seniorGradient Descent
What is convergence rate in Gradient Descent?
Updated May 16, 2026
Short answer
Convergence rate describes how fast Gradient Descent approaches the optimum.
Deep explanation
Depending on function properties, GD may converge linearly, sublinearly, or superlinearly. Strongly convex functions achieve linear convergence, while non-convex problems often have slower guarantees.
Real-world example
Training efficiency comparison across optimization algorithms.
Common mistakes
- Assuming all optimization problems converge equally fast.
Follow-up questions
- What affects convergence rate?
- Which optimizers converge faster?