Why Gradient Descent Works in High-Dimensional Non-Convex Landscapes

Discussions explain why gradient descent works in high-dimensional non-convex landscapes: most critical points are saddle points rather than bad local minima. Though surrounded by high-error plateaus that slow learning, they do not trap the optimizer, enabling effective large-scale training.

2026-09-02 ~ 2026-09-02 · 2 related posts

• Why Gradient Descent Works in Enormous Dimensions? — burny_tech · 2026-09-02
• Why gradient descent works in high dimensions: The saddle point problem — burny_tech · 2026-09-02