FSF3971 Optimal Stochastic Control and Backward Stochastic Differential Equations 7.5 credits

1. Optimal control. Dynamic programming, the HJB Equation, the maximum principle
2. Backward stochastic differential equations.
3. Forward-backward stochastic differential equations. Duality.
4. Mean-field type control
5. Mean-field games
6. Applications

After completing the course the students are expectedto

• Explain the dynamic programming principle and its connection to partial differential equations

• Have a good understanding of the maximum principle

• Have a good understanding of backward stochastic differential equations.

• Outline the foundations mean-field games and its relation to control and BSDEs

• Explain and motivate the methods in different applications

• Be able to solve problems and discuss research questions related to the theory. 

A Master’s degree in mathematics, applied mathematics or related field including at least 30 ECTS in mathematics and SF3940 Probability. 

The literature consists of a draft of the following book:

T. Basar, B. Djehiche and H. Tembine (2014-2017), Mean-Field-Type Game: Foundations and New Directions

If the course is discontinued, students may request to be examined during the following two academic years.

• HEM1 - Home assignments, 3.5 credits, grading scale: P, F
• TENM - Oral exam, 4.0 credits, grading scale: P, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Homework and an oral exam.

Homework and an oral exam.

Boualem Djehiche

• All members of a group are responsible for the group's work.
• In any assessment, every student shall honestly disclose any help received and sources used.
• In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.