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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 226–250 of 536 papers

TitleStatusHype
Distributed Optimization of Graph Convolutional Network using Subgraph Variance—0
Distributed Optimization over Block-Cyclic Data—0
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates—0
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication—0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping—0
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement—0
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks—0
Distributed Personalized Gradient Tracking with Convex Parametric Models—0
Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Distributed Random Reshuffling Methods with Improved Convergence—0
Distributed Random Reshuffling over Networks—0
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems—0
Correlated quantization for distributed mean estimation and optimization—0
Distributed saddle point problems for strongly concave-convex functions—0
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds—0
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems—0
Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity—0
Distributed System Identification for Linear Stochastic Systems with Binary Sensors—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
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