What is curvature-adaptive learning rate?

Updated May 16, 2026

Short answer

It adjusts learning rate based on local curvature of the loss surface.

Deep explanation

Curvature-adaptive methods scale learning rates inversely with curvature (second derivative information). In steep curvature directions, smaller steps are taken; in flat regions, larger steps are allowed. This improves stability and convergence speed.

Real-world example

Adaptive optimizers like RMSProp and Adam approximate this idea.

Common mistakes

  • Ignoring curvature leads to unstable updates.

Follow-up questions

  • What is diagonal approximation?
  • Why not full Hessian?

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