Distributed saddle point problems for strongly concave-convex functions
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In this paper, we propose GT-GDA, a distributed optimization method to solve saddle point problems of the form: min_𝐱max_𝐲{F(𝐱,𝐲) :=G(𝐱) + ⟨𝐲, P𝐱⟩ - H(𝐲)}, where the functions G(·), H(·), and the the coupling matrix P are distributed over a strongly connected network of nodes. GT-GDA is a first-order method that uses gradient tracking to eliminate the dissimilarity caused by heterogeneous data distribution among the nodes. In the most general form, GT-GDA includes a consensus over the local coupling matrices to achieve the optimal (unique) saddle point, however, at the expense of increased communication. To avoid this, we propose a more efficient variant GT-GDA-Lite that does not incur the additional communication and analyze its convergence in various scenarios. We show that GT-GDA converges linearly to the unique saddle point solution when G(·) is smooth and convex, H(·) is smooth and strongly convex, and the global coupling matrix P has full column rank. We further characterize the regime under which GT-GDA exhibits a network topology-independent convergence behavior. We next show the linear convergence of GT-GDA to an error around the unique saddle point, which goes to zero when the coupling cost ⟨𝐲, P𝐱⟩ is common to all nodes, or when G(·) and H(·) are quadratic. Numerical experiments illustrate the convergence properties and importance of GT-GDA and GT-GDA-Lite for several applications.
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