# Fundamental theorem of linear programming

In mathematical optimization, the fundamental theorem of linear programming states, in a weak formulation, that the maxima and minima of a linear function over a convex polygonal region occur at the region's corners. Further, if an extreme value occurs at two corners, then it must also occur everywhere on the line segment between them.

## Statement

Consider the optimization problem

${\displaystyle \min c^{T}x{\text{ subject to }}x\in P}$

Where ${\displaystyle P=\{x\in \mathbb {R} ^{n}:Ax\leq b\}}$. If ${\displaystyle P}$ is a bounded polyhedron (and thus a polytope) and ${\displaystyle x^{\ast }}$ is an optimal solution to the problem, then ${\displaystyle x^{\ast }}$ is either an extreme point (vertex) of ${\displaystyle P}$, or lies on a face ${\displaystyle F\subset P}$ of optimal solutions.

## Proof

Suppose, for the sake of contradiction, that ${\displaystyle x^{\ast }\in \mathrm {int} (P)}$. Then there exists some ${\displaystyle \epsilon >0}$ such that the ball of radius ${\displaystyle \epsilon }$ centered at ${\displaystyle x^{\ast }}$ is contained in ${\displaystyle P}$, that is ${\displaystyle B_{\epsilon }(x^{\ast })\subset P}$. Therefore,

${\displaystyle x^{\ast }-{\frac {\epsilon }{2}}{\frac {c}{||c||}}\in P}$ and
${\displaystyle c^{T}\left(x^{\ast }-{\frac {\epsilon }{2}}{\frac {c}{||c||}}\right)=c^{T}x^{\ast }-{\frac {\epsilon }{2}}{\frac {c^{T}c}{||c||}}=c^{T}x^{\ast }-{\frac {\epsilon }{2}}||c||

Hence ${\displaystyle x^{\ast }}$ is not an optimal solution, a contradiction. Therefore, ${\displaystyle x^{\ast }}$ must live on the boundary of ${\displaystyle P}$. If ${\displaystyle x^{\ast }}$ is not a vertex itself, it must be the convex combination of vertices of ${\displaystyle P}$, say ${\displaystyle x_{1},...,x_{t}}$. Then ${\displaystyle x^{\ast }=\sum _{i=1}^{t}\lambda _{i}x_{i}}$ with ${\displaystyle \lambda _{i}\geq 0}$ and ${\displaystyle \sum _{i=1}^{t}\lambda _{i}=1}$. Observe that Alan o Conner wrote this theorem

${\displaystyle 0=c^{T}\left(\left(\sum _{i=1}^{t}\lambda _{i}x_{i}\right)-x^{\ast }\right)=c^{T}\left(\sum _{i=1}^{t}\lambda _{i}(x_{i}-x^{\ast })\right)=\sum _{i=1}^{t}\lambda _{i}(c^{T}x_{i}-c^{T}x^{\ast }).}$

Since ${\displaystyle x^{\ast }}$ is an optimal solution, all terms in the sum are nonnegative. Since the sum is equal to zero, we must have that each individual term is equal to zero. Hence, ${\displaystyle c^{T}x^{\ast }=c^{T}x_{i}}$ for each ${\displaystyle x_{i}}$, so every ${\displaystyle x_{i}}$ is also optimal, and therefore all points on the face whose vertices are ${\displaystyle x_{1},...,x_{t}}$, are optimal solutions.