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401-0647-00L 5 Credits BSC , MSC D-USYS , D-BAUG , D-INFK , D-ARCH , D-MAVT , D-MATH , D-ITET , D-BSSE

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Introduction to Mathematical Optimization

Lecturers & Examiners: Dr. David Adjiashvili

VVZ CR 4.4

Last Updated: 2026-02-05 15:35:51

Abstract

Introduction to basic techniques and problems in mathematical optimization, and their applications to a variety of problems in engineering.

Objective

The goal of the course is to obtain a good understanding of some of the most fundamental mathematical optimization techniques used to solve linear programs and basic combinatorial optimization problems. The students will also practice applying the learned models to problems in engineering.

Content

Topics covered in this course include: - Linear programming (simplex method, duality theory, shadow prices, ...). - Basic combinatorial optimization problems (spanning trees, shortest paths, network flows, ...). - Modelling with mathematical optimization: applications of mathematical programming in engineering.

Resources

Literature

Information about relevant literature will be given in the lecture.

General Information

Language: English
Levels: BSC , MSC
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 120 minutes
Aids: none

An interim examination is offered some time in the middle of the semester during one of the lecture times (90 min written exam, perhaps 7th or 8th week). The interim examination is optional, so it is possible to skip it, in which case the grade in the final examination is all that counts. Definite Final = max { Final, 0.3 Interim + 0.7 Final }

Course Components

Type	Title	Time & Place	Hours
lecture	Introduction to Mathematical Optimization "Hybrid" Online except in September/October 2020 for students in the Computational Science and Engineering Bachelor's Programme, where this course is mandatory. Those students will be invited by the lecturer to the classroom teaching (Tue 16-18 ETH Zentrum campus). As of November 2020 ONLINE for all students. The lecturers will communicate the exact lesson times of ONLINE courses. URL for live streaming:	Tue 16:00-18:00 (ON LI NE)	2 h weekly
exercise	Introduction to Mathematical Optimization Groups are selected in myStudies. Wed 12-13 or Wed 16-17 The lecturers will communicate the exact lesson times of ONLINE courses.	Wed 12:15-13:00 (HG D 1.2) Wed 16:00-17:00 (ON LI NE) Wed 16:15-17:00 (HG D 1.2)	1 h weekly

Offered In

Computational Science and Engineering Bachelor
- Bachelor Studies (Programme Regulations 2018)
  - Basic Courses
    - Block G1
Mechanical Engineering Master
- Core Courses
  - Mechanics, Materials, Structures (The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor.)
Civil Engineering Master
- Master Studies (Programme Regulations 2020)
  - 1. Semester
    - Major Courses
      - Major in Transport Systems
Agricultural Sciences Master
- Major in Agriculture Economics
  - Methodology Competences
    - Methods in Agricultural Economics
Spatial Development and Infrastructure Systems Master
- 1. Semester
  - Major Courses
    - Major in Transport Systems and Behaviour
Computational Biology and Bioinformatics Master (More informations at: )
- Advanced Courses (A total of 30 ECTS needs to be acquired in the Advanced Courses category. Thereof 18 ECTS in the Theory and 12 ECTS in the Biology category. Note that some of the lectures are being recorded: )
  - Theory (At least 18 ECTS need to be acquired in this category.)
Electrical Engineering and Information Technology Master
- Master Studies (Programme Regulations 2018)
  - Systems and Control (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Systems and Control", see . The individual study plan is subject to the tutor's approval.)
    - Specialisation Courses (These specialisation courses are particularly recommended for the area of "Systems and Control", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the Master's Programme.)
  - Signal Processing and Machine Learning (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Signal Processing and Machine Learning ", see . The individual study plan is subject to the tutor's approval.)
    - Specialisation Courses (These specialisation courses are particularly recommended for the area of "Signal Processing and Machine Learning", but you are free to choose courses from any other field in agreement with your tutor. A minimum of 40 credits must be obtained from specialisation courses during the MSc EEIT.)
- Master Studies (Programme Regulations 2008)
  - Major Courses (A total of 42 CP must be achieved during the Master Programme. The individual study plan is subject to the tutor's approval.)
    - Systems and Control
      - Recommended Subjects (These courses are recommended, but you are free to choose courses from any other special field. Please consult your tutor.)
    - Signal Processing and Machine Learning
      - Recommended Subjects
Integrated Building Systems Master
- Main Courses
  - Specialised Courses