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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 51–75 of 536 papers

TitleStatusHype
Dynamic communication topologies for distributed heuristics in energy system optimization algorithmsCode0
A primal-dual perspective for distributed TD-learningCode0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUsCode0
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over NetworksCode0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
Distributed Optimization using Heterogeneous Compute SystemsCode0
Distributed optimization for nonrigid nano-tomographyCode0
Distributed Optimization with Arbitrary Local SolversCode0
Distributed Markov Chain Monte Carlo Sampling based on the Alternating Direction Method of MultipliersCode0
Differentially Private Distributed Estimation and LearningCode0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation AnalysisCode0
Byzantine-Robust Loopless Stochastic Variance-Reduced GradientCode0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
CoCoA: A General Framework for Communication-Efficient Distributed OptimizationCode0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
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