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<br> <u></u>  
 
<br> <u></u>  
  
<u>Theorem 7:</u> If a matrix A = [a<sub>ij</sub>] is upper (lower) triangular, then; det(A) = a<sub>11</sub>*a<sub>12</sub>...a<sub>nn </sub>; tha is, the determinant of a triangular matrix is the product of the element on themain diagonal. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>
+
<u>Theorem 7:</u> If a matrix A = [a<sub>ij</sub>] is upper (lower) triangular, then; det(A) = a<sub>11</sub>*a<sub>12</sub>...a<sub>nn </sub>; tha is, the determinant of a triangular matrix is the product of the element on themain diagonal. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp;<br>  
 +
 
 +
<br>
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<u>Theorem 8:</u> If A is an n x n matrix, then A is nonsingular if and only if '''det(A) not equal 0'''
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<br> <u>Theorem 9:</u> If A and B are n x n matrices, then; '''det(AB) = det(A)det(B)'''
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 +
<br>
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 +
----
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 +
----
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 +
'''''<u>Cofactor Expansion:&nbsp;</u>'''''
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'''''<u></u>'''''
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The cofactor expansion is a method for evaluating the determinant of an n xn matrix that reduces the problem to the evaluation of determinants of matrices of order n - 1. We should repeat the proces of&nbsp;(n-1) x (n-1) until we have a 2 x 2 matrices.&nbsp;
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 +
<br>
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Let A = [a<sub>ij</sub>] be an ''n'' x ''n'' matrix. Let Mij be the (n-1) x (n-1) submatrix of A obtained by deleting the ''i''th row and ''j''th row column of A. The determinant det(M<sub>ij</sub>) is called the '''minor '''a<sub>ij</sub>. Also, Let A = [a<sub>ij</sub>] be an n x n matrix. The '''cofactor''' A<sub>ij</sub> of a<sub>ij</sub> is defined as A<sub>ij</sub> = (-1)<sup>i+j&nbsp;</sup>det(M<sub>ij</sub>)
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<br>
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<u>Theorem 10:</u> Let A = [aij] be an n x n matrix. then;
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&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; det(A) = a<sub>i1</sub>A<sub>i1</sub>+a<sub>i2</sub>A<sub>i2</sub>+...+a<sub>in</sub>A<sub>in</sub> &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; and &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;det(A)=a<sub>1j</sub>A<sub>1j</sub>+a<sub>2j</sub>A<sub>2j</sub>+...+a<sub>nj</sub>A<sub>nj</sub>
 +
 
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&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[expansion of det(A) along the ''i''th row] &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;[expansion of det(A) along the ''j''th column]
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<br>
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----
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----
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<u>'''''Inverse of a Matrix:'''''</u>
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<u>''</u>
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Theorem 11:&nbsp;

Revision as of 16:45, 7 December 2011

Determinants




Introduction:


If A is a square matrix then the determinant function is denoted by det and det(A)

For an instance we have a 2 x 2 matrix denominated A, therefore:


                                                                                         det(A) = [a11 ,  a12 ; a21 , a22 ]

As we already defined the determinant function we can write some formulas. The formulas for any 2 x 2 and 3 x 3 matrix will be:

                     

                      The determinant function for a 2 x 2 matrix is:


                                                                                      $ det(A)=\left(\begin{array}{cccc}a11&a12\\a21&a22\end{array}\right) $ 

                                                                                               = (a11 * a22) - (a12 * a21 )                        

                   

                      The determinant function for a 3 x 3 matrix is: 


                                                                               $ det(A)=\left(\begin{array}{cccc}a11&a12&a13\\a21&a22&a23\\a31&a32&a33\end{array}\right) $

                                         = (a11 * a22 * a33) + (a12 * a23 * a31) + (a13 * a21 * a32) - (a12 * a21 * a33) - (a11 * a23 * a32) - (a13 * a22 * a31



Properties of Determinants:


Theorem 1: Let A be an n x n matrix then; det(A) = det(At)


Theorem 2: If a matrix B results from matrix A by interchanging two different rows (columns) of A, then; det(B) = - det(A) 


Theorem 3: If two rows (columns) of A are equal, then; det(A) = 0


Theorem 4: If a row (column) of A consists entirely of zeros, then; det(A) = 0


Theorem 5: If B obtained from A by multiplying a row (column) of A by a real number k, then;det(B) = kdet(A)    

 

Theorem 6: If B = [bij] is obained from A = [aij] by adding to each element of the rth row (column) of A, k times the corresponding element of the sth row (column), r not equal s, of A, then; det(B) = det(A)


Theorem 7: If a matrix A = [aij] is upper (lower) triangular, then; det(A) = a11*a12...ann ; tha is, the determinant of a triangular matrix is the product of the element on themain diagonal.                                                       


Theorem 8: If A is an n x n matrix, then A is nonsingular if and only if det(A) not equal 0


Theorem 9: If A and B are n x n matrices, then; det(AB) = det(A)det(B)




Cofactor Expansion: 

The cofactor expansion is a method for evaluating the determinant of an n xn matrix that reduces the problem to the evaluation of determinants of matrices of order n - 1. We should repeat the proces of (n-1) x (n-1) until we have a 2 x 2 matrices. 


Let A = [aij] be an n x n matrix. Let Mij be the (n-1) x (n-1) submatrix of A obtained by deleting the ith row and jth row column of A. The determinant det(Mij) is called the minor aij. Also, Let A = [aij] be an n x n matrix. The cofactor Aij of aij is defined as Aij = (-1)i+j det(Mij)


Theorem 10: Let A = [aij] be an n x n matrix. then;

                      det(A) = ai1Ai1+ai2Ai2+...+ainAin                             and                        det(A)=a1jA1j+a2jA2j+...+anjAnj

                   [expansion of det(A) along the ith row]                                                [expansion of det(A) along the jth column]




Inverse of a Matrix:

Theorem 11: 

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