Main

## Main.ObjectiveVariables History

April 02, 2017, at 05:50 PM by 45.56.3.173 -
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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.

to:

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.

April 02, 2017, at 05:50 PM by 45.56.3.173 -
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The APMonitor 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:

to:

The APMonitor 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:

April 02, 2017, at 05:46 PM by 45.56.3.173 -
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The objective function is always minimized with APMonitor. 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 APMonitor. 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 APMonitor 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.

Changed lines 27-41 from:
 ! 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

to:
 ! Example model with an objective variable
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:)

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## Objective Variables

June 16, 2015, at 06:45 PM by 45.56.3.184 -
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(:table border=1 width=50% align=left bgcolor=#EEEEEE cellspacing=0:)

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(:table border=1 width=100% align=left bgcolor=#EEEEEE cellspacing=0:)

September 25, 2008, at 07:44 PM by 158.35.225.230 -
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(:cellnr:)

September 25, 2008, at 07:44 PM by 158.35.225.230 -

## 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 APMonitor. 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:)