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Recent advances in optimization theory have shown that smooth strongly convex finite sums can be minimized faster than by treating them as a black box "batch" problem.
On an approach to the construction of optimal methods of minimization of smooth convex functions
Nesterov, Yu · 1988
Earlier work this paper cites.
Introductory Lectures On Convex Programming
Nesterov, Yu · 1998
Earlier work this paper cites.
Stochastic Optimization: Algorithms and Applications , chapter Convergence Rate of Incremental Subgradient Algorithms
Nedic, Angelia and Bertsekas, Dimitri · 2000
Earlier work this paper cites.
Incremental gradient, subgradient, and proximal methods for convex optimization: A survey
Bertsekas, Dimitri P · 2010
Earlier work this paper cites.
Efficiency of coordinate descent methods on huge-scale optimization problems
Nesterov, Yu · 2010
Cited alongside, same era.
Iteration complexity of randomized block-coordinate descent methods for minimizing a composite function
Richtarik, Peter and Takac, Martin · 2011
Cited alongside, same era.
Beneath the valley of the noncommutative arithmetic-geometric mean inequality: conjectures, case-studies, and consequences
Recht, Benjamin and Re, Christopher · 2012
Cited alongside, same era.
Optimization with first-order surrogate functions
Mairal, Julien · 2013
Later among the works it cites.
Minimizing finite sums with the stochastic average gradient
Schmidt, Mark, Roux, Nicolas Le, and Bach, Francis · 2013
Later among the works it cites.
Stochastic dual coordinate ascent methods for regularized loss minimization
Shalev-Shwartz, Shai and Zhang, Tong · 2013
Later among the works it cites.
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