Since the eigenvectors of are the right singular vectors, and the eigenvectors of are the left singular vectors, it might seem natural to use and to find right and left singular vectors, respectively. But as we pointed out earlier, left (or right) singular vectors for nonzero singular values can be recovered from right (or left) singular vectors by multiplying them by (or ), so either or can be used, whichever is cheaper.

The eigenvalues of and are the squares of the singular values of . In contrast, the eigenvalues of are positive and negative singular values of (as well as additional zero eigenvalues). Since the speed with which all algorithms converge depends on the distribution and location of eigenvalues, there are significant differences between and on the one hand, and on the other hand.

and are appropriate for computing the largest singular values, since squaring keeps the largest singular values largest, and many of the algorithms of Chapter 4 converge most easily to the largest eigenvalues. Indeed, squaring increases any gaps between the largest singular values and the rest of the spectrum, accelerating convergence.

On the other hand, the smallest singular values are squared too. In the most extreme case, a singular value near the square root of machine precision turns into after squaring, so its value may be entirely obscured by rounding errors. Also, small singular values that are clustered appear even more clustered after squaring. In other words, getting the smallest singular values can be challenging.

does not square singular values, so the problems just mentioned do not arise. On the other hand, since the eigenvalues of are plus and minus the singular values of , tiny singular values of are near the middle of the spectrum of . These are the hardest eigenvalues to find and generally require shift-and-invert or Jacobi-Davidson to find. In summary, the most accurate (but most expensive) way to find the smallest singular values is to use shift-and-invert on .