30,056 research outputs found
ADMM-Tracking Gradient for Distributed Optimization over Asynchronous and Unreliable Networks
In this paper, we propose (i) a novel distributed algorithm for consensus
optimization over networks and (ii) a robust extension tailored to deal with
asynchronous agents and packet losses. The key idea is to achieve dynamic
consensus on (i) the agents' average and (ii) the global descent direction by
iteratively solving an online auxiliary optimization problem through a
distributed implementation of the Alternating Direction Method of Multipliers
(ADMM). Such a mechanism is suitably interlaced with a local proportional
action steering each agent estimate to the solution of the original consensus
optimization problem. First, in the case of ideal networks, by using tools from
system theory, we prove the linear convergence of the scheme with strongly
convex costs. Then, by exploiting the averaging theory, we extend such a first
result to prove that the robust extension of our method preserves linear
convergence in the case of asynchronous agents and packet losses. Further, by
using the notion of Input-to-State Stability, we also guarantee the robustness
of the schemes with respect to additional, generic errors affecting the agents'
updates. Finally, some numerical simulations confirm our theoretical findings
and show that the proposed methods outperform the existing state-of-the-art
distributed methods for consensus optimization
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
Distributed Online Optimization with Coupled Inequality Constraints over Unbalanced Directed Networks
This paper studies a distributed online convex optimization problem, where
agents in an unbalanced network cooperatively minimize the sum of their
time-varying local cost functions subject to a coupled inequality constraint.
To solve this problem, we propose a distributed dual subgradient tracking
algorithm, called DUST, which attempts to optimize a dual objective by means of
tracking the primal constraint violations and integrating dual subgradient and
push sum techniques. Different from most existing works, we allow the
underlying network to be unbalanced with a column stochastic mixing matrix. We
show that DUST achieves sublinear dynamic regret and constraint violations,
provided that the accumulated variation of the optimal sequence grows
sublinearly. If the standard Slater's condition is additionally imposed, DUST
acquires a smaller constraint violation bound than the alternative existing
methods applicable to unbalanced networks. Simulations on a plug-in electric
vehicle charging problem demonstrate the superior convergence of DUST
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