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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 326–350 of 536 papers

TitleStatusHype
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point—0
Multi-frequency calibration for DOA estimation with distributed sensors—0
Multi-Message Shuffled Privacy in Federated Learning—0
Multi-objective Distributed Optimization for Zonal Distribution System with Multi-Microgrids—0
Multi-Timescale Gradient Sliding for Distributed Optimization—0
MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods—0
Network-aware EV charging and discharging in unbalanced distribution grids: A distributed, robust approach against communication failures—0
Network-Aware Value Stacking of Community Battery via Asynchronous Distributed Optimization—0
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus—0
Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing—0
On Communication Compression for Distributed Optimization on Heterogeneous Data—0
On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations—0
On Distributed Adaptive Optimization with Gradient Compression—0
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking—0
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading—0
Online Distributed Optimization on Dynamic Networks—0
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
On the Convergence of Decentralized Adaptive Gradient Methods—0
On the Convergence of Local Descent Methods in Federated Learning—0
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control—0
Optimal Algorithms for Distributed Optimization—0
Optimal Data Splitting in Distributed Optimization for Machine Learning—0
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity—0
Optimally Managing the Impacts of Convergence Tolerance for Distributed Optimal Power Flow—0
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