Computational Physics - Department of Physics

(Axel Boer) #1
6.2 Mathematical Intermezzo 155


  1. Upper triangular ifai j= 0 fori>j, which for a 4 × 4 matrix is of the form

    


a 11 a 12 a 13 a 14
0 a 22 a 23 a 24
0 0 a 33 a 34
0 0 0 ann






  1. Lower triangular ifai j= 0 fori<j

    


a 11 0 0 0
a 21 a 22 0 0
a 31 a 32 a 33 0
a 41 a 42 a 43 a 44






  1. Upper Hessenberg ifai j= 0 fori>j+ 1 , which is similar to a upper triangular except that
    it has non-zero elements for the first subdiagonal row

    


a 11 a 12 a 13 a 14
a 21 a 22 a 23 a 24
0 a 32 a 33 a 34
0 0 a 43 a 44






  1. Lower Hessenberg ifai j= 0 fori<j+ 1





a 11 a 12 0 0
a 21 a 22 a 23 0
a 31 a 32 a 33 a 34
a 41 a 42 a 43 a 44






  1. Tridiagonal ifai j= 0 for|i−j|> 1

    


a 11 a 12 0 0
a 21 a 22 a 23 0
0 a 32 a 33 a 34
0 0 a 43 a 44





There are many more examples, such as lower banded with bandwidthpforai j= 0 fori>j+p,
upper banded with bandwidthpforai j= 0 fori<j+p, block upper triangular, block lower
triangular etc.
For a realn×nmatrixAthe following properties are all equivalent


  1. If the inverse ofAexists,Ais nonsingular.

  2. The equationAx= 0 impliesx= 0.

  3. The rows ofAform a basis ofRn.

  4. The columns ofAform a basis ofRn.
    5.Ais a product of elementary matrices.

  5. 0 is not an eigenvalue ofA.


The basic matrix operations that we will deal with are addition and subtraction

A=B±C=⇒ai j=bi j±ci j, (6.2)

scalar-matrix multiplication
A=γB=⇒ai j=γbi j,
vector-matrix multiplication
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