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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

Showing 501536 of 536 papers

TitleStatusHype
Distributed optimization for nonrigid nano-tomographyCode0
Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUsCode0
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive SynchronizationCode0
A primal-dual perspective for distributed TD-learningCode0
PowerSGD: Practical Low-Rank Gradient Compression for Distributed OptimizationCode0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningCode0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved RatesCode0
Manifold Identification for Ultimately Communication-Efficient Distributed OptimizationCode0
Robust Learning from Untrusted SourcesCode0
Opportunistic Routing in Wireless Communications via Learnable State-Augmented PoliciesCode0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
An Accelerated Communication-Efficient Primal-Dual Optimization Framework for Structured Machine LearningCode0
Communication Efficient Distributed Optimization using an Approximate Newton-type MethodCode0
Communication- and Computation-Efficient Distributed Submodular Optimization in Robot Mesh NetworksCode0
Optimal algorithms for smooth and strongly convex distributed optimization in networksCode0
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual FrameworkCode0
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
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex OptimizationCode0
Optimization for Large-Scale Machine Learning with Distributed Features and ObservationsCode0
SlowMo: Improving Communication-Efficient Distributed SGD with Slow MomentumCode0
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk MinimizationCode0
FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation RetrievalCode0
OverSketched Newton: Fast Convex Optimization for Serverless SystemsCode0
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