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Distributed Optimization
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization
Multi-Timescale Gradient Sliding for Distributed Optimization
Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking
Distributed gradient methods under heavy-tailed communication noise
Online distributed optimization for spatio-temporally constrained real-time peer-to-peer energy trading
Privacy-Preserving Peer-to-Peer Energy Trading via Hybrid Secure Computations
Federated Learning: From Theory to Practice
Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs
LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization
Distributed Optimization with Efficient Communication, Event-Triggered Solution Enhancement, and Operation Stopping
Distributed model predictive control without terminal cost under inexact distributed optimization
Hessian Riemannian Flow For Multi-Population Wardrop Equilibrium
Residual-Evasive Attacks on ADMM in Distributed Optimization
Distributed Optimization with Gradient Tracking over Heterogeneous Delay-Prone Directed Networks
Graph Neural Network-Based Distributed Optimal Control for Linear Networked Systems: An Online Distributed Training Approach
Reducing the Communication of Distributed Model Predictive Control: Autoencoders and Formation Control
Dynamic Incentive Strategies for Smart EV Charging Stations: An LLM-Driven User Digital Twin Approach
ALADIN-$β$: A Distributed Optimization Algorithm for Solving MPCC Problems
Combining Graph Attention Networks and Distributed Optimization for Multi-Robot Mixed-Integer Convex Programming
Convergence Theory of Flexible ALADIN for Distributed Optimization
Collaborative Satisfaction of Long-Term Spatial Constraints in Multi-Agent Systems: A Distributed Optimization Approach (extended version)
Communication Efficient Federated Learning with Linear Convergence on Heterogeneous Data
Byzantine-Resilient Federated Learning via Distributed Optimization
Accelerated Distributed Optimization with Compression and Error Feedback
Distributed Pose Graph Optimization using the Splitting Method based on the Alternating Direction Method of Multipliers
From Centralized to Decentralized Federated Learning: Theoretical Insights, Privacy Preservation, and Robustness Challenges
Opportunistic Routing in Wireless Communications via Learnable State-Augmented Policies
Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs
Smoothed Normalization for Efficient Distributed Private Optimization
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness
Efficient Distributed Optimization under Heavy-Tailed Noise
A Survey of Optimization Methods for Training DL Models: Theoretical Perspective on Convergence and Generalization
Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization
Communication-Efficient Distributed Kalman Filtering using ADMM
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