To maximize the objective function subject to the constraints:
We will first rewrite the constraints in a more manageable form and then find the feasible region. After that, we will evaluate the objective function at the vertices of the feasible region to find the maximum value.
Step 1: Rewrite the Constraints
From , we can express it as .
The second constraint remains as is.
The non-negativity constraints and also remain.
Step 2: Find the Feasible Region
Now we will find the intersection points of the lines defined by the constraints:
Intersection of and :
So, the point is .
Intersection of and :
So, the point is .
Intersection of and :
This does not provide a new point since is the x-axis.
Step 3: Evaluate the Objective Function at the Vertices
The vertices of the feasible region are:
Now we will evaluate at these points:
At :
At :
At :
Step 4: Determine the Maximum Value
The maximum value of occurs at the point where .
Conclusion
The maximum value of subject to the given constraints is at the point .
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To tackle the problem of maximizing subject to the constraints given, first, we need to identify the feasible region defined by the inequalities.
Constraints Analysis:
For the constraint , we can express in terms of :
For the constraint , this simply bounds from above.
The constraints and ensure we are within the first quadrant.
Finding Intersection Points:
Let’s find the points where the lines intersect:
Set in the first inequality:
This means we have a line segment with critical points at and where (which gives ). Thus, we find the intersection at .
Therefore, our vertices of the feasible region that we can check for maximizing are , , and (where can be zero, leading to point ).
Evaluating Objective Function at Vertices:
At :
At :
At :
Conclusion:
The maximum of occurs at the point with . Hence, the maximum value of under the given constraints is .