What is the mathematical intuition behind Gradient Descent?
Updated May 16, 2026
Short answer
Gradient Descent uses first-order Taylor approximation to iteratively minimize a function.
Deep explanation
Gradient Descent relies on local linear approximation of a function using derivatives. The gradient indicates direction of steepest ascent; moving opposite reduces function value iteratively until convergence.
Real-world example
Optimizing cost in supply chain systems by incremental adjustments.
Common mistakes
- Ignoring curvature information leading to slow convergence.
Follow-up questions
- What is Taylor expansion role?
- Why first-order only?