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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 401–425 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
Distributed Optimization by Network Flows with Spatio-Temporal Compression—0
Spatio-Temporal Communication Compression in Distributed Prime-Dual Flows—0
StochaLM: a Stochastic alternate Linearization Method for distributed optimization—0
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network—0
Stochastic, Distributed and Federated Optimization for Machine Learning—0
Stochastic Distributed Optimization for Machine Learning from Decentralized Features—0
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis—0
Straggler Mitigation in Distributed Optimization Through Data Encoding—0
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers—0
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction—0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization—0
Supervised MPC control of large-scale electricity networks via clustering methods—0
Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems—0
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial Participation—0
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