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Autore principale: Liu, Shenyu
Natura: Preprint
Pubblicazione: 2024
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Accesso online:https://arxiv.org/abs/2411.06883
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author Liu, Shenyu
author_facet Liu, Shenyu
contents In this work, we propose a novel discrete-time distributed algorithm for finding least-squares solutions of linear algebraic equations with a scheduling protocol to further enhance its scalability. Each agent in the network is assumed to know some rows of the coefficient matrix and the corresponding entries in the observation vector. Unlike typical distributed algorithms, our approach considers communication bandwidth limits, allowing agents to transmit only a portion of their ``guessed" solution, independent of its dimension. A cyclic scheduling protocol determines which portion is transmitted at each iteration. Assuming a small fixed step size and a diagonalizable algorithm matrix, we prove that agents' ``guessed" solutions converge exponentially to a least squares solution. For cases where the observation vectors are time-varying, a modified algorithm guarantees practical convergence, with tracking error bounded by the single-step variation in the observation vector. Simulations and comparisons with state-of-the-art algorithms validate our algorithm's feasibility and scalability.
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publishDate 2024
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spellingShingle Scalable Distributed Least Squares Algorithm for Linear Algebraic Equations via Periodic Scheduling
Liu, Shenyu
Systems and Control
In this work, we propose a novel discrete-time distributed algorithm for finding least-squares solutions of linear algebraic equations with a scheduling protocol to further enhance its scalability. Each agent in the network is assumed to know some rows of the coefficient matrix and the corresponding entries in the observation vector. Unlike typical distributed algorithms, our approach considers communication bandwidth limits, allowing agents to transmit only a portion of their ``guessed" solution, independent of its dimension. A cyclic scheduling protocol determines which portion is transmitted at each iteration. Assuming a small fixed step size and a diagonalizable algorithm matrix, we prove that agents' ``guessed" solutions converge exponentially to a least squares solution. For cases where the observation vectors are time-varying, a modified algorithm guarantees practical convergence, with tracking error bounded by the single-step variation in the observation vector. Simulations and comparisons with state-of-the-art algorithms validate our algorithm's feasibility and scalability.
title Scalable Distributed Least Squares Algorithm for Linear Algebraic Equations via Periodic Scheduling
topic Systems and Control
url https://arxiv.org/abs/2411.06883