MAX/MINS with multivariables

  • Thread starter Giuseppe
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In summary, the conversation discusses finding critical points for the equation f(x,y) = xy^2 in the circular disk x^2+y^2<=3. The person took the gradient and found that the point (0,0) can be considered. They then parameterized the equation and took the derivative, but there was a mistake in the derivative. The correct critical points are (0,0), (0,radical3), (0,-radical3), (radical3,0), and (-radical3,0), two of which are maxima and two of which are minima.
  • #1
Giuseppe
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Hey I was wondering if anyone can tell me if i am doing this right.

f(x,y) = xy^2 ; R is the circular disk x^2+y^2<=3

So first i took the gradient, since I know a mix/min can exist if the gradient is equal to 0.

Gradient of X= y^s
Gradient of Y= 2xy

So the point (0,0) can be considered right?

Anyway, I know I have to test region edges, so I parameterized the equation.

r(t) = <radical 3 cos(t),radical 3 sin(t)>

i plugged those values of x and y into my equation , and then took the derivative.

After some simplification, I came up with

sin(t)(3*radical3*cos(t)^2-3*radical3)

t= 0, pi, pi/2, 3pi/2 (right?)

so i found the x and y value when t is equal to those values

in conclusion, i have these points.

(0,0)
(0,radical3)
(0,-radical3)
(radical3,0)
(-radical3,0)

I tested these values in the equation xy^2 = f(x,y)

and found that there is no max or min...i don't think this is right.

Can someone help me find my mistake?
 
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  • #2
Your mistake is in the derivative you took. The gradient of f(x,y) should be <y^2,2xy>. You should have gotten sin(t)(3*radical3*cos(t)^2+3*radical3)when you took the derivative. Then, when you plug in 0, pi, pi/2, 3pi/2 for t, you should get four critical points, two of which are maxima and two of which are minima.
 
  • #3


Your approach seems to be correct. However, it is important to note that while finding critical points (where the gradient is equal to 0) is a necessary condition for max/min, it is not sufficient. In other words, just because the gradient is 0 at a certain point does not guarantee that it is a max/min.

In order to determine if the critical points you found are actually max/min, you need to use the second derivative test. This involves taking the second partial derivatives and plugging in the values of x and y at the critical points. If the second derivative test is inconclusive, then you may need to consider other methods such as using Lagrange multipliers.

Additionally, when parameterizing the equation, make sure to specify the range of t. In this case, t should range from 0 to 2π in order to cover the entire circular disk.

Overall, your approach is correct but you may need to do some additional calculations to determine if the critical points are actually max/min.
 

What is the concept of MAX/MINS with multivariables?

MAX/MINS with multivariables refers to the process of finding the maximum or minimum value of a function that has multiple independent variables.

Why is it important to find MAX/MINS with multivariables?

Finding the maximum or minimum value of a function with multivariables is important in many fields of science, such as physics, economics, and engineering. It allows us to optimize and understand complex systems in a more efficient and accurate way.

What is the difference between local and global MAX/MINS with multivariables?

A local MAX/MIN refers to the highest or lowest point in a specific region of a function. A global MAX/MIN refers to the highest or lowest point of the entire function. In other words, a local MAX/MIN is a relative extremum while a global MAX/MIN is an absolute extremum.

How do you find MAX/MINS with multivariables?

To find the MAX/MIN of a function with multiple variables, you need to use methods such as partial derivatives, critical points, and the second derivative test. These methods involve finding where the gradient of the function is equal to zero or does not exist.

What are some real-life applications of MAX/MINS with multivariables?

MAX/MINS with multivariables have many real-life applications, such as optimizing production processes in manufacturing, minimizing costs in business operations, and maximizing efficiency in transportation routes. It is also used in fields like meteorology to predict weather patterns and in medical research to analyze drug interactions.

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