Stochastische Optimierung

What it is about

You will learn the basics and methods of stochastic optimization and practice formulating and numerically solving real decision problems under uncertainty as stochastic optimization problems. In the end you can understand two- and multi-stage models, incorporate risk measures, and apply solution approaches with software (MATLAB + numerical solvers).

What you will be able to do

• Understand fundamental theory of stochastic optimization
• Critically reflect on the limits and assumptions of the theory
• Implement solution approaches in MATLAB with numerical solvers
• Formulate real problems under uncertainty as stochastic optimization models
• Communicate results scientifically

What the module consists of

• VorlesungConveying the theoretical foundations of stochastic optimization
• Lab/ÜbungApplication of theory in exercises and MATLAB implementations; presentation of solutions
• HausaufgabenPractical work on case studies for deepening understanding and assessment

Teaching method

• VorlesungExplains the theory on which the practical exercises are built
• Übung/LabEnables practical application, programming, and presentation of results
• SelbststudiumComplement to lecture content and serves for preparation and completion of tasks
• Diskussionen/PräsentationenEncourage critical reflection of theory and scientific communication