Let $Ax \leq b$, $x \geq 0$ define the feasible region. Each constraint defines a hyperplane in $R^n$ and the distance from a point $\hat x$ to a hyperplane is
$d_i(\hat x)$ = $(b_i - A_{i,*} \hat x)$ / $|| A_{i,*} ||$ with $|| A_{i,*} ||$ = ($\Sigma^n_{j=1}$ $A^2_{i,j}$)$^{1/2}$
$|| A_{i,*} ||$ is also known as the L-2 norm.
Let $ \bar b_i$ = ($b_i$ / $|| A_{i,*} ||$) and $\bar A_{i,*}$ = ($A_{i,*} \hat x$ / $|| A_{i,*} ||$) and $d_i(\hat x)$ = $\bar b_i - \bar A_{i,*}$. $d_i(\hat x)$ is both the distance from $\hat x$ to the ith hyperplane as well as the slack in the newly scaled ith constraint.
The center can be determined from the optimization problem
Maximize($Minimum_{i=1,...,m}$ $d_i$)
subject to: $\bar A$x + Id = $\bar b$
$x \geq 0$
$d \geq 0$
How to reformulate this optimization problem as a linear program?