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Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis

https://doi.org/10.1109/tsp.2021.3086579
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Introductory Lectures on Convex Optimization
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Fast linear iterations for distributed averaging
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Topics in Matrix Analysis
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Robust convergence analysis of distributed optimization algorithms
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A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization
inferredcites →https://doi.org/10.1109/cdc.2012.6425904openalex-snapshot
Distributed Alternating Direction Method of Multipliers
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Augmented distributed gradient methods for multi-agent optimization under uncoordinated constant stepsizes
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A General Framework of Exact Primal-Dual First-Order Algorithms for Distributed Optimization
inferredcites →https://doi.org/10.1109/cdc42340.2020.9303791openalex-snapshot
A Unified Algorithmic Framework for Distributed Composite Optimization
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A Linear Algorithm for Optimization Over Directed Graphs With Geometric Convergence
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Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization
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Convergence of Asynchronous Distributed Gradient Methods Over Stochastic Networks
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A Bregman Splitting Scheme for Distributed Optimization Over Networks
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Push–Pull Gradient Methods for Distributed Optimization in Networks
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Decentralized Proximal Gradient Algorithms With Linear Convergence Rates
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Harnessing Smoothness to Accelerate Distributed Optimization
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NEXT: In-Network Nonconvex Optimization
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A Unification and Generalization of Exact Distributed First-Order Methods
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A Proximal Gradient Algorithm for Decentralized Composite Optimization
inferredcites →https://doi.org/10.1109/tsp.2018.2875883openalex-snapshot
Exact Diffusion for Distributed Optimization and Learning—Part II: Convergence Analysis
inferredcites →https://doi.org/10.1109/tsp.2018.2875898openalex-snapshot
Exact Diffusion for Distributed Optimization and Learning—Part I: Algorithm Development
inferredcites →https://doi.org/10.1109/tsp.2019.2926022openalex-snapshot
A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates
inferredcites →https://doi.org/10.1109/tsp.2021.3086579openalex-snapshot
Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis
inferredcites →https://doi.org/10.1137/14096668xopenalex-snapshot
EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization
inferredcites →https://doi.org/10.1137/16m1084316openalex-snapshot
Achieving Geometric Convergence for Distributed Optimization Over Time-Varying Graphs
inferredcites →https://doi.org/10.1137/19m1259973openalex-snapshot
Distributed Optimization Based on Gradient Tracking Revisited: Enhancing Convergence Rate via Surrogation
inferredcites →https://doi.org/10.2307/2005542openalex-snapshot
Iterative Solution of Large Linear Systems
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Iterative Solution of Large Linear Systems.
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Geometrically convergent distributed optimization with uncoordinated step-sizes
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A Canonical Form for First-Order Distributed Optimization Algorithms
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Communication Efficient Distributed Optimization using an Approximate Newton-type Method
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Optimal algorithms for smooth and strongly convex distributed optimization in networks
inferredcites →https://doi.org/10.48550/arxiv.1905.07996openalex-snapshot
A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization
inferredcites →https://doi.org/10.48550/arxiv.1910.10666openalex-snapshot
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Table of Contents
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2021 Index IEEE Transactions on Signal Processing Vol. 69
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A Unified and Refined Convergence Analysis for Non-Convex Decentralized Learning
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Decentralized Inexact Proximal Gradient Method With Network-Independent Stepsizes for Convex Composite Optimization
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A Dual Inexact Nonsmooth Newton Method for Distributed Optimization
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Differentially Private Distributed Optimization Over Time-Varying Unbalanced Networks With Linear Convergence Rates
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A Convergence-Motivated Learning-to-Optimize Framework for Decentralized Optimization
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Distributed Optimization Based on Gradient Tracking Revisited: Enhancing Convergence Rate via Surrogation
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Decentralized Conjugate Gradient Tracking for Sum-of-Nonconvex Optimization
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A Unified Framework for Robust Distributed Optimization Under Bounded Disturbances
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Federated Learning with Privacy and Communication Efficiency on Heterogeneous Data
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Distributed and Inexact Proximal Gradient Method for Online Convex Optimization
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First-Order Algorithms for Nonlinear Structured Optimization
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Decentralized Primal-Dual Proximal Operator Algorithm for Constrained Nonsmooth Composite Optimization Problems over Networks
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A Distributed Primal-Dual Framework for Composite Optimization with Nonseparable Coupled NonSmooth Function
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Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization
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Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems
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A Hybrid Variance-Reduced Method for Decentralized Stochastic Non-Convex Optimization
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Decentralized Composite Optimization with Compression
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A Stochastic Proximal Gradient Framework for Decentralized Non-Convex Composite Optimization: Topology-Independent Sample Complexity and Communication Efficiency
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