Convergence Properties Of Message-Passing Algorithm For Distributed Convex Optimisation With Scaled Diagonal Dominance

IEEE TRANSACTIONS ON SIGNAL PROCESSING(2021)

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摘要
This paper studies the convergence properties of the well-known message-passing algorithm for convex optimisation. Under the assumption of pairwise separability and scaled diagonal dominance, asymptotic convergence is established and a simple bound for the convergence rate is provided for message-passing. In comparison with previous results, our results do not require the given convex program to have known convex pairwise components and that our bound for the convergence rate is tighter and simpler. When specialised to quadratic optimisation, we generalise known results by providing a very simple bound for the convergence rate.
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关键词
Convergence, Optimization, Signal processing algorithms, Linear programming, Couplings, Belief propagation, Task analysis, Distributed optimisation, message passing, belief propagation, min-sum algorithm, convex optimisation
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