Inertial accelerated primal-dual methods for linear equality constrained convex optimization problems

DOI10.1007/s11075-021-01246-yzbMath1496.90056arXiv2103.12937OpenAlexW4206228871MaRDI QIDQ2159429

Rong Hu, Ya-Ping Fang, Xin He

Publication date: 1 August 2022

Published in: Numerical Algorithms (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2103.12937

zbMATH Keywords

inexactness\( \mathcal{O}(1/k^2)\) convergence rateinertial accelerated primal-dual methodlinear equality constrained convex optimization problem

Mathematics Subject Classification ID

Analysis of algorithms (68W40) Convex programming (90C25) Large-scale problems in mathematical programming (90C06) Decomposition methods (49M27)

Related Items (5)

Reducing the Complexity of Two Classes of Optimization Problems by Inexact Accelerated Proximal Gradient Method ⋮ Fast augmented Lagrangian method in the convex regime with convergence guarantees for the iterates ⋮ Inertial primal-dual dynamics with damping and scaling for linearly constrained convex optimization problems ⋮ Time rescaling of a primal-dual dynamical system with asymptotically vanishing damping ⋮ Fast primal-dual algorithm via dynamical system for a linearly constrained convex optimization problem

Uses Software

• PDCO
• SPGL1

Cites Work

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• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems
• Gradient methods for minimizing composite functions
• Accelerated Bregman method for linearly constrained \(\ell _1-\ell _2\) minimization
• Fast alternating linearization methods for minimizing the sum of two convex functions
• A class of customized proximal point algorithms for linearly constrained convex optimization
• Customized proximal point algorithms for linearly constrained convex minimization and saddle-point problems: a unified approach
• Inexact accelerated augmented Lagrangian methods
• A proximal-based deomposition method for compositions method for convex minimization problems
• Randomized optimal consensus of multi-agent systems
• Accelerated linearized Bregman method
• Convergence rate of inertial forward-backward algorithm beyond Nesterov's rule
• Accelerated alternating direction method of multipliers: an optimal \(O(1 / K)\) nonergodic analysis
• Fast convergence of inertial dynamics and algorithms with asymptotic vanishing viscosity
• Atomic Decomposition by Basis Pursuit
• The Rate of Convergence of Nesterov's Accelerated Forward-Backward Method is Actually Faster Than $1/k^2$
• A Differential Equation for Modeling Nesterov's Accelerated Gradient Method: Theory and Insights
• Convergence Rates of Inertial Primal-Dual Dynamical Methods for Separable Convex Optimization Problems
• Probing the Pareto Frontier for Basis Pursuit Solutions
• Non-stationary First-Order Primal-Dual Algorithms with Faster Convergence Rates
• Accelerated First-Order Primal-Dual Proximal Methods for Linearly Constrained Composite Convex Programming
• A Dynamical Approach to an Inertial Forward-Backward Algorithm for Convex Minimization
• On the Nonergodic Convergence Rate of an Inexact Augmented Lagrangian Framework for Composite Convex Programming
• Bregman Iterative Algorithms for $\ell_1$-Minimization with Applications to Compressed Sensing

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