In machine learning, the best parameters for a model are chosen so as to minimize the training objective. Strictly convex functions are paticularly interesting because they have a unique global minimum.

Furthermore, for strict and non-sctrict convex functions, every local minimum is a global minimum.

Visually, a convex function “curves up”, without any bends the other way.

## What is convexity?

A function is convex if and only if a segment joining two points on its curve always stays above the curve. :

The function is *strictly convex* when the inequality is strict.

## Caracterization of convex functions

- Sum of convex functions are also convex.
- A differentiable function of one variable is convex on an interval iff it lies above all of its tangents: .
- A differentiable function of several variables is convex on a compact iff it lies above its linearization:
- A twice differentiable function of one variable is convex on an interval iff its second derivative is non-negative.