Quadratic functions are the workhorse of the analysis of iterative algorithms such as gradient-based optimization. They lead, in discrete and continuous time, to closed-form dynamics that treat all eigensubspaces of the Hessian matrix independently. This leads to simple math for understanding convergence behaviors (maximal step-size, condition number, acceleration, scaling laws for gradient descent or its…