Asynchronous Broadcast-Based Convex Optimization Over a Network
By Angelia Nedić. 2011
301 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Mathematical Optimization
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In Computer Networks and Communications
In Mathematical Optimization
Citations in the corpus
- Personalized and Private Peer-to-Peer Machine Learning (2017, methodology): “For communication, we rely on a broadcast-based model (Aysal et al., 2009; Nedic, 2011) where agents send messages to all”
- Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions (2016, methodology): “Since (δk(t))t≥1 is a Bernoulli process of parameter 1/pk, by the law of iterative logarithms (Dudley, 2010), (Nedić, 2011, Lemma 3) one has with probability 1 and for any q > 0 lim t→+∞ |mk(t)− t| t 1 2+q = 0, and the result holds.”
- An Overview of Recent Progress in the Study of Distributed Multi-Agent Coordination (2012, background): “Further results in this topic can be found in [244]–[246], where a similar distributed multi-agent optimization problem was studied within various scenarios , such as under constraints [ 244], over random networks [ 245], and with broadcast-based communications in an asynchronou s setting [246].”
- Distributed optimization over time-varying directed graphs (2013, neutral): “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].”
- 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].”
- Decentralized Consensus Optimization With Asynchrony and Delays (2016, background): “Gossip [15], [16] Reduced No Yes Average Consensus Yes Randomized Consensus [17], [18] Reduced No Yes Average Consensus Yes Randomized DGD [19], [26] Reduced No Yes Aggregate Optimization No Asynchronous Diffusion [27]–[29] Reduced No Yes Aggregate stochastic Optimization No Randomized ADMM [20], [2”
- Asynchronous Decentralized Accelerated Stochastic Gradient Descent (2018, background): “While asynchronous decentralized stochastic firstorder methods [33], [34] established error bounds when fi is (strongly) convex, [35] achieved O(1/ 2) rate of convergence for smooth and convex problems.”
- Performance of a Distributed Stochastic Approximation Algorithm (2012, unknown): “It is known that convergence of stochastic approximation algorithms to an attractive set is established provided that the sequence remains in a compact set with probability one and is, with probability one, infinitely often in the domain of attraction of this attractive set.”
- Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes (2015, methodology): “In [21], an asynchronous broadcast-based algorithm is designed to handle the problem of random link failures as well as uncoordinated update over random networks.”
- Balancing Communication and Computation in Distributed Optimization (2017, background): “A number of later contributions [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30] extended DGD to other settings, including stochastic networks, constrained problems, and noisy environments.”
- A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates (2017, background): “It and its variants [16, 33] are intuitive and simple but usually slow due to the diminishing step-size that is needed to obtain a consensual and optimal solution, even if the objective functions are differentiable and strongly convex.”
- Distributed Algorithms for Aggregative Games on Graphs (2016, methodology): “This idea has attracted a lot of attention recently in an effort to extend the algorithm of [57] to more general and broader range of problems [35, 46, 45, 19, 18, 36, 54, 33, 26, 58, 48, 59, 5].”
- Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization (2011, methodology): “In order to distinguish both broadcast algorithms, we will designate the algorithm of [23] as the broadcast algorithm with sleeping phases, referring to the fact that each agent does not update its estimates as long as it is not the recipient of a message.”