Advanced Stochastic Optimization

The course provides knowledge of advanced models and methods for optimization under uncertainty. The course covers the following topics:

• Risk-averse stochastic optimization
• Distributionally robust stochastic optimization
• Mixed-integer stochastic optimization
• Stochastic optimization with endogenous uncertainty
• Implementing stochastic optimization models using appropriate software
• Applications of stochastic optimization in, e.g., energy

The course is designed for PhD students who work with theoretical and practical optimization problems under uncertainty, in industry and services.

The course will convey the following knowledge: The theoretical foundation necessary for formulation, analysis and solution of stochastic programming problems and relevant applications. The knowledge necessary to conduct research in the field of optimization under uncertainty. The course builds on and extends IØ8403 Stochastic Optimization, focusing more on advanced models and software.

The course will develop the following skills: Training to build and solve optimization models for solution of planning and economic problems under uncertainty.

Lectures and non-obligatory exercises. The course can be given in form of intensive lectures with several hours per day, several days per week, during a limited number of weeks in the semester.