juniorGradient Descent
What is a local minimum in Gradient Descent?
Updated May 16, 2026
Short answer
A local minimum is a point where the function is lower than nearby points but not necessarily global lowest.
Deep explanation
Gradient Descent can get stuck in local minima in non-convex functions. These are points where gradient is zero but better solutions may exist elsewhere.
Real-world example
Neural networks training with multiple error valleys.
Common mistakes
- Assuming GD always finds global minimum.
Follow-up questions
- What is global minimum?
- How to escape local minima?