The OPTIMUM SOLUTION is expressly created and automatically delivered by the program.
The technology advantages are:
1. NO Partial, NO Negative Math Solutions
2. No Endless Guess & Trials
3. Yes, Effective Solutions ‘Under Pressure’
This technology plays the crucial role in highly sensitive and complex
Volumetric With Pores Stone SKELETON Grading Optimization, Providing Consistently Optimum ’Spatial Packing’
As a Result
User Gains Desired Product Solution and pockets SUBSTANTIAL SAVINGS.
This Above Is THE CORE of the ASPHALT EXPERT SYSTEM®©
In addition to the above in brief described key optimization technology, we use some other modified technologies such as the one derivation from a
Sequential Unconstrained Minimization Technique Which Uses Mixed Penalty Function
Constrained nonlinear optimization problems are composed of a nonlinear objective function and may be subject to linear and nonlinear constraints. The ASPHALT EXPERT SYSTEM®© uses several brand new, modified, and derived methods to solve these problems. Some of them are based on the following known methods: trust-region and active set sequential quadratic programming.
ASPHALT EXPERT SYSTEM®© has
various unique ‘built-in’ optimization technologies that represent a
significant departures from three well-known methods for solving nonlinear
least squares problems: Trust-region, Levenberg-Marquardt,
and Gauss-Newton in order to solve nonlinear least squares problems, data
fitting problems, and systems of nonlinear equations.
· Trust-region is used for bound constrained problems.
· Levenberg-Marquardt is a line search method whose search direction is a cross between the Gauss-Newton and steepest descent directions.
· Gauss-Newton is a line search method that chooses a search direction based on the solution to a linear least-squares problem.
ASPHALT EXPERT SYSTEM®© also includes a specialized interface for
data-fitting problems to find the member of a family of nonlinear functions
that best fits a set of data points. ASPHALT EXPERT
SYSTEM®© uses the same methods
for data-fitting problems as it uses for nonlinear least-squares problems.