Multivariable Minimization Question

In summary, the conversation discusses the problem of an insect confined to move on a surface, with the goal of finding the direction in which the temperature decreases the fastest. The solution involves finding the gradient of the temperature function, which is then used to determine the equation for the tangent plane at a given point. The use of Lagrange Multipliers is mentioned, but the constraints for their use are not clear. However, the person ultimately finds the solution before the deadline.
  • #1
SigurRos
25
0
I got this question as a take home exam question, and I can't figure it out for the life of me:


The temperature T(x,y,z) throughout a region in space is given by:

T(x,y,z) = 3*x^2*y*2+z^2

An insect is confined to move on the surface S : x^2 + y^2 = z. The insect is at the point P(1,1,2) on S and wishes to move in the direction in which T decreases the fastest. However, the insect can only move in directions tangential to S.

In which direction should the insect move from P(1,1,2)?

Here's what I've got so far:

The Temperature will decrease the fastest in the direction opposite to the Gradient of T(x,y,z), which is:

-<6*x*y^2 x, 6*x^2*y y, 2*z z> , which at (1,1,2) is equal to <-6 x, -6 y, -4 z>.

The equation for the tangent plane at (1,1,2) is:

PLANE: 2*(x-1)+2*(y-1)-(z-2) = 0

My professor says that Lagrange Multipliers may be used, but I'm not quite sure how. I'm not sure if the constraints are the plane, the surface S, or both. I tried computing using both the plane and S as constraints, but it didnt work because they only touch at (1,1,2). Any suggestions?

This is due in exactly 12 hours from right now, any help will be greatly appreciated.
 
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  • #2
Good news:
I got the solution.
 

Related to Multivariable Minimization Question

1. What is multivariable minimization and why is it important?

Multivariable minimization is a mathematical process of finding the minimum value of a function with multiple variables. It is important because it allows us to optimize complex systems and make informed decisions based on various factors.

2. What techniques are commonly used for multivariable minimization?

Some common techniques used for multivariable minimization include gradient descent, Newton's method, and the Method of Lagrange Multipliers. These methods involve iteratively adjusting the variables to find the minimum value of the function.

3. How is multivariable minimization used in real-world applications?

Multivariable minimization has numerous applications in various fields such as economics, engineering, and data analysis. It can be used to optimize production processes, design efficient systems, and analyze large datasets to make predictions and decisions.

4. What are the challenges of multivariable minimization?

One of the main challenges of multivariable minimization is finding the global minimum, as there may be multiple local minima in a complex function. Additionally, the process can be computationally intensive and require advanced mathematical knowledge and programming skills.

5. How can I improve my understanding of multivariable minimization?

To improve your understanding of multivariable minimization, you can study relevant mathematical concepts such as calculus, optimization algorithms, and linear algebra. You can also practice solving various multivariable minimization problems and explore their applications in different fields.

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