Ort und Zeit
Übung / flipped classroom: Di, 14-16 Uhr, HS II, Albertstr. 23b
Klausur: Datum wird noch bekanntgegeben
Lehre
Dozent:in: Moritz Diehl
Assistenz: Léo Simpson
Sprache: auf Englisch
Inhalt
The aim of the course is to give an introduction into numerical methods for the solution of optimization problems in
science and engineering. The focus is on continuous nonlinear optimization in finite dimensions, covering both convex
and nonconvex problems. The course divided into four major parts:
- Fundamental Concepts of Optimization: Definitions, Types of Optimization Problems, Convexity, Duality, Compu-
ting Derivatives
- Unconstrained Optimization and Newton-Type Algorithms: Exact Newton, Quasi-Newton, BFGS, Gauss-Newton,
Globalization
- Equality Constrained Optimization: Optimality Conditions, Newton-Lagrange and Constrained Gauss–Newton,
Quasi-Newton, Globalization
- Inequality Constrained Optimization Algorithms: Karush-Kuhn-Tucker Conditions, Active Set Methods, Interior
Point Methods, Sequential Quadratic Programming
The course is organized as inverted classroom based on lecture recordings and a lecture manuscript, with weekly
alternating Q&A sessions and exercise sessions. The lecture is accompanied by intensive computer exercises offered in Python (6 ECTS) and an optional project (3 ECTS). The project consists in the formulation
and implementation of a self-chosen optimization problem or numerical solution method, resulting in documented
computer code, a project report, and a public presentation. Please check the website for further information.
Vorkenntnisse
notwendig: Analysis I–II, Lineare Algebra I–II
nützlich: Einführung in die Numerik
Verwendbarkeit
Wahlmodul im Optionsbereich (2HfB21)
Wahlpflichtmodul Mathematik (BSc21)
Mathematische Ergänzung (MEd18)
Angewandte Mathematik (MSc14)
Mathematik (MSc14)
Vertiefungsmodul (MSc14)
Wahlmodul (MSc14)
Elective in Data (MScData24)