Topic outline

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    MS-EV0017 - Stochastic programming and robust optimization

    Why this course?

    In this course, we will learn about mathematical programming methods for modelling and solving optimisation problems under uncertainty. This is critical for the use of mathematical programming approaches in real settings, where the uncertainty related to the input data must be taken into account. 

    We will learn about the two main paradigms for uncertainty consideration: stochastic programming and robust optimisation. Our focus will be primarily practical, meaning we will learn about good modelling practice and uncertainty representation. 

    The course will be organised in two parts. In Part I, there will be lectures covering key topics such as: 

    1. Two- and multi-stage stochastic programming
    2. Scenario generation and sampling average approximation
    3. Chance constraints and risk management
    4. Static and adjustable robust optimisation
    5. Specialised solution methods

    In Part II, the course turns into a seminar course, in which the students will present scientific papers regarding either application of stochastic and robust optimisation and/or novel techniques recently developed in the literature.

    Practical matters

    • Lecturer: Fabricio Oliveira
    • Head TA: Paula Weller
    • Lectures: Periods I and II, Wednesdays, 9.15h-12.00h
    • Assessment methods: homework assignments and paper presentations (participation mandatory). 
    • Grading scale: 0-5
    • Study material: lecture slides, computational exercises, scientific papers.
    • Prerequisites: knowledge of mathematical programming (linear programming (optimisation)) is mandatory. At Aalto University, this refers to the content in MS-C2105 - Introduction to Optimization, MS-E2121 - Linear Optimization D, or MS-E2122 - Nonlinear Optimization D.

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      Course syllabus describing the course organisation, assessment and schedule.

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