Discrete Optimization

TDA206/DIT370, Period 3, 2017

Final course results

We are done grading, and you should receive your final course grade soon. The figure below shows the final point distribution for the course (homework + exam). Students who have not taken the exam were excluded from the figure. The number of attainable points was 50, partial point totals for the course have been rounded up to the next integer. Since there is this obvious peak at 22 points, we decided to make 22 instead of 23 the threshold for a passing grade (3 or G).

The exam solutions can be found here.

A supplementary task to improve grades can be found here. The deadline is Monday, May 1, 2017, 16:00. Please refer any questions to Birgit.

Exam FAQ – Live Updates



Grading is based on hand-in exercises (30% of the final grade) and a final take-home exam (70% of the final grade).

Since one week was canceled and we therefore only covered 5/6-th of the material, the final grade will be calculated by rescaling the original 60 points (18 HW + 42 exam) as follows:

Point range

Chalmers grade

GU grade














Student representatives

There are three course evaluators:


Lecture Notes

Lecture notes and other material will be available here. Note that the lecture plan is tentative and subject to change. If you find any typos or errors, please feel free to send an email to John. Lecture notes contain a timestamp at the bottom of every page, so you can make sure you always have the latest revision.

Course literature

The lectures and evaluation will be based on the teacher's lecture notes, the material covered in class and the exercise sets. Therefore, you do not need to buy any book, but the lecture notes are partly based on the books below, which you can read if you are eager to know more:

Brief Course Description and Goals

See also the syllabus of this course.

This course is intended to provide a basic understanding about mathematical optimization and its applications. In optimization problems, the main goal is to obtain the maximum (or minimum) amount of an objective value by a proper selection of a number of individual variables. This goal is desirable in a large variety of applications, but may be difficult to achieve. In this course, you learn how to use mathematical disciplines to treat optimization problems. Examples from different application domains will be presented. In particular, optimization problems with discrete-valued parameters are discussed.

After the course you should be able to:

Update: By popular request, here is a list of possible future topics. Note that, due to time constraints, this list is very tentative and subject to change, and we will almost certainly not be able to cover everything in the remainder of this course. It should be treated as a rough guideline for students who want to get deeper into the literature, but nothing on this list is mandatory unless covered in the lectures:


Further knowledge of graphs will be helpful. However, sufficient explanation for the graph-theoretic concepts will be provided when required.

Exercise sets

Each set of exercises should be submitted before the announced deadline.

We will use the MATLAB based optimization tool CVX.

Find links to download the systems here: MATLAB, CVX.

Run cvx_setup in matlabs command window to get started with the optimization tool.

All exercises are submitted individually in the FIRE SYSTEM. Deadlines are sharp. No resubmissions or late submissions will be possible.

You are allowed and encouraged to work in groups, but you must write down your solutions yourself (no verbatim copies, as far as that’s feasible). You also have to include the names of the people that you discussed with. Groups can be different each week.

A check list for your submissions:

Final Examination

The exam is in the take-home format. The take-home exam will be Wednesday, March 15, 10:00 am to Thursday, March 16, 10:00 am. Details to follow on this website.