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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 351–400 of 536 papers

TitleStatusHype
Distributed Training of Graph Convolutional Networks—0
A Unified Linear Speedup Analysis of Federated Averaging and Nesterov FedAvg—0
Distributed optimization for nonrigid nano-tomographyCode0
Federated Learning with Compression: Unified Analysis and Sharp Guarantees—0
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking—0
Linear Convergent Decentralized Optimization with Compression—0
Advances in Asynchronous Parallel and Distributed Optimization—0
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
Approximate Gradient Coding with Optimal Decoding—0
Distributed Newton Can Communicate Less and Resist Byzantine Workers—0
Distributed Optimization for Massive Connectivity—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
ALADIN-α -- An open-source MATLAB toolbox for distributed non-convex optimization—0
Recurrent Averaging Inequalities in Multi-Agent Control and Social Dynamics Modeling—0
FedSplit: An algorithmic framework for fast federated optimization—0
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems—0
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural NetworksCode0
Supervised MPC control of large-scale electricity networks via clustering methods—0
StochaLM: a Stochastic alternate Linearization Method for distributed optimization—0
Uncertain Multi-Agent Systems with Distributed Constrained Optimization Missions and Event-Triggered Communications: Application to Resource Allocation—0
DC-DistADMM: ADMM Algorithm for Constrained Distributed Optimization over Directed Graphs—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method—0
Communication-efficient Variance-reduced Stochastic Gradient Descent—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Decentralized gradient methods: does topology matter?—0
Improving Rate of Convergence via Gain Adaptation in Multi-Agent Distributed ADMM Framework—0
Multi-frequency calibration for DOA estimation with distributed sensors—0
Revisiting EXTRA for Smooth Distributed Optimization—0
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Adaptive Sampling Distributed Stochastic Variance Reduced Gradient for Heterogeneous Distributed Datasets—0
The Geometry of Sign Gradient Descent—0
Distributed Optimization over Block-Cyclic Data—0
Is Local SGD Better than Minibatch SGD?—0
Distributed Averaging Methods for Randomized Second Order Optimization—0
Differentially Quantized Gradient Methods—0
Estimating the Error of Randomized Newton Methods: A Bootstrap Approach—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Manifold Identification for Ultimately Communication-Efficient Distributed OptimizationCode0
Graph Learning Under Partial Observability—0
A Distributed Quasi-Newton Algorithm for Primal and Dual Regularized Empirical Risk MinimizationCode0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local ComputationsCode0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees—0
vqSGD: Vector Quantized Stochastic Gradient Descent—0
Learning-Accelerated ADMM for Distributed Optimal Power Flow—0
On the Convergence of Local Descent Methods in Federated Learning—0
Local SGD with Periodic Averaging: Tighter Analysis and Adaptive SynchronizationCode0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
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