There are many algorithms for minimization when the objective function is differentiable or convex, but few options when it is neither. We describe two simple algorithmic approaches for minimization of nonsmooth, nonconvex objectives: BFGS (a new look at an old method), and Gradient Sampling (a method that, although computationally intensive, has a nice convergence theory, which we will describe). Both methods require the user to provide a routine that computes the function value and, if it exists, the gradient at a given point,the idea being that the function is virtually never evaluated at a point where it is not differentiable, even though it is typically not differentiable at local optimizers. Applications abound in engineering, particularly control.