  A few iterations of alternating least squares with a random starting point
provably suffice to produce nearly optimal spectral- and Frobenius-norm
accuracies of low-rank approximations to a matrix; iterating to convergence is
unnecessary. Thus, software implementing alternating least squares can be
retrofitted via appropriate setting of parameters to calculate nearly optimally
accurate low-rank approximations highly efficiently, with no need for
convergence.
