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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 301–350 of 536 papers

TitleStatusHype
Distributed System Identification for Linear Stochastic Systems with Binary Sensors—0
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
Robust Distributed Optimization With Randomly Corrupted Gradients—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Asynchronous Stochastic Optimization Robust to Arbitrary Delays—0
Learning Autonomy in Management of Wireless Random Networks—0
Decentralized Personalized Federated Learning for Min-Max Problems—0
Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques—0
Auction-based and Distributed Optimization Approaches for Scheduling Observations in Satellite Constellations with Exclusive Orbit Portions—0
Assessing the Impacts of Nonideal Communications on Distributed Optimal Power Flow Algorithms—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
Optimization in Open Networks via Dual Averaging—0
LocalNewton: Reducing Communication Bottleneck for Distributed Learning—0
Innovation Compression for Communication-efficient Distributed Optimization with Linear Convergence—0
Improving the Transient Times for Distributed Stochastic Gradient Methods—0
Distributed Energy Trading Management for Renewable Prosumers with HVAC and Energy Storage—0
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
Distributed Experiment Design and Control for Multi-agent Systems with Gaussian Processes—0
Distributed Newton-like Algorithms and Learning for Optimized Power Dispatch—0
Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication BudgetCode0
Gradient-Tracking over Directed Graphs for solving Leaderless Multi-Cluster Games—0
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization—0
Straggler-Resilient Distributed Machine Learning with Dynamic Backup Workers—0
Bias-Variance Reduced Local SGD for Less Heterogeneous Federated Learning—0
Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization—0
Delayed Projection Techniques for Linearly Constrained Problems: Convergence Rates, Acceleration, and Applications—0
Cost-efficient SVRG with Arbitrary Sampling—0
Design of heterogeneous multi-agent system for distributed computation—0
Convergent Adaptive Gradient Methods in Decentralized Optimization—0
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
Wyner-Ziv Estimators for Distributed Mean Estimation with Side Information and OptimizationCode0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
Sparse sketches with small inversion bias—0
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems—0
Widely-distributed Radar Imaging Based on Consensus ADMM—0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms—0
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
Asynchronous Distributed Optimization with Stochastic Delays—0
Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuous-time Dynamics—0
Graph neural networks-based Scheduler for Production planning problems using Reinforcement Learning—0
Distributed Optimization, Averaging via ADMM, and Network TopologyCode0
On Communication Compression for Distributed Optimization on Heterogeneous Data—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Projected Push-Sum Gradient Descent-Ascent for Convex Optimizationwith Application to Economic Dispatch Problems—0
Distributed Personalized Gradient Tracking with Convex Parametric Models—0
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