VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Mathematical Optimization Lab
Last Updated: 2026-06-03 00:14:30
Abstract
Hands-on coding-based course on using mathematical optimization methods and software to solve a variety of optimization problems.
Objective
The goal of this course is to learn how to put mathematical optimization theory into practice by using modern mathematical optimization libraries in python. At the end of this course, students should be able to implement algorithms that can tackle a wide variety of mathematical optimization problems.
Content
Key topics include: - Fundamental techniques in applied optimization. - Modeling computational questions in terms of classical mathematical optimization problems, and implementing algorithms to solve these fast.
Resources
Lecture Notes
See moodle page.
Literature
Necessary materials will be provided on moodle.
General Information
- Language
- English
- Levels
- BSC , MSC
- Frequency
- Yearly recurring
Examination
- Type
- ungraded semester performance
- Digital
- The examination takes place on your own device. Installation of SEB required.
Registration & Places
- Signup End
- 06.02.2026
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise |
Mathematical Optimization Lab
Number of participants is limited.
|
|
3 h weekly |
Offered In
-
-
-
-
Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
-
-
-
Electives (Students may also choose courses from the Master's program in Computer Science. It is their responsibility to make sure that they meet the requirements and conditions for these courses.)
-
-
-
Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
-
-
-
Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 14 of the required 26 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
-
-
-
Statistics Master (The following courses belong to the curriculum of the Master's Programme in Statistics. The corresponding credits do not count as external credits even for course units where an enrolment at ETH Zurich is not possible.)