Push–Pull Gradient Methods for Distributed Optimization in Networks
From MaRDI portal
Publication:5853980
DOI10.1109/TAC.2020.2972824MaRDI QIDQ5853980
Shi Pu, Angelia Nedić, Jin-Ming Xu, Wei Shi
Publication date: 12 March 2021
Published in: IEEE Transactions on Automatic Control (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1810.06653
Related Items (25)
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks ⋮ Distributed multi-step subgradient projection algorithm with adaptive event-triggering protocols: a framework of multiagent systems ⋮ Distributed smooth optimisation with event-triggered proportional-integral algorithms ⋮ Cooperative fixed-time/finite-time distributed robust optimization of multi-agent systems ⋮ An event-triggering algorithm for decentralized stochastic optimization over networks ⋮ Distributed mirror descent algorithm over unbalanced digraphs based on gradient weighting technique ⋮ An accelerated exact distributed first-order algorithm for optimization over directed networks ⋮ A causal filter of gradient information for enhanced robustness and resilience in distributed convex optimization ⋮ A resilient distributed optimization strategy against false data injection attacks ⋮ A fixed step distributed proximal gradient push‐pull algorithm based on integral quadratic constraint ⋮ A Fenchel dual gradient method enabling regularization for nonsmooth distributed optimization over time-varying networks ⋮ Distributed online convex optimization with multiple coupled constraints: a double accelerated push-pull algorithm ⋮ Event-triggered primal-dual design with linear convergence for distributed nonstrongly convex optimization ⋮ Distributed stochastic compositional optimization problems over directed networks ⋮ Unnamed Item ⋮ Distributed stochastic gradient tracking methods ⋮ A study on distributed optimization over large-scale networked systems ⋮ A privacy-masking learning algorithm for online distributed optimization over time-varying unbalanced digraphs ⋮ Fully asynchronous policy evaluation in distributed reinforcement learning over networks ⋮ Solving leaderless multi-cluster games over directed graphs ⋮ Distributed gradient tracking methods with finite data rates ⋮ Multi-agent control: a graph-theoretic perspective ⋮ An accelerated distributed gradient method with local memory ⋮ Surplus-based accelerated algorithms for distributed optimization over directed networks ⋮ Triggered gradient tracking for asynchronous distributed optimization
This page was built for publication: Push–Pull Gradient Methods for Distributed Optimization in Networks