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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 111–120 of 536 papers

TitleStatusHype
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty—0
Estimation Network Design framework for efficient distributed optimization—0
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization—0
Distributed Fractional Bayesian Learning for Adaptive Optimization—0
Federated Optimization with Doubly Regularized Drift Correction—0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
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