AI engineering glossary · Calculus optimisation
What is strong convexity?
Strong convexity: A property meaning a function curves upward at least as fast as a quadratic with some positive constant mu: its Hessian has all eigenvalues at least mu. It guarantees a unique minimum and lets gradient descent converge at a geometric rate.
Strong convexity: an example
f(x) = 3x^2 is strongly convex with mu = 6, since its second derivative is always 6.
Related terms
More Calculus optimisation terms
- Subgradient
- Subgradient method
- Sum rule
- Symbolic differentiation
- Tangent line
- Taylor series
- Total derivative
- Trust region method
Where you learn it
Strong convexity is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.