Scientific Computing

This module demonstrates the method of steepest descent for minimizing a nonlinear function in two dimensions. From a given starting point, a one-dimensional minimization of the objective function is performed along the negative of its gradient vector. The process is then repeated from the new point until convergence, which can be very slow.

The user selects a problem either by choosing a preset example or
typing in a desired objective function *f*(*x*,
*y*)*x*, *y*)*x*, *y*)*f* along
*f*

**Reference:** Michael T. Heath, *Scientific Computing,
An Introductory Survey*, 2nd edition, McGraw-Hill, New York,
2002. See Section 6.5.2, especially Algorithm 6.3 and Example 6.11.

**Developers:** Jeffrey Naisbitt and Michael Heath