5 credits
30.0 h + 22.5 h
Q1
Teacher(s)
Jungers Raphaël;
Language
English
Prerequisites
Basic knowledge (1st cycle) in linear algebra and numerical analysis.
Main themes
The course builds on the solid mathematical foundations of Matrix theory in order to elaborate algorithmic solutions to major challenges involving computations with/on matrices.
- Matrices defined over a field/ring/nonnegative: applications and challenges
- Canonical forms, decompositions, eigen- and singular values
- Norms, convexity, structured matrices: sparse/adjacency matrices
- Recent computational challenges: Nonnegative Matrix Factorization, matrix semigroups
Aims
At the end of this learning unit, the student is able to : | |
| 1 | Contribution of the course to the program objectives :
After successful completion of this course, the student will :
|
The contribution of this Teaching Unit to the development and command of the skills and learning outcomes of the programme(s) can be accessed at the end of this sheet, in the section entitled “Programmes/courses offering this Teaching Unit”.
Content
After an introduction recalling some basic notions, we discuss the following topics:
- Complements on determinants
- The singular value decomposition and its applications. Angles between subspaces, generalized inverses, projectors, least-squares problems
- Eigenvalue decomposition: Schur and Jordan form
- Approximations and variational characterization of eigenvalues
- Congruence and stability: inertia, Lyapunov equation, stability analysis of dynamical systems
- Structured and Polynomial matrices: Euclid algorithm, Smith normal form, fast algorithms.
- Nonnegative matrices: Perron-Frobenius theorem, stochastic matrices
- Matrix semigroups: algebraic structure, algorithms and applications (NMF, Joint Spectral Characteristics)
Teaching methods
- Regular classes with a schedule fixed by the EPL.
- Exercises or homeworks made individually or in small groups, with the possibility to consult teaching assistants. Solutions to the problems are made available afterwards.
Evaluation methods
The evaluation of the students is partly based on an exam organized according to the rules imposed by the EPL. The exam material corresponds to the contents of the course material, with the possible exception of certain parts specified in a document available on Moodle after the last session of the course.
The other part of the evaluation is based on the homeworks made during the semester.
More elaborate information on the on the evaluation procedure is given in the course plan, made available on Moodle at the beginning of the academic year.
The other part of the evaluation is based on the homeworks made during the semester.
More elaborate information on the on the evaluation procedure is given in the course plan, made available on Moodle at the beginning of the academic year.
Online resources
Bibliography
Le support de cours se compose d'ouvrages de référence, de notes de cours détaillées et de documents complémentaires disponibles sur Moodle.
Ouvrages de référence :
Ouvrages de référence :
- G.H. Golub and C.F. Van Loan (1989). Matrix Computations, 2nd Ed, Johns Hopkins University Press, Baltimore.
- P. Lancaster and M. Tismenetsky (1985). The Theory of Matrices, 2nd Ed, Academic Press, New York
Faculty or entity
MAP
Programmes / formations proposant cette unité d'enseignement (UE)
Title of the programme
Sigle
Credits
Prerequisites
Aims
Master [120] in Data Science Engineering
Master [120] in Electrical Engineering
Master [120] in Statistics: General
Master [120] in Mathematical Engineering
Master [120] in Mathematics
Master [120] in Physics
Master [120] in data Science: Information technology