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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 426–450 of 536 papers

TitleStatusHype
OverSketched Newton: Fast Convex Optimization for Serverless SystemsCode0
Byzantine Fault Tolerant Distributed Linear Regression—0
Differentially Private Consensus-Based Distributed Optimization—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
Sparsity Constrained Distributed Unmixing of Hyperspectral Data—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Do Subsampled Newton Methods Work for High-Dimensional Data?—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Robust Learning from Untrusted SourcesCode0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Trajectory Normalized Gradients for Distributed Optimization—0
Distributed Nesterov gradient methods over arbitrary graphs—0
A continuous-time analysis of distributed stochastic gradient—0
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Stochastic Distributed Optimization for Machine Learning from Decentralized Features—0
Solving Non-smooth Constrained Programs with Lower Complexity than O(1/ ): A Primal-Dual Homotopy Smoothing Approach—0
Markov Chain Block Coordinate Descent—0
Distributed Convex Optimization With Limited Communications—0
Distributed optimization in wireless sensor networks: an island-model framework—0
Sparsified SGD with MemoryCode0
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks—0
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
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