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Chain rule
Let's take an arbitrary function f that takes variables x and y as input, and there is some change in either variable so that . Using this, we can find the change in f using the following:
![](https://epubservercos.yuewen.com/FF11E0/19470372701459106/epubprivate/OEBPS/Images/Chapter_1343.jpg?sign=1738888556-K1YOV09X7bri96Id7cS4Ze3h4ird0YH3-0-3db2adf7a9eeedb4f63aee79ef53ce48)
This leads us to the following equation:
![](https://epubservercos.yuewen.com/FF11E0/19470372701459106/epubprivate/OEBPS/Images/Chapter_1002.jpg?sign=1738888556-HEuRsbbFSpcmbjQOD5GEA6aMPsBQnmkD-0-ed6e9b73405901968c3eaa30721d001f)
Then, by taking the limit of the function as , we can derive the chain rule for partial derivatives.
We express this as follows:
![](https://epubservercos.yuewen.com/FF11E0/19470372701459106/epubprivate/OEBPS/Images/Chapter_1285.jpg?sign=1738888556-4reeDPDGKhQL4iu4vzLPsUSi02nTEK4N-0-8aa299c582698032c33bb1acd5ff7d5b)
We now divide this equation by an additional small quantity (t) on which x and y are dependent, to find the gradient along . The preceding equation then becomes this one:
![](https://epubservercos.yuewen.com/FF11E0/19470372701459106/epubprivate/OEBPS/Images/Chapter_1007.jpg?sign=1738888556-3nu6V7PTjIErtHLgPBhGM9cFMaNTHqPh-0-cdf60f5d08675b8e109398928743c710)
The differentiation rules that we came across earlier still apply here and can be extended to the multivariable case.