Introduction to Numerical Linear Algebra
Einführung in die Numerische Lineare Algebra
6 LPSoSeEN
Voraussetzungen
Empfohlen, im Wortlaut
Basic knowledge of mathematics as conveyed by mathematical courses in computer science or engineering from the first three to four semesters, e.g.: • Module 11101 Lineare Algebra und analytische Geometrie I, and • Module 11103 Analysis I or • Module 11112 Mathematik IT-1 (Diskrete Mathematik) • Module 11113 Mathematik IT-2 (Lineare Algebra) • Module 11213 Mathematik IT-3 (Analysis) or • Module Höhere Mathematik - T1 • Module 11108 Höhere Mathematik - T2 • Module 11206 Höhere Mathematik - T3
Inhalte
The module focuses on methods and algorithms suitable for solving linear sets of equations as they typically arise in many applications such as solving/discretzising partial differential equations in engineering sciences or machine learning algorithms. In particular we will cover: • Classic iterative methods for solving linear systems of equations (Jacobi, Gauß-Seidel, SOR) • Projection type methods for solving linear systems of equations (CG, GMRES) • Direct methods for sparse linear systems of equations • Jacobi eigenvalue algorithm, power iteration, QR iteration Additionally, we will address practical issues of solving large sparse systems of linear equations such as storage schemes and parallelisation strategies.
Lernziele
After successful completion of the course the students know and understand classic and state of the art numerical methods and algorithms for solving linear systems of equations and to compute eigenvalues and eigenvectors. Through programming exercises they have acquired the practical skills to implement and validate numerical methods for scientific computing applications. The students have learned to use the programming language Python and common Python libraries/toolboxes (Numpy, Scipy) for an efficient and performant implementation methods used in scientific computing.
Prüfungsleistung
• three written examinations during the lecture or exercise period, 30 minutes each (1/3 each; 70% in total) • three programming tasks (1/3 each; 30% in total)
Literatur (4)
- G. H. Golub, C. F. van Loan: Matrix Computations
- L. N. Trefethen, D. Bau: Numerical Linear Algebra, SIAM
- Y. Saad: Iterative Methods for Sparse Linear Systems
- T. A. Davis: Direct Methods for Sparse Linear Systems
Bemerkungen
• Study programme Angewandte Mathematik M.Sc.: Compulsory elective module in complex „Numerics“ • Study programme Informatik B.Sc.: Compulsory elective module in complex „Mathematik“ or in field of application „Mathematik“ • Study programme Informatik M.Sc.: Compulsory elective module in complex „Mathematik“ or in field of application „Mathematik“ • Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Advanded Methods“ • Study programme Physics M.Sc.: Compulsory elective module in complex „Minor Subject“ • Study programme Mathematics M.Sc.: Compulsory elective module in complex „Numerics“ • Study programme Mathematical Data Science M.Sc.: Compulsory elective module in complex „Fundamentals of Data Science“ Change from MAP to MCA. Registration for repitition of the MAP module only possible via Student Services.
Quelle: Modulbeschreibung der BTU · abgerufen 19.10.2026Original