Introduction to differentials

Differential of a vector function

Let $f(x)$ be a function that maps ${\mathbb{R}}^{n} \to {\mathbb{R}}^{m}$. The derivative $\partial f(x) / \partial x$ is an $m \times n$ matrix that defines the linear approximation

\[f(x + u) \approx f(x) + \frac{\partial f(x)}{\partial x} \times u \enspace .\]

The differential $df$ is an alternative way of expressing this approximation

\[f(x + u) \approx f(x) + df(x; u)\]

where $df(x; u)$ must be linear in $u$.

Chain rule for differentials

The chain rule for differentials states that if $h(x) = g(f(x))$ then

\[dh(x; u) = dg(f(x); df(x; u)) \enspace .\]

The differential $dh$ is clearly linear in $u$ because it is the composition of two linear functions.

Differential of a variable

The chain rule enables some useful notation. Let us introduce the variable $y = f(x)$ and write the differential of the variable $y$ as a function of the differential of $x$

\[dy = df(x; dx) \enspace .\]

We can similarly introduce $z = g(y)$ and write the differential of $z$

\[dz = dg(y; dy) \enspace .\]

While these expressions were obtained in isolation, each without knowledge of the other, they can be combined and the result agrees with the chain rule

\[dz = dg(y; df(x; dx)) \enspace .\]

Therefore, knowledge of the entire system in which the expression resides is not required to take differentials. That is, the expression of $dz$ in terms of $dy$ was valid whether $dy$ was an arbitrary vector or a function of $dx$. This is known as Cauchy’s rule of invariance.

Common rules in differential form

The product rule becomes

\[d(a \otimes b) = da \otimes b + a \otimes db\]

where the product $\odot$ can be any operation that distributes over addition

\[a \otimes (b + c) = a \otimes b + a \otimes c \enspace .\]

This includes the element-wise product, matrix multiplication, the inner product, the Kronecker product, etc.

Extension to matrices

The differential form is easily extended to matrix functions $F : {\mathbb{R}}^{m \times n} \to {\mathbb{R}}^{p \times q}$

\[F(X + U) \approx F(X) + dF(X; U)\]

where the differential $dF(X; U)$ is linear in the elements of $U$.

We will now use matrix differentials and the product rule to find the derivative of the $n \times n$ matrix inverse $Y = f(X) = X^{-1}$. This function can alternatively be defined implicitly $X Y = I$. Taking differentials of both sides gives

\[\begin{aligned} dY X + Y dX & = 0 \\ dY & = -Y dX Y\end{aligned}\]

This gives an expression for the differential $dY = df(X; dX)$ that is linear in $dX$ as expected.

Finding derivatives using differentials

Note that the differential provides the derivative as an operator

\[u \mapsto \frac{\partial f(x)}{\partial x} \times u\]

rather than as an array of values. The explicit derivative can be trivially extracted from an expression for the differential $df(x; dx)$ by taking the coefficients of $dx$ since

\[dy_{i} = df_{i}(x; dx) = \sum_{j} \frac{\partial f_{i}(x)}{\partial x_{j}} dx_{j} \enspace .\]

This is known as the identification theorem.

For the example of the matrix inverse, this must be generalized to matrices

\[dY_{i j} = dF_{i j}(X; dX) = \sum_{k = 1}^{n} \sum_{\ell = 1}^{n} \frac{\partial F_{i j}(X)}{\partial X_{k \ell}} dX_{k \ell} \enspace .\]

Comparing this to the element-wise expression of the differential

\[dY_{i j} = - \sum_{k = 1}^{n} \sum_{\ell = 1}^{n} Y_{i k} dX_{k \ell} Y_{\ell j}\]

provides the derivative

\[\frac{\partial F_{i j}(X)}{\partial X_{k \ell}} = -Y_{i k} Y_{\ell j} \enspace .\]

Back-propagation and differentials


Back-propagation is an algorithm for computing the derivatives of a scalar energy $E$, which is a composition of $n$ functions

\[E = f_{n}( \cdots f_{2}(f_{1}( x , \theta_{1}), \theta_{2}) \cdots , \theta_{n}) \enspace ,\]

with respect to the parameters $\theta_{i}$ of each function $f_{i}$. That is, we want to find

\[\frac{\partial E}{\partial \theta_{1}}, \frac{\partial E}{\partial \theta_{2}}, \dots, \frac{\partial E}{\partial \theta_{n}} \enspace .\]

Let the variables $y_{i}$ represent the intermediate results so that

\[y_{i} = f_{i}(y_{i-1}, \theta_{i}) \enspace ,\]

with $y_{0} = x$ and $y_{n} = E$.

It is trivial to obtain the derivatives with respect to $y_{n-1}$ and $\theta_{n}$ from the derivatives of $f_{n}$ according to

\[\begin{aligned} \frac{\partial E}{\partial y_{n-1}} & = \frac{\partial f_{n}(y_{n-1}, \theta_{n})}{\partial y_{n-1}} \enspace , & \frac{\partial E}{\partial \theta_{n}} & = \frac{\partial f_{n}(y_{n-1}, \theta_{n})}{\partial \theta_{n}} \enspace .\end{aligned}\]

Back-propagation proceeds by obtaining the derivatives with respect to $\theta_{i}$ and $y_{i-1}$ from the derivative with respect to $y_{i}$

\[\begin{aligned} \frac{\partial E}{\partial \theta_{i}} & = \frac{\partial E}{\partial y_{i}} \times \frac{\partial y_{i}}{\partial \theta_{i}} = \frac{\partial E}{\partial y_{i}} \times \frac{\partial f_{i}(y_{i-1}, \theta_{i})}{\partial \theta_{i}} \\ \frac{\partial E}{\partial y_{i-1}} & = \frac{\partial E}{\partial y_{i}} \times \frac{\partial y_{i}}{\partial y_{i-1}} = \frac{\partial E}{\partial y_{i}} \times \frac{\partial f_{i}(y_{i-1}, \theta_{i})}{\partial y_{i-1}} \enspace .\end{aligned}\]

and so on.

Forward propagation using differentials

Whereas back-propagation computes

\[\frac{\partial E}{\partial \theta_{i}} = \left( \left( \cdots \left( \frac{\partial E}{\partial y_{n-1}} \times \frac{\partial y_{n-1}}{\partial y_{n-2}} \right) \times \cdots \right) \times \frac{\partial y_{i+1}}{\partial y_{i}} \right) \times \frac{\partial y_{i}}{\partial \theta_{i}} \enspace ,\]

forward propagation computes

\[\frac{\partial E}{\partial \theta_{i}} = \frac{\partial E}{\partial y_{n-1}} \times \left( \frac{\partial y_{n-1}}{\partial y_{n-2}} \times \left( \cdots \times \left( \frac{\partial y_{i+1}}{\partial y_{i}} \times \frac{\partial y_{i}}{\partial \theta_{i}} \right) \cdots \right) \right) \enspace .\]

Forward propagation is much less efficient for two main reasons: the computation is not shared for different $i$, and one of the matrix dimensions is always 1 in back propagation.

Breaking forward propagation down into steps, the first step is to compute

\[\frac{\partial y_{i}}{\partial \theta_{i}} = \frac{\partial f_{i}(y_{i-1}, \theta_{i})}{\partial \theta_{i}}\]

and the next steps are to compute, for $j = i+1, \dots, n$,

\[\frac{\partial y_{j}}{\partial \theta_{i}} = \frac{\partial f_{j}(y_{j-1}, \theta_{j})}{\partial y_{j-1}} \times \frac{\partial y_{j-1}}{\partial \theta_{i}} \enspace .\]

Note that this product is exactly the operator that is defined by the differential. Therefore, if we have the differential $df_{j}(y_{j-1}, \theta_{j}; dy_{j-1}, d\theta_{j})$, we can use it to perform forward propagation

\[\frac{\partial y_{j}}{\partial \theta_{i}} = df_{j}\bigg(y_{j-1}, \theta_{j}; \frac{\partial y_{j-1}}{\partial \theta_{i}}, 0\bigg) \enspace .\]

However, recall that forward propagation is much less efficient than back-propagation. The question remains: how to use differentials to obtain an operator for back-propagation?

Back-propagation using differentials

Consider a scalar cost $E = g(y)$ where $y = f(x)$, and define $h(x) = g(f(x))$. The definition of the differential and Cauchy’s rule of invariance provide

\[dE = \frac{\partial E}{\partial y} \times dy = \frac{\partial E}{\partial x} \times dx \enspace .\]

Since $E$ is a scalar, it’s more natural to write this as an inner product

\[\langle \nabla_{y} E, dy \rangle = \langle \nabla_{x} E, dx \rangle\]

where the gradient $\nabla_{x} E$ denotes a vector in the same space as $x$, instead of the operator $\partial E / \partial x$ which maps that space to ${\mathbb{R}}$. When $x$ is a vector, the gradient is the transpose of the derivative $\nabla_{x} E = (\partial E / \partial x)^{T}$. A useful short-hand is to adopt $\bar{x} = \nabla_{x} E$, since no specific knowledge of $E$ is required for this identity

\[\langle \bar{y}, dy \rangle = \langle \bar{x}, dx \rangle \enspace .\]

Manipulation of this equation after substituting an expression for $dy$ in terms of $dx$ can provide an expression for $\bar{x} = \nabla_{x} E$ as a linear function of $\bar{y} = \nabla_{y} E$, precisely the operator required for back-propagation!

Critically, observe that it is much easier to preserve the structure of the intermediate variables with the back-propagation operator, whereas the forward-propagation operator requires all variables to be vectorized.

Let’s return to the example of the matrix inverse. For matrices, it’s useful to recall the inner product $\langle A, B \rangle = {\operatorname{tr}}(A^{T} B)$ and the trace rotation ${\operatorname{tr}}(A B C) = {\operatorname{tr}}(C A B) = {\operatorname{tr}}(B C A)$ for compatible dimensions. Substituting the expression for $dY$ in the identity gives

\[\begin{aligned} \langle \bar{X}, dX \rangle = \langle \bar{Y}, dY \rangle & = \langle \bar{Y}, -Y dX Y \rangle \\ & = -{\operatorname{tr}}(\bar{Y}^{T} Y dX Y) \\ & = \langle -Y^{T} \bar{Y} Y^{T}, dX \rangle\end{aligned}\]

and therefore

\[\nabla_{X} E = -Y^{T} \left(\nabla_{Y} E\right) Y^{T}\]

In the case of the matrix inverse, this expression could have been obtained from the forward operator using vectorization and Kronecker product identities

\[\begin{aligned} {\operatorname{vec}}(dY) & = {\operatorname{vec}}(-Y dX Y) \\ \frac{\partial {\operatorname{vec}}(Y)}{\partial {\operatorname{vec}}(X)} \times {\operatorname{vec}}(dX) & = -(Y^{T} \otimes Y) {\operatorname{vec}}(dX) \\ \frac{\partial {\operatorname{vec}}(Y)}{\partial {\operatorname{vec}}(X)} & = -Y^{T} \otimes Y\end{aligned}\]

and then

\[\begin{aligned} \frac{\partial E}{\partial {\operatorname{vec}}(X)} & = \frac{\partial E}{\partial {\operatorname{vec}}(Y)} \times \frac{\partial {\operatorname{vec}}(Y)}{\partial {\operatorname{vec}}(X)} \\ {\operatorname{vec}}(\nabla_{X} E)^{T} & = {\operatorname{vec}}(\nabla_{Y} E)^{T} \times (-Y^{T} \otimes Y) \\ \nabla_{X} E & = -Y^{T} \left(\nabla_{Y} E\right) Y^{T} \enspace .\end{aligned}\]

However, it is somewhat messier. Since we used differentials to find the forward operator, we might as well use them to find the backward operator.

More examples

Gram matrix

Consider the function $Y = f(X) = X^{T} X$. Taking differentials and applying the product rule gives

\[dY = dX^{T} X + X^{T} dX\]

then making the substitution gives

\[\begin{aligned} \langle \bar{Y}, dY \rangle & = \langle \bar{Y}, dX^{T} X + X^{T} dX \rangle \\ & = {\operatorname{tr}}(\bar{Y}^{T} dX^{T} X) + {\operatorname{tr}}(\bar{Y}^{T} X^{T} dX) \\ & = {\operatorname{tr}}(dX^{T} X \bar{Y}^{T}) + {\operatorname{tr}}[(X \bar{Y})^{T} dX] \\ & = \langle dX, X \bar{Y}^{T} + X \bar{Y} \rangle = \langle \bar{X}, dX \rangle\end{aligned}\]

and therefore $\bar{X} = X (\bar{Y} + \bar{Y}^{T})$.

Circular convolution

Consider the function $y = f(x) = a * x$. Taking differentials and applying the product rule gives

\[dy = da * x + a * dx\]

then making the substitution gives

\[\langle \bar{y}, dy \rangle = \langle \bar{y}, da * x + a * dx \rangle \enspace .\]

This inner product has an equivalent expression in the Fourier domain since inner products are preserved

\[\begin{aligned} \langle F\bar{y}, F\{da * x + a * dx\} \rangle & = \langle \hat{\bar{y}}, \hat{da} \circ \hat{x} \rangle + \langle \hat{\bar{y}}, \hat{a} \circ \hat{dx} \rangle \\ & = \langle \hat{\bar{y}} \circ \hat{x}^*, \hat{da} \rangle + \langle \hat{\bar{y}} \circ \hat{a}^*, \hat{dx} \rangle\end{aligned}\]

and therefore

\[\begin{aligned} F \bar{a} & = \hat{\bar{y}} \circ \hat{x}^* & F \bar{x} & = \hat{\bar{y}} \circ \hat{a}^* \\ \bar{a} & = x \star \bar{y} & \bar{x} & = a \star \bar{y}\end{aligned}\]

where $\star$ denotes circular cross-correlation.