back to search

Convex Optimization Laboratory

EI72561Examination Performance6 ECTSEnglishsummer semesterDepartment Computer Engineering
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You will receive practical guidance on the design and implementation of algorithms for convex optimization with applications particularly from information and communication technology. In the end you will be able to mathematically model optimization problems, develop suitable solution methods and numerically implement them (including standard methods such as simplex, gradient descents, Newton methods and basic interior-point methods).

What you will be able to do

  • Mathematical modeling of typical optimization problems in ICT systems
  • Implementation of numerical algorithms for convex optimization problems
  • Reformulation into primal and dual problems and primal reconstruction
  • Implementation of gradient and subgradient methods with step-size control
  • Application and implementation of the cutting-plane method
  • Implementation of simplex, gradient-descent, Newton and basic interior-point methods
  • Application of common general-purpose solvers for convex optimization

What the module consists of

  • Labor/PraktikumBearbeitung mehrerer Einheiten mit Modellierung, Lösungsentwurf und Implementierung; Teamarbeit möglich; aufeinander aufbauende Einheiten

Teaching method

  • Schriftliche InstruktionenLeiten jede Labor-Einheit an und geben Aufgabenstellung und Anforderungen vor
  • Selbstständige Bearbeitung zu HauseDu erarbeitest Modell, Lösungskonzept und Implementierung eigenständig oder im Team
  • Präsentation und DiskussionErgebnisse werden präsentiert und diskutiert, darauf aufbauend startet die nächste Einheit
No dates in the current semester
There are no course dates for this module this semester, or they haven't been matched yet.

Module ratings

No ratings for this module yet.

Rate this module

Only fill in the categories you can judge – for each one, either stars and text together or nothing at all.

Lecture
Tutorial
Exam

Reviews are automatically checked before they are published.

Official page in TUMonline · Details are not binding.