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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 226–250 of 536 papers

TitleStatusHype
Distributed Optimization of Graph Convolutional Network using Subgraph Variance—0
A Mirror Descent-Based Algorithm for Corruption-Tolerant Distributed Gradient Descent—0
Distributed Optimization Strategy for Multi Area Economic Dispatch Based on Electro Search Optimization Algorithm—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
Distributed Optimization via Energy Conservation Laws in Dilated Coordinates—0
Deep Distributed Optimization for Large-Scale Quadratic Programming—0
Distributed Optimization via Kernelized Multi-armed Bandits—0
DEED: A General Quantization Scheme for Communication Efficiency in Bits—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
Information-Geometric Barycenters for Bayesian Federated Learning—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
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—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
Adaptive Consensus ADMM for Distributed Optimization—0
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