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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 351–375 of 536 papers

TitleStatusHype
Approximate Gradient Coding with Optimal Decoding—0
Federated Accelerated Stochastic Gradient DescentCode1
Byzantine-Robust Learning on Heterogeneous Datasets via BucketingCode1
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
MANGO: A Python Library for Parallel Hyperparameter TuningCode1
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
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General FrameworkCode1
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
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