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summary Matrices and Linear Transformations

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A course on matrices and vector spaces, also known as linear algebra, is a fundamental subject in mathematics that deals with linear equations, systems of equations, and the properties of vectors and matrices. The course starts with an introduction to matrices, which are rectangular arrays of numbers or symbols that can be used to represent data or solve systems of equations. Topics covered include matrix operations such as addition, multiplication, and inversion, as well as determinants, eigenvalues, and eigenvectors. The course then moves on to vector spaces, which are sets of vectors that have certain properties such as closure under addition and scalar multiplication. Topics covered include vector space axioms, subspaces, linear independence, bases, and dimension. Throughout the course, there is an emphasis on the interplay between matrices and vector spaces. Students learn how to use matrices to represent linear transformations between vector spaces and to solve linear systems. They also learn how to apply concepts from vector spaces to matrices, such as diagonalization and the spectral theorem. Overall, a course on matrices and vector spaces provides students with a solid foundation in linear algebra, which is essential for many areas of mathematics, science, and engineering. It also has practical applications in fields such as computer graphics, signal processing, and machine learning.

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Cours d’algèbre 1
Filière MIP-MIPC
premier semestre

........................................
Chapitre : Calcul matriciel et systèmes linéa
((Chapitre complet))
[Séance ..]




Karim KREIT

FST-UCA

,Copyright © 2020-2021 Karim KREIT

Copying

All rights reserved.

Art. No xxxxx
ISBN xxx–xx–xxxx–xx–x
Edition 0.0

Published by FST-UCA

,Table des matières



1 Matrices (Définitions et propriétés). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.1 Calcul Matriciel ....................................................................................... 4
1.1.1 Définition et notations ...................................................................... 4
1.1.2 Matrices particulières ....................................................................... 5
1.1.3 Opérations sur les matrices .............................................................. 8
1.2 Quelques propriétés des matrices carrées ................................................ 14
1.2.1 Puissance d’une matrice ................................................................. 14
1.2.2 Matrices symétriques-Matrices antisymétriques ................................ 16
1.2.3 La trace ........................................................................................ 17
1.2.4 Matrices inversibles ....................................................................... 19

2 Résolution des systèmes linéaires (Pivot de Gauss) . . . . . . . . . . . . . . . . . . . 23
2.0.1 Les matrices élémentaires .............................................................. 23
2.1 Matrices échelonnées ............................................................................. 25
2.1.1 Équivalence à une matrice échelonnée ............................................. 26
2.1.2 Inverse d’une matrice - Méthode Gausse .......................................... 29
2.2 Systèmes linéaires ................................................................................. 32
2.2.1 Matrices et systèmes linéaires ........................................................ 32
2.2.2 Matrices inversibles et systèmes linéaires ........................................ 32




3

, 1. Matrices (Définitions et propriétés).

1.1 Calcul Matriciel ........................................................ 4
1.2 Quelques propriétés des matrices carrées .................... 14




Dans ce chapitre, K désigne un corps. On peut penser à Q, R ou C.


1.1 Calcul Matriciel

1.1.1 Définition et notations
Definition 1.1.

Soit (n, p) ∈ N∗ .
On appelle matrice de type (n, p) ou de format n×p à coefficients dans K, un tableau
rectangulaire A à n lignes et p colonnes d’éléments de K, c’est-à-dire

a1,1 a1,2 ... a1,j ... a1,p 
 
a2,1 a2,2 ... a2,j ... a2,p 
 
 
 . . . ... ... ... ... . . . 
A = 
 ai,1 ai,2 ... ai,j ... ai,p 

 
 . . . ... ... ... ... . . . 

an,1 an,2 ... an,j ... an,p


 
Et en abrégé: A = ai,j 16i6n ou plus simplement (aij ) lorsqu’il n’y a pas de confu-
16j6p
sion.

• Les ai,j sont appelés coefficients de la matrice A. L’élément ai,j correspond a
la valeur de l’intersection de la ligne i et de la colonne j.

• On dit que A est de taille n × p.

L’ensemble des matrices à n lignes et p colonnes à coefficients dans K est noté
Mn,p (K). Si K = R les éléments de Mn,p (R) sont appelés matrices réelles.




Exemple 1.1 !
1 −2 5
A=
0 3 7
est une matrice 2 × 3 avec, par exemple, a1,1 = 1 et a2,3 = 7.



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