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Time-varying dual accelerated gradient ascent: A fast network optimization algorithm
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DOI：10.1016/j.jpdc.2022.03.014.png)
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Abstract
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We propose a time-varying dual accelerated gradient method for minimizing the average of n strongly convex and smooth functions over a time-varying network with n nodes. We prove that the time-varying dual accelerated gradient ascent method converges at an R-linear rate with the time to reach an epsilon-neighborhood of the solution being of O(1/ln(1/c) ln M/epsilon), where c is a constant depending on the graph and objective function parameters and M is a constant depending on the initial values. We test the proposed method on two classes of problems: L-2-regularized least squares and logistic classification problems. For each class, we generate 1000 problems and use the Dolan-More performance profiles to compare our obtained results with the ones obtained by several state-of-the-art algorithms to illustrate the efficiency of our method. (C) 2022 Elsevier Inc. All rights reserved.
Keywords:
Distributed convex optimization
Time-varying network optimization
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