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Strukturbaum
Keine Einordnung ins Vorlesungsverzeichnis vorhanden.
Veranstaltung ist aus dem Semester
WiSe 2023/24
, Aktuelles Semester: SoSe 2024
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Nonlinear Optimization Methods
Sprache: Englisch
Keine Belegung möglich
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(Keine Nummer)
Vorlesung/Übung
WiSe 2023/24
4 SWS
keine Übernahme
ECTS-Punkte: 6
https://moodle.uni-due.de/course/view.php?id=43071
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Abteilungen:
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Bauwissenschaften
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M-CM-19, Computational Mechanics
(
1.
-
4.
Semester )
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B5, Bauingenieurwesen (M.Sc.)
(
1.
-
4.
Semester )
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Master of Science Computational Mechanics, ISE, Abschluss 87, Master of Science Computational Mechanics, ISE (87E96)
(
1.
-
4.
Semester )
- Kategorie : WA
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Master of Science Bauingenieurwesen, Abschluss 87, Master of Science Bauingenieurwesen (87257)
(
1.
-
4.
Semester )
- Kategorie : WA
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Zugeordnete Lehrpersonen:
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Schneider
verantwort
,
Mehta
begleitend
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Termin:
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findet statt
Mittwoch
12:00
-
13:30
wöch.
Beginn : 18.10.2023
Ende : 31.01.2024
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Raum :
SE 005
S - E
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Lecture
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findet statt
Mittwoch
13:30
-
15:00
wöch.
Beginn : 18.10.2023
Ende : 31.01.2024
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Raum :
SE 005
S - E
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Exercises
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| Kommentar: |
Course Contents
Optimization problems are a central topic for almost any working engineer. Examples
include dimensioning of components, minimizing the elastic energy within finite
element methods of modern AI (artificial intelligence) methods. This course introduces
the participants to the basics of nonlinear optimization of differentiable functions.
Furthermore, an overview of different classes of optimization algorithms presented,
discussing which method to apply to a specific problem. In the associated exercise
sessions, solution methods discussed in the lectures will be implemented, also
discussing how to use freely available optimization packages in Python.
Syllabus
• Necessary and sufficient optimizality conditions for unconstrained optimization
• Gradient methods
• Fast and conjugate gradient methods
• Newton and Quasi-Newton methods
• Optimality conditions for constrained optimization
• Projection methods for simple constraints
• Lagrange duality, penalty methods and the method of multipliers
• Interior point methods
• Active set strategies
• Alternating Direction Method of Multipliers (ADMM)
see also https://www.uni-due.de/ingmath/courses.php |
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| Literatur: |
[1] Nocedal, J. und Wright, S. J.: Numerical optimization. Springer, 1999.
[2] Boyd, S. und Vandenberghe, L.: Convex optimization. Cambridge University Press, 2004. |
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| Voraussetzungen: |
Basic training in advanced mathematics. |
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