Understanding Jacobian Matrices - Working Through Examples

In summary: I'm still trying to wrap my head around systems. Systems are just functions that take more than one input.
  • #1
Shamshiel
2
0
I get the idea of Jacobian matrices. I think. Working through different examples, I don't have any problems.

For example,

f1 = x^2 + y^2
f2 = 3x + 4y

would result in

[2x 2y]
[3 4]

Similarly, by my understanding, something like

x^2 + y^2
3y + 4x

would result in

[2x 2y]
[4 3]

But when going up to systems like, say

f1 = x + x^2 + x^3 + y

...I'm baffled. I don't see how that could work. I'm under the impression that the Jacobian matrix should have two columns, but I'd have four there. I've had great difficulty finding any examples or explanations online, and I suspect my problem is I don't really understand them to begin with. And I really want to understand the math behind this, because otherwise I'm not really getting it.

Thanks!
 
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  • #2
What do you mean by "systems" like that? A single formula is not really a system is it?

In any case, the Jacobean of a function from Rm (m variables) to Rn (n values) is an m by n matrix- there are m columns, one for each variable, and n rows, one for each value. Yes, your first two examples are from R2 to R2 and so are 2 by 2 matrices as you show.

Your third example is from R2 (two variables, x and y) to R1 (one value, f1) and so would be a "2 by 1" matrix, with two rows and one column:
[tex]\begin{bmatrix}1+2x+ 3x^2 \\ 1\end{bmatrix}[/tex]
 
  • #3
To generalize: for each function f_i (x1,...,xn) that you have, you will have n
partial derivatives; one partial with respect to each variable.

This matrix describes the linear map that approximates the change of the function
in the neighborhood of a point, just like f'(x) does, in a map f:R-->R.
 
  • #4
I suggest you understand Jacobians as coordinate representations of the differentials of functions.
 
  • #5
Thanks guys! That helps a lot. :) Sorry for my lack of clarity with that third example, though.
 

What is a Jacobian matrix?

A Jacobian matrix is a matrix of partial derivatives. It represents the rates of change of one set of variables with respect to another set of variables. It is often used in multivariate calculus and optimization problems.

Why are Jacobian matrices important?

Jacobian matrices are important because they allow us to understand the relationship between different variables in a system and how they change with respect to each other. They are also useful in solving optimization problems and in physics and engineering applications.

How do you calculate a Jacobian matrix?

To calculate a Jacobian matrix, you take the partial derivatives of one set of variables with respect to another set of variables and arrange them in a matrix. The number of rows in the matrix is equal to the number of variables in the output and the number of columns is equal to the number of variables in the input.

What are some real-world applications of Jacobian matrices?

Jacobian matrices have many real-world applications, including in physics, engineering, economics, and machine learning. They are used to model physical systems, optimize processes, and understand the relationship between different variables in complex systems.

How can understanding Jacobian matrices help in solving optimization problems?

Jacobian matrices can help in solving optimization problems by providing information about how different variables in a system are related and how they change with respect to each other. This information can be used to find the maximum or minimum values of a function, which is often the goal in optimization problems.

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