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We present a novel accelerated primal-dual (APD) method for solving a class of deterministic and stochastic saddle point problems (SPP).
A stochastic approximation method
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A general framework for a class of first order primal-dual algorithms for convex optimization in imaging science
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Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization, Part II: shrinking procedures and optimal algorithms
S. Ghadimi and G. Lan · 2010
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A first-order primal-dual algorithm for convex problems with applications to imaging
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First-Order Methods for Nonsmooth Convex Large-Scale Optimization, II: Utilizing problems structure
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Primal-dual first-order methods with 𝒪 ( 1 / ϵ ) {\cal O}(1/\epsilon) iteration-complexity for cone programming
G. Lan, Z. Lu, and R. D. C. Monteiro · 2011
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Optimal stochastic approximation algorithms for strongly convex stochastic composite optimization, Part I: a generic algorithmic framework
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Convergence analysis of primal-dual algorithms for a saddle-point problem: from contraction perspective
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An optimal method for stochastic composite optimization
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Validation analysis of mirror descent stochastic approximation method
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Bundle-level type methods uniformly optimal for smooth and non-smooth convex optimization
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