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== THE DETERMINANT OF A MATRIX ==
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== THE DETERMINANT OF A MATRIX ==
  
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== Definition  ==
  
== Definition ==
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Definition: Let A = <nowiki>[aij]</nowiki> be an n x n matrix. The '''determinant''' function, denoted by '''det''', is defined by  
Definition: Let A = <nowiki>[aij]</nowiki> be an n x n matrix. The '''determinant''' function, denoted by '''det''', is defined by
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det(A) =   <math>\sum{a1j1 a2j2... anjn}</math>
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det(A) = <math>\sum{a1j1 a2j2... anjn}</math>  
  
where the summation is over all permutations j1, j2... jn of the set S = {1, 2, ..., n}. The sign is taken as + or - according to whether the permutation j1, j2, ... jn is even or odd.
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where the summation is over all permutations j1, j2... jn of the set S = {1, 2, ..., n}. The sign is taken as + or - according to whether the permutation j1, j2, ... jn is even or odd.  
  
'''2 x 2 Matrices'''
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'''2 x 2 Matrices'''  
  
'''3 x 3 Matrices'''
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'''3 x 3 Matrices'''  
  
'''4 x 4 Matrices'''
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'''4 x 4 Matrices'''  
  
== Example (2 x 2 Matrix) ==
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== Example (2 x 2 Matrix) ==
<math>\matrix{2, 4 ; -3, 1}</math>
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== Example (3 x 3 Matrix) ==
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[4, 2 ; -3 ; 1]
  
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== Example (3 x 3 Matrix)  ==
  
== Example (4 x 4 Matrix) ==
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== Example (4 x 4 Matrix) ==
  
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== Important Properties of the Determinant  ==
  
== Important Properties of the Determinant ==
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- The determinant of the transpose of a matrix will be the same as the determinant of the original matrix.  
- The determinant of the transpose of a matrix will be the same as the determinant of the original matrix.
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- If two rows in the matrix are interchanged, the determinant will be the negative of the determinant of the original matrix.
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- If two rows in the matrix are interchanged, the determinant will be the negative of the determinant of the original matrix.  
  
- If two rows or columns in a matrix are the same, the determinant will be zero.
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- If two rows or columns in a matrix are the same, the determinant will be zero.  
  
- If there is a row of zeros in the matrix, the determinant will be zero.
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- If there is a row of zeros in the matrix, the determinant will be zero.  
  
- If a row or column in a matrix was multiplied by a real number, then the determinant will be that real number multiplied by the determinant of the original matrix.
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- If a row or column in a matrix was multiplied by a real number, then the determinant will be that real number multiplied by the determinant of the original matrix.  
  
- If the matrix is either an upper or a lower triangular matrix, the determinant will be the product of the elements on the main diagonal.
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- If the matrix is either an upper or a lower triangular matrix, the determinant will be the product of the elements on the main diagonal.  
  
- If two matrices are n x n, then the determinant of one multiplied by the determinant of the other is the determinant of the full matrix of the two next to each other.
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- If two matrices are n x n, then the determinant of one multiplied by the determinant of the other is the determinant of the full matrix of the two next to each other.  
  
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<br>
  
== Nonsingular and Similar Matrices ==
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== Nonsingular and Similar Matrices ==
Definition: A square matrix is called '''nonsingular''' if its determinant is not equal to zero.
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- For square matrices, then the linear combination of the matrix has a nontrivial solution if and only if the matrix is NOT nonsingular.
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Definition: A square matrix is called '''nonsingular''' if its determinant is not equal to zero.  
  
- For nonsingular matrices, the determinant of the inverse of that matrix is equal to 1 over the determinant of the matrix.
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- For square matrices, then the linear combination of the matrix has a nontrivial solution if and only if the matrix is NOT nonsingular.  
  
- If two matrices are similar matrices, then the determinants of the two are equal.
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- For nonsingular matrices, the determinant of the inverse of that matrix is equal to 1 over the determinant of the matrix.  
  
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- If two matrices are similar matrices, then the determinants of the two are equal.
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<br>
  
 
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[[Category:MA265Fall2012Alvarado]]
 
[[Category:MA265Fall2012Alvarado]]

Revision as of 13:22, 8 December 2012

THE DETERMINANT OF A MATRIX

Definition

Definition: Let A = [aij] be an n x n matrix. The determinant function, denoted by det, is defined by

det(A) = $ \sum{a1j1 a2j2... anjn} $

where the summation is over all permutations j1, j2... jn of the set S = {1, 2, ..., n}. The sign is taken as + or - according to whether the permutation j1, j2, ... jn is even or odd.

2 x 2 Matrices

3 x 3 Matrices

4 x 4 Matrices

Example (2 x 2 Matrix)

[4, 2 ; -3 ; 1]

Example (3 x 3 Matrix)

Example (4 x 4 Matrix)

Important Properties of the Determinant

- The determinant of the transpose of a matrix will be the same as the determinant of the original matrix.

- If two rows in the matrix are interchanged, the determinant will be the negative of the determinant of the original matrix.

- If two rows or columns in a matrix are the same, the determinant will be zero.

- If there is a row of zeros in the matrix, the determinant will be zero.

- If a row or column in a matrix was multiplied by a real number, then the determinant will be that real number multiplied by the determinant of the original matrix.

- If the matrix is either an upper or a lower triangular matrix, the determinant will be the product of the elements on the main diagonal.

- If two matrices are n x n, then the determinant of one multiplied by the determinant of the other is the determinant of the full matrix of the two next to each other.


Nonsingular and Similar Matrices

Definition: A square matrix is called nonsingular if its determinant is not equal to zero.

- For square matrices, then the linear combination of the matrix has a nontrivial solution if and only if the matrix is NOT nonsingular.

- For nonsingular matrices, the determinant of the inverse of that matrix is equal to 1 over the determinant of the matrix.

- If two matrices are similar matrices, then the determinants of the two are equal.



Alumni Liaison

BSEE 2004, current Ph.D. student researching signal and image processing.

Landis Huffman