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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 201–250 of 536 papers

TitleStatusHype
When Evolutionary Computation Meets Privacy—0
On the Convergence of Decentralized Federated Learning Under Imperfect Information SharingCode0
Byzantine-Robust Loopless Stochastic Variance-Reduced GradientCode0
Differentially Private Distributed Convex Optimization—0
Multi-Message Shuffled Privacy in Federated Learning—0
TAMUNA: Doubly Accelerated Distributed Optimization with Local Training, Compression, and Partial ParticipationCode0
Distributed Optimization for Reactive Power Sharing and Stability of Inverter-Based Resources Under Voltage Limits—0
Sparse-SignSGD with Majority Vote for Communication-Efficient Distributed Learning—0
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Machine Learning for Large-Scale Optimization in 6G Wireless Networks—0
Decentralized Stochastic Multi-Player Multi-Armed Walking Bandits—0
Simulation-Integrated Distributed Optimal Power Flow for Unbalanced Power Distribution Systems—0
BALPA: A Balanced Primal-Dual Algorithm for Nonsmooth Optimization with Application to Distributed Optimization—0
Continual Learning with Distributed Optimization: Does CoCoA Forget?—0
Simple and Scalable Algorithms for Cluster-Aware Precision Medicine—0
Distributed Optimization with Quantized Gradient Descent—0
Impact of Redundancy on Resilience in Distributed Optimization and Learning—0
Fast Adaptive Federated Bilevel Optimization—0
GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational ComplexityCode0
Provably Doubly Accelerated Federated Learning: The First Theoretically Successful Combination of Local Training and Communication Compression—0
Distributed MPC for Self-Organized Cooperation of Multiagent Systems -- Extended Version—0
Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence—0
Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reductionCode0
Cooperative Tuning of Multi-Agent Optimal Control SystemsCode0
Distributed CPU Scheduling Subject to Nonlinear Constraints—0
Real-Time Distributed Model Predictive Control with Limited Communication Data Rates—0
Decentralized Optimization with Distributed Features and Non-Smooth Objective Functions—0
Consensus optimization approach for distributed Kalman filtering: performance recovery of centralized filtering with proofs—0
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Coordinating Flexible Ramping Products with Dynamics of the Natural Gas Network—0
Convergence Theory of Generalized Distributed Subgradient Method with Random Quantization—0
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking—0
Distributed Learning of Neural Lyapunov Functions for Large-Scale Networked Dissipative Systems—0
Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated LearningCode0
Simultaneous Contact-Rich Grasping and Locomotion via Distributed Optimization Enabling Free-Climbing for Multi-Limbed Robots—0
Can Competition Outperform Collaboration? The Role of Misbehaving Agents—0
On the Convergence of Distributed Stochastic Bilevel Optimization Algorithms over a Network—0
Distributed Adversarial Training to Robustify Deep Neural Networks at ScaleCode0
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression—0
A Computation and Communication Efficient Method for Distributed Nonconvex Problems in the Partial Participation Setting—0
Optimal Gradient Sliding and its Application to Distributed Optimization Under Similarity—0
Byzantine Machine Learning Made Easy by Resilient Averaging of Momentums—0
Distributed Optimization in Distribution Systems with Grid-Forming and Grid-Supporting Inverters—0
On Distributed Adaptive Optimization with Gradient Compression—0
EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed OptimizationCode0
Understanding A Class of Decentralized and Federated Optimization Algorithms: A Multi-Rate Feedback Control Perspective—0
Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement—0
Distributed Dynamic Safe Screening Algorithms for Sparse Regularization—0
FedADMM: A Federated Primal-Dual Algorithm Allowing Partial Participation—0
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