Compare and contrast the theoretical convergence rates of the Gradient Descent algorithm for three distinct scenarios: 1) Lipschitz continuous gradients (general convex), 2) Strongly convex functions (linear convergence), and 3) Non-convex functions (critical point convergence). Explain how the condition number of the Hessian matrix affects the speed of convergence.