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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 51–75 of 536 papers

TitleStatusHype
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Acceleration in Distributed Optimization under Similarity—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
A Reinforcement Learning Approach to Parameter Selection for Distributed Optimal Power Flow—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
A Sequential Approximation Framework for Coded Distributed Optimization—0
A Stochastic Large-scale Machine Learning Algorithm for Distributed Features and Observations—0
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—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
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Accelerating variational quantum algorithms with multiple quantum processors—0
An Equivalent Circuit Approach to Distributed Optimization—0
Accelerated Distributed Optimization with Compression and Error Feedback—0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
Information-Geometric Barycenters for Bayesian Federated Learning—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
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