed stochastic subgradient-push algorithm, called DP-DSSP, is proposed…”
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Fast Convergence Rates of Distributed Subgradient Methods With Adaptive Quantization Autor Doan, Thinh T., Maguluri, Siva Theja, Romberg, Justin

“… Recent advances using the distributed gradient algorithm with a quantization scheme at a fixed resolution have established convergence, but at rates significantly slower than when the communications are unquantized…”
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Distributed Subgradient Method With Edge-Based Event-Triggered Communication Autor Kajiyama, Yuichi, Hayashi, Naoki, Takai, Shigemasa

“…This paper proposes a distributed subgradient method for constrained optimization with event-triggered communications…”
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Distributed Subgradient Method in Open Multiagent Systems Autor Hayashi, Naoki

“… Each active agent updates its estimation by a distributed subgradient-based algorithm with information exchange of the estimation with neighboring active agents…”
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Distributed quasi-monotone subgradient algorithm for nonsmooth convex optimization over directed graphs Autor Liang, Shu, Wang, Leyi, Yin, George

“… matrix to the agents. In this brief paper, a distributed subgradient-based algorithm is proposed to solve nonsmooth convex optimization problems…”
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Distributed multi-step subgradient projection algorithm with adaptive event-triggering protocols: a framework of multiagent systems Autor An, Wenjing, Zhao, Peifeng, Liu, Hongjian, Hu, Jun

“… To this addressed problem, a distributed multi-step subgradient projection algorithm is developed, where an adaptive event-triggering protocol is designed to govern the information exchange…”
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Distributed Optimization for MPC of Linear Networks With Uncertain Dynamics Autor Camponogara, E., de Lima, M. L.

“… This technical note develops a distributed algorithm to operate a linear dynamic network with a network of agents that implement a distributed model predictive control strategy…”
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Distributed Primal-Dual Subgradient Method for Multiagent Optimization via Consensus Algorithms Autor Deming Yuan, Shengyuan Xu, Huanyu Zhao

“… primal-dual subgradient method, to provide approximate saddle points of the Lagrangian function, based on the distributed average consensus algorithms…”
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On Distributed Nonconvex Optimization: Projected Subgradient Method for Weakly Convex Problems in Networks Autor Chen, Shixiang, Garcia, Alfredo, Shahrampour, Shahin

“…The stochastic subgradient method is a widely used algorithm for solving large-scale optimization problems arising in machine learning…”
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