Proving convexity of a function whose Hessian is positive semidefinite over a convex set Let $C$ be a convex set in $\mathbb{R}^n$ and let $f:{\mathbb{R}}^n \rightarrow \mathbb{R}$ be twice continuously differentiable over $C$.
The Hessian of $f$ is positive semidefinite over $C$, and I want to show that $f$ is therefore a convex function.
I am currently trying to apply Taylor's Theorem to replace $f(x)$ with an expression that includes its Hessian.
 A: I'm assuming what you would like to show is that if $f$ has positive semidefinite Hessian, then for all $\mathbf{x}, \mathbf{y}$ in the domain, and $t \in [0,1]$, we have:
$$
f(t \mathbf{x} + (1-t) \mathbf{y}) \le t f(\mathbf{x})  + (1-t) f(\mathbf{y})
$$
To reduce it to the one-dimensional case, fix $\mathbf{x}$ and $\mathbf{y}$ and look at the function restricted to the line segment connecting those points. That is, define the one-dimensional function:
$$g(t) = f(t \mathbf{x} + (1-t) \mathbf{y})$$
Then we can compute the derivatives of $g$:
$$g'(t) = ( \mathbf{x} - \mathbf{y})^T \mathbf{\nabla}f(t \mathbf{x} + (1-t) \mathbf{y})$$
$$g''(t) = ( \mathbf{x} - \mathbf{y})^T \mathbf{\nabla^2}f(t \mathbf{x} + (1-t) \mathbf{y}) ( \mathbf{x} - \mathbf{y})$$
Since the Hessian is positive semidefinite, we have $g''(t) \ge 0$ for all $t$. Then we use this with Taylor's theorem to prove that:
$$
\begin{aligned}
g(0) &\ge g(t) + g'(t)(-t)\\
g(1) &\ge g(t) + g'(t)(1-t)
\end{aligned}
$$
Then if $t \in [0,1]$, these can then be combined to give:
$$
g(t) \le tg(1) + (1-t)g(0)
$$
which is equivalent to the inequality we wanted to prove.
