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

IRIX version compatibility: 5.1, 5.2

Greg Mead

Sales Manager
Aptech Systems, Inc.
23804 S.E. Kent-Kangley Rd.
Maple Valley, WA 98038
USA
206-432-7855
206-432-7832 (fax)
gregm@aptech.com

For applications in related solution areas, see the following indices: Statistics & Data Analysis, the developer index for Aptech Systems, Inc. and the product category index for Math, Physics, Other Sciences.

The GAUSS Application is a Trademark of Aptech Systems, Inc.