Stochastic Dual Coordinate Ascent with Alternating Direction Method of Multipliers

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SAGA: A Fast Incremental Gradient Method With Support for Non-Strongly Convex Composite Objectives (2014, neutral): “3 ? 3[6] 3 7 Non-smooth 7 7 3 7 7 Low Storage Cost 7 7 7 3 7 Simple(-ish) Proof 3 7 3 3 3 Adaptive to SC 3 3 7 ? ? Figure 1: Basic summary of method properties.”
Minimizing finite sums with the stochastic average gradient (2013, methodology): “6 of the extended version of this paper we review other algorithms that have been discovered to have similar convergence rates, including stochastic dual coordinate ascent (SDCA) [54], incremental surrogate optimization (MISO) [35], stochastic variance-reduced gradient (SVRG) methods [23,26,34] whic”
Group-Based Alternating Direction Method of Multipliers for Distributed Linear Classification (2017, methodology): “To further test the scalability of the proposed GADMM method, we extend the SDCA-ADMM [32] to the framework of GADMM (namely SDCA-GADMM), and make a comparison with SDCA-ADMM under the same distributed cluster.”
Stochastic Primal-Dual Coordinate Method for Regularized Empirical Risk Minimization (2014, background): “Suzuki [38] considered a problem similar to (1), but with more complex regularization function g, meaning that g does not have a simple proximal mapping.”