Numerical Optimization
Tutorial / flipped classroom: Di, 14-16h, HS II, Albertstr. 23b
Sit-in exam: date to be announced
Teacher: Moritz Diehl
Language: in English
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:
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.
necessary: Analysis I–II, Linear Algebra I–II
useful: Introduction to Numerics
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Applied Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective in Data (MScData24)
Lecture: Di, Do, 8-10h, SR 404, Ernst-Zermelo-Str. 1
Exercise session: Do, 10-12h, HS II, Albertstr. 23b
Programming exercise: 2 hours, date to be determined
Requirements on examinations, assessments and coursework will be described in the supplements of the module handbooks to be published as part of the course cataloque by end of October.
Teacher: Sören Bartels, Moritz Diehl, Thorsten Schmidt
Assistant: Alen Kushova
Language: in English
This course provides an introduction into the basic concepts, notions, definitions and results in probability theory, numerics and optimization, accompanied with programming projects in Python. Besides deepen mathematical skills in principle, the course lays the foundation of further classes in these three areas.
None that go beyond admission to the degree programme.
Basics in Applied Mathematics (MScData24)
Tutorial / flipped classroom: Di, 14-16h, HS II, Albertstr. 23b
Lecture: asynchronous (videos)
Sit-in exam: date to be announced
Requirements on examinations, assessments and coursework will be described in the supplements of the module handbooks to be published as part of the course cataloque by end of October.
Teacher: Moritz Diehl
Language: in English
The aim of the course is to give an introduction to numerical methods for the solution of optimal control problems in science and engineering. The focus is on both discrete time and continuous time optimal control in continuous state spaces. It is intended for a mixed audience of students from mathematics, engineering and computer science.
The course covers the following topics:
The lecture is accompanied by intensive weekly computer exercises offered both in MATLAB and Python (6~ECTS) and an optional project (3~ECTS). The project consists in the formulation and implementation of a self-chosen optimal control problem and numerical solution method, resulting in documented computer code, a project report, and a public presentation.
Required: Analysis I and II, Linear Algebra I and II \
Recommended: Numerics I, Ordinary Differential Equations, Numerical Optimization
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Applied Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective in Data (MScData24)
Lecture: Mo, Mi, 8-10h, HS 00-026, Georges-Köhler-Allee 101
Tutorial: 2 hours, various dates
Course offered by the Faculty of Engineering. For contents, prerequisites, and requirements see the module handbook M.Sc. Computer Science.
Teacher: Moritz Diehl
Elective in Data (MScData24)
Course offered by the Faculty of Engineering. For contents, prerequisites, and requirements see the course website.
Registration for this event is done using a separate registration form!
Teacher: Joschka Boedecker, Moritz Diehl, Sebastien Gros
Elective in Data (MScData24)
Further courses can be admitted as Elective in Data or as Elective after consultation with the Examination Board.
Numerical Optimization
Teacher: Moritz Diehl
Assistant: Léo Simpson
Language: in English
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:
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.
necessary: Analysis I–II, Linear Algebra I–II
useful: Introduction to Numerics
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Applied Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective in Data (MScData24)
Lecture: Di, Do, 8-10h, HS II, Albertstr. 23b
Tutorial: 2 hours, date to be determined and announced in class
Programming exercise: 2 hours, date to be determined
Teacher: Moritz Diehl, Patrick Dondl, Angelika Rohde
Assistant: Ben Deitmar, Coffi Aristide Hounkpe
Language: in English
This course provides an introduction into the basic concepts, notions, definitions and results in probability theory, numerics and optimization, accompanied with programming projects in Python. Besides deepen mathematical skills in principle, the course lays the foundation of further classes in these three areas.
None that go beyond admission to the degree programme.
Basics in Applied Mathematics (MScData24)
Tutorial / flipped classroom: Di, 14-16h, HS II, Albertstr. 23b
Teacher: Moritz Diehl
Assistant: Florian Messerer
Language: in English
The aim of the course is to give an introduction to numerical methods for the solution of optimal control problems in science and engineering. The focus is on both discrete time and continuous time optimal control in continuous state spaces. It is intended for a mixed audience of students from mathematics, engineering and computer science.
The course covers the following topics:
The lecture is accompanied by intensive weekly computer exercises offered both in MATLAB and Python (6~ECTS) and an optional project (3~ECTS). The project consists in the formulation and implementation of a self-chosen optimal control problem and numerical solution method, resulting in documented computer code, a project report, and a public presentation.
Required: Analysis~I and II, Linear Algebra~I and II \ Recommended: Numerics I, Ordinary Differential Equations, Numerical Optimization
Elective (Option Area) (2HfB21)
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Applied Mathematics (MSc14)
Mathematics (MSc14)
Concentration Module (MSc14)
Elective (MSc14)
Elective in Data (MScData24)
Lecture: Mo, 8:30-10h, HS 00-026, Georges-Köhler-Allee 101, Mi, 8:30-10h, HS 00-036, Georges-Köhler-Allee 101
Tutorial: 2 hours, various dates
Course offered by the Faculty of Engineering. For contents, prerequisites, and requirements see the module handbook M.Sc. Computer Science.
Teacher: Moritz Diehl
Language: in English
Elective in Data (MScData24)
Further courses can be admitted as Elective in Data or as Elective after consultation with the Examination Board.
Numerical Optimization
Exercise session: Di, 14-16h, HS Weismann-Haus, Albertstr. 21a
Teacher: Moritz Diehl
Assistant: Armin Nurkanivic
general:
Compulsory Elective in Mathematics (BSc21)
Numerical Optimal Control in Science and Engineering
Exercise session: Di, 14-16h, HS II, Albertstr. 23b
Teacher: Moritz Diehl
Assistant: Armin Nurkanivic
general:
Compulsory Elective in Mathematics (BSc21)
Exercise session: Fr, 14-16h, HS II, Albertstr. 23b
Sit-in exam 08.03., 09:00-12:00
Teacher: Moritz Diehl
Assistant: Florian Messerer
general:
Compulsory Elective in Mathematics (BSc21)
Exercise session: Do, 14-16h, SR 226, Hermann-Herder-Str. 10
Sit-in exam 22.09., 13:00-00:00
Sit-in exam (resit) 06.03., 13:00-16:00
Teacher: Moritz Diehl
Assistant: Florian Messerer
general:
Compulsory Elective in Mathematics (BSc21)
Exercise session: Fr, 10-12h, HS II, Albertstr. 23b
Teacher: Moritz Diehl
Assistant: Florian Messerer
general:
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Numerical Optimal Control in Science and Engineering
Teacher: Moritz Diehl
Assistant: Florian Messerer
general:
Compulsory Elective in Mathematics (BSc21)
Numerical Optimal Control in Science and Engineering
Exercise session: Fr, 10-12h, SR 226, Hermann-Herder-Str. 10
Teacher: Moritz Diehl
Assistant: Florian Messerer
general:
Numerical Optimization
Do, 10-12h, HS II, Albertstr. 23b, project
Exercise session: Fr, 14-16h, HS II, Albertstr. 23b
Teacher: Moritz Diehl
general:
Compulsory Elective in Mathematics (BSc21)
Supplementary Module in Mathematics (MEd18)
Numerical Optimal Control in Science and Engineering
Numerical Optimal Control in Science and Engineering
Fr, 14-16h, HS II, Albertstr. 23b
Teacher: Moritz Diehl
general:
Numerical Optimization
Exercise session: Di, 14-16h, HS II, Albertstr. 23b
Teacher: Moritz Diehl
general:
Compulsory Elective in Mathematics (BSc21)
Numerical Optimal Control in Science and Engineering
Numerical Optimal Control in Science and Engineering
Fr, 10-12h, HS II, Albertstr. 23b
Sit-in exam 25.09., 09:00-12:00
Teacher: Moritz Diehl
general:
Numerical Optimization