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Shu Lu, Department of Statistics and Operations Research

The course will provide an overview on nonlinear programming algorithms and theory. The following is a list of topics to be covered; the plan is subject to change.

  1. Unconstrained optimization (recognizing a local minimum, line search methods, trust-region methods, the conjugate gradient method).
  2. Theory of constrained optimization (Karush-Kuhn-Tucker conditions).
  3. Quadratic Programming (duality and special algorithms).
  4. Algorithms for general constrained optimization (penalty, interior-point, augmented Lagrangian, sequential quadratic programming).