Distributed Asynchronous Constrained Stochastic Optimization

By Kunal Srivastava, Angelia Nedić. 2011

352 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

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Citations in the corpus

Asynchronous Decentralized Parallel Stochastic Gradient Descent (2017, background): “Srivastava and Nedic [2011], Sundhar Ram et al. [2010] are similar.”
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent (2017, methodology): “…gradient evaluations we need to get a e -approximation solution, which is deﬁned in (3). comparison [Bianchi et al., 2013, Ram et al., 2009a, Srivastava and Nedic, 2011, Sundhar Ram et al., 2010] or implicitly indicate that decentralized algorithms were much worse than centralized…”
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].”
Fast Distributed Gradient Methods (2011, background): “e cost functions and give convergence rates for the running local averages (1=k) P k t=1 x i(t); in contrast, we provide rates of convergence for the current estimates x i(k). Unlike [9], [16], [14], [27], [28], this paper considers unconstrained optimization problems and that the underlying network”
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].”
Distributed Continuous-Time Optimization: Nonuniform Gradient Gains, Finite-Time Convergence, and Convex Constraint Set (2017, methodology): “In [5], a distributed algorithm with nonuniform subgradient gains was also given to solve the distributed optimization problem, but the algorithm can only be used in the stochastic sense.”
Asynchronous Incremental Stochastic Dual Descent Algorithm for Network Resource Allocation (2017, background): “This approach is followed in [18]–[20], all of which consider various consensus-based distributed subgradient algorithms.”
Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes (2015, background): “Some extension has also been made for cases under constraints [11], [12], cases where only noisy gradient is available [13], [14] and directed graphs [15].”
Adaptive Networks (2014, methodology): “…techniques that rely on the use of similar projection operations require the constraint conditions to be relatively simple in order for the distributed algorithm to be able to compute the necessary projections analytically (such as projecting onto the nonnegative orthant) [119], [120], [166].”
A Non-Asymptotic Analysis of Network Independence for Distributed Stochastic Gradient Descent (2019, background): “Recently, distributed implementation of stochastic gradient algorithms has received considerable interest [37, 41, 12, 3, 5, 42, 6, 7, 22, 17, 18, 30, 31, 38, 40, 14, 33, 29, 44, 1].”
Asynchronous Saddle Point Algorithm for Stochastic Optimization in Heterogeneous Networks (2019, background): “Asynchrony in online optimization has taken on different forms, such as, for instance, maintaining a local Poisson clock for each agent [25] or a distribution-free generic bounded delay [6], [26], which is our focus.”
NEXT: In-Network Nonconvex Optimization (2016, background): “Consider a network composed of I autonomous agents aiming to cooperatively and distributively solve Problem (1).”
Distributed Optimization Over Time-Varying Directed Graphs (2015, neutral): “2364096 Distributed optimization of a sum of convex functions has received a surge of interest in recent years [3], [5], [8], [11]– [13], [15], [23], [27].”
Balancing Communication and Computation in Distributed Optimization (2017, background): “A number of later contributions [15]–[30] extended DGD to other settings, including stochastic networks, constrained problems, and noisy environments.”
Distributed Algorithms for Aggregative Games on Graphs (2016, background): “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].”