Higher-Order Derivatives
Higher-order derivatives can capture information about a function that first-order derivatives on their own cannot capture.
First-order derivatives can capture important information, such as the rate of change, but on their own they cannot distinguish between local minima or maxima, where the rate of change is zero for both. Several optimization algorithms address this limitation by exploiting the use of higher-order derivatives, such as in Newton’s method where the second-order derivatives are used to reach the local minimum of an optimization function.
In this tutorial, you will discover how to compute higher-order univariate and multivariate derivatives.
After completing this tutorial, you will know:
- How to compute the higher-order derivatives of univariate functions.