Overview

Part 2 - Convexity I

Outline

In this part, we will cover the following topics:

  • Convex sets
  • Polytopes
  • Polyhedra
  • Cones

We will start by defining what makes a set convex.

Convex Sets

Definition 1 (Convex Set)

A set S⊆RnS \subseteq \mathbb{R}^n is called convex if,

λx1+(1−λ)x2∈S{∀(x1,x2)∈Sλ∈(0,1)\lambda \mathbf{x}_1 + (1 - \lambda) \mathbf{x}_2 \in S \begin{cases} \forall (\mathbf{x}_1, \mathbf{x}_2) \in S \newline \lambda \in (0,1) \end{cases}
Example of a convex set and non-convex set
Figure 1: Example of a convex set and non-convex set

Examples

By definition, ∅\emptyset is convex.

The set {x∈Rn∣∥x∥≤a}\{\mathbf{x} \in \mathbb{R}^n \mid \Vert \mathbf{x} \Vert \leq a \} is convex ∀a∈R\forall a \in \mathbb{R}.

However, the set {x∈Rn∣∥x∥=a}\{\mathbf{x} \in \mathbb{R}^n \mid \Vert \mathbf{x} \Vert = a \} is not convex for any a>0a > 0.

Example of a convex set and non-convex set
Figure 2: Example of a convex set and non-convex set
Proposition 1 (Convex Set Intersection)

Let Sk∈RnS_k \in \mathbb{R}^n for k=1,…,Kk = 1, \ldots, K be convex.

Then, the intersection,

S≔⋂k=1KSk  is convex.S \coloneqq \bigcap_{k=1}^K S_k \, \text{ is convex.}
Proof

Let x1,x2∈S\mathbf{x}_1, \mathbf{x}_2 \in S and λ∈(0,1)\lambda \in (0,1). Since x1,x2∈S\mathbf{x}_1, \mathbf{x}_2 \in S, we have that x1,x2∈Sk\mathbf{x}_1, \mathbf{x}_2 \in S_k for all k=1,…,Kk = 1, \ldots, K. Each SkS_k is convex, thus,

λx1+(1−λ)x2∈Sk,∀k=1,…,K\lambda \mathbf{x}_1 + (1 - \lambda) \mathbf{x}_2 \in S_k, \quad \forall k = 1, \ldots, K

Hence, λx1+(1−λ)x2\lambda \mathbf{x}_1 + (1 - \lambda) \mathbf{x}_2 lies in all the SkS_k simultaneously, i.e.,

λx1+(1−λ)x2∈S=⋂k=1KSk ■\lambda \mathbf{x}_1 + (1 - \lambda) \mathbf{x}_2 \in S = \bigcap_{k=1}^K S_k \ _\blacksquare
Note

Unions of convex sets might not be convex.

Examples of the union of convex sets
Figure 3: Examples of the union of convex sets

Convex Hull

Definition 2 (Convex Hull)

The convex hull of a finite set of points, {v1,…,vk}⊆Rn\{\mathbf{v}_1, \ldots, \mathbf{v}_k \} \subseteq \mathbb{R}^n is defined as,

conv V≔{λ1v1+…+λkvk∣λ1,…,λk≥0∑i=1kλi=1}\mathrm{conv} \, V \coloneqq \{\lambda_1 \mathbf{v}_1 + \ldots + \lambda_k \mathbf{v}_k \mid \lambda_1, \ldots, \lambda_k \geq 0 \sum_{i=1}^k \lambda_i = 1 \}
Definition 3 (Properties of the Convex Hull)

The convex hull of the set S⊆RnS \subseteq \mathbb{R}^n, is the set with any of the following properties,

  • (1) It is the unique minimal convex set containing SS.
  • (2) It is the intersection of all convex sets containing SS.
  • (3) It is the set of all convex combinations of points in SS.
Construction of the convex hull
Figure 4: Construction of the convex hull

From (3), any point in the convex hull can be expressed as a convex combination of points in SS.

An interesting question is how many points do we need to construct the convex hull?

Theorem 1 (Carathéodory’s Theorem)

Let α∈conv(S)\alpha \in \mathrm{conv}(S), where S⊆RnS \subseteq \mathbb{R}^n.

Then α\alpha can be expressed as a convex combination of at most n+1n+1 points in SS.

Polytopes

Definition 4 (Polytope)

A set P⊆RnP \subseteq \mathbb{R}^n is called a polytope if it is the convex hull of finitely many points.

Note

A polytope will always have straight edges. So, a circle is not a polytope (since the circumference is curved and can not be expressed with finitely many points).

Definition 5 (Extreme Point)

A point v\mathbf{v} of a convex set SS is called an extreme point if,

v=x1=x2{v=λx1+(1−λ)x2x1,x2∈Sλ∈(0,1)\mathbf{v} = \mathbf{x}_1 = \mathbf{x}_2 \begin{cases} \mathbf{v} = \lambda \mathbf{x}_1 + (1 - \lambda) \mathbf{x}_2 \newline \mathbf{x}_1, \mathbf{x}_2 \in S \newline \lambda \in (0,1) \end{cases}
Theorem 2 (Extreme Points of a Polytope)

Let PP be the polytope conv(V)\mathrm{conv}(V), where V={v1,…,vk}V = \{\mathbf{v}_1, \ldots, \mathbf{v}_k \}. Then, PP is equal to the convex hull of its extreme points.

Extreme points of different polytopes
Figure 5: Extreme points of different polytopes

Polyhedra

Definition 6 (Polyhedron)

A set PP is called a polyhedron if there exists a matrix A∈Rn×mA \in \mathbb{R}^{n \times m} and a vector b∈Rn\mathbf{b} \in \mathbb{R}^n such that,

P≔{x∈Rm∣Ax≤b}P \coloneqq \{\mathbf{x} \in \mathbb{R}^m \mid A\mathbf{x} \leq \mathbf{b} \}
Note

Ax≤bA\mathbf{x} \leq \mathbf{b} means that,

aiTx≤bi,∀i=1,…,n\mathbf{a}_i^T \mathbf{x} \leq b_i, \quad \forall i = 1, \ldots, n

where aiT\mathbf{a}_i^T is the ii-th row of AA and bib_i is the ii-th element of b\mathbf{b}. Further, {x∈Rm∣Ax≤b}\{\mathbf{x} \in \mathbb{R}^m \mid A\mathbf{x} \leq \mathbf{b} \} is a half-space. A polyhedron is the intersection of nn half-spaces   ⟹  \implies a polyhedron is a convex set (by our previous proposition).

Half-space defined by a linear inequality
Figure 6: Half-space defined by a linear inequality
Example 1 (Polyhedron)

Let A=[12−210−1]A = \begin{bmatrix} 1 & 2 \newline -2 & 1 \newline 0 & -1 \end{bmatrix} and b=[6−2−1]\mathbf{b} = \begin{bmatrix} 6 \newline -2 \newline -1 \end{bmatrix}. This gives the following system of inequalities,

{x1+2x2≤6−2x1+x2≤−2−x2≤−1\begin{cases} x_1 + 2x_2 \leq 6 \newline -2x_1 + x_2 \leq -2 \newline -x_2 \leq -1 \end{cases}
Polyhedron defined by the intersection of half-spaces
Figure 7: Polyhedron defined by the intersection of half-spaces
Theorem 3 (Extreme Points of a Polyhedron)

Let x′∈P{x∈Rm∣Ax≤b}\mathbf{x}^{\prime} \in P \{\mathbf{x} \in \mathbb{R}^m \mid A\mathbf{x} \leq \mathbf{b} \}, where A∈Rn×mA \in \mathbb{R}^{n \times m}, rank(A)=m\mathrm{rank}(A) = m and b∈Rn\mathbf{b} \in \mathbb{R}^n.

Further, let A′x′=b′A^{\prime} \mathbf{x}^{\prime} = \mathbf{b}^{\prime} be the equality subsystem of Ax≤bA\mathbf{x} \leq \mathbf{b}, i.e., A′\mathbf{A}^{\prime} contains all rows of AA where we have (Ax′)i=bi(A \mathbf{x}^{\prime})_i = b_i.

Then, x′\mathbf{x}^{\prime} is an extreme point of PP if and only if rank(A′)=m\mathrm{rank}(A^{\prime}) = m.

Example 2 (Continued)

Let x′=[321]\mathbf{x}^{\prime} = \begin{bmatrix} \frac{3}{2} \newline 1 \end{bmatrix}. Then,

Ax′=[72−2−1],b=[6−2−1]Ax′≤b  ⟹  [72−2−1]≤[6−2−1]\begin{align*} A \mathbf{x}^{\prime} & = \begin{bmatrix} \frac{7}{2} \newline -2 \newline -1 \end{bmatrix}, \mathbf{b} = \begin{bmatrix} 6 \newline -2 \newline -1 \end{bmatrix} \newline A \mathbf{x}^{\prime} \leq \mathbf{b} & \implies \begin{bmatrix} \frac{7}{2} \newline -2 \newline -1 \end{bmatrix} \leq \begin{bmatrix} 6 \newline -2 \newline -1 \end{bmatrix} \end{align*}

Which means,

A′=[−210−1],b′=[−2−1]\mathbf{A}^{\prime} = \begin{bmatrix} -2 & 1 \newline 0 & -1 \end{bmatrix}, \mathbf{b}^{\prime} = \begin{bmatrix} -2 \newline -1 \end{bmatrix}

We can see that rank(A′)=2=m\mathrm{rank}(\mathbf{A}^{\prime}) = 2 = m, thus x′\mathbf{x}^{\prime} is an extreme point of PP.

Example 3 (Continued)

Now, consider a new point x2′=[21]\mathbf{x}^{\prime}_2 = \begin{bmatrix} 2 \newline 1 \end{bmatrix}. Then,

Ax2′=[4−3−1]Ax2′≤b  ⟹  [4−3−1]≤[6−2−1]\begin{align*} A \mathbf{x}^{\prime}_2 & = \begin{bmatrix} 4 \newline -3 \newline -1 \end{bmatrix} \newline A \mathbf{x}^{\prime}_2 \leq \mathbf{b} & \implies \begin{bmatrix} 4 \newline -3 \newline -1 \end{bmatrix} \leq \begin{bmatrix} 6 \newline -2 \newline -1 \end{bmatrix} \end{align*}

Which means,

A2′=[0−1],b2′=[−1]\mathbf{A}^{\prime}_2 = \begin{bmatrix} 0 & -1 \end{bmatrix}, \mathbf{b}^{\prime}_2 = \begin{bmatrix} -1 \end{bmatrix}

We can see that rank(A2′)=1≠m\mathrm{rank}(\mathbf{A}^{\prime}_2) = 1 \neq m, thus x2′\mathbf{x}^{\prime}_2 is not an extreme point of PP.

Cones

Definition 7 (Cone)

A set C⊆RnC \subseteq \mathbb{R}^n is a cone if,

λx∈C,∀x∈C,λ>0\lambda \mathbf{x} \in C, \quad \forall \mathbf{x} \in C, \quad \lambda > 0
Examples of cones
Figure 8: Examples of cones
Example 4 (Convex Cone)

Let A⊆Rn×mA \subseteq \mathbb{R}^{n \times m} then, C≔{x∈Rm∣Ax≤0}C \coloneqq \{\mathbf{x} \in \mathbb{R}^m \mid A\mathbf{x} \leq 0 \} is a convex cone.

It is quite simple to prove this, assume that x∈C\mathbf{x} \in C and λ>0\lambda > 0,

A(λx)=λ⏟>0Ax⏟≤0⏟≤0 ■A(\lambda \mathbf{x}) = \underbrace{\underbrace{\lambda}_{> 0} \underbrace{A\mathbf{x}}_{\leq 0}}_{\leq 0} \ _\blacksquare

Representation Theorem

Theorem 4 (Representation Theorem)

Let Q={x∈Rm∣Ax≤b}Q = \{\mathbf{x} \in \mathbb{R}^m \mid A\mathbf{x} \leq \mathbf{b} \} (polyhedron) and let {v1,…,vk}\{\mathbf{v}_1, \ldots, \mathbf{v}_k \} be its extreme points. Further, we define P≔conv({v1,…,vk})P \coloneqq \mathrm{conv}(\{\mathbf{v}_1, \ldots, \mathbf{v}_k \}) (polytope) and C≔{x∈Rm∣Ax≤0}C \coloneqq \{\mathbf{x} \in \mathbb{R}^m \mid A\mathbf{x} \leq 0 \} (cone). Then,

Q=P+C={x∈Rm∣x=u+v,u∈P,v∈C}\begin{align*} Q & = P + C \newline & = \{\mathbf{x} \in \mathbb{R}^m \mid \mathbf{x} = \mathbf{u} + \mathbf{v}, \quad \mathbf{u} \in P, \quad \mathbf{v} \in C \} \end{align*}
Corollary 1 (Bounded Polyhedron)

A bounded polyhedron is a polytope.

Let’s state the pros and cons of polytopes and polyhedra.

  • Polytope
    • Pros: Easy to get extreme points (by definition).
    • Cons: Hard to check if new point is inside.
  • Polyhedron
    • Pros: Easy to check if new point is inside.
    • Cons: Hard to get extreme points (need to check intersection of half-spaces, grows combinatorially, infeasible for large problems).

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