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The Compressed Row and Column (in the next section) Storage formats are the most general: they make absolutely no assumptions about the sparsity structure of the matrix, and they don't store any unnecessary elements. On the other hand, they are not very efficient, needing an indirect addressing step for every single scalar operation in a matrix-vector product or preconditioner solve.

The Compressed Row Storage (CRS) format puts the subsequent nonzeros of the
matrix rows in contiguous memory locations.
Assuming we have a nonsymmetric sparse matrix , we create 3 vectors:
one for floating-point numbers (` val`), and the other two for
integers (` col_ind`, ` row_ptr`). The ` val` vector
stores the values of the nonzero elements of the
matrix , as they are traversed in a row-wise fashion.
The ` col_ind` vector stores
the column indexes of the elements in the ` val` vector.
That is, if then .
The ` row_ptr` vector stores
the locations in the ` val` vector that start a row, that is,
if then .
By convention, we define , where is
the number of nonzeros in the matrix . The storage savings for this
approach is significant. Instead of storing elements,
we need only storage locations.

As an example, consider the nonsymmetric matrix defined by

The CRS format for this matrix is then specified by the arrays
{` val`, ` col_ind`, ` row_ptr`} given below

.

If the matrix is symmetric, we need only store the upper (or lower) triangular portion of the matrix. The trade-off is a more complicated algorithm with a somewhat different pattern of data access.