We are concerned with the solution of large-scale linear least-squares problems

In applications of partial Golub-Kahan bidiagonalization to the solution of least-squares problems (1.

then select an incoming column solving the least-squares problem

3]/3 operations are needed for solving the least-squares problem (1).

In Section 2 we give a brief overview of tensor methods for nonlinear least-squares problems (tensor methods for nonlinear equations can be regarded as a special case of these).

1992], and Schnabel and Frank [1984] for more details on tensor methods for nonlinear equations and nonlinear least-squares problems.

p], then the approximate solution obtained by solving the least-squares problem (1.

VAN DER VORST, SIRT- and CG-type methods for the iterative solution of sparse linear least-squares problems, Linear Algebra Appl.

Solving the regularized

least-squares problems via ADM.

In the discussion of Section 9 we comment on an iterative algorithm for solving the true nonlinear

least-squares problemWilkinson, The

least-squares problem and pseudo-inverses, Comput.

of Paris 6) completely rewrites his 1987 edition to include material on Monte-Carlo methods, least-squares discrete problems, and

least-squares problems involving functions.

It is derived as in [23] for regular

least-squares problems and is an unbiased estimator of the predictive risk, hence its name.

Pereyra, The differentiation of pseudo-inverses and nonlinear

least-squares problems whose variables separate, SIAM J.

This work led to a comprehensive suite of algorithms and software for solving large, sparse, symmetric positive definite systems of equations and

least-squares problems.