4 papers
AdGT: Decentralized Gradient Tracking with Adaptive Per-Agent Stepsizes
Diyako Ghaderyan, Stefan Werner
In decentralized optimization, gradient-tracking methods typically rely on a single global stepsize. This choice can be conservative when agents have local objectives with differen…
Federated Smoothing Proximal Gradient for Quantile Regression with Non-Convex Penalties
Reza Mirzaeifard, Diyako Ghaderyan, Stefan Werner
Distributed sensors in the internet-of-things (IoT) generate vast amounts of sparse data. Analyzing this high-dimensional data and identifying relevant predictors pose substantial…
Decentralized Smoothing ADMM for Quantile Regression with Non-Convex Sparse Penalties
Reza Mirzaeifard, Diyako Ghaderyan, Stefan Werner
In the rapidly evolving internet-of-things (IoT) ecosystem, effective data analysis techniques are crucial for handling distributed data generated by sensors. Addressing the limita…
A Fast Row-Stochastic Decentralized Method for Distributed Optimization Over Directed Graphs
Diyako Ghaderyan, Necdet Serhat Aybat, A. Pedro Aguiar +1
In this paper, we introduce a fast row-stochastic decentralized algorithm, referred to as FRSD, to solve consensus optimization problems over directed communication graphs. The pro…