Objective Variables
Main.ObjectiveVariables History
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This obtains the current objective function value. An objective may consist of multiple objectives that are maximized or minimized. They are all converted to minimization functions and added together.
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This obtains the current objective function value with input arguments ''s=server'' and ''a=application name''. An objective may consist of multiple objectives that are maximized or minimized. They are all converted to minimization functions and added together.
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The %blue%A%red%P%black%Monitor objective is reported in the minimized form. Thus, a maximized objective with a result of +17 is reported as -17. This is retrieved as the parameter ''nlc.objfcnval'' in a programming script from the ''apm_tag'' function such as:
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The %blue%A%red%P%black%Monitor objective is reported in the minimized form. Thus, a maximized objective with a result of +17 is reported as -17.
!!! Retrieve Objective Function
The objective function is retrieved as the parameter ''nlc.objfcnval'' in a programming script from the ''apm_tag'' function such as:
!!! Retrieve Objective Function
The objective function is retrieved as the parameter ''nlc.objfcnval'' in a programming script from the ''apm_tag'' function such as:
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The objective function is always minimized with %blue%A%red%P%black%Monitor. Objective function maximization is accomplished by defining a new variable that is the negative of the minimized objective.
to:
The objective function is always minimized with %blue%A%red%P%black%Monitor. Maximizing an objective function is accomplished by minimizing the negative of the original objective.
! original objective
maximize z
The objective is modified by minimizing the negative of the original objective function.
! modified objective
minimize -z
The %blue%A%red%P%black%Monitor objective is reported in the minimized form. Thus, a maximized objective with a result of +17 is reported as -17. This is retrieved as the parameter ''nlc.objfcnval'' in a programming script from the ''apm_tag'' function such as:
obj = apm_tag(s,a,'nlc.objfcnval')
This obtains the current objective function value. An objective may consist of multiple objectives that are maximized or minimized. They are all converted to minimization functions and added together.
! original objective
maximize z
The objective is modified by minimizing the negative of the original objective function.
! modified objective
minimize -z
The %blue%A%red%P%black%Monitor objective is reported in the minimized form. Thus, a maximized objective with a result of +17 is reported as -17. This is retrieved as the parameter ''nlc.objfcnval'' in a programming script from the ''apm_tag'' function such as:
obj = apm_tag(s,a,'nlc.objfcnval')
This obtains the current objective function value. An objective may consist of multiple objectives that are maximized or minimized. They are all converted to minimization functions and added together.
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! Example model with an objective function
Model example
Parameters
p1 = 5
End Parameters
Variables
objective
v1 > 6
End Variables
Equations
objective = (v1 -p1)^2
End Equations
End Model
Model example
v1 > 6
End Variables
Equations
objective = (v1 -
End Equations
End Model
to:
! Example model with an objective variable
Parameters
p1 = 5
Variables
objective
v1 > 6
Equations
objective = (v1 - p1)^2
Parameters
p1 = 5
Variables
objective
v1 > 6
Equations
objective = (v1 - p1)^2
Changed lines 36-51 from:
to:
! Equivalent model with a minimize objective statement
Parameters
p1 = 5
Variables
v1 > 6
Equations
minimize (v1 - p1)^2
(:cellnr:)
! Equivalent model with a maximize objective statement
Parameters
p1 = 5
Variables
v1 > 6
Equations
maximize -(v1 - p1)^2
(:cellnr:)
Parameters
p1 = 5
Variables
v1 > 6
Equations
minimize (v1 - p1)^2
(:cellnr:)
! Equivalent model with a maximize objective statement
Parameters
p1 = 5
Variables
v1 > 6
Equations
maximize -(v1 - p1)^2
(:cellnr:)
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(:table border=1 width=50% align=left bgcolor=#EEEEEE cellspacing=0:)
to:
(:table border=1 width=100% align=left bgcolor=#EEEEEE cellspacing=0:)
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!! Objective Variables
Objective variables are defined to construct an objective function. The objective function is a summation of all variables that are designated as objective-type. Variables are defined as objective function contributions by starting with '''obj'''. Thus, the variables ''obj1'', ''objective'', ''object[1]'' would be included in the objective function summation.
Additionally, slack variables are included in the objective function. These variables begin with the key letters '''slk''' and are defined with a lower bound of zero.
!!! Minimize vs. Maximize
The objective function is always minimized with %blue%A%red%P%black%Monitor. Objective function maximization is accomplished by defining a new variable that is the negative of the minimized objective.
!!! Example
(:table border=1 width=50% align=left bgcolor=#EEEEEE cellspacing=0:)
(:cellnr:)
! Example model with an objective function
Model example
Parameters
p1 = 5
End Parameters
Variables
objective
v1 > 6
End Variables
Equations
objective = (v1 - p1)^2
End Equations
End Model
Solution
p1 = 5
v1 = 6
objective = 1
(:tableend:)
Objective variables are defined to construct an objective function. The objective function is a summation of all variables that are designated as objective-type. Variables are defined as objective function contributions by starting with '''obj'''. Thus, the variables ''obj1'', ''objective'', ''object[1]'' would be included in the objective function summation.
Additionally, slack variables are included in the objective function. These variables begin with the key letters '''slk''' and are defined with a lower bound of zero.
!!! Minimize vs. Maximize
The objective function is always minimized with %blue%A%red%P%black%Monitor. Objective function maximization is accomplished by defining a new variable that is the negative of the minimized objective.
!!! Example
(:table border=1 width=50% align=left bgcolor=#EEEEEE cellspacing=0:)
(:cellnr:)
! Example model with an objective function
Model example
Parameters
p1 = 5
End Parameters
Variables
objective
v1 > 6
End Variables
Equations
objective = (v1 - p1)^2
End Equations
End Model
Solution
p1 = 5
v1 = 6
objective = 1
(:tableend:)