Can the Jacobian be written as $\mathbf{f}\nabla^T$?

Question

I was messing around with the various representations of the Jacobian and noticed that it should be able to be written as follows:

$$\mathbf{J} = \mathbf{f}\nabla^T$$

Where $\mathbf{f}$ is a multivariate vector function:

$$\mathbf{f}(x, y, z) = (f(x, y, z), g(x, y, z), h(x, y, z))$$

This expression for the Jacobian would expand to the following:

$$\mathbf{J} = \begin{bmatrix}f \\ g \\ h\end{bmatrix}\begin{bmatrix} \frac{\partial}{\partial x} & \frac{\partial}{\partial y} & \frac{\partial}{\partial z} \end{bmatrix}$$

This seems to work, but surprisingly, I can't find reference to this form anywhere. Does this work or is there some technicality I'm missing? If it does work, why is it never used?

Answer 1

As @ZainJabbar alluded to, $\nabla$ operates from the left, so instead of the Jacobian from your expression we obtain a "rescale-$\mathbf f$-by-divergence-of-a-second-vector-field" operator. Nevertheless, the Jacobian can be written as a Kronecker product:

$$J=\nabla^T \otimes \mathbf f$$

Answer 2

This is correct so long as it's understood that $\nabla$ is differentiating $\mathbf f$. To elaborate a bit more, the Jacobian is the matrix representation of the total differential $\mathrm d\mathbf f$ of $\mathbf f$. It turns out that the total differential is given by the directional derivatives $D_{\mathbf h}$: $$ \mathrm d\mathbf f(\mathbf h) = D_{\mathbf h}\mathbf f. $$ However, the operator $D_{\mathbf h}$ is precisely $\mathbf h\cdot\nabla$ (with $\mathbf h$ undifferentiated): $$ \mathrm d\mathbf f(\mathbf h) = (\mathbf h\cdot\nabla)\mathbf f. $$ Actually, this can be taken as a definition of $\nabla$ (insofar as it gives us the gradient of a scalar function). Now, if we represent vectors as column matrices then the dot product is $$ \mathbf h\cdot\nabla = \mathbf h^T\nabla = \nabla^T\mathbf h $$ where again we must note that $\mathbf h$ is undifferentiated. Let us put a dot $\dot\nabla$ and a dot $\dot{\mathbf f}$ to make it clear that $\nabla$ only differentiates $\mathbf f$. Then because $\nabla^T\mathbf h$ is scalar-like $$ \mathrm d\mathbf f(\mathbf h) = (\dot\nabla^T\mathbf h)\dot{\mathbf f} = \dot{\mathbf f}(\dot\nabla^T\mathbf h) = (\dot{\mathbf f}\dot\nabla^T)\mathbf h $$ so we see directly that $\dot{\mathbf f}\dot\nabla^T$ is the matrix representation of $\mathrm d\mathbf f$, making it by definition the Jacobian.

Answer 3

Aloha,

The symbol $\nabla = \begin{bmatrix} \frac{\partial}{\partial x} \\ \frac{\partial}{\partial y} \\ \frac{\partial}{\partial z} \end{bmatrix}$ is known as a vector differential operator. This is a type of function which inputs one function and returns another function.

Because $\nabla$ is a function, this means that the notational rules around "$\nabla$" are the same as functions. Consider a familiar function $\sin$, the expression $x \sin$ is different than $\sin x$. In the case of $\nabla$, this means that writing a function to the left ($f \nabla$) is understood as multiplying $\nabla$ by $f$. Writing the function to the right ($\nabla f$) is understood as application of $\nabla$ to $f$.

Let us do an example with the one dimensional derivative.

$$ \frac{d}{dx} x $$

Is the differential operator $ \frac{d}{dx} $ applied to the function $x$. This results in the constant function $1$.

Writing in a different order gives,

$$ x \frac{d}{dx} $$

This is now another differential operator. This is an object which inputs a function and returns $x$ times the derivative of that function. Here are some examples. I will use the notation

$$\text{Input} \mapsto \text{Output}$$

So,

$$ x \mapsto x \frac{d}{dx}(x) = x \cdot 1 = x $$ $$ x^3 \mapsto x \frac{d}{dx}(x^3) = x \cdot (3x^2) = 3x^3 $$ $$ e^x \mapsto x \frac{d}{dx}(e^x) = x \cdot (e^x) = x e^x $$

We now note that we lose "commutativity" in this sense of writing down functions. Writing in a different order implies a different operation. Let us do the multiplication and see what kind of operator we get.

$$\begin{bmatrix} f \\ g \\ h \end{bmatrix} \begin{bmatrix} \frac{\partial}{\partial x} & \frac{\partial}{\partial y} & \frac{\partial}{\partial z} \end{bmatrix} = \begin{bmatrix} f \frac{\partial}{\partial x} & f \frac{\partial}{\partial y} & f \frac{\partial}{\partial z} \\ g \frac{\partial}{\partial x} & g \frac{\partial}{\partial y} & g \frac{\partial}{\partial z} \\ h \frac{\partial}{\partial x} & h \frac{\partial}{\partial y} & h \frac{\partial}{\partial z} \end{bmatrix}$$

Wanna have a go at some function and multiply this matrix out? Keep in mind our discussion about multiplication order.

Here is something very cool that you have discovered though! Consider the product of two matrices with real elements, $A, B$. Then (assuming the sizes match up)

$$ (AB)^T = B^T A^T $$

For example,

$$ \left( \begin{bmatrix} 1 & 1 & 1 \\ 2 & 3 & 4 \end{bmatrix} \begin{bmatrix} 1 \\ 0 \\ 0 \end{bmatrix} \right)^T = \begin{bmatrix} 1 \\ 2 \end{bmatrix}^T = \begin{bmatrix} 1 & 2 \end{bmatrix} = \begin{bmatrix} 1 & 0 & 0 \end{bmatrix} \begin{bmatrix} 1 & 2 \\ 1 & 3 \\ 1 & 4 \end{bmatrix} $$

And this is an honest theorem (which you may prove). Why does it work here? It's because the order of writing products inside the matrix does not matter. That is, real number multiplication is commutative ($2 \cdot 3 = 3 \cdot 2$). This is in contrast to the derivative above.

Let me know what I may clarify / correct. I like that you are playing around with the math and demonstrating working knowledge with the material. Keep it up!