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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 301–325 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
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