Are you an EPFL student looking for a semester project?
Work with us on data science and visualisation projects, and deploy your project as an app on top of Graph Search.
This lecture focuses on specifying the error between f'(x0) and its approximation using a backward finite difference formula, presenting theorem 2.1 for any function f and x0 in R. The error is bounded by c times h, where c can depend on f and x0 but not on h. The result implies that the error is halved when h is halved, or divided by ten when h is divided by ten. The demonstration, left as an exercise, is similar to the previous one, involving the exhibition of a constant c that depends on f and x0 but not on h, being half the maximum of the absolute values of second derivatives between x0 and x0-h.