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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 211–220 of 536 papers

TitleStatusHype
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Distributed Online Optimization with Byzantine Adversarial Agents—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights—0
Distributed Optimization for Massive Connectivity—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
Deep Distributed Optimization for Large-Scale Quadratic Programming—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
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