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Solution and Optimization Methods for Large Scale Problems

Description

n this course, we present the state of the art for linear as well as nonlinear multilevel and multigrid methods. The solution of large linear and nonlinear systems of equations is one of the most important tasks in numerical simulation. Since standard solution methods do not scale optimally , alternative solution strategies have been developed during the last decades. In particular multilevel or multiscale solution strategies have been developed, which are often employed due to their high efficiency. Prominent examples are multilevel or domain decomposition methods for linear elliptic problems, In this course, we start from well known subspace correction methods for linear problems and proceed to more recent developments as are nonlinear multigrid and monotone multigrid. Finally, we will consider (recursive) trust-region methods and their application to minimization problems in computational mechanics. For all methods, we will also discuss their parallelization.

 

RECOMMENDED COURSES
High-Performance Computing

 

 

REFERENCES

  • Multigrid Tutorial; William L. Briggs, Van Emden Henson, and Steve F. McCormick; Second Edition, SIAM, 2000 (book home page), ISBN 0-89871-4621.
  • Multigrid Methods and Applications; Wolfgang Hackbusch; Springer, 1985.
  • An Introduction to Multigrid Methods; Pieter Wesseling; Corrected Reprint. Philadelphia: R.T. Edwards, Inc., 2004. ISBN 1-930217-08-0.
  • Matrix computations; Gene H. Golub and Charles F. Van Loan. Domain Decomposition Methods; Toselli, Widlund Nocedal Wright Numerical Optimisation; Trust-Region Methods; Conn Gould Toint. Practical Methods of Optimisation; R. Fletcher. Numerical Optimisation, Series: Springer Series in Operations Research and Financial Engineering, Nocedal, Jorge, Wright, Stephen 2nd ed., 2006, XXII, 664 p. 85 illus., www.springer.com/mathematics/book/978-0-387-30303-1

People

 

Krause R.

Course director

Kopanicakova A.

Assistant

Additional information

Semester
Spring
Academic year
2018-2019
ECTS
6
Language
English
Education
Master of Science in Artificial Intelligence, Elective course, Lecture, 1st year

Master of Science in Artificial Intelligence, Elective course, Lecture, 2nd year

Master of Science in Computational Science, Core course, Lecture, 1st year

PhD programme of the Faculty of Informatics, Elective course, 2nd year (4 ECTS)

PhD programme of the Faculty of Informatics, Elective course, 1st year (4 ECTS)