4.7 Article

A Unifying Approximate Method of Multipliers for Distributed Composite Optimization

Related references

Note: Only part of the references are listed.
Article Automation & Control Systems

Push-Pull Gradient Methods for Distributed Optimization in Networks

Shi Pu et al.

Summary: In this article, a distributed convex optimization approach is introduced, which achieves minimal cost functions through the push-pull gradient method for information exchange between nodes in a network. Experimental results demonstrate that this algorithm exhibits linear convergence in various network architectures, especially showing significant performance in random-gossip settings.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2021)

Article Automation & Control Systems

Distributed Big-Data Optimization via Blockwise Gradient Tracking

Ivano Notarnicola et al.

Summary: This study focuses on distributed big-data nonconvex optimization in multiagent networks. A novel distributed solution method is proposed, where agents update one block of the entire decision vector in an uncoordinated fashion to address nonconvexity and reduce communication overhead in large-scale problems. Numerical results demonstrate the effectiveness of the algorithm and highlight the impact of block dimension on communication overhead and convergence speed.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2021)

Article Automation & Control Systems

A Second-Order Proximal Algorithm for Consensus Optimization

Xuyang Wu et al.

Summary: The proposed SoPro algorithm is a distributed second-order proximal algorithm that solves consensus optimization in networks. It converges linearly to the exact optimal solution when the global cost function is locally restricted strongly convex, relaxing the standard global strong convexity condition required by existing distributed optimization algorithms. Moreover, SoPro is demonstrated to be computation- and communication-efficient compared to state-of-the-art distributed second-order methods, with extensive simulations showing its competitive convergence performance.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2021)

Article Automation & Control Systems

Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

Sulaiman A. Alghunaim et al.

Summary: This article investigates a type of nonsmooth decentralized multiagent optimization problems and proposes a general algorithmic framework that achieves linear convergence in the presence of nonsmooth terms. However, for specific problems with nonsmooth terms, some algorithms fail to achieve linear convergence for strongly convex objectives and different nonsmooth terms.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2021)

Article Engineering, Electrical & Electronic

Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis

Jinming Xu et al.

Summary: The study introduces a general algorithmic framework for distributed composite optimization over networks, with a focus on operator splitting theory for convergence analysis. The algorithm exhibits both linear and sublinear convergence rates, along with the ability to adjust the ratio between communication and computation.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2021)

Article Engineering, Electrical & Electronic

An Improved Convergence Analysis for Decentralized Online Stochastic Non-Convex Optimization

Ran Xin et al.

Summary: This paper investigates decentralized online stochastic non-convex optimization over a network of nodes. By integrating gradient tracking technique, the GT-DSGD algorithm is shown to have desirable characteristics towards minimizing a sum of smooth non-convex functions, achieving network-independent performances that match the centralized minibatch SGD.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2021)

Article Automation & Control Systems

A Fast Distributed Asynchronous Newton-Based Optimization Algorithm

Fatemeh Mansoori et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2020)

Article Computer Science, Software Engineering

Distributed nonconvex constrained optimization over time-varying digraphs

Gesualdo Scutari et al.

MATHEMATICAL PROGRAMMING (2019)

Article Engineering, Electrical & Electronic

Communication-Censored ADMM for Decentralized Consensus Optimization

Yaohua Liu et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2019)

Article Engineering, Electrical & Electronic

A Decentralized Proximal-Gradient Method With Network Independent Step-Sizes and Separated Convergence Rates

Zhi Li et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2019)

Article Automation & Control Systems

Fenchel Dual Gradient Methods for Distributed Convex Optimization Over Time-Varying Networks

Xuyang Wu et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2019)

Article Automation & Control Systems

Optimal distributed stochastic mirror descent for strongly convex optimization

Deming Yuan et al.

AUTOMATICA (2018)

Article Automation & Control Systems

Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization

N. S. Aybat et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2018)

Article Automation & Control Systems

A Bregman Splitting Scheme for Distributed Optimization Over Networks

Jinming Xu et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2018)

Article Automation & Control Systems

Harnessing Smoothness to Accelerate Distributed Optimization

Guannan Qu et al.

IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS (2018)

Article Automation & Control Systems

Asynchronous Distributed Optimization Via Randomized Dual Proximal Gradient

Ivano Notarnicola et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2017)

Article Automation & Control Systems

Convergence Rate of Distributed ADMM Over Networks

Ali Makhdoumi et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2017)

Article Engineering, Electrical & Electronic

Stochastic Proximal Gradient Consensus Over Random Networks

Mingyi Hong et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2017)

Article Mathematics, Applied

ACHIEVING GEOMETRIC CONVERGENCE FOR DISTRIBUTED OPTIMIZATION OVER TIME-VARYING GRAPHS

Angelia Nedic et al.

SIAM JOURNAL ON OPTIMIZATION (2017)

Article Mathematics, Applied

NEWTON-LIKE METHOD WITH DIAGONAL CORRECTION FOR DISTRIBUTED OPTIMIZATION

Dragana Bajovic et al.

SIAM JOURNAL ON OPTIMIZATION (2017)

Article Automation & Control Systems

A fast proximal gradient algorithm for decentralized composite optimization over directed networks

Jinshan Zeng et al.

SYSTEMS & CONTROL LETTERS (2017)

Article Automation & Control Systems

A Coordinate Descent Primal-Dual Algorithm and Application to Distributed Asynchronous Optimization

Pascal Bianchi et al.

IEEE TRANSACTIONS ON AUTOMATIC CONTROL (2016)

Article Engineering, Electrical & Electronic

DQM: Decentralized Quadratically Approximated Alternating Direction Method of Multipliers

Aryan Mokhtari et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2016)

Article Mathematics, Applied

ON THE CONVERGENCE OF DECENTRALIZED GRADIENT DESCENT

Kun Yuan et al.

SIAM JOURNAL ON OPTIMIZATION (2016)

Article Automation & Control Systems

Primal-dual algorithm for distributed constrained optimization

Jinlong Lei et al.

SYSTEMS & CONTROL LETTERS (2016)

Article Engineering, Electrical & Electronic

NEXT: In-Network Nonconvex Optimization

Paolo Di Lorenzo et al.

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS (2016)

Article Engineering, Electrical & Electronic

A Decentralized Second-Order Method with Exact Linear Convergence Rate for Consensus Optimization

Aryan Mokhtari et al.

IEEE TRANSACTIONS ON SIGNAL AND INFORMATION PROCESSING OVER NETWORKS (2016)

Article Engineering, Electrical & Electronic

A Proximal Gradient Algorithm for Decentralized Composite Optimization

Wei Shi et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2015)

Article Mathematics, Applied

EXTRA: AN EXACT FIRST-ORDER ALGORITHM FOR DECENTRALIZED CONSENSUS OPTIMIZATION

Wei Shi et al.

SIAM JOURNAL ON OPTIMIZATION (2015)

Article Engineering, Electrical & Electronic

On the Linear Convergence of the ADMM in Decentralized Consensus Optimization

Wei Shi et al.

IEEE TRANSACTIONS ON SIGNAL PROCESSING (2014)

Article Operations Research & Management Science

Mirror descent and nonlinear projected subgradient methods for convex optimization

A Beck et al.

OPERATIONS RESEARCH LETTERS (2003)