Differentiating expressions involving multivariable vector valued functions

In summary, you are asking for help with differentiation rules for a function that is from R2 to R3. If you want the derivative function, you will have to think of it as a function from R2 to the set of matrices from R2 to R6.
  • #1
flyingtabmow
2
0
Not really a homework problem... just a general question (this seemed like the place to put it...). Say I have three functions:

[tex]f,g,h:\mathbb{R}^2\rightarrow\mathbb{R}^3[/tex]

and an expression along the lines of:

[tex]\left\langle f(u_1,u_2),g(u_1,u_2)\right\rangle h(u_1,u_2)[/tex]

What differentiation rules allow me to compute

[tex]\frac{\partial}{\partial \vec{u}}(\left\langle f(u_1,u_2),g(u_1,u_2)\right\rangle h(u_1,u_2))[/tex]

My problem is that I'm unclear on how to order/transpose the Jacobians and vectors such that all of the multiplications make sense. It's possible to order them as follows:

[tex]\left\langle f(\vec{u}),g(\vec{u})\right\rangle \frac{\partial h}{\partial \vec{u}} + h(\vec{u})\cdot f(\vec{u})^\mathrm{T}\cdot \frac{\partial g}{\partial \vec{u}} + h(\vec{u})\cdot g(\vec{u})^\mathrm{T}\cdot \frac{\partial f}{\partial \vec{u}}[/tex]

Everything works out there, and the result is a 3x2 matrix. However I'm not clear on what rules allow me to actually arrive at this result (other than moving things around until it all fits).

If anyone knows a good (preferably online) resource or book that discusses these sorts of expressions, that would be very helpful as well.

Thanks!
 
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  • #2
No rules. You have to go back to the definition. Your function is from R2 to R3 so the derivative, at a given point in R2 is the linear transformation from R2 to R3 that most closely approximates f- if f is differentiable so that is unique.

In a particular coordinate system, so that [itex]F(u_1,u_2)= \left< f(u_1,u_2), g(u_1,u_2), h(u_1,u_2)\right>[/itex] the derivative, at [itex](u_1, u_2)[/itex] can be represented by the 2 by 3 matrix
[tex]\left[\begin{array}{cc}\frac{\partial f}{\partial u_1} & \frac{\partial f}{\partial u_2} \\ \frac{\partial g}{\partial u_1} & \frac{\partial g}{\partial u_2} \\ \frac{\partial h}{\partial u_1} & \frac{\partial h}{\partial u_2}\end{array}\right][/tex]
Strictly speaking, the derivative is the linear transformation represented by that matrix.

Again, that is at a particular point in R2- at a particular (u1, u2). If you want the derivative FUNCTION, you will have to think of it as a function from R2 to the set of matrices from R2 to R3 which can, itself, be represented as a function from R2 to R6.
 
  • #3
Sorry, I must not have been clear. The three functions [tex]f[/tex], [tex]g[/tex], and [tex]h[/tex] are each from [tex]\mathbb{R}^2\rightarrow\mathbb{R}^3[/tex]. That is, they each have coordinate functions [tex]f_i[/tex], [tex]g_i[/tex], and [tex]h_i[/tex] ([tex]f[/tex], [tex]g[/tex], and [tex]h[/tex] are not the coordinate functions of some [tex]F:\mathbb{R}^2\rightarrow\mathbb{R}^3[/tex]). The use of the angle bracket notation was to signify an inner product, not different components of a vector. Thus [tex]\left\langle f(u_1,u_2),g(u_1,u_2)\right\rangle[/tex] is a scalar, and [tex]\left\langle f(u_1,u_2),g(u_1,u_2)\right\rangle h(u_1,u_2)[/tex] is a column vector, and I'm wondering how to compute

[tex]\frac{\partial}{\partial \vec{u}}(\left\langle f(u_1,u_2),g(u_1,u_2)\right\rangle h(u_1,u_2))[/tex]

and what differentiation rules apply.

Thanks!
 

FAQ: Differentiating expressions involving multivariable vector valued functions

What is the definition of a multivariable vector valued function?

A multivariable vector valued function is a function that takes multiple variables as input and outputs a vector, which is a mathematical object with both magnitude and direction. In other words, it is a function that maps a set of numbers to a set of vectors.

How do you differentiate a multivariable vector valued function?

To differentiate a multivariable vector valued function, you must take the partial derivative of each component of the vector with respect to each variable. This means that each variable is treated as constant when taking the derivative of the other variables.

What is the purpose of differentiating expressions involving multivariable vector valued functions?

The purpose of differentiating expressions involving multivariable vector valued functions is to find the rate of change of the function with respect to each variable. This is useful in many areas of science, such as physics, engineering, and economics.

What are some common techniques for differentiating expressions involving multivariable vector valued functions?

Some common techniques for differentiating expressions involving multivariable vector valued functions include using the chain rule, product rule, and quotient rule. Additionally, the gradient and directional derivative can be used to find the rate of change in a specific direction.

Can you give an example of differentiating a multivariable vector valued function?

Sure, let's say we have the function f(x,y) = (xy, x+y). To differentiate this function, we would take the partial derivative of the first component with respect to x, which would be y. Then, we would take the partial derivative of the second component with respect to y, which would be 1. Therefore, the derivative of this multivariable vector valued function would be f'(x,y) = (y, 1).

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