Distributed optimization in sensor networks

By Michael G. Rabbat, Robert D. Nowak. 2004

967 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

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Citations in the corpus

An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, methodology): “In wireless sensor networks [109], the estimation problem is usually modeled as a distributed multi-agent optimization problem.”
Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling (2010, neutral): “For instance, problems such as multi-agent coordination, distributed tracking and localization, estimation problems in sensor networks and packet routing are all naturally cast as distributed convex minimization [BT89, LWHS02, LOT03, RN04, XBK07].”
Distributed Stochastic Subgradient Projection Algorithms for Convex Optimization (2008, background): “A number of problems that arise in the context of peer to peer networks can be posed as the constrained minimization of a sum of convex functions, where each component function is known only to one agent in the network [1–13].”
Communication-efficient algorithms for decentralized and stochastic optimization (2017, neutral): “uction Decentralized optimization problems deﬁned over complex multiagent networks are ubiquitous in signal processing, machine learning, control, and other areas in science and engineering (see e.g. [47,21,50,15]). In this paper, we consider the following decentralized optimization problem which is”
Distributed optimization over time-varying directed graphs (2013, background): “Distributed optimization of a sum of convex functions has received a surge of interest in recent years [13], [15], [6], [11], [9], [10], [19], [12], [2], [16], [4].”
Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs (2016, background): “Some examples include distributed averaging [7,50,72], distributed spectrum sensing [1], formation control [49, 58], power system control [21, 55], statistical inference and learning [20, 45, 53].”
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm (2015, background): “In decentralized optimization, agents try to minimize their local functions f n ( x n ) while ensuring that their local variables x n coincide with the variables x m of all neighbors m ∈ N n – which, given that the network is connected, ensures that the variables x n of all nodes are the same and…”
Stochastic Gradient-Push for Strongly Convex Functions on Time-Varying Directed Graphs (2014, methodology): “Our work here contributes to the growing literature on distributed methods for optimization over networks [5], [9], [12], [14]–[16], [19]–[23], [28], [32], [34], [40], [43].”
An Exact Quantized Decentralized Gradient Descent Algorithm (2018, background): “D ISTRIBUTED optimization of a sum of convex functions has a variety of applications in different areas including decentralized control systems [2], wireless systems [3], sensor networks [4], networked multiagent systems [5], multirobot networks [6], and large scale machine learning [7].”
DEXTRA: A Fast Algorithm for Optimization Over Directed Graphs (2017, background): “D ISTRIBUTED computation and optimization have gained great interests due to their widespread applications in, e.g., large-scale machine learning [1], [2], model predictive con-trol [3], cognitive networks [4], [5], source localization [6], [7], resource scheduling [8], and message routing [9].”
D-ADMM: A Communication-Efficient Distributed Algorithm for Separable Optimization (2012, background): “Gradient and subgradient methods, including incremental versions, are long known to yield distributed algorithms (in the sense deﬁned before); see, e.g., [6], [7], [8].”
Performance of a Distributed Stochastic Approximation Algorithm (2012, neutral): “The fitted model is with (see [32] for a similar model).”
Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms (2019, background): “Nonetheless, such a controller may not exist in most aforementioned applications, due to either cost, scalability, or robustness concerns (Rabbat and Nowak, 2004; Dall’Anese et al., 2013; Zhang et al., 2019).”
Linear Time Average Consensus on Fixed Graphs and Implications for Decentralized Optimization and Multi-Agent Control (2014, positive): “For speciﬁc application areas where this problem appears, we mention robust statistical inference [70], non-autonomous power control [72], network resource allocation [7], distributed message routing [60], and spectrum access coordination [49].”
Asynchronous Decentralized Accelerated Stochastic Gradient Descent (2018, background): “Decentralized optimization problems deﬁned over complex multi-agent networks are ubiquitous in signal processing, machine learning, control, and other areas in science and engineering (see e.g. [23, 13, 24, 8]).”
Information fusion for wireless sensor networks: Methods, models, and classifications (2007, methodology): “Different square-error metrics can be used (minimized) such as the ordinary squared error [Brown et al. 1992], the Huber loss function [Rabbat and Nowak 2004], and the root mean squared error [Guestrin et al.”
Locating the nodes: cooperative localization in wireless sensor networks (2005, background): “The energy efficiency of centralized and distributed estimation approaches can be compared [79]; in general, when the average number of hops to the central processor exceeds the necessary number of iterations, distributed algorithms will likely save communication energy costs.”