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  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
In the discussion of Section 9 we comment on an iterative algorithm for solving the true nonlinear least-squares problem
Wilkinson, 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
It is derived as in  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