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Convex Sets in Linear Programming

Lecture 4

N – Dimensional Space

All definitions displayed here exist within an n-dimensional space, where n is an integer > or = to 2 (greater than or equal to 2).

What is a convex set?

A convex set means that between the two points in a Set, once a line joins them both, all other points on the line between are physically, completely lying within the set.

Definitions of a Convex Set: Featuring:

$$\text{ A set S residing in } R^n \text{ is said to be convex if for all X, Y in S, we have } \lambda X + (1-\lambda)Y \text{ also in S, with} 0 \le \lambda \le 1 $$

Again, its convex when the line joining two points within the set , lies completely within the set…

First example discussed that I need to understand:

Confirm convexity:

$$ S1=[X such \text{ } that |X| \le 1]$$
S1 = {X such that |X|<=1 } whatever the X value is…

NB to self;

Proof:

$$\text{Let} X_1, X_2 \epsilon S_1$$ $$\text{Then} |X_1| \le 1, |X_2| \le 1 \text{ where } 0 \le \lambda \le 1$$ $$\text{So that, }| \lambda X_1 +(1- \lambda X_2 | \le | \lambda X_1 | + | (1- \lambda ) X_2 | $$ $$ = \lambda| X_1 | +( 1 – \lambda ) | X_2| $$ $$ \le 1 $$ $$ \lambda X_1 + (1- \lambda ) X_2 \epsilon S_1 $$ $$ S_1 \text { is therefore a convex set }$$

2nd example discussed that I need to understand:

Check convexity

$$ S_2=[X s.t. |X|=1] where X \epsilon R^n $$

Proof:

$$ Let X_1, X_2 \text{ be in } S_2, s.t. X_1 \ne X_2, |X1|=|X_2|=1 $$ $$ Now | \lambda X_1 + (1-\lambda) X_2 | \le \lambda |X_1| + (1-\lambda)|X_2|, 0 \le \lambda \le 1, $$ $$ \text{with equality possible if and only if either} $$ $$\text{1. } X_1=0$$ $$ \text{2. } X_2 = 0 $$ $$ \text{3. } \lambda X_1 = \mu (1-\lambda) X_2, \mu >0 $$

Method to show by contradiction that the line joining the 2 points does not belong to the set…

Theorem 1:

Theorem 2:

Action Required – do these again…

Theorem 3:

Theorem 4:

Theorem 5:

Theorem

Theorem 6:

Convex Polyhedron

Theorem 7:

Theorem 8

Hyperplanes

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