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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 401–410 of 536 papers

TitleStatusHype
Single Point-Based Distributed Zeroth-Order Optimization with a Non-Convex Stochastic Objective Function—0
Smoothed Normalization for Efficient Distributed Private Optimization—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization—0
Solving Non-smooth Constrained Programs with Lower Complexity than O(1/ ): A Primal-Dual Homotopy Smoothing Approach—0
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning—0
Sparse sketches with small inversion bias—0
Sparsification as a Remedy for Staleness in Distributed Asynchronous SGD—0
Sparsity Constrained Distributed Unmixing of Hyperspectral Data—0
Spatial Reuse in Dense Wireless Areas: A Cross-layer Optimization Approach via ADMM—0
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