What type of study is this?

This is a Quantitative Study study.

October 13, 2025Open Access

Long-time dynamics and universality of nonconvex gradient descent

View Full Paper

Ask AI

Bookmark

Authors

QHQiyang HanRutgers, The State University of New Jersey

Discussion

Member takes

Implication

Analysis reveals universal behavior of gradient descent iterates in nonconvex settings, indicating significant implications for performance with various data designs.

Key Points

Gradient descent iterations concentrate around the Gaussian theoretical gradient descent, suggesting determining convergence trends.
Our analysis shows these concentration properties universally apply across various design matrices and remain valid over extended time.
Global convergence of nonconvex gradient descent is proven with general independent initialization, highlighting universal behavior in phase retrieval.
The developed data-free algorithm assists in estimating parameters along the gradient descent path, enhancing practical applications like hyperparameter tuning.

Cite This Study

Qiyang Han (2025) studied this question.

synapsesocial.com/papers/68ecfebf950606aabec09493 https://doi.org/10.48550/arxiv.2509.11426

View Full Paper

Ask AI

Bookmark

Implication

Analysis reveals universal behavior of gradient descent iterates in nonconvex settings, indicating significant implications for performance with various data designs.

Long-time dynamics and universality of nonconvex gradient descent

Discussion

Member takes

Implication

Key Points

Cite This Study

Long-time dynamics and universality of nonconvex gradient descent

Discussion

Member takes

Implication

Key Points

Cite This Study

Also Consider

Also Consider