Convex Optimization and Equilibrium Modeling
Equilibrium modeling has proved to be very effective in solving various problems in economics and transportation. Examples include finding traffic equilibria, spatial price equilibria, and Cournot-Nash equilibria. One important area of application involves energy markets, where equilibrium models may be used to monitor the abuse of market power and predict the effects of decarbonization policies.
This course starts with an introduction to convex optimization. Convex optimization problems arise in a broad number of fields and applications. After introducing the concepts of convex sets and convex functions, we are able to recognize certain optimization problems as being convex. We proceed to cover duality theory and optimality conditions for these problems. Finally, we discuss solution methods for unconstrained and constrained convex optimization problems.
The second part of the course focuses on equilibrium modeling. A short introduction to game theory allows to understand the notion of a Nash equilibrium. We furthermore consider complementarity problems and variational inequalities, with an emphasis on the relationship between these problems and convex optimization.
The content of the course is as follows:
A. Convex optimization:
- A1. Convex sets.
- A2. Convex functions.
- A3. Convex optimization problems.
- A4. Duality and optimality.
B. Equilibrium modeling:
- B1. Nash equilibria.
- B2. Complementarity problems.
- B3. Variational inequalities.
MSc Programme in Actuarial Mathematics
MSc Programme in Mathematics-Economics
- Properties of convex sets, convex functions, and convex optimization problems
- Solution methods for convex optimization problems
- Definitions of a Nash equilibrium, complementarity problem, and variational inequality
- Classify convex optimization problems
- Recognize and formulate convex optimization and equilibrium problems
- Implement and solve a given optimization or equilibrium problem using appropriate software
- Understand and reproduce the proofs presented in the course
- Explain how to exploit duality theory and optimality conditions for convex optimization problems in the design of a solution method
- Describe the differences and relationships between convex optimization problems, complementarity problems, and variational inequalities
- Formulate, implement and solve a practical problem and justify the formulation and solution method
2 x 2 hours of lectures and 1 x 2 hours exercises/project work per week for 7 weeks.
Operations Research 1 (OR1) or similar is required.
Academic qualifications equivalent to a BSc degree is recommended.
Individual written feedback will be given on the mandatory assignment.
- 7,5 ECTS
- Type of assessment
Oral examination, 30 minutes
- Type of assessment details
- Without preparation time
- Written aids allowed
- Marking scale
- 7-point grading scale
- Censorship form
- No external censorship
Several internal examiners
Criteria for exam assessment
The student should convincingly and accurately demonstrate the knowledge, skills and competences described under Intended learning outcome.
Single subject courses (day)
- Theory exercises
- Project work
- Course number
- 7,5 ECTS
- Programme level
- Full Degree Master
- Block 2
- No limit.
The number of seats may be reduced in the late registration period.
- Study Board of Mathematics and Computer Science
- Department of Mathematical Sciences
- Faculty of Science
- Nena Batenburg (9-646376677064777469426f63766a306d7730666d)
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