Primal-dual mirror descent method for constraint stochastic optimization problems
DOI10.1134/S0965542518110039zbMath1412.90097OpenAlexW2902894923WikidataQ128821247 ScholiaQ128821247MaRDI QIDQ2416753
E. V. Gasnikova, Alexander V. Gasnikov, A. S. Bayandina, S. V. Matsievskii
Publication date: 24 May 2019
Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1134/s0965542518110039
constrained optimizationrandomizationmirror descent methodconvex stochastic optimizationprobability of large deviations
Convex programming (90C25) Optimality conditions and duality in mathematical programming (90C46) Stochastic programming (90C15)
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- The CoMirror algorithm for solving nonsmooth constrained convex problems
- Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints
- Algorithms for stochastic optimization with function or expectation constraints
- Decomposition techniques for bilinear saddle point problems and variational inequalities with affine monotone operators
- Stochastic online optimization. Single-point and multi-point non-linear multi-armed bandits. Convex and strongly-convex case
- Lexicographic differentiation of nonsmooth functions
- Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization
- Primal-Dual Subgradient Method for Huge-Scale Linear Conic Problems
- Accuracy Certificates for Computational Problems with Convex Structure
- Robust Stochastic Approximation Approach to Stochastic Programming
- Solving variational inequalities with Stochastic Mirror-Prox algorithm
- Accuracy Guarantees for <formula formulatype="inline"> <tex Notation="TeX">$\ell_1$</tex></formula>-Recovery
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