Version vom 26. Juni 2013, 13:38 Uhr

Parametrical objective function is a special part of linear optimization. The foundation of parametrical optimization is the sensitivity analysis. Compared to the sensitivity analysis the Parametrical objective function makes a statement about large changes in the input data.

=Basic Knowledge=

To include large changes in the input data you have to add a new variable "q". For simple cases you just summate the new variable "q" multiplicated with a constant vektor "ß" to the objective function. In this case the constant vektor is the value which change the input parameter "c".

The objectiv function is now with a parameter:

c(q)=c+q*ß

Thereby there is a new optimization problem which can be solved.

P(q)=(c+q*ß)^T x --> max!

${\displaystyle Ax=b}$

${\displaystyle x}$ Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): \ge

${\displaystyle 0}$

Exemplification