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For each algorithm we have distinguished between different ways of running the algorithm:
is direct application, where we multiply with $A$ and solve with $B$.
is shift and invert, which solves systems $(A-\sigma B)x=b$ for $x$ and multiplies with $B$. This gives the ability to compute a wider choice of eigenvalues in fewer iterations.
means application with a preconditioner, for instance, a sparse approximate factorization. This needs less space than shift and invert, but most often it also needs a larger number of matrix-vector multiplies.

Susan Blackford 2000-11-20