Note from June 29, 2020
Although we do not yet know how long the social distancing related to the Covid19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been  or will be  communicated to the students by the teachers, as soon as possible.
Although we do not yet know how long the social distancing related to the Covid19 pandemic will last, and regardless of the changes that had to be made in the evaluation of the June 2020 session in relation to what is provided for in this learning unit description, new learnig unit evaluation methods may still be adopted by the teachers; details of these methods have been  or will be  communicated to the students by the teachers, as soon as possible.
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 :

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, leastsquares 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: PerronFrobenius 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.
 A seminar with presentations by the students is organized at the end of the quadrimester.
 Exercises or homeworks made individually or in small groups, with the possibility to consult teaching assistants..
 Details announced during the first class.
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 after the last session of the course.
The other part of the evaluation is based on the homeworks and presentations made during the semester.
More elaborate information on the evaluation procedure is given in the course plan, made available at the beginning of the academic year.
The other part of the evaluation is based on the homeworks and presentations made during the semester.
More elaborate information on the evaluation procedure is given in the course plan, made available 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
Teaching materials
 LINMA 2380 Course notes by R.J. et al.
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 Mathematics
Master [120] in Mathematical Engineering
Master [120] in Statistic: General
Master [120] in Electrical Engineering
Master [120] in Data Science: Information Technology