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Mathematics 14 Online
OpenStudy (anonymous):

If there are two points where an objective function is optimized, what can be said about the linear programming problem? The feasible region has only two vertices. Two of the constraints are parallel to each other. The feasible region contains the origin (0, 0). The objective function is parallel to one of the constraints.

OpenStudy (anonymous):

@texaschic101

OpenStudy (anonymous):

@perl

OpenStudy (perl):

Do you have any notes on linear programming problems?

OpenStudy (anonymous):

hold up

OpenStudy (anonymous):

Its ok if you dont know :/

OpenStudy (anonymous):

You want to minimize (or maximize) a particular function of several unknowns called the *objective* function, subject to a collection of inequalities called *constraints*. For linear programming, the constraints are a linear function of the unknowns. "

OpenStudy (perl):

im going to do an example which has multiple solutions, one moment

OpenStudy (anonymous):

ok

OpenStudy (perl):

this is an example with more than one solution: Max 6X + 4y subject to: X + 2y <= 16 3X + 2y <= 24 all decision variables >= 0.

OpenStudy (anonymous):

x=0?

OpenStudy (anonymous):

>0 does not work :(

OpenStudy (anonymous):

so The only solid answer would be A

OpenStudy (anonymous):

nah not A

OpenStudy (anonymous):

There are parallel constraints

OpenStudy (perl):

right, it looks like 6x + 4y is parallel to 3x + 2y

OpenStudy (anonymous):

yea It does

OpenStudy (anonymous):

If there are two points where an objective function is optimized

OpenStudy (perl):

In some instances, the objective function will be parallel to one of the constraint lines that forms a boundary of the feasible solution space. When this happens, every combination of x1 and x2 on the segment of the constraint line that touches the feasible solution space represents an optimal solution. Hence, there are multiple optimal solutions to the problem.

OpenStudy (anonymous):

so the answer is D

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