Solve Optimization Problem: Find Min & Max of f(x,y)

In summary, the conversation discusses finding the minimum and maximum values of a function with given constraints. The speaker used the method of bounded and closed domain to find the minimum and maximum points on a triangular region. They obtained the minimum value at (0,-2) and the maximum at (3,-2). They also mention that the published answers for the sum of the minimum and maximum values may be incorrect. Another speaker suggests using Lagrange multipliers, but the first speaker did not use this method.
  • #1
Yankel
395
0
Hello all

I am trying to find minimum and maximum of the following function:

\[f(x,y)=4x^{2}-y^{2}-xy-2x+6y\]

under the constraints:

\[y=4-2x\]

\[x\geq 0\]

\[y\geq -2\]I tried solving this problem using the method of the method of bounded and closed domain, understanding that the constraints creates a triangle. I checked every line in the triangle, the edge points and the local min and max for each line (if there were any).

I got that the minimum value was f(0,-2)=-16 and the maximum was f(3,-2)=20
(should I have used Lagrange multipliers ?)

The problem is:

1. I entered this problem to MAPLE, and got max like mine, but min at f(0.5,3)=7.5. I found this point, but it isn't the absolute minimum.

2. In the answers sheet for this problem there are 4 possible answers for the sum of the min+max: 30.5, -7, 0. 16.
Non of them are according to my solution or MAPLE's.

Can you please assist me with solving this problem ?

Thank you !
 
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  • #2
Using Lagrange multipliers, I find:

\(\displaystyle f_{\min}=\frac{15}{2}\)

\(\displaystyle f_{\max}=20\)

Post your work using Lagrange, and I will be glad to look it over. :D
 
  • #3
I didn't use Lagrange, I looked at it as an optimization problem in a closed region, using a triangle.

I notice that the point (0,-2) which is my smallest, is not even in the region the constraint creates. Therefore, if I ignore it, I get the same results you got using Lagrange. This means that the published answers are wrong (if you, me and MAPLE say the same thing...).
 
  • #4
Hi,

It's even easier if you simply use the equality constrain to go through a single valued function and derive the function.

I agree with your answers, so maybe the provided ones are wrong.
 

FAQ: Solve Optimization Problem: Find Min & Max of f(x,y)

What is an optimization problem?

An optimization problem is a mathematical problem that seeks to find the best solution from a set of possible solutions. In the case of finding the minimum and maximum of a function, the goal is to find the values of the independent variables that result in the smallest and largest values of the function, respectively.

How do you solve an optimization problem?

To solve an optimization problem, you need to first determine the objective function, which represents the quantity that you want to minimize or maximize. Then, you need to identify the constraints, which are any limitations on the values of the independent variables. With this information, you can use various mathematical techniques such as calculus, linear programming, or heuristics to find the optimal solution.

What is the difference between a local and global minimum/maximum?

A local minimum/maximum is a point where the function has the smallest/largest value in a small region around that point. This means that there may be other points with smaller/larger values in other regions of the function. In contrast, a global minimum/maximum is the smallest/largest value of the function over its entire domain, meaning that there are no other points with smaller/larger values.

What is the role of the gradient in solving an optimization problem?

The gradient is a vector that points in the direction of the steepest increase of a function. In optimization problems, it is used to find the direction in which the function is changing the fastest, and therefore, the most efficient direction to move towards the optimal solution. The gradient also helps determine whether a point is a local minimum, maximum, or saddle point.

Are there any real-world applications of solving optimization problems?

Yes, there are many real-world applications of solving optimization problems including in economics, engineering, and operations research. Some examples include minimizing production costs, maximizing profits, optimizing resource allocation, and finding the most efficient route for transportation. The ability to solve optimization problems is crucial for making data-driven decisions and improving overall efficiency and effectiveness in various industries.

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