What is it about?
This paper improves known iteration complexities of algorithms that solve nonconvex optimization problems asymptotically based on refining an argument that is used to prove iteration complexities of these algorithms.
Featured Image
Why is it important?
This paper hints that state-of-the-art iteration complexities of algorithms that solve nonconvex optimization problems can be further improved.
Perspectives
The refined argument upon which iteration complexities of known algorithms that solves nonconvex optimization problems is improved is based on a simple fact on convergence of sequences.
Dr Chee Khian Sim
University of Portsmouth
Read the Original
This page is a summary of: Refining asymptotic complexity bounds for nonconvex optimization methods, including why steepest descent is $$o(\epsilon ^{-2})$$ rather than $$\mathcal{O}(\epsilon ^{-2})$$, Computational Optimization and Applications, July 2025, Springer Science + Business Media,
DOI: 10.1007/s10589-025-00709-5.
You can read the full text:
Contributors
The following have contributed to this page
