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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 376–400 of 536 papers

TitleStatusHype
Iterative Pre-Conditioning to Expedite the Gradient-Descent Method—0
KKT Conditions, First-Order and Second-Order Optimization, and Distributed Optimization: Tutorial and Survey—0
Model Aggregation via Good-Enough Model Spaces—0
LAGO: Few-shot Crosslingual Embedding Inversion Attacks via Language Similarity-Aware Graph Optimization—0
LASER: Linear Compression in Wireless Distributed Optimization—0
Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees—0
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension—0
Learning-Accelerated ADMM for Distributed Optimal Power Flow—0
Learning Autonomy in Management of Wireless Random Networks—0
Distributed Model Predictive Control Design for Multi-agent Systems via Bayesian Optimization—0
Learning (With) Distributed Optimization—0
Leveraging Function Space Aggregation for Federated Learning at Scale—0
Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM—0
Linear Convergence of Distributed Mirror Descent with Integral Feedback for Strongly Convex Problems—0
On Linear Convergence of PI Consensus Algorithm under the Restricted Secant Inequality—0
Linear Convergent Decentralized Optimization with Compression—0
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data—0
Local Methods with Adaptivity via Scaling—0
LocalNewton: Reducing Communication Bottleneck for Distributed Learning—0
Distributed Saddle-Point Problems: Lower Bounds, Near-Optimal and Robust Algorithms—0
Local SGD Optimizes Overparameterized Neural Networks in Polynomial Time—0
LoCoDL: Communication-Efficient Distributed Learning with Local Training and Compression—0
Logarithmically Quantized Distributed Optimization over Dynamic Multi-Agent Networks—0
Log-Scale Quantization in Distributed First-Order Methods: Gradient-based Learning from Distributed Data—0
Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression—0
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