## Computer design

The cheapest, fastest and most reliable components of a computer system are those that aren't there.

(Gordon Bell)

## Parrot walking

## Cycling at its best

## Partying at its best

## Life is pain

## Finger

## Quote of the day

"Change is certain. Progress is not."

(E.H. Carr)

## Have a beer

## Delivering pizza

## Does a convex objective subject to a polytope attain its optimum at a vertex?

Today, while thinking about a work-related optimisation problem, I asked myself whether, given a domain corresponding to a nonempty polytope \(P\) and a convex objective, is there always an optimal solution which is a vertex of \(P\) ?

To warm up let's look at the **linear programming case** first. Given a nonempty polytope \(P = \{x \in \mathbb{R}^n: Ax \geq b \}\) and a linear objective \(c \in \mathbb{R}^n\) it's well known that there is a vertex \(v \in P\) which is optimal for \(\text{min } c \cdot x \text{ s.t. } x \in P\).
We can prove this as follows: Let \(Q\) be the set of all optimal solutions and let \(w \in \mathbb{R}\) be the optimal objective value. Then, \(Q = \{ x \in \mathbb{R}^n : Ax \geq b, c \cdot x = w \}\) is itself a polytope. Since \(Q \subset P\), it does not contain any lines implying it has at least one vertex. Let \(v \in Q\) be a vertex of \(Q\). We show that \(v\) is also a vertex of \(P\). Suppose \(v\) is not a vertex of \(P\). Then, there are \(x, y \in P\) such that \(x \neq v, y \neq v\) and some \(\lambda \in [0,1]\) such that \(v = \lambda x + (1-\lambda)y\). Moreover, we get $$w = c \cdot v = \lambda c \cdot x + (1-\lambda) c \cdot y$$. The latter can be used to show that \(x \in Q\) and \(y \in Q\) which contradicts that \(v\) is a vertex of \(Q\).

Given a **convex** objective function, it turns out that we have to distinguish between minimisation and maximisation unlike in the linear programming case.

**Minimisation:** Let the polytope \(P \subset \mathbb{R}^2\) be given by the convex combination of the four vertices \( (1,0), (0,-1), (-1,0), (0,1) \) and let \(f : P \rightarrow \mathbb{R}\) be given by $$f(x) = x_1^2 + x_2^2 + x_1 x_2. $$ Consider \(\text{min } f(x) \text{ s.t. } x \in P\). It's not hard to see that none of the vertices corresponds to an optimal solution which is given by the interior point \((0,0) \in P\). In other words, if we minimise a convex function subject to a polytope, then we can generally not assume that an optimal solution is given by a vertex.

**Maximisation:** ...

## Big vs. small

## Cheerleading at its best

## Movie recommendation

Watched this one a few days ago. I came across it because it won the 'Palme d'Or' and several European film awards. The trailer really hooked me and I was very happy when I found out that it's currently on Netflix.