TITLE: Can a "continuous" convex combination not be element of the convex hull?
QUESTION [10 upvotes]: Short version of question: can a "continuous" convex combination not be element of the convex hull?
I am not a mathematician, so please excuse me if I am not precise. I consider first, e.g., 4 dimensional real valued vectors $a \in \mathbb{R}^4$. Now consider a set of $n$ vectors $a_i, i={1,2,...,n}$ and the set containing all convex combinations of these vectors 
\begin{equation}
C=\left\{\sum_{i=1}^k \hat{w}_i a_i | k\in\{1,2,...,n\}, i\in\{1,2,...,n\}, \sum_{i=1}^k \hat{w}_i = 1, \hat{w}_i \geq 0 \forall i\right\} \ .
\end{equation}
As far as I understand the definition of the convex hull, see 3.definition in Wikipedia, the set $C$ is the convex hull of these vectors and trivially any convex combination of vectors lies in $C$.
Now, I am taking a look at the following problem over a non-convex region $\Omega \subset \mathbb{R}^2$ for vector valued vector functions $a(x) \in \mathbb{R}^4$ and $x \in \Omega$ 
\begin{equation}
\lambda = \int_\Omega w(x) a(x) dx \in \mathbb{R}^4
\end{equation}
with real valued $w(x) \in \mathbb{R}$ with the following properties
\begin{equation}
\int_\Omega w(x) dx = 1 , \quad
w(x) \geq 0 \quad \forall x \in \Omega
\end{equation}
at what $w(x)$ is a distribution. Due to the properties of $w(x)$, I interpret for any $w(x)$ the integral $\lambda$ to be a "continuous" convex combination of the values of $a(x)$ over $\Omega$. The set of all possible $\lambda$ for all distributions $w(x)$ having the properties mentioned above will be denoted as
\begin{equation}
\Lambda = \left\{\lambda | \lambda = \int_\Omega w(x) a(x) dx , \int_\Omega w(x) dx = 1 ,
w(x) \geq 0 \quad \forall x \in \Omega\right\}
\end{equation}
and the convex hull of all values of $a(x)$ as
\begin{equation}
\Gamma = \left\{\sum_{i=1}^k \hat{w}_i a(x_i) | k\in\mathbb{N}, x_i \in \Omega, \sum_{i=1}^k \hat{w}_i = 1, \hat{w}_i \geq 0 \forall i\right\} \ .
\end{equation}
Question: are the sets $\Lambda$ and $\Gamma$ the same or can I find a $w(x)$ such that the resulting $\lambda \not\in \Gamma$? This would be somehow very unintuitive for me, but I am not a mathematician. I keep thinking of this with Dirac distributions defined for $\Omega$ and the $n$ going to infinity in the case of simple vectors, as sketched at the beginning. Therefore, I can not imagine any case for which I should be able to combine values of $a(x)$ and end outside of $\Gamma$. But the more I read about distributions, the more weird things are possible! Any help is very appreciated. Thanks a lot!

REPLY [5 votes]: As others have said the two sets are the same. The fact that $\Gamma\subset \Lambda$ essentially follows from the fact that a convex combination $\sum_1^k w_i a_i$ is equal to $\int a(x)w(x)\, dx$ with $w=\sum_1^k w_i\delta_{a_i}$ (Dirac deltas concentrated at $a_i$). 
The opposite inclusion follows from Jensen's inequality. Consider the function (this is called characteristic (or indicator) function in convex analysis) 
$$I_\Gamma(x)=\begin{cases} 0 & x\in \Gamma \\ +\infty & x\notin \Gamma\end{cases}$$
This function is convex and $\Gamma=\{x\ :\ I_\Gamma(x)=0\}$. Now let $\lambda = \int a(x)w(x)\, dx\in\Lambda$. By Jensen's inequality 
$$
I_\Gamma(\lambda)\le \int I_\Gamma(a(x))w(x)\, dx=0, $$
so $I_\Gamma(\lambda)=0$, which means that $\lambda \in \Gamma$.