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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 151–200 of 536 papers

TitleStatusHype
Learning (With) Distributed Optimization—0
Continuous-Time Distributed Dynamic Programming for Networked Multi-Agent Markov Decision Processes—0
Federated K-Means Clustering via Dual Decomposition-based Distributed Optimization—0
Differentially Private Distributed Estimation and LearningCode0
Distributed Random Reshuffling Methods with Improved Convergence—0
Just One Byte (per gradient): A Note on Low-Bandwidth Decentralized Language Model Finetuning Using Shared RandomnessCode1
Robust Optimization, Structure/Control co-design, Distributed Optimization, Monolithic Optimization, Robust Control, Parametric Uncertainty—0
Towards Scalable Multi-View Reconstruction of Geometry and Materials—0
Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?—0
An Equivalent Circuit Approach to Distributed Optimization—0
Error Feedback Shines when Features are RareCode0
Coordinated Frequency-Constrained Stochastic Economic Dispatch for Integrated Transmission and Distribution System via Distributed Optimization—0
Q-SHED: Distributed Optimization at the Edge via Hessian Eigenvectors Quantization—0
On the Finite-Time Behavior of Suboptimal Linear Model Predictive Control—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling—0
Network-GIANT: Fully distributed Newton-type optimization via harmonic Hessian consensus—0
Distributed Coordination of Multi-Microgrids in Active Distribution Networks for Provisioning Ancillary Services—0
A Novel Decentralized Algorithm for Coordinating the Optimal Power and Traffic Flows with EVs based on Variable Inner Loop Selection—0
Stochastic Distributed Optimization under Average Second-order Similarity: Algorithms and Analysis—0
D-SVM over Networked Systems with Non-Ideal Linking Conditions—0
A Survey on Distributed Evolutionary Computation—0
CEC: Crowdsourcing-based Evolutionary Computation for Distributed Optimization—0
Distributed Optimization for Quadratic Cost Functions over Large-Scale Networks with Quantized Communication and Finite-Time Convergence—0
On Degeneracy Issues in Multi-parametric Programming and Critical Region Exploration based Distributed Optimization in Smart Grid Operations—0
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
Federated Learning as Variational Inference: A Scalable Expectation Propagation ApproachCode1
CEDAS: A Compressed Decentralized Stochastic Gradient Method with Improved Convergence—0
Algorithm Unrolling-Based Distributed Optimization for RIS-Assisted Cell-Free Networks—0
Beyond spectral gap (extended): The role of the topology in decentralized learningCode1
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
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