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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 301–350 of 536 papers

TitleStatusHype
Learning (With) Distributed Optimization—0
Leveraging Function Space Aggregation for Federated Learning at Scale—0
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM—0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality—0
Linear Convergent Decentralized Optimization with Compression—0
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data—0
Local Methods with Adaptivity via Scaling—0
LocalNewton: Reducing Communication Bottleneck for Distributed Learning—0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms—0
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time—0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks—0
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data—0
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression—0
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 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
Optimal Methods for Convex Risk Averse Distributed Optimization—0
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement—0
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