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The SSOR preconditioner like the Jacobi preconditioner, can be derived from the coefficient matrix without any work.

If the original, symmetric, matrix is decomposed as

in its diagonal, lower, and upper triangular part, the SSOR matrix is defined as

or, parametrized by

The optimal value of the parameter, like the parameter in the SOR method, will reduce the number of iterations to a lower order. Specifically, the spectral condition number is attainable, see Axelsson and Barker[14]. In practice, however, the spectral information needed to calculate the optimal is prohibitively expensive to compute.

The SSOR matrix is given in factored form, so this preconditioner
shares many properties of other factorization-based methods (see
below). For instance, its suitability for vector processors or
parallel architectures depends strongly on the
ordering of the variables. On the other hand, since this factorization
is given * a priori*, there is no possibility of breakdown as in
the construction phase of incomplete factorization methods.