Implement the Gradient Descent method as a function:

function xstar $=$ gradDescent$($f$,$ alpha, x$1,$ tol$)$

We seek a local minimum of the anonymous function given an initial guess $.$ The objective function f is a scalar$-$valued function with vector input. The scalar alpha is the scale on the step size in the gradient direction. We will return the final iterate as our approximation xstar.

As a convention, please use the variable n: number of steps. This will be important because if alpha is chosen to be too small, the algorithm may take a very long time to converge. If the algorithm takes more than $10,000$ iterations, stop and return a warning.

We will stop iterating once the norm of the gradient is smaller than $,$ i$.$e$.,$ OR you have computed $10,000$ iterations. This second condition is used to indicate the method is slow for that particular example.