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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 401–450 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
Byzantine Fault Tolerant Distributed Linear Regression—0
Differentially Private Consensus-Based Distributed Optimization—0
SLSGD: Secure and Efficient Distributed On-device Machine Learning—0
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems—0
On Maintaining Linear Convergence of Distributed Learning and Optimization under Limited Communication—0
Sparsity Constrained Distributed Unmixing of Hyperspectral Data—0
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free—0
Do Subsampled Newton Methods Work for High-Dimensional Data?—0
Predict Globally, Correct Locally: Parallel-in-Time Optimal Control of Neural Networks—0
Robust Learning from Untrusted SourcesCode0
99% of Distributed Optimization is a Waste of Time: The Issue and How to Fix it—0
Trajectory Normalized Gradients for Distributed Optimization—0
Distributed Nesterov gradient methods over arbitrary graphs—0
A continuous-time analysis of distributed stochastic gradient—0
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Stochastic Distributed Optimization for Machine Learning from Decentralized Features—0
Federated Optimization in Heterogeneous NetworksCode1
Solving Non-smooth Constrained Programs with Lower Complexity than O(1/ ): A Primal-Dual Homotopy Smoothing Approach—0
Markov Chain Block Coordinate Descent—0
Distributed Convex Optimization With Limited Communications—0
Distributed optimization in wireless sensor networks: an island-model framework—0
Sparsified SGD with MemoryCode0
A Dual Approach for Optimal Algorithms in Distributed Optimization over Networks—0
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks—0
An Exact Quantized Decentralized Gradient Descent Algorithm—0
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