  This paper documents a computational implementation of a {\em projection and
rescaling algorithm} for finding most interior solutions to the pair of
feasibility problems \[ \text{find} \; x\in L\cap\mathbb{R}^n_{+} \;\;\;\;
\text{ and } \; \;\;\;\; \text{find} \; \hat x\in L^\perp\cap\mathbb{R}^n_{+},
\] where $L$ denotes a linear subspace in $\mathbb{R}^n$ and $L^\perp$ denotes
its orthogonal complement. The projection and rescaling algorithm is a recently
developed method that combines a {\em basic procedure} involving only low-cost
operations with a periodic {\em rescaling step.} We give a full description of
a MATLAB implementation of this algorithm and present multiple sets of
numerical experiments on synthetic problem instances with varied levels of
conditioning. Our computational experiments provide promising evidence of the
effectiveness of the projection and rescaling algorithm.
  Our MATLAB code is publicly available. Furthermore, the simplicity of the
algorithm makes a computational implementation in other environments completely
straightforward.
