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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 401–425 of 536 papers

TitleStatusHype
SlowMo: Improving Communication-Efficient Distributed SGD with Slow MomentumCode0
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model—0
Gradient-Consensus: Linearly Convergent Distributed Optimization Algorithm over Directed Graphs—0
Communication-Efficient Distributed Optimization in Networks with Gradient Tracking and Variance ReductionCode1
Convex Set Disjointness, Distributed Learning of Halfspaces, and LP Feasibility—0
Proximal gradient flow and Douglas-Rachford splitting dynamics: global exponential stability via integral quadratic constraints—0
Federated Learning: Challenges, Methods, and Future DirectionsCode0
Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization—0
Popt4jlib: A Parallel/Distributed Optimization Library for Java—0
Centralised and Distributed Optimization for Aggregated Flexibility Services Provision—0
Data Encoding for Byzantine-Resilient Distributed Optimization—0
Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex OptimizationCode0
Asymptotic Network Independence in Distributed Stochastic Optimization for Machine Learning—0
Distributed Optimization for Smart Cyber-Physical Networks—0
Secure Architectures Implementing Trusted Coalitions for Blockchained Distributed Learning (TCLearn)—0
Distributed Optimization for Over-Parameterized Learning—0
The Communication Complexity of Optimization—0
Communication-Efficient Accurate Statistical Estimation—0
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computations—0
Deep Learning for Distributed Optimization: Applications to Wireless Resource Management—0
PowerSGD: Practical Low-Rank Gradient Compression for Distributed OptimizationCode0
Accelerated Sparsified SGD with Error Feedback—0
Distributed estimation of the inverse Hessian by determinantal averaging—0
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension—0
OverSketched Newton: Fast Convex Optimization for Serverless SystemsCode0
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