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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 126–150 of 536 papers

TitleStatusHype
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
A Plug and Play Distributed Secondary Controller for Microgrids with Grid-Forming Inverters—0
Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategies—0
Anytime MiniBatch: Exploiting Stragglers in Online Distributed Optimization—0
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision—0
Cell Zooming with Masked Data for Off-Grid Small Cell Networks: Distributed Optimization Approach—0
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection—0
A Distributed ADMM-based Deep Learning Approach for Thermal Control in Multi-Zone Buildings under Demand Response Events—0
Acceleration for Compressed Gradient Descent in Distributed Optimization—0
Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity—0
Accelerated consensus via Min-Sum Splitting—0
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization—0
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise—0
CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression—0
Can Competition Outperform Collaboration? The Role of Misbehaving Agents—0
An Integrated Optimization + Learning Approach to Optimal Dynamic Pricing for the Retailer with Multi-type Customers in Smart Grids—0
A Differential Private Method for Distributed Optimization in Directed Networks via State Decomposition—0
Byzantine-Robust Learning on Heterogeneous Datasets via Resampling—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
Byzantine-Resilient Output Optimization of Multiagent via Self-Triggered Hybrid Detection Approach—0
SHED: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing—0
Debiased distributed learning for sparse partial linear models in high dimensions—0
Accelerating variational quantum algorithms with multiple quantum processors—0
Byzantine-Resilient Non-Convex Stochastic Gradient Descent—0
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