This paper provides a description of a practically efficient minimal-representation algorithm for polytopes. The algorithm is based on a primal active-set method that heavily exploits warm-starts and low-rank updates of matrix factorizations in order to reduce the required computational work. By using a primal active-set method, several nonredundant inequalities can be identified for each solved linear program. Implementation details are provided both for the minimal-representation algorithm and for the underlying active-set method.