How do I Find the Jacobi Matrix and Its Determinant for a Given Transformation?

In summary, the conversation discusses the definition of a transformation function and the process of finding its Jacobian matrix and determinant. The speaker also asks for clarification on the difference between the two. They are then given a task to find a function that satisfies a certain integral equation and are asked to find an explicit formula for that function.
  • #1
Kork
33
0

Homework Statement



The transformation f is defined by: R^2 --> R^2 and is defined by:
f(x,y) = (y^5, x^3)

Find the jacobi matrix and its determinant

Homework Equations



f(x,y) = (y^5, x^3)

The Attempt at a Solution



I would start by differentiating y^5 with respect to x and then y, then differentiate x^3 with respect to x and then y.

I end up with:

Df = (0... 5y^4 )
...(3x^2... 0 )

And from here I don't know what to do. Usually I would be told, that Df = (2,1) for example, and then I would place them instead of y and x, but here I am not given any other information than what I have written above. How do I find the determinant?
 
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  • #2
How about just doing what you were told to do? Yes, if you were asked to find the Jacobian matrix and its determinant at, say, (1, 2) you would replace x and y with those. But you aren't asked to do that so just leave them as "x" and "y".
 
  • #3
HallsofIvy said:
How about just doing what you were told to do? Yes, if you were asked to find the Jacobian matrix and its determinant at, say, (1, 2) you would replace x and y with those. But you aren't asked to do that so just leave them as "x" and "y".

Okay, thank you.

But I have another question, what is the difference between the Jacobi Matrix and the Jacobi determinant?
 
  • #4
I am also asked to:

let D be the set of points (x,y) in R^2 doe which 0 ≤ x ≤ 1 and

0 ≤ y ≤ 1. Find a function g: R^2 --> R for which

1010 h(x,y)dxdy =
1010 h(y5, x3*g(x,y)dxdy

is true for all functions h: D --> R integrable over D
 

What is a Jacobi Matrix?

A Jacobi Matrix, also known as a Jacobian, is a matrix of partial derivatives used in multivariate calculus to represent the linearization of a multivariate function. It is commonly used in optimization and numerical analysis to find the rate of change of a function with respect to its variables.

Why is it important to make a Jacobi Matrix?

The Jacobi Matrix is important because it allows us to analyze the behavior of a multivariate function by breaking it down into simpler, linear functions. It also helps us to find critical points and to optimize functions in a more efficient way.

What are the steps for making a Jacobi Matrix?

The steps for making a Jacobi Matrix include:
1. Identify the variables of the multivariate function.
2. Take the partial derivative of the function with respect to each variable.
3. Arrange the partial derivatives in a matrix format, with each row representing a variable and each column representing a partial derivative.
4. Simplify the matrix by evaluating the partial derivatives at a specific point or leaving them as variables.
5. Use the resulting matrix to analyze the behavior of the function or to solve optimization problems.

What are some common applications of the Jacobi Matrix?

The Jacobi Matrix has various applications in different fields. Some common ones include:
1. Optimization: The Jacobi Matrix is used to optimize functions in fields such as economics, engineering, and physics.
2. Robotics: It is used to model the movement of robotic arms and to control their motion.
3. Machine Learning: The Jacobi Matrix is used in algorithms for training and updating neural networks.
4. Differential Equations: It is used to find solutions to differential equations and to study their stability.
5. Image Processing: The Jacobi Matrix is used to analyze and manipulate images in computer vision.

Are there any limitations to making a Jacobi Matrix?

Yes, there are some limitations to making a Jacobi Matrix, including:
1. It can only be used for functions that are differentiable.
2. The resulting matrix may be very large and complex for functions with many variables.
3. It may be challenging to find the partial derivatives for some functions.
4. The Jacobi Matrix may not accurately represent the behavior of a function if the variables are highly correlated.
5. It may not be applicable to nonlinear functions.

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