The efficiency of most of the iterative methods considered in this book is determined primarily by the performance of the matrix-vector product and therefore on the storage scheme used for the matrix. Often, the storage scheme used arises naturally from the specific application problem.
In this section we will review some of the more popular sparse matrix formats that have been used in numerical software packages such as ITPACK [263], NSPCG [345], and SPARSPAK [191], some of which have more recently been adopted as part of a new software standard by the BLAS Technical Forum (see ETHOME for further information). In §10.2.2, we demonstrate how the matrix-vector product is formulated using two of the sparse matrix formats.
If the coefficient matrix 
is sparse, large scale eigenvalue problems
can be most efficiently solved if the
zero elements of are neither manipulated nor stored.
Sparse storage schemes allocate
contiguous storage in memory for
the nonzero elements of the matrix, and perhaps a limited number of
zeros. This, of course, requires a scheme for knowing
where the elements fit into the full matrix.
There are many methods for storing the data (see, for instance, Saad [386] and Eijkhout [156]). Here we will discuss compressed row and column storage, block compressed row storage, diagonal storage, jagged diagonal storage, and skyline storage.