# How to determine whether a function of many variables is convex or non-convex?

A function $f$ is convex if $$f(\theta x + (1 − \theta)y) \leq \theta f(x) + (1 − \theta)f(y)$$ for all $x, y \in \mathcal{D}(f)$, the domain of $f$, and $\theta \in [0, 1]$.

How do I determine whether a function of many variables is convex or non-convex?

If I find the Hessian of my function and they are not all positive, is it a non-convex function?

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First i think that the domain must be convex. Second it is depends on your function. If it is only continuous, you have only continuous techniques to prove it. If it is $C^1$ you have more techniques and if it is $C^2$, you can compute the Hessian and verify if it is positive. –  Tomás Feb 2 '13 at 16:28
Convexity makes sense for functions $f:A\rightarrow \mathbb R$ where $A$ is a convex subset of a vectorial space $V$. In particular, $f$ must be real-valued. Otherwise the codomain should be equipped with a partial order relation such that the definition of convexity still makes sense. –  AndreasT Feb 2 '13 at 16:40
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## 1 Answer

If the function is twice differentiable and the Hessian is positive semidefinite in the entire domain, then the function is convex. Note that the domain must be assumed to be convex too. If the Hessian has a negative eigenvalue at a point in the interior of the domain, then the function is not convex.

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