Linear Convergence of Randomized Feasible Descent Methods Under the Weak Strong Convexity Assumption

By Chenxin Ma, Rachael Tappenden, Martin Takác. 2016

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Citations in the corpus

Linear Convergence of Gradient and Proximal-Gradient Methods Under the Polyak-Łojasiewicz Condition (2016, neutral): “But these problems have also been shown to satisfy proximal variants of the EB condition [Tseng, 2010, Necoara and Clipici, 2016], and thus by the equivalence in Appendix G we have that any L1-regularized least squares problem satisﬁes the proximal-PL inequality.”
CoCoA: A General Framework for Communication-Efficient Distributed Optimization (2016, methodology): “(Ma et al., 2015b, Remark 7) If the data points x i are normalized such that x i (cid:107) ≤ 1 , ∀ i ∈ [ n ] , then σ k ≤”
Distributed optimization with arbitrary local solvers (2015, neutral): “…with the massive growth of available datasets, major roadblocks still persist in the distributed setting, where data no longer ﬁts in the memory of a single computer, and computation must be split across multiple machines in a network [3, 7, 12, 18, 22, 28, 32, 34, 37, 46, 50, 58, 60, 62, 76].”
Stochastic, Distributed and Federated Optimization for Machine Learning (2017, background): “However, with the massive growth of available datasets, major roadblocks still persist in the distributed setting, where data no longer fits in the memory of a single computer, and computation must be split across multiple machines in a network [19, 111, 106, 8, 149, 53, 173, 57, 32, 108, 166, 167, ”
Primal-Dual Rates and Certificates (2016, background): “The proof is partially motivated by proofs in (Ma et al., 2015a; Shalev-Shwartz & Zhang, 2013) but come with some crucial unique steps and tricks.”