Singular value decomposition (SVD) is a general linear-algebraic tool
for dealing with singular or near-singular matrices (Noble &
Daniel 1977). It is a generalization of
eigenfunction analysis to systems split into two domains, such as the
sky and the *u*,*v* planes. SVD determines the form of *S* which has
minimum length by discarding the singular or near-singular terms in
the formal algebraic solution. Briggs
(1995) briefly discussed the use of SVD for
deconvolving VLBA data; he showed that for about 3000 pixel
intensities and visibilities, SVD produces an image whose quality is
subjectively on a par with that of `CLEAN', but at the cost of large
memory use and of long running time (on IBM RS/6000-580 workstations).

1996 November 4

10:52:31 EST