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Recent theoretical advances in decentralized distributed convex optimization - MaRDI portal

Recent theoretical advances in decentralized distributed convex optimization (Q6354638)

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scientific article; zbMATH DE number 7598280

Language	Label	Description	Also known as
English	Recent theoretical advances in decentralized distributed convex optimization	scientific article; zbMATH DE number 7598280

Statements

scholarly article

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publication date

26 November 2020

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arXiv classification

math.OC

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Eduard Gorbunov

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Alexander Rogozin

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Aleksandr Beznosikov

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Darina Dvinskikh

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MaRDI profile type

MaRDI publication profile

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Ergodicity of Continuous-Time Distributed Averaging Dynamics: A Spanning Directed Rooted Tree Approach

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Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

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Katyusha: the first direct acceleration of stochastic gradient methods

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Dual approaches to the minimization of strongly convex functionals with a simple structure under affine constraints

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Mirror descent and convex optimization problems with non-smooth inequality constraints

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Near-Optimal Decentralized Algorithms for Saddle Point Problems over Time-Varying Networks

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Gradient-Free Methods with Inexact Oracle for Convex-Concave Stochastic Saddle-Point Problem

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A first-order primal-dual algorithm for convex problems with applications to imaging

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A Smoothed Dual Approach for Variational Wasserstein Problems

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First-order methods of smooth convex optimization with inexact oracle

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Decentralized and parallel primal and dual accelerated methods for stochastic convex programming problems

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Accelerated and unaccelerated stochastic gradient descent in model generality

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Stochastic intermediate gradient method for convex problems with stochastic inexact oracle

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Finite-Dimensional Variational Inequalities and Complementarity Problems

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Accelerated meta-algorithm for convex optimization problems

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Gradient-free proximal methods with inexact oracle for convex stochastic nonsmooth optimization problems on the simplex

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Universal method for stochastic composite optimization problems

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Optimal Stochastic Approximation Algorithms for Strongly Convex Stochastic Composite Optimization I: A Generic Algorithmic Framework

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Stochastic First- and Zeroth-Order Methods for Nonconvex Stochastic Programming

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An Accelerated Method for Derivative-Free Smooth Stochastic Convex Optimization

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On the upper bound for the expectation of the norm of a vector uniformly distributed on the sphere and the phenomenon of concentration of uniform measure on the sphere

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Alternating minimization methods for strongly convex optimization

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An Optimal Algorithm for Decentralized Finite-Sum Optimization

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Fast Distributed Gradient Methods

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Solving variational inequalities with Stochastic Mirror-Prox algorithm

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Deterministic and stochastic primal-dual subgradient algorithms for uniformly convex minimization

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Decomposition into functions in the minimization problem

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An optimal method for stochastic composite optimization

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Gradient sliding for composite optimization

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First-order and stochastic optimization methods for machine learning

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Communication-efficient algorithms for decentralized and stochastic optimization

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Random Gradient Extrapolation for Distributed and Stochastic Optimization

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Algorithms for stochastic optimization with function or expectation constraints

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Derivative-free optimization methods

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Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters

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Revisiting EXTRA for Smooth Distributed Optimization

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Gradient-free method for nonsmooth distributed optimization

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Monotone (nonlinear) operators in Hilbert space

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First- and second-order diffusive methods for rapid, coarse, distributed load balancing

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Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs

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Distributed Subgradient Methods for Multi-Agent Optimization

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Prox-Method with Rate of Convergence <i>O</i>(1/<i>t</i>) for Variational Inequalities with Lipschitz Continuous Monotone Operators and Smooth Convex-Concave Saddle Point Problems

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Robust Stochastic Approximation Approach to Stochastic Programming

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Introductory lectures on convex optimization. A basic course.

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Lectures on convex optimization

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Random gradient-free minimization of convex functions

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Distributed stochastic gradient tracking methods

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Harnessing Smoothness to Accelerate Distributed Optimization

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Accelerated Distributed Nesterov Gradient Descent

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Entropic optimal transport is maximum-likelihood deconvolution

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A Stochastic Approximation Method

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Penalty-based method for decentralized optimization over time-varying graphs

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Towards accelerated rates for distributed optimization over time-varying networks

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Optimal Algorithms for Non-Smooth Distributed Optimization in Networks

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Minimizing finite sums with the stochastic average gradient

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Understanding Machine Learning

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An Optimal Algorithm for Bandit and Zero-Order Convex Optimization with Two-Point Feedback

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EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization

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Parametric estimation. Finite sample theory

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Gradient methods for problems with inexact model of the objective

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Inexact model: a framework for optimization and variational inequalities

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Distributed Optimization Based on Gradient Tracking Revisited: Enhancing Convergence Rate via Surrogation

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Distributed Zero-Order Algorithms for Nonconvex Multiagent Optimization

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A dual approach for optimal algorithms in distributed optimization over networks

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Fast linear iterations for distributed averaging

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On the Convergence of Decentralized Gradient Descent

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Identifiers

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Mathematics Subject Classification ID

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zbMATH DE Number

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zbMATH Open document ID

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10.1007/978-3-031-00832-0_8

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Sitelinks

Mathematics(1 entry)

mardi Publication:6354638

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