Optimization

Constrained Optimization minimizes an arbitrary function including linear and nonlinear, equality and inequality constraints on parameters using the Sequential Quadratic Programming method. The descent methods include the Gauss-Newton, BFGS, and DFP methods. Derivatives and Jocobians may be computed numerically, or procedures may be provided by the user. Features:

- Linear and nonlinear constraints on parameters

- Equality and inequality constraints on parameters.

The GAUSS Application - Constrained Optimization is provided in GAUSS source code, allowing the user flexibility to customize and extend it's capabilities.

Greg Mead

Sales Manager