Optimization I
Description and Content
The course consists of a two-hour lecture and a one-hour tutorial. The course provides an introduction to the research field of numerical optimization. Unconstrained optimization problems are considered and different numerical solution methods are introduced, e.g. the conjugate directions method, the gradient method and the Newton method. Furthermore, the topic of linear programming is treated. In addition to the numerical implementation, the analysis of the methods also plays an important role. In the exercises, programming tasks in the programming environment PYTHON are an essential part.
Prerequisites for the course are Analysis I and II, Linear Algebra, Numerics I, PYTHON programming knowledge.
The following topics will be covered:
- Optimality conditions
- Convex optimization
- Descent methods and step size strategies
- Gradient methods
- The conjugate gradient method
- Convergence rate
- The Newton method
- Quasi-Newton method
Teaching material
The course is administrated via ILIAS. Please log in to the corresponding course on Zeus so that you can join the ILIAS course.