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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 401–450 of 536 papers

TitleStatusHype
Machine Learning for Large-Scale Optimization in 6G Wireless Networks—0
Machine Learning Infused Distributed Optimization for Coordinating Virtual Power Plant Assets—0
Markov Chain Block Coordinate Descent—0
Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing—0
A Proximal Gradient Method With Probabilistic Multi-Gossip Communications for Decentralized Composite Optimization—0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications to Parallel Machine Learning and Multi-Label Image Segmentation—0
Mixed Robust/Average Submodular Partitioning: Fast Algorithms, Guarantees, and Applications—0
Moreau Envelope ADMM for Decentralized Weakly Convex Optimization—0
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 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
Optimal Methods for Convex Risk Averse Distributed Optimization—0
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement—0
Optimization for Reinforcement Learning: From Single Agent to Cooperative Agents—0
Optimization in Open Networks via Dual Averaging—0
Parallel Feedforward Compensation for Output Synchronization: Fully Distributed Control and Indefinite Laplacian—0
Partitioning Data on Features or Samples in Communication-Efficient Distributed Optimization?—0
Peer-to-Peer Learning Dynamics of Wide Neural Networks—0
Pixel super-resolved lensless on-chip sensor with scattering multiplexing—0
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model—0
Asynchronous Decentralized SGD with Quantized and Local Updates—0
Popt4jlib: A Parallel/Distributed Optimization Library for Java—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Prescribed-time Convergent Distributed Multiobjective Optimization with Dynamic Event-triggered Communication—0
Coordinated Day-ahead Dispatch of Multiple Power Distribution Grids hosting Stochastic Resources: An ADMM-based Framework—0
Privacy-Preserving Distributed Market Mechanism for Active Distribution Networks—0
Privacy-Preserving Distributed Optimization and Learning—0
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations—0
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