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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 441–450 of 536 papers

TitleStatusHype
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?—0
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation—0
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective—0
Utilizing Redundancy in Cost Functions for Resilience in Distributed Optimization and Learning—0
Variance Reduction in Deep Learning: More Momentum is All You Need—0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
When Evolutionary Computation Meets Privacy—0
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Without-Replacement Sampling for Stochastic Gradient Methods: Convergence Results and Application to Distributed Optimization—0
Without-Replacement Sampling for Stochastic Gradient Methods—0
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