Partial Derivatives
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Partial derivatives follow directly from derivatives you have seen in single variable calculus. Calculation is pretty straightforward but, as is common in multi-variable calculus, you need to watch your notation carefully.
To calculate partial derivatives, you are given a function, usually of more than one variable, and you are asked to take the derivative with respect to one of the variables. To do so, you consider the other variable as a constant. Before we go any further, let's discuss notation.
Remember from single variable calculus, if you are given a function, say \(f(x)\) and you are asked to take the derivative (with respect to x, of course, since x is the only variable ). For partial derivatives, you have more than one variable, say \(g(x,y)\). Here is a comparison of how you write the derivative of g with respect to x in both cases.
single variable | multi-variable | |
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\(\displaystyle{ \frac{df}{dx} }\) or \(f'(x)\) | \(\displaystyle{ \frac{\partial g}{ \partial x} }\) or \(g_x(x,y)\) |
The \(d\) to indicate derivative for a single variable function is replaced by \( \partial \) for a partial derivative. This can also be written with a subscript to indicate the variable that we are taking the derivative with respect to. Notice that with partial derivatives, there is no 'prime' notation, since there is no way to determine the derivative variable. We need to show the variable somewhere to make any sense out of a partial derivative.
Example - - Okay, we are ready for an example. Let's find both partial derivatives of \(g(x,y) = x^2y\), meaning \(\partial g/\partial x\) and \(\partial g/\partial y\).
partial derivative of \(g\) with respect to \(x\) \(\left[ \partial g/\partial x \right]\) |
partial derivative of \(g\) with respect to \(y\) \(\left[ \partial g/\partial y \right]\) | |
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y is a constant |
x is a constant | |
\(\displaystyle{ \frac{\partial g}{\partial x} = }\) \(\displaystyle{ \frac{\partial}{\partial x}[x^2y] = }\) \(\displaystyle{ y \frac{\partial}{\partial x}[x^2] = }\) \(\displaystyle{ y(2x) = 2xy }\) |
\(\displaystyle{ \frac{\partial g}{\partial y} = }\) \(\displaystyle{ \frac{\partial}{\partial y}[x^2y] = }\) \(\displaystyle{ x^2 \frac{\partial}{\partial y}[y] = }\) \(\displaystyle{ x^2 (1) = x^2 }\) | |
\(\displaystyle{\frac{\partial}{\partial x}[x^2y] = 2xy}\) |
\(\displaystyle{ \frac{\partial}{\partial y}[x^2y] = x^2 }\) |
Notice that in each case, we could pull out the other variable, since it is considered a constant, and then we take the derivative just as we would in a single variable equation.
For partial derivatives, there are similar rules for products and quotients of functions. Here is a quick video showing those equations. | |
Chain Rule for Partial Derivatives |
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Here is a very short video clip discussing a more general version of the chain rule for functions of more than two variables. | |
One of the main uses of the first derivative is in the First Derivative Test, similar to what you learned in single variable calculus. Multi-variable functions can be tested with the partial derivative version of the First Derivative Test. This next panel shows a proof of how it works. We recommend that you watch this video since it can give you a better feel for partial derivatives.
Second Order Partial Derivatives |
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As you learned in single variable calculus, you can take higher order derivatives of functions. This is also true for multi-variable functions. However, for second order partial derivatives, there are actually four second order derivatives, compared to two for single variable functions. Using subscript notation, we have these four partial derivatives.
\(f_{xx}\) | \(f_{xy}\) | \(f_{yx}\) | \(f_{yy}\) |
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An important, but not obvious, result is that the two mixed partials, \(f_{xy}\) and \(f_{yx}\) are always equal, i.e. \(f_{xy} = f_{yx}\).
Here is a great video clip explaining these second order equations in more detail using other, very common, notation. | |
Okay, time for some practice problems. Once you are done with those, an important application of partial derivatives can be found on the next page where we discuss gradients and directional derivatives.