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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 501–525 of 536 papers

TitleStatusHype
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
A Survey of Resilient Coordination for Cyber-Physical Systems Against Malicious Attacks—0
A Survey on Distributed Evolutionary Computation—0
A survey on secure decentralized optimization and learning—0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis—0
Asynchronous Adaptation and Learning over Networks - Part II: Performance Analysis—0
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis—0
Asynchronous Distributed Optimization with Stochastic Delays—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
Asynchronous Forward Bounding for Distributed COPs—0
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees—0
Asynchronous Stochastic Optimization Robust to Arbitrary Delays—0
A Tight Convergence Analysis for Stochastic Gradient Descent with Delayed Updates—0
AttentionX: Exploiting Consensus Discrepancy In Attention from A Distributed Optimization Perspective—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning—0
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Byzantine Fault Tolerant Distributed Linear Regression—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Byzantine-Resilient Federated Learning via Distributed Optimization—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
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