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Convex Optimization
Last Updated: 2026-02-05 16:07:36
Abstract
Introduction to Convex Optimization with a focus on algorithms and the numerous applications of Convex Optimization.
Objective
The main goal of this course is to obtain a solid understanding of classical Convex Optimization techniques and their numerous applications, including in Data Science, Machine Learning, and, more generally, in science and engineering. Apart from building up a solid foundational understanding of Convex Optimization, students also get hands-on experience through regular coding exercises. This aims at providing a holistic view on the process of identifying, modeling, and solving a wide range of computational questions that can be cast as Convex Optimization problems.
Content
Key topics include: - Introduction to Convex Optimization. - Subclasses of Convex Optimization: Semidefinite Programming, Second-Order Cone Programming and Geometric Programming. - Applications of Convex Optimization in science and engineering. - Algorithms for Convex Optimization.
Resources
Lecture Notes
A script will be provided.
Literature
- Boyd, S., \& Vandenberghe, L. (2004). Convex Optimization. Cambridge: Cambridge University Press. doi:10.1017/CBO9780511804441
General Information
- Language
- English
- Levels
- BSC , MSC
Examination
- Type
- end-of-semester examination
- Mode
- written 180 minutes
- Aids
- None
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Convex Optimization |
|
3 h weekly |
Offered In
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Electives (For the Master's degree in Applied Mathematics the following additional condition (not manifest in myStudies) must be obeyed: At least 15 of the required 28 credits from core courses and electives must be acquired in areas of applied mathematics and further application-oriented fields.)
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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.)
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