WebCab Optimization (J2EE Edition) 2.6

Refined procedures for solving and performing sensitivity analysis on uni and multi dimensional, local or global optimization problems which may or may not have linear constraints. Specialized Linear programming algorithms based on the Simplex Algorithm and duality are included along with a framework for sensitivity analysis w.r.t. boundaries (duality, or direct approach), or object function coefficients.

File size: 6201 Kb

WebCab Functions for .NET 2.0

Add refined numerical procedures to either construct a function of one or two variables from a set of points (i.e. interpolate), or solve an equation of one variable; to your .NET, COM, and XML Web service Apps. Interpolate using Newton poly., Lagrange's formula, Burlisch-Stoer algorithm, Cubic/Bicubic splines (natural and free); Solve using Newton-Raphson, Bisection, Brent, secant and false position, Ridders' Method,...

File size: 3320 Kb

WebCab Functions (J2SE Edition) 2.0

Java API Components offering refined numerical procedures to either construct a function of one or two variables from a set of points (i.e. interpolate), or solve an equation of one variable. The interpolation procedures provided include Newton polynomials, Lagrange's formula, Burlisch-Stoer algorithm, Cubic splines (natural and free), Bicubic interpolation and procedures for find the interpolation functions coefficients.

File size: 5080 Kb

WebCab Functions (J2EE Edition) 2.0

EJB Suite offering refined numerical procedures to either construct a function of one or two variables from a set of points (i.e. interpolate), or solve an equation of one variable. The interpolation procedures provided: Newton poly., Lagrange's formula, Burlisch-Stoer algorithm, Cubic splines (natural and free), Bicubic interpolation and procedures for find the interpolation functions coefficients. Solve using Newton-Raphson, Bisection,...

File size: 6728 Kb

Regression Analysis and Forecasting 1.0

The Multiple Regression Analysis and Forecasting model provides a solid basis for identifying value drivers and forecasting business plan data. While it utilises a range of commonly employed statistical measures to test the validity of the analysis, results are summarized in text for ease of use. Once relationships have been identified, forecasting can be accomplished based on a range of available methodologies.

File size: 162 Kb