We discuss the algorithmic steps involved in common sparse matrix problems, with particular emphasis on linear programming by the revised simplex method. We then propose new architectural mechanisms which are being built into an experimental machine, the Edinburgh Sparse Processor, and which enable vector instructions to operate efficiently on sparse vectors stored in compressed form. Finally, we review the use of these new mechanisms on the linear programming problem.