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
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