The paper proposes a novel single-loop algorithm, called SLDBO, for efficiently solving decentralized bilevel optimization (DBO) problems. The key features of SLDBO are:
The convergence rate analysis of SLDBO shows that it achieves the best-known sublinear convergence rate of O(1/K) for a stationarity measure, without requiring any heterogeneity assumptions.
The paper also presents experimental results on hyperparameter optimization problems using both synthetic and MNIST datasets. The results demonstrate the efficiency and effectiveness of the proposed SLDBO algorithm, especially in high-dimensional and heterogeneous data settings.
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Youran Dong,... at arxiv.org 04-24-2024
https://arxiv.org/pdf/2311.08945.pdfDeeper Inquiries