Algorithm 1 computes an explicit inverse, which could cause numerical instability if the matrix is ill-conditioned. The inverse free Algorithm 2 provides an alternative approach for achieving better numerical stability. There are some very difficult problems where Algorithm 2 gives a more accurate answer than Algorithm 1. Numerical examples can be found in [7]. However, neither algorithm avoids all accuracy and convergence difficulties associated with eigenvalues very close to the boundary of the selected region.
The stability advantage of the inverse free approach is obtained at the
cost of more storage and arithmetic.
Algorithm 2 needs 
more storage space than Algorithm 1.
This will certainly limit the problem size we will be able to solve.
Furthermore, one step of the Algorithm 2 does about 6 to 7
times more arithmetic than the one step of Algorithm 1.
QR decomposition, the major component of Algorithm 2, and matrix inversion,
the main component of Algorithm 1, require comparable amounts of communication
per flop. (See table 4 for details.) Therefore,
Algorithm 2 can be expected to run significantly slower than Algorithm 1.
Since Algorithm 1 is faster but somewhat less stable than
Algorithm 2, and since testing stability is easy
(compute 
),
we may use the following 3 step algorithm:
If a fast parallel version of the QR algorithm[32] becomes
available, it would probably be faster than the inverse free algorithm
and hence would obviate the need for the second step listed above.
Algorithm 2 would still be of interest if only a subset of the
spectrum is desired (the QR algorithm necessarily computes the entire
spectrum), or for the generalized eigenproblem of a matrix pencil 
.