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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 521–530 of 536 papers

TitleStatusHype
Dynamic communication topologies for distributed heuristics in energy system optimization algorithmsCode0
Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem PrincipleCode0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
CoCoA: A General Framework for Communication-Efficient Distributed OptimizationCode0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth RegularizationCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Error Feedback Shines when Features are RareCode0
Byzantine-Robust Loopless Stochastic Variance-Reduced GradientCode0
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