Strong consistency of random gradient‐free algorithms for distributed optimization

DOI10.1002/oca.2254zbMath1362.93172OpenAlexW2344594310MaRDI QIDQ5346596

Xing-Min Chen, Chao Gao

Publication date: 26 May 2017

Published in: Optimal Control Applications and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/oca.2254

zbMATH Keywords

convergence analysismulti-agent systemsdistributed optimizationGaussian smoothingrandom gradient-free method

Mathematics Subject Classification ID

Transformations (93B17) Agent technology and artificial intelligence (68T42)

Related Items

Asymptotic properties of dual averaging algorithm for constrained distributed stochastic optimization ⋮ Gradient-free distributed optimization with exact convergence ⋮ A gradient‐free distributed optimization method for convex sum of nonconvex cost functions ⋮ A distributed accelerated optimization algorithm over time‐varying directed graphs with uncoordinated step‐sizes ⋮ A resilient distributed optimization strategy against false data injection attacks ⋮ A fixed step distributed proximal gradient push‐pull algorithm based on integral quadratic constraint

Cites Work

• Unnamed Item
• Distributed stochastic subgradient projection algorithms for convex optimization
• Randomized optimal consensus of multi-agent systems
• Gradient-free method for nonsmooth distributed optimization
• Incremental Subgradient Methods for Nondifferentiable Optimization
• Incremental Stochastic Subgradient Algorithms for Convex Optimization
• A Randomized Incremental Subgradient Method for Distributed Optimization in Networked Systems
• Distributed Subgradient Methods for Multi-Agent Optimization
• Constrained Consensus and Optimization in Multi-Agent Networks
• Gradient‐free method for distributed multi‐agent optimization via push‐sum algorithms
• Convergence of a Multi-Agent Projected Stochastic Gradient Algorithm for Non-Convex Optimization