  Let $ SM_n(\mathbb{R})^g$ denote $g$-tuples of $n \times n$ real symmetric
matrices. Given tuples $X=(X_1, \dots, X_g) \in SM_{n_1}(\mathbb{R})^g$ and
$Y=(Y_1, \dots, Y_g) \in SM_{n_2}(\mathbb{R})^g$, a matrix convex combination
of $X$ and $Y$ is a sum of the form \[ V_1^* XV_1+V_2^* Y V_2 \quad \quad \quad
V_1^* V_1+V_2^* V_2=I_n \] where $V_1:\mathbb{R}^n \to \mathbb{R}^{n_1}$ and
$V_2:\mathbb{R}^n \to \mathbb{R}^{n_2}$ are contractions. Matrix convex sets
are sets which are closed under matrix convex combinations. A key feature of
matrix convex combinations is that the $g$-tuples $X, Y$, and $V_1^* XV_1+V_2^*
Y V_2$ do not need to have the same size. As a result, matrix convex sets are a
dimension free analog of convex sets.
  While in the classical setting there is only one notion of an extreme point,
there are three main notions of extreme points for matrix convex sets:
ordinary, matrix, and absolute extreme points. Absolute extreme points are
closely related to the classical Arveson boundary. A central goal in the theory
of matrix convex sets is to determine if one of these types of extreme points
for a matrix convex set minimally recovers the set through matrix convex
combinations.
  This article shows that every real compact matrix convex set which is defined
by a linear matrix inequality is the matrix convex hull of its absolute extreme
points, and that the absolute extreme points are the minimal set with this
property. Furthermore, we give an algorithm which expresses a tuple as a matrix
convex combination of absolute extreme points with optimal bounds. Similar
results hold when working over the field of complex numbers rather than the
reals.
