TY - JOUR
T1 - Superfast and stable structured solvers for Toeplitz least squares via randomized sampling
AU - Xi, Yuanzhe
AU - Xia, Jianlin
AU - Cauley, Stephen
AU - Balakrishnan, Venkataramanan
PY - 2014
Y1 - 2014
N2 - We present some superfast (O((m + n) log2(m + n)) complexity) and stable structured direct solvers for m× n Toeplitz least squares problems. Based on the displacement equation, a Toeplitz matrix T is first transformed into a Cauchy-like matrix C, which can be shown to have small off-diagonal numerical ranks when the diagonal blocks are rectangular. We generalize standard hierarchically semiseparable (HSS) matrix representations to rectangular ones, and construct a rectangular HSS approximation to C in nearly linear complexity with randomized sampling and fast multiplications of C with vectors. A new URV HSS factorization and a URV HSS solution are designed for the least squares solution. We also present two structured normal equation methods. Systematic error and stability analysis for our HSS methods is given, which is also useful for studying other HSS and rank structured methods. We derive the growth factors and the backward error bounds in the HSS factorizations, and show that the stability results are generally much better than those in dense LU factorizations with partial pivoting. Such analysis has not been done before for HSS matrices. The solvers are tested on various classical Toeplitz examples ranging from well-conditioned to highly ill-conditioned ones. Comparisons with some recent fast and superfast solvers are given. Our new methods are generally much faster, and give better (or at least comparable) accuracies, especially for ill-conditioned problems.
AB - We present some superfast (O((m + n) log2(m + n)) complexity) and stable structured direct solvers for m× n Toeplitz least squares problems. Based on the displacement equation, a Toeplitz matrix T is first transformed into a Cauchy-like matrix C, which can be shown to have small off-diagonal numerical ranks when the diagonal blocks are rectangular. We generalize standard hierarchically semiseparable (HSS) matrix representations to rectangular ones, and construct a rectangular HSS approximation to C in nearly linear complexity with randomized sampling and fast multiplications of C with vectors. A new URV HSS factorization and a URV HSS solution are designed for the least squares solution. We also present two structured normal equation methods. Systematic error and stability analysis for our HSS methods is given, which is also useful for studying other HSS and rank structured methods. We derive the growth factors and the backward error bounds in the HSS factorizations, and show that the stability results are generally much better than those in dense LU factorizations with partial pivoting. Such analysis has not been done before for HSS matrices. The solvers are tested on various classical Toeplitz examples ranging from well-conditioned to highly ill-conditioned ones. Comparisons with some recent fast and superfast solvers are given. Our new methods are generally much faster, and give better (or at least comparable) accuracies, especially for ill-conditioned problems.
KW - HSS error and stability analysis
KW - Randomized sampling
KW - Rectangular HSS matrix
KW - Superfast and stable solvers
KW - Toeplitz least squares
KW - URV factorization
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U2 - 10.1137/120895755
DO - 10.1137/120895755
M3 - Article
AN - SCOPUS:84897528667
SN - 0895-4798
VL - 35
SP - 44
EP - 72
JO - SIAM Journal on Matrix Analysis and Applications
JF - SIAM Journal on Matrix Analysis and Applications
IS - 1
ER -