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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 151–200 of 536 papers

TitleStatusHype
DEED: A General Quantization Scheme for Communication Efficiency in Bits—0
Deep Distributed Optimization for Large-Scale Quadratic Programming—0
Asynchronous Iterations in Optimization: New Sequence Results and Sharper Algorithmic Guarantees—0
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Design of heterogeneous multi-agent system for distributed computation—0
Detecting Shared Data Manipulation in Distributed Optimization Algorithms—0
Differentially Private Consensus-Based Distributed Optimization—0
Differentially Private Distributed Convex Optimization—0
Cost-efficient SVRG with Arbitrary Sampling—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
Correlation Aware Sparsified Mean Estimation Using Random Projection—0
DiLoCo: Distributed Low-Communication Training of Language Models—0
Discretized Distributed Optimization over Dynamic Digraphs—0
Distributed Continuous-Time Optimization with Uncertain Time-Varying Quadratic Cost Functions—0
Asynchronous Forward Bounding for Distributed COPs—0
Distributed Algorithms for Feature Extraction Off-loading in Multi-Camera Visual Sensor Networks—0
Distributed and time-varying primal-dual dynamics via contraction analysis—0
Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent—0
Distributed Averaging Methods for Randomized Second Order Optimization—0
Distributed Collision-Free Motion Coordination on a Sphere: A Conic Control Barrier Function Approach—0
Distributed Convex Optimization With Limited Communications—0
Distributed Convex Optimization with State-Dependent (Social) Interactions over Random Networks—0
Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services—0
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Distributed Delay-Tolerant Strategies for Equality-Constraint Sum-Preserving Resource Allocation—0
Distributed Difference of Convex Optimization—0
Distributed Dual Quaternion Based Localization of Visual Sensor Networks—0
Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes—0
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization—0
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage—0
Distributed estimation of the inverse Hessian by determinantal averaging—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Fractional Bayesian Learning for Adaptive Optimization—0
Distributed gradient-based optimization in the presence of dependent aperiodic communication—0
Distributed gradient methods under heavy-tailed communication noise—0
Distributed Training of Graph Convolutional Networks—0
Distributed Graph Learning with Smooth Data Priors—0
Distributed Inexact Damped Newton Method: Data Partitioning and Load-Balancing—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
Distributed Learning of Generalized Linear Causal Networks—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Distributed learning with compressed gradients—0
Distributed Linear Regression with Compositional Covariates—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
Distributed Maximum Consensus over Noisy Links—0
New Bounds For Distributed Mean Estimation and Variance Reduction—0
Correlated quantization for distributed mean estimation and optimization—0
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