seniorGradient Descent
What is curvature-aware optimization in Gradient Descent?
Updated May 16, 2026
Short answer
Curvature-aware optimization uses second-order information to adjust updates.
Deep explanation
These methods approximate curvature using Hessian or quasi-Newton techniques to adapt step sizes per direction, improving convergence in ill-conditioned problems.
Real-world example
Optimizing logistic regression with Newton-Raphson methods.
Common mistakes
- Assuming curvature methods always scale to deep learning.
Follow-up questions
- What is L-BFGS?
- Why use curvature?