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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 201–250 of 536 papers

TitleStatusHype
Distributed Mirror Descent with Integral Feedback: Asymptotic Convergence Analysis of Continuous-time Dynamics—0
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking—0
Distributed model predictive control without terminal cost under inexact distributed optimization—0
Parallel Momentum Methods Under Biased Gradient Estimations—0
Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version—0
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in Voltage Control—0
Distributed Multi-Task Relationship Learning—0
Distributed Nesterov gradient methods over arbitrary graphs—0
Distributed Newton Can Communicate Less and Resist Byzantine Workers—0
Distributed Newton-like Algorithms and Learning for Optimized Power Dispatch—0
Distributed Online Optimization in Dynamic Environments Using Mirror Descent—0
GTAdam: Gradient Tracking with Adaptive Momentum for Distributed Online Optimization—0
Distributed Online Optimization with Byzantine Adversarial Agents—0
Communication Efficient, Differentially Private Distributed Optimization using Correlation-Aware Sketching—0
Distributed Optimization for Client-Server Architecture with Negative Gradient Weights—0
Distributed Optimization for Massive Connectivity—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
Distributed Optimization for Over-Parameterized Learning—0
Asynchronous Forward Bounding for Distributed COPs—0
Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits—0
Distributed Optimization for Smart Cyber-Physical Networks—0
Distributed Optimization for Traffic Light Control and Connected Automated Vehicle Coordination in Mixed-Traffic Intersections—0
Correlated Quantization for Faster Nonconvex Distributed Optimization—0
Distributed optimization in wireless sensor networks: an island-model framework—0
Distributed Optimization of Convex Sum of Non-Convex Functions—0
Distributed Optimization of Graph Convolutional Network using Subgraph Variance—0
Distributed Optimization over Block-Cyclic Data—0
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm—0
Communication-Efficient Distributed SGD with Compressed Sensing—0
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates—0
Distributed Optimization via Gradient Descent with Event-Triggered Zooming over Quantized Communication—0
Distributed Optimization via Kernelized Multi-armed Bandits—0
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data—0
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping—0
Distributed Optimization with Finite Bit Adaptive Quantization for Efficient Communication and Precision Enhancement—0
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks—0
Distributed Personalized Gradient Tracking with Convex Parametric Models—0
Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers—0
Correlated quantization for distributed mean estimation and optimization—0
Distributed Random Reshuffling Methods with Improved Convergence—0
Distributed Random Reshuffling over Networks—0
Distributed Resource Allocation Algorithms for Multi-Operator Cognitive Communication Systems—0
Asynchronous Distributed Optimization with Delay-free Parameters—0
Distributed saddle point problems for strongly concave-convex functions—0
Distributed Second Order Methods with Fast Rates and Compressed Communication—0
Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds—0
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems—0
Distributed Stochastic Variance Reduced Gradient Methods and A Lower Bound for Communication Complexity—0
Distributed System Identification for Linear Stochastic Systems with Binary Sensors—0
Aiding Global Convergence in Federated Learning via Local Perturbation and Mutual Similarity Information—0
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