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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=1739284283-03Mstg1Uav59BstCAbX18jAqk8AWr8w9-0-b9f1dafd035a4a6ffa5e3fe9b3d71ff0)
This leads us to the following equation:
![](https://epubservercos.yuewen.com/FF11E0/19470372701459106/epubprivate/OEBPS/Images/Chapter_1002.jpg?sign=1739284283-6phLReVqfMA9DUPWavyvqVhipb1Wyndo-0-e66e1ec55acb56c08ab964dd73078c95)
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=1739284283-lT9mSnOtoaA3ebyihezLDx9wuPb2dyR0-0-43defe83c2280a406aa25f47bbdefb73)
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=1739284283-q56x2rUpYmutXR6vwzgcQcbyX53YbYgU-0-bd2d5e7278ebda42654639fb1495139c)
The differentiation rules that we came across earlier still apply here and can be extended to the multivariable case.