Linear optimization: Parametrical objective function 4: Unterschied zwischen den Versionen
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'''Parametrical objective function''' is a special part of linear optimization. | '''Parametrical objective function''' is a special part of linear optimization. | ||
The foundation of parametrical optimization is the sensitivity analysis. | 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''. | Compared to the [[sensitivity analysis]] the Parametrical objective function ''makes a statement about large changes in the input data''. | ||
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=Basic Knowledge= | =Basic Knowledge= | ||
Version vom 26. Juni 2013, 13:28 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!
Ax=b
Fehler beim Parsen (http://mathoid.testme.wmflabs.org Serverantwort ist ungültiges JSON.): x=>0
