Stochastic Optimization

The course provides knowledge of methods and models for optimization based decision support under uncertainty and risk. The course consists of three related parts:

• Theory
• Algorithms and software
• Applications in finance, production planning, energy, telecommunications among others

The course will be given next time Fall 2022.

Position and function within the study program:

The course is designed for PhD students of IØT and other departments who work with theoretical and practical optimization problems in different branches of industry and services with substantial uncertainty about problem data and other elements of problem formulation. The course is built upon optimization courses in IØT's master program, knowledge of probability theory or similar knowledge.

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 will develop the following skills: Training to utilize optimization models for solution of planning and economic problems under uncertainty in energy, production, logistics, transportation, finance, telecom.

Other important learning objectives: Give training in implementation and use of relevant software for solution of optimization models under uncertainty.

Lectures and exercises. Non-obligatory exercises. Course can be given in form of intensive lectures with several hours per day, several days per week, during limited number of weeks in semester.