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Mathematical Optimization

Achieving good solutions at minimum cost, steering processes in optimal ways, attaining minimum energy states, etc., are goals that arise frequently in a variety of contexts. They are examples of optimization problems and the task of mathematical optimization is to analyze and solve them using mathematical methods. Our research group works on optimization theory and on efficient, tailored solvers for optimization problems.

Optimization theory and algorithms are applicable in many areas of engineering, natural sciences, and economics. Examples include optimal control, shape optimization, image processing, portfolio optimization, and many more. More recent application areas include AI, data science, and machine learning.

Students interested in these topics can take the following courses to deepen their knowledge:

  • Einführung in die Optimierung (Bachelor, MA2012, German).
  • Nonlinear Optimization: Advanced (Bachelor/Master, MA3503)
  • Seminar: Recent developments in Nonlinear Optimization (Bachelor/Master)
  • Case Studies: Nonlinear Optimization (Master, MA4513)
  • Modern Methods in Nonlinear Optimization (with changing focus, Master, MA4503)

Research groups in the Department of Mathematics that teach and research in related areas are Discrete Optimization and Optimal Control.