For a simple problem like this, we can compute a closed form solution using calculus to find the optimal beta parameters that minimize our loss function. But as a cost function grows in complexity, finding a closed form solution with calculus is no longer feasible. This is the motivation for an iterative approach called **gradient descent**, which allows us to minimize a complex loss function.