15.084  Nonlinear Optimization

  The goal of this course is to provide an analytical and computational approach to nonlinear convex optimization problems. Nonlinear optimization problems arise in a wide variety of applications ranging from machine learning and portfolio selection to signal processing. Course topics include constrained optimization, linear and conic optimization, duality theory, convex and self-concordant functions. Algorithms to solve nonlinear optimization problems include gradient descent and interior-point methods.