Faces of a simplex, convex hull of the canonical basis of \$\mathbb{R}^n\$

In summary, we have defined the face of a convex set and have two things to prove. For the first part, we can use an indirect proof to show that if $F$ is a face, then $F$ must be equal to $F(A)$ for some subset $A$ of the canonical basis of $\mathbb{R}^n$. For the second part, we can show that any element in $F(A)$ can be written as a convex combination of elements in $\{e_i \ | \ i \in A\}$, thus proving that $F(A)$ is equal to the convex hull of these elements.
  • #1
Samwise1
15
0
Let $I_n := \{1,2,...,n \}, \ p \in \Delta_n = \{(p_1, ..., p_n) \ | \ p_i \ge 0, \sum_{i=1}^n =1\}$

$ \text{supp (p)}= \{ i \in I_n \ | \ p_i \neq 0\}$

For a convex set $C$ we define $F$ to be its face if $F$ is convex and $\forall x,y \in C, \lambda \in (0,1) : \lambda x + (1- \lambda ) y \in F \Rightarrow x,y \in F$

For $A \subset I_n$ we define $F(A) : = \{ p \in \Delta_n \ | \ \text{supp(p)} \subset A \}$

I have two things to prove:

$1) F \text{ is a face } \iff \exists A \subset I_n : F = F(A)$

Here, $\Leftarrow$ is no problem. When it comes to $\Rightarrow$, I've been thinking about indirect proof. That is, let's assume that $F$ is a face but for every $A \subset I_n, \ F(A) \neq F$. But then, if we take $A$ to be a subset of canonical basis of $\mathbb{R}^n$ we get a contradiction. Is that correct?

$2) \text{conv} (\{ e_i, i \in A \} ) = F(A)$

here $e_i$ are elements of the canonical basis of $\mathbb{R}^n$

Now, when it comes to $\subset, \ F(A)$ is a convex set and evidently $\{ e_i \ , \ i \in A \} \subset F(A)$ so $F(A)$ is one of the convex sets containing $\{ e_i \ , \ i \in A \} $, so the interesection of those sets must be a subset of $F(A)$.

I have problems proving the other inclusion.

Could you help me with that?

Thank you
 
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  • #2
!For the first part, your idea of using an indirect proof is good. However, instead of assuming that $F(A) \neq F$ for all $A \subset I_n$, you can assume that $F(A) \neq F$ for some specific $A$, say $A=\{1,2\}$. Then, you can use the definition of a face to show that $F$ must contain all convex combinations of elements in $F(A)$, which would lead to a contradiction.

For the second part, one direction is as you mentioned. For the other direction, we need to show that any $p \in F(A)$ can be written as a convex combination of elements in $\{e_i \ | \ i \in A\}$. Since $p \in F(A)$, $\text{supp}(p) \subset A$. This means that $p$ can be written as a convex combination of elements in $A$, i.e. $p = \sum_{i \in A} \lambda_i e_i$ for some $\lambda_i \geq 0$ with $\sum_{i \in A} \lambda_i = 1$. Since $A \subset I_n$, we can extend this to a convex combination of all the canonical basis elements, i.e. $p = \sum_{i \in I_n} \gamma_i e_i$ where $\gamma_i = \lambda_i$ for $i \in A$ and $\gamma_i = 0$ for $i \in I_n \setminus A$. This shows that $p \in \text{conv}(\{e_i \ | \ i \in A\})$, completing the proof.
 

1. What is a simplex?

A simplex is a geometric shape that is the generalization of a triangle to higher dimensions. In two dimensions, a simplex is a triangle, while in three dimensions it is a tetrahedron. In higher dimensions, a simplex can be thought of as the simplest polytope (a geometric object with flat sides) with a certain number of vertices.

2. What is the convex hull of the canonical basis of \$\mathbb{R}^n\$?

The convex hull of the canonical basis of \$\mathbb{R}^n\$ is the smallest convex set that contains all the points of the canonical basis. In other words, it is the smallest convex shape that contains all the unit vectors in \$\mathbb{R}^n\$. It is a fundamental concept in convex geometry and has many applications in various fields of mathematics and science.

3. How do you find the convex hull of the canonical basis of \$\mathbb{R}^n\$?

There are various algorithms for finding the convex hull of the canonical basis of \$\mathbb{R}^n\$. One approach is to use the gift wrapping algorithm, also known as the Jarvis march algorithm. This algorithm starts with a point on the boundary of the convex hull and iteratively adds the next point that forms the smallest angle with the previous point. The algorithm terminates when it reaches the starting point again.

4. What is the significance of the convex hull of the canonical basis of \$\mathbb{R}^n\$?

The convex hull of the canonical basis of \$\mathbb{R}^n\$ has many important applications in fields such as optimization, computer graphics, and data science. It is used in linear programming and optimization problems to find the optimal solution. In computer graphics, it is used to create 3D models and animations. In data science, it is used to identify patterns and outliers in data sets.

5. Can the convex hull of the canonical basis of \$\mathbb{R}^n\$ be visualized?

Yes, the convex hull of the canonical basis of \$\mathbb{R}^n\$ can be visualized in two or three dimensions. In two dimensions, it would look like a triangle connecting the three unit vectors in \$\mathbb{R}^2\$. In three dimensions, it would look like a tetrahedron connecting the four unit vectors in \$\mathbb{R}^3\$. In higher dimensions, it becomes difficult to visualize, but it can still be represented mathematically and used in various applications.

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