Distributed dual subgradient methods with averaging and applications to grid optimization
From MaRDI portal
Publication:6644268
DOI10.1007/s10957-024-02385-7MaRDI QIDQ6644268
Carolyn L. Beck, Thinh T. Doan, Subhonmesh Bose, Ye Guo, Haitian Liu, Hoa Dinh Nguyen
Publication date: 27 November 2024
Published in: Journal of Optimization Theory and Applications (Search for Journal in Brave)
Cites Work
- Unnamed Item
- Unnamed Item
- Primal recovery from consensus-based dual decomposition for distributed convex optimization
- Coordinated dispatch of regional transmission organizations: theory and example
- Convex proximal bundle methods in depth: a unified analysis for inexact oracles
- Dual subgradient method with averaging for optimal resource allocation
- Introductory lectures on convex optimization. A basic course.
- Dual decomposition for multi-agent distributed optimization with coupling constraints
- On linear convergence of a distributed dual gradient algorithm for linearly constrained separable convex problems
- Ergodic, primal convergence in dual subgradient schemes for convex programming
- A stochastic primal-dual method for optimization with conditional value at risk constraints
- Primal convergence from dual subgradient methods for convex optimization
- Price-Based Coordinated Aggregation of Networked Distributed Energy Resources
- Exact Convex Relaxation of Optimal Power Flow in Radial Networks
- Approximate Primal Solutions and Rate Analysis for Dual Subgradient Methods
- Matrix Analysis
- Distributed Coordination of DERs With Storage for Dynamic Economic Dispatch
- Incremental Bundle Methods using Upper Models
- An Incentive-Based Online Optimization Framework for Distribution Grids
- Constraint-Coupled Distributed Optimization: A Relaxation and Duality Approach
- Distributed Subgradient Methods for Multi-Agent Optimization
- Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis
- Accelerated Distributed Nesterov Gradient Descent
- Distributed Smooth Convex Optimization With Coupled Constraints
- Distributed Saddle-Point Subgradient Algorithms With Laplacian Averaging
- Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
- Convex Relaxation of Optimal Power Flow—Part II: Exactness
- Quadratically Constrained Quadratic Programs on Acyclic Graphs With Application to Power Flow
- Nonlinear Programming
- A unified distributed method for constrained networked optimization via saddle-point dynamics
This page was built for publication: Distributed dual subgradient methods with averaging and applications to grid optimization