Scientific Computing

This module demonstrates the Gauss-Newton method for nonlinear least squares. Given an approximate solution, a new approximate solution is computed based on local linearization about the current point using the Jacobian matrix, which results in a linear least squares problem to be solved for the step to the new approximate solution. This process is repeated until convergence.

The user selects a problem either by choosing a preset example or
typing in a desired function *f* of parameters
*x*, *y*. Contours of the least squares residual are drawn
on the plot. An initial guess *x*, *y*)*x*, *y*)** s**
is computed by solving the linear least squares problem

**Reference:** Michael T. Heath, *Scientific Computing,
An Introductory Survey*, 2nd edition, McGraw-Hill, New York,
2002. See Section 6.6.1, especially Example 6.15.

**Developers:** Jeffrey Naisbitt and Michael Heath