Linear optimization: Parametrical objective function 4

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! Ax=b

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