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Distributed Optimization

The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.

Source: Analysis of Distributed StochasticDual Coordinate Ascent

Papers

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Showing 351–375 of 536 papers

TitleStatusHype
GIANT: Globally Improved Approximate Newton Method for Distributed Optimization—0
Goal-Oriented Wireless Communication Resource Allocation for Cyber-Physical Systems—0
GoSGD: Distributed Optimization for Deep Learning with Gossip Exchange—0
Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs—0
Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization—0
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks—0
Gradient Sparsification for Communication-Efficient Distributed Optimization—0
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games—0
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach—0
Graph Neural Networks Gone Hogwild—0
Graphon Particle Systems, Part II: Dynamics of Distributed Stochastic Continuum Optimization—0
Hemingway: Modeling Distributed Optimization Algorithms—0
Hessian Riemannian Flow For Multi-Population Wardrop Equilibrium—0
Simple and Scalable Algorithms for Cluster-Aware Precision Medicine—0
High-performance Kernel Machines with Implicit Distributed Optimization and Randomization—0
High-Probability Convergence for Composite and Distributed Stochastic Minimization and Variational Inequalities with Heavy-Tailed Noise—0
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence—0
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Impact of Redundancy on Resilience in Distributed Optimization and Learning—0
Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework—0
Improving the Transient Times for Distributed Stochastic Gradient Methods—0
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity—0
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence—0
Graph Learning Under Partial Observability—0
Is Local SGD Better than Minibatch SGD?—0
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