Courses in Stud.IP

current semester
link to course in Stud.IP Studip_icon
Constraint Satisfaction Problems (VL)
Subtitle:
This course is part of the module: Constraint Satisfaction Problems
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3002_s24
Lecturer:
Prof. Dr. Antoine Mottet
Description:

This course gives an introduction to the topic of constraint satisfaction problems and their complexity. A constraint satisfaction problem (CSP) is a computational problem of the form "Given variables and constraints on the variables, does there exist an assignment of the variables to some concrete domain that satisfies all the constraints?" The framework of CSPs is very general, and in fact every computational problem is equivalent to a CSP. The study of CSPs has been very prolific in the past, both in practice (e.g., with SAT solvers) and in complexity theory, a prominent field of theoretical computer science.

In this course, we will review the theoretical aspects of CSPs. The course will cover the basics of the theory such as the universal-algebraic approach to constraint satisfaction and several classical algorithms such as local consistency checking and the Bulatov-Dalmau algorithm.

Basic knowledge in predicate logic and an affinity to abstract mathematical thinking are highly recommended in order to follow this course.

Performance accreditation:
m1812-2021 - Constraint Satisfaction Problems<ul><li>p1803-2021 - Constraint Satisfaction Problems: mündlich</li></ul>
ECTS credit points:
3
Stud.IP informationen about this course:
Home institute: Theoretische Informatik (E-EXK6)
Registered participants in Stud.IP: 18
Postings: 2
Documents: 6
former semester
link to course in Stud.IP Studip_icon
Constraint Satisfaction Problems (VL)
Subtitle:
This course is part of the module: Constraint Satisfaction Problems
Semester:
SoSe 24
Course type:
Lecture
Course number:
lv3002_s24
Lecturer:
Prof. Dr. Antoine Mottet
Description:

This course gives an introduction to the topic of constraint satisfaction problems and their complexity. A constraint satisfaction problem (CSP) is a computational problem of the form "Given variables and constraints on the variables, does there exist an assignment of the variables to some concrete domain that satisfies all the constraints?" The framework of CSPs is very general, and in fact every computational problem is equivalent to a CSP. The study of CSPs has been very prolific in the past, both in practice (e.g., with SAT solvers) and in complexity theory, a prominent field of theoretical computer science.

In this course, we will review the theoretical aspects of CSPs. The course will cover the basics of the theory such as the universal-algebraic approach to constraint satisfaction and several classical algorithms such as local consistency checking and the Bulatov-Dalmau algorithm.

Basic knowledge in predicate logic and an affinity to abstract mathematical thinking are highly recommended in order to follow this course.

Performance accreditation:
m1812-2021 - Constraint Satisfaction Problems<ul><li>p1803-2021 - Constraint Satisfaction Problems: mündlich</li></ul>
ECTS credit points:
3
Stud.IP informationen about this course:
Home institute: Theoretische Informatik (E-EXK6)
Registered participants in Stud.IP: 18
Postings: 2
Documents: 6

Courses

For information on courses and modules, please refer to the current course catalogue and module manual of your degree programme.

Module / Course Period ECTS Credit Points
Module: Electrical Power Systems I: Introduction to Electrical Power Systems WiSe 6
Module: Electrical Power Systems II: Operation and Information Systems of Electrical Power Grids WiSe 6
Module: Electrical Power Systems III: Dynamics and Stability of Electrical Power Systems SuSe 6
Module: Electrical Engineering II: Alternating Current Networks and Basic Devices SuSe 6
Module: Electrical Engineering Project Laboratory SuSe 6
Module: Process Measurement Engineering SuSe 4
Module: Smart Grid Technologies WiSe, SuSe 6

Course: Seminar on Electromagnetic Compatibility and Electrical Power Systems

Further Information

WiSe, SuSe 2

SuSe: Summer Semester
WiSe: Winter Semester