Mathematical optimization
The application of optimization span many fields. A simple example is attempting to find the smallest possible difference in the distance of two objects in two-dimensional space (x and y). In this context, the derivative of the function that gives the difference is taken in order to find the minimum. A more complicated example is in Machine Learning, in which the optimization function attempts to find the global minimum of the loss function in order to minimize the difference or loss between the algorithm’s predictions and the actual values. This example is more difficult as Machine learning algorithms often utilize multidimensional data usually in the form of tensors yielding more complicated functions.