Eco-panda: A Computationally Economic, Geometrically Converging Dual Optimization Method on Time-varying Undirected Graphs | IEEE Conference Publication | IEEE Xplore

Eco-panda: A Computationally Economic, Geometrically Converging Dual Optimization Method on Time-varying Undirected Graphs


Abstract:

In this paper we consider distributed convex optimization over time-varying undirected graphs. We propose a linearized version of primarily averaged network dual ascent (...Show More

Abstract:

In this paper we consider distributed convex optimization over time-varying undirected graphs. We propose a linearized version of primarily averaged network dual ascent (PANDA) that keeps the advantages of PANDA while requiring less computational costs. The proposed method, economic primarily averaged network dual ascent (Eco-PANDA), provably converges at R-linear rate to the optimal point given that the agents' objective functions are strongly convex and have Lipschitz continuous gradients. Therefore, the method is competitive, in terms of type of rate, with both DIGing and PANDA. The proposed method halves the communication costs of methods like DIGing while still converging R-linearly and having the same per iterate complexity.
Date of Conference: 12-17 May 2019
Date Added to IEEE Xplore: 17 April 2019
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Conference Location: Brighton, UK
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