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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 76–100 of 536 papers

TitleStatusHype
Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps—0
Graph Neural Networks Gone Hogwild—0
Distributed Utility Optimization in Vehicular Communication Systems—0
A KL-based Analysis Framework with Applications to Non-Descent Optimization Methods—0
ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM TrainingCode1
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data—0
Local Methods with Adaptivity via Scaling—0
Differentially-Private Distributed Model Predictive Control of Linear Discrete-Time Systems with Global Constraints—0
MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable ConvergenceCode1
The Limits and Potentials of Local SGD for Distributed Heterogeneous Learning with Intermittent Communication—0
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance—0
Structured Reinforcement Learning for Incentivized Stochastic Covert Optimization—0
Distributed Traffic Signal Control via Coordinated Maximum Pressure-plus-Penalty—0
Estimation Network Design framework for efficient distributed optimization—0
Rate Analysis of Coupled Distributed Stochastic Approximation for Misspecified Optimization—0
Distributed Fractional Bayesian Learning for Adaptive Optimization—0
Federated Optimization with Doubly Regularized Drift Correction—0
PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory SystemCode0
Generalized Gradient Descent is a Hypergraph Functor—0
Distributed Maximum Consensus over Noisy Links—0
Network-Aware Value Stacking of Community Battery via Asynchronous Distributed Optimization—0
Quantization Avoids Saddle Points in Distributed Optimization—0
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction—0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
MUSIC: Accelerated Convergence for Distributed Optimization With Inexact and Exact Methods—0
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