Moses - Optimization Algorithms

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Diese Modulbeschreibung gilt nicht im aktuellen Semester.

Die Modulbeschreibung gilt nur für den Zeitraum WiSe 2020/21 - SoSe 2023. Die Version für das aktuelle Semester (SoSe 2024) finden Sie hier: Modulbeschreibung des SoSe 2024

Module / Version:
#41016 / #1

Validity:
WiSe 2020/21 - SoSe 2023

Default display language:
English

Module title:
Optimization Algorithms

Credits:
6

Responsible person:
Toussaint, Marc

Grading:
benotet

Type of exam:
Schriftliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Technische Informatik und Mikroelektronik

Area of expertise:
34342100 FG Intelligent Systems

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 4-4

Contact person:
Toussaint, Marc

E-mail address:
office@lis.tu-berlin.de

Website:

https://www.user.tu-berlin.de/mtoussai/teaching/index.html

Learning Outcomes

The students will be able to develop and apply optimization algorithms. They can formulate real-world problems appropriately as mathematical programs. They have a detailed understanding of the different categories of optimization problems, and methods to approach them. They have a basic understanding of the theory behind and properties of optimization algorithms. They have an overview of and experience with existing optimization software and are able to apply them to solve optimization problems.

Content

The course is on continuous optimization problems, with focus on non-linear mathematical programming (constrained optimization). Part 1 introduces efficient downhill algorithms in the unconstrained case: * gradient descent, backtracking, Wolfe conditions, convergence properties * covariant gradients, Newton, quasi-Newton methods, BFGS Part 2 will introduce efficient algorithms for constrained optimization: * Basics on KKT * Log-barriers, Augmented Lagrangian, primal-dual Newton * Phase I optimization Part 3 will dive into large-scale, sparse, and structured solvers, dealing with non-convexity, and applications * Existing libraries, CERES, structured NLPs, solving constraint graphs * Optimization plus sampling to handle non-convexity * Branch-and-bound-type methods * Applications in AI, Robotics, & ML

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Optimization Algorithms	IV		WiSe	English	4

Workload and Credit Points

Optimization Algorithms (IV):

Workload description	Multiplier	Hours	Total
Attendance	15.0	4.0h	60.0h
Pre/post processing	15.0	8.0h	120.0h
			180.0h(~6 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

weekly lectures, exercise sessions, and homeworks

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

Good knowledge in linear algebra and calculus Basic programming knowledge, programming in C++ or Python

Mandatory requirements for the module test application:

1. Requirement

Unbenoteter Übungsschein

Module completion

Grading

graded

Type of exam

Written exam

Language

English

Duration/Extent

120 min

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Wintersemester.

Maximum Number of Participants

This module is not limited to a number of students.

Registration Procedures

cf. course webpage

Recommended literature
No recommended literature given

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

	Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
This module is not used in any degree program.

Miscellaneous

No information

Optimization Algorithms