What type of study is this?

This is a Quantitative Study study.

October 3, 2025Open Access

Scalable Hessian-free Proximal Conjugate Gradient Method for Nonconvex and Nonsmooth Optimization

View Full Paper

Ask AI

Bookmark

Authors

YZYiming ZhouImperial College London WDWei DaiArmy Medical University

Discussion

Member takes

Implication

This approach demonstrates efficiency in nonconvex optimization problems in machine learning and signal processing, indicating significant improvements over existing methods.

Key Points

The proposed method shows improved efficiency in solving nonconvex and nonsmooth optimization problems, addressing challenges in large-scale instances.
Key innovation includes an approximation to the Newton direction that maintains fast convergence while minimizing computation complexity.
This approach involves developing a curvature-aware majorization surrogate using CG iterations, ensuring effective optimization.
Empirical results indicate the method's superiority over benchmark techniques, particularly in applications like CS-MRI and dictionary learning.

Cite This Study

Zhou et al. (2025) studied this question.

synapsesocial.com/papers/68e040eda99c246f578b33b8 https://doi.org/10.48550/arxiv.2509.15973

View Full Paper

Ask AI

Bookmark

Implication

This approach demonstrates efficiency in nonconvex optimization problems in machine learning and signal processing, indicating significant improvements over existing methods.

Scalable Hessian-free Proximal Conjugate Gradient Method for Nonconvex and Nonsmooth Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

Scalable Hessian-free Proximal Conjugate Gradient Method for Nonconvex and Nonsmooth Optimization

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider