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Lambda matrices, matrix polynomials, division of λ-matrices, remainder theorem, scalar matrix polynomials, Cayley-Hamilton theorem


 

Lambda matrix. A matrix whose elements are polynomials in the variable λ.


Let F[λ] be a polynomial domain consisting of the set of all polynomials in λ with coefficients in field F. A non-zero mxn matrix over F[λ]


                              ole.gif


is called a λ-matrix.



Example.


      ole1.gif






Matrix polynomial.. A matrix polynomial can take any of the following three forms:


   ole2.gif  


where the coefficients A0, A1, .... , Ap are mxn matrices over a field F and the indeterminate λ is a number.



 Example.


  ole3.gif  




 




  ole4.gif


where the coefficients a0, a1, .... , ap are numbers and the indeterminate C is a matrix.






  ole5.gif


where the coefficients A0, A1, .... , Ap are matrices and the indeterminate C is a matrix.






Representation of a λ-matrix as a matrix polynomial. Any mxn λ-matrix can be written as a matrix polynomial. Let “p” be the degree of the polynomial of highest degree found in A(λ). Then A(λ) can be written as the following matrix polynomial:


          ole6.gif


where A0, A1, .... , Ap are mxn matrices.



Example.


    ole7.gif


                         ole8.gif


  

Singular and non-singular λ-matrices. The determinant of an n-square λ-matrix is a polynomial in λ and if this determinant vanishes identically we call the matrix singular. Otherwise it is called non-singular.



Proper and improper λ-matrices. An n-square λ-matrix A(λ) is called proper if the matrix Ap in the matrix polynomial


     ole9.gif                                                                                                 


is non-singular. It is called improper if matrix Ap is singular.




Operations with λ-matrices. Consider the two n-square λ-matrices A(λ) and B(λ) and their matrix polynomial equivalents:


   ole10.gif

and

   ole11.gif



Equality of two λ-matrices. Two λ-matrices A(λ) and B(λ) are said to be equal if p = q and Ai = Bi (i = 0, 1, 2, ... , p) in their matrix polynomial representations.


Sum of two λ-matrices. The sum of A(λ) and B(λ), A(λ) + B(λ), is a λ-matrix C(λ) obtained by adding corresponding elements of A(λ) and B(λ).


The product A(λ) B(λ) is a λ-matrix or matrix polynomial of degree at most p + q. If either A(λ) or B(λ) is non-singular, the degree of A(λ) B(λ) and also B(λ) A(λ) is exactly p + q.


A λ-matrix and its matrix polynomial equivalent are identically equal and the equality is not disturbed by replacing λ with any scalar k of F. For example, putting λ = k in (4) yields


                ole12.gif


However, when λ is replaced by an n-square matrix C, two results can be obtained due to the fact that, in general, two n-square matrices do not commute. These two results correspond to


    ole13.gif

and

   ole14.gif


where AR(C) is called the right functional value of A(λ) and AL(C) is called the left functional value of A(λ).



Division of λ-matrices. Consider the matrix polynomials


                ole15.gif

and

                ole16.gif


If B(λ) is non-singular, then there exist unique matrix polynomials Q1(λ), R1(λ), Q2(λ), and R2(λ) where R1(λ) and R2(λ) are either zero or of degree less than that of B(λ), such that


             ole17.gif

 

             ole18.gif   


If R1(λ) = 0 , B(λ) is called a right divisor of A(λ).

If R2(λ) = 0 , B(λ) is called a left divisor of A(λ).



Scalar matrix polynomials. Let


          ole19.gif


where the coefficients b0, b1, .... , bq and the indeterminate λ are scalars from a number field F. A matrix polynomial B(λ) of the form


        ole20.gif

                    ole21.gif

                   ole22.gif

                                       

(where In is the identity matrix) is called a scalar matrix polynomial. A scalar matrix polynomial is a matrix polynomial whose coefficients are scalar matrices.


Example. The following is a scalar matrix polynomial:


    ole23.gif




Theorem 1. A scalar matrix polynomial B(λ) = b(λ)∙In commutes with every n-square matrix polynomial.



Theorem 2. If

                           ole24.gif

and

                        ole25.gif    

 

then there exist unique matrix polynomials Q1(λ) and R1(λ) such that


         ole26.gif

 

                               ole27.gif  


and if R1(λ) = 0 , b(λ)∙In divides A(λ).




Theorem 3. A matrix polynomial



                       ole28.gif



of degree n is divisible by a scalar matrix polynomial B(λ) = b(λ)∙In if and only if every aij(λ) in A(λ) is divisible by b(λ).





The Remainder Theorem. Let A(λ) be an n-square λ-matrix over the polynomial domain F[λ]


     ole29.gif


and let B = [bij] be an n-square matrix over field F. Since λI - B is non-singular, we may write


                                       ole30.gif   

and

                                      ole31.gif  


where R1 and R2 are free of λ.



Theorem 4. If A(λ) is divided by λI - B, where B = [bij] is n-square, until remainders R1 and R2, free of λ, are obtained, then


             ole32.gif    

and

            ole33.gif  


(where AR(B) and AL(B) are the right and left functional values of A(λ)).



When A(λ) is a scalar matrix polynomial


      ole34.gif    


the remainders are identical so that


         ole35.gif




Theorem 5. If a scalar matrix polynomial f(λ)∙In is divided by λI - B until a remainder R, free of λ, is obtained, then R = f(B).




Theorem 6. A scalar matrix polynomial f(λ)∙In is divisible by λI - B if and only if f(B) = 0.



 

Cayley-Hamilton Theorem. Every square matrix A = [aij] satisfies its characteristic equation Φ(λ) = 0.


Proof. Let A be a n-square matrix having characteristic matrix (λI - A) and characteristic equation Φ(λ) = |λI - A| = 0. A theorem on adjoints states that for any matrix A


                        A(adj A) = |A| In .


Applying this theorem to the characteristic matrix (λI - A) we get


                        (λI - A) ·adj (λI - A) = Φ(λ)·I .


Thus Φ(λ)·I is divisible by λI - A and, by Theorem 6, Φ(A) = 0.




References.

  Ayres. Matrices (Schaum).



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