This approach can guarantee that the identified parameters are optimal,by solving a one-dimensional polynomial equation instead of a nonlinear least square problem.

This survey is concerned with some recent results on convergence of the Newton method for solving nonlinear operator (equation) and the Gauss-Newton method for solving the least square problems and the convex composite optimization problems.

For solving large-scale sparse least square problems,the author discusses the convergence of two-block AOR iterative method;and gives the necessary and sufficient conditions and the domain of its convergence;and further demonstrates that the spectral radius p (L(2)γ,ω) of two-block AOR optimal iterative matrix.

In this paper,we study the convergence of USSOR iterative methods for solving least squares problems.

This paper deals with the problem of finding a solution of a constrained least squares problem from the solution set of its unconstrained problem.

In this paper, we consider the unbalanced Procrustes problem with orthogonal constraint: given matrix A ∈ R(n×n) and B ∈ R(n×k), n > k, minimize the residual \\AQ — B\\f over the Steifel manifold of orthonormal matrices.

A neural network model for solving nonlinear least squares problem is given.

Trust region method is a kind of efficient methods to solve the general unconstrained optimization and its special situation, the nonlinear least squares problems.

for finding the approximation solution of nonlinear least squares problems min F(x)=[SX(]1[]2[SX)]f(x) T f(x).

We present an iterative method for solving nonlinear least squares problems,on the basis of Jacobi method for solving system of linear equations,where numerical experiments show quite promising performance of the parallel algorithm.