Completing the course
There are 2 problem sets with homework assignments per week, due on Sunday evening and Tuesday evening. These are based on the lecture material in question and the corresponding solved exercise problems. There are 0-6 presentation items in each problem set. The students are asked to prepare the problem sets before each exercise class and inform the assistant about their willingness to present their solutions.
for practical information about submitting, deadlines, and grading of homework assignments and the modalities for the students' presentation exercises.
The course can be completed by participating in the exercise classes and taking the exam. The course can also be completed by just taking the exam.
The students obtain 2 alternative grades on the scale 0–5:
(1) just based on the result of their exam;
(2) based on the result of their exam and their exercise submissions.
From these two grades, the higher grade is automatically valid. The alternative grades are computed as follows:
(1) The points in the exam (0–30) determine the final grade E;
(2) The combined points are computed by scaling the points of the exam (0–30) linearly to 0–70, scaling the points of the homework exercises (0–h_max) linearly to 0–20, and by scaling the points for the presentation exercises 0–10 (0–p_max), and summing these up. The combined points (0–100) determine the combined grade C. The homework problems make up 20 % of total points, and presentation problems make up 10 %, while the exam yields 70 %. However, for passing the course, at least 12 points in the exam are necessary.
From the grades E and C, the higher grade is chosen. In both cases, 40% of the exam points (i.e., 12 points) are necessary for passing the course with grade 1.
The precise thresholds for the particular grades are reported after the grading of the exams.
Targeted learning outcomes
After the course, the student has learned to perform the following tasks:
- Solving continuous time problems using calculus of variations, including different end point conditions. Lectures 1-2 (Kirk Section 4).
- Solving optimal control problems, including different end point conditions, and minimum principle. Lectures 3-5 (Kirk Section 5).
- Solving infinite horizon problems. Lecture 4 (Bertsekas Vol 2, Section 1).
- Solving discrete time/state problems using DP algorithm, including stochastic problems. Lectures 6-7 (Bertsekas Vol 1, Sections 1-5).
- Solving continuous time problems using Hamilton-Jacobi-Bellman equation. Lecture 8 (Kirk Section 3.11).
- Discounted problems and numerical methods, e.g. value- and policy iterations. Lecture 9 (Bertsekas Vol 1 Section 7).
- Learning definitions for relevant concepts and terminology used in the course. These definitions are often asked in course exams.