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Controlling algorithm stability through pivoting

When performing Gaussian elimination, we showed in Chapter 3 that it was necessary to interchange rows to avoid a zero pivot. In Section 4.3, we illustrated that the use of a small pivot can lead to growth in the size of the computed entries. On our 3-digit computer, the small pivot led to a large multiplier resulting in the addition -2420 + 1.58, which destroys the value 1.58. Interchanges are again necessary for stability, this time to control growth in the resulting numbers. We study a variety of pivoting strategies aimed at controlling growth in the course of the factorization.

Partial pivoting

One reason why we might get growth that destroys smaller values in the course of the computation comes from having a very large multiplier. A solution to this is to require that the inequality |/j- | < 1 should hold for the coefficients of the matrix L. This is readily achieved by reordering the rows of the matrix so that the new pivot satisfies the inequalities

The k-th column must be scanned below the main diagonal to determine its entry of largest magnitude. The row containing this entry is exchanged with the k-th row and so becomes the new k-th row. This strategy is called partial pivoting.

Applying this strategy to example (4.3.1), we would interchange rows one and two of A before beginning the reduction and would compute the factors

Using these factors, we compute

which is almost correct to three decimal places (x = 1.176).

Note that this strategy for computation subject to rounding errors is similar to the strategy used for treating a zero pivot in exact arithmetic. In the exact case, we were concerned only to avoid a zero pivot; in the presence of rounding, small pivots must also be avoided. Note also that a zero pivot gives the ultimate disaster of infinite growth and total loss of accuracy.

In practice, Gaussian elimination with partial pivoting is considered to be a stable algorithm. This is based on experience rather than rigorous analysis, since the best a priori bound that can be given for a dense matrix is

This bound is easy to establish (see Exercise 4.5), but generally very pessimistic, particularly in the case where n is very large.

 
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