Ergodic Stochastic Optimization Algorithms for Wireless Communication and Networking

By Alejandro Ribeiro. 2010

165 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

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Citations in the corpus

Robust and Communication-Efficient Collaborative Learning (2019, background): “…learning naturally arises in various applications such as distributed deep learning (LeCun et al., 2015; Dean et al., 2012), multi-agent robotics and path planning (Choi and How, 2010; Jha et al., 2016), distributed resource allocation in wireless networks (Ribeiro, 2010), to name a few.”
Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication (2018, background): “(1) The formulation (1) captures problems in sensor network, mobile computation, and multi-agent control, where either efficiently centralizing data or globally aggregate intermediate results is unfeasible (Johansson, 2008; Bullo et al., 2009; Forero et al., 2010; Ribeiro, 2010).”
DSA: Decentralized Double Stochastic Averaging Gradient Algorithm (2015, background): “Deﬁne x n ∈ R p as a local copy of the variable x that is kept at node n .”
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].”
Quantized Decentralized Consensus Optimization (2018, background): “Distributed optimization of a sum of convex functions has a variety of applications in different areas including decentralized control systems [1], wireless systems [2], sensor networks [3], networked multiagent systems [4], multirobot networks [5], and large scale machine learning [6].”
Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation (2017, methodology): “(7)-(8)], it is known that under (A1)-(A3) and (A6), the average resource allocations x̄ := 1 T ∑T t=1x i t are asymptotically feasible and near-optimal [9].”
DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers (2015, background): “The nodes’ goal is to ﬁnd the optimal argument of the global cost function Problems of this form arise in, e.g., decentralized control [2]– [4], wireless communication [5], [6], sensor networks [7]–[9], and large scale machine learning [10]–[12].”
Proximity Without Consensus in Online Multiagent Optimization (2016, background): “Distributed optimization has a rich history in networked control systems, [5]–[8] wireless systems [9], [10], sensor networks [11], [12], and machine learning [13], [14].”
A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks (2017, background): “-2,-4,-5: Capacity limits, reliability and ED constraints Stochastic subgradient algorithm [133], [134].”
Optimization of Radio and Computational Resources for Energy Efficiency in Latency-Constrained Application Offloading (2014, unknown): “m solution or simple algorithm to obtain the optimum solution to this average formulation is quite complicated. A possible (and simpler) approach would consist in applying the philosophy presented in [30], which proposes an instantaneous stochastic gradient search algorithm to deal with this kind of”
IQN: An Incremental Quasi-Newton Method with Local Superlinear Convergence Rate (2017, background): “Problems of this form arise in machine learning [3, 2, 30, 8], control [5, 7, 15], and wireless communications [27, 23, 24].”
Decentralized Online Learning With Kernels (2017, background): “In this work, we focus on extensions of the formulation in (1) to the case where data is scattered across an interconnected network that represents, for instance, robotic teams [10], communication systems [41], or sensor networks [4].”