SlideShare a Scribd company logo
1 of 22
MATRICES


          BY ALFIA MAGFIRONA
              D100102004
   CIVIL ENGINEERING DEPARTEMENT
         ENGINEERING FACULTY
MUHAMMADIYAH UNIVERSITY OF SURAKARTA
MATRICES - OPERATIONS
 MINORS

If A is an n x n matrix and one row and one column are deleted, the resulting
matrix is an (n-1) x (n-1) submatrix of A.
The determinant of such a submatrix is called a minor of A and is designated
by mij , where i and j correspond to the deleted
row and column, respectively.
mij is the minor of the element aij in A.
eg.
                      a11       a12     a13
              A       a21       a22     a23
                      a31       a32     a33
Each element in A has a minor
Delete first row and column from A .
The determinant of the remaining 2 x 2 submatrix is the minor of a11




                                      a22     a23
                        m11
                                      a32     a33
Therefore the minor of a12 is:


                                 a21   a23
                   m12
                                 a31   a33
  And the minor for a13 is:



                                 a21   a22
                     m13
                                 a31   a32
E. COFACTOR OF MATRIX
If A is a square matrix, then the minor of its entry aij, also known as the
i,j, or (i,j), or (i,j)th minor of A, is denoted by Mij and is defined to be the
determinant of the submatrix obtained by removing from A its i-th row
and j-th column. It follows:

                       Cij      ( 1)i j mij
When the sum of a row number i and column j is even, cij = mij and
when i+j is odd, cij =-mij
               c11 (i 1, j 1)             ( 1)1 1 m11           m11
               c12 (i 1, j         2)      ( 1)1 2 m12            m12
                                                 1 3
               c13 (i 1, j         3)     ( 1)         m13       m13
The Formula :

  C11 C12 C13   M 11   M 12 M 13
  C21 C22 C23   M 21 M 22    M 23
  C31 C32 C33   M 31   M 32 M 33
DETERMINANTS CONTINUED


The determinant of an n x n matrix A can now be defined as


      A      det A a11c11 a12c12  a1nc1n

The determinant of A is therefore the sum of the products of the
elements of the first row of A and their corresponding cofactors.
(It is possible to define |A| in terms of any other row or column but for
simplicity, the first row only is used)
Therefore the 2 x 2 matrix :

                         a11   a12
                  A
                         a21 a22
Has cofactors :

               c11     m11     a22    a22
 And:
              c12       m12     a21    a21
For a 3 x 3 matrix:

                       a11    a12     a13
             A         a21    a22     a23
                       a31    a32     a33
The cofactors of the first row are:

                  a22        a23
          c11                         a22 a33 a23a32
                  a32        a33
                       a21 a23
          c12                               (a21a33 a23a31 )
                       a31 a33
                  a21 a22
          c13                         a21a32 a22 a31
                  a31 a32
F. ADJOINT OF MATRIX

   The adjoint matrix for 2 x 2 square matrix

    A=         , so Adjoint of matrix A is

    Elements in the first diagonal of matrix is
    exchanged, and the second diagonal of matrix is
    just changed mark.
A=                        second diagonal of
                          matrix



               first diagonal of
               matrix




     Adj A =
PROBLEM

Find Adjoint of matrix



We can use the formula of The adjoint matrix for 2 x 2
square matrix.
So,
           Adj
   The adjoint matrix for 3 x 3 square matrix




                                        OR
To determine the adjoint matrix for 3 x 3 square
matrix is used cofactor matrix in each elements in the
square of matrix.
It uses cofactor of matrix A1.1 to fill in
fisrt rows of A and for the others we
must use others cofactor.




         Don’t forget to obseve the
         mark : (+) or (-)
PROBLEM

Find Adjoint of matrix



Solution :




                         OR
Adj



or



     Adj
G. INVERSE OF MATRIX
It is easy to show that the inverse of matrix is uniqe and the
inverse of the inverse of A is A-1 but there is also many
properties inverse matix; that is,
                          ������������������ ������
a.           ������−������ =                          the inverse of matrix ������ = (������������������ )
                             ������
b. ������������−������ = ������ ������ = ������ (������������������������������������������������)
                         −������
                                                      For any nonsingular matrix A
c. ������������������������ ������ = ������ ������������������������ = ������ ������                  For any square matrix A
                    ������
d. ������−������ =                            If A is nonsingular
                    ������
e.       ������������ = ������, ������ = ������−������ ������                  If A is an m x n nonsingular matrix,
        ������������ = ������, ������ = ������������−������                    If B is an n x m matrix, and there
                                                  exist matrix X
f.    ������������   −������   = ������−������ ������−������          For any two nonsingular matrices A and B
   A square matrix that has an inverse is called
    a nonsingular matrix
   A matrix that does not have an inverse is
    called a singular matrix
   Square matrices have inverses except when
    the determinant is zero
   When the determinant of a matrix is zero the
    matrix is singular
EXAMPLE

                                 1       2
                    A=
                                 3 4


                1    1 4             2        0.4       0.2
            A
                    10 3         1            0.3      0.1

 To check                                    AA-1 = A-1 A = I


                             1       1 2 0.4                0.2     1 0
                    AA                                                    I
                                      3 4 0.3              0.1      0 1
                         1       0.4           0.2     1        2   1 0
                    A A                                                   I
                                 0.3          0.1       3 4         0 1
Example 2

                       3      1     1
                A      2    1       0
                       1    2           1

The determinant of A is

        |A| = (3)(-1-0)-(-1)(-2-0)+(1)(4-1) = -2

The elements of the cofactor matrix are

         c11      ( 1),           c12       ( 2),   c13   (3),
         c21      ( 1),           c22       ( 4),   c23   (7),
         c31      ( 1),           c32       ( 2),   c33   (5),
The cofactor matrix is therefore

                       1       2       3
              C       1        4           7
                       1       2       5

   so
                           1       1           1
         adjA C T          2       4           2
                           3       7           5
   and
                               1       1           1   0.5   0.5   0.5
         1   adjA     1
     A                   2             4           2   1.0   2.0   1.0
              A        2
                         3             7           5   1.5   3.5   2.5

More Related Content

What's hot

Matrices & determinants
Matrices & determinantsMatrices & determinants
Matrices & determinants
indu thakur
 
6.4 inverse matrices
6.4 inverse matrices6.4 inverse matrices
6.4 inverse matrices
math260
 
MATRICES
MATRICESMATRICES
MATRICES
daferro
 

What's hot (20)

Presentation on matrix
Presentation on matrixPresentation on matrix
Presentation on matrix
 
Matrices & determinants
Matrices & determinantsMatrices & determinants
Matrices & determinants
 
Matrices and determinants
Matrices and determinantsMatrices and determinants
Matrices and determinants
 
Matrices and determinants-1
Matrices and determinants-1Matrices and determinants-1
Matrices and determinants-1
 
Determinants
DeterminantsDeterminants
Determinants
 
Matrix algebra
Matrix algebraMatrix algebra
Matrix algebra
 
Matrices and determinants
Matrices and determinantsMatrices and determinants
Matrices and determinants
 
Determinants
DeterminantsDeterminants
Determinants
 
Ppt on matrices and Determinants
Ppt on matrices and DeterminantsPpt on matrices and Determinants
Ppt on matrices and Determinants
 
6.4 inverse matrices
6.4 inverse matrices6.4 inverse matrices
6.4 inverse matrices
 
Introduction of matrix
Introduction of matrixIntroduction of matrix
Introduction of matrix
 
Matrix presentation By DHEERAJ KATARIA
Matrix presentation By DHEERAJ KATARIAMatrix presentation By DHEERAJ KATARIA
Matrix presentation By DHEERAJ KATARIA
 
Lecture 5 inverse of matrices - section 2-2 and 2-3
Lecture 5   inverse of matrices - section 2-2 and 2-3Lecture 5   inverse of matrices - section 2-2 and 2-3
Lecture 5 inverse of matrices - section 2-2 and 2-3
 
Matrix Algebra seminar ppt
Matrix Algebra seminar pptMatrix Algebra seminar ppt
Matrix Algebra seminar ppt
 
Matrices
MatricesMatrices
Matrices
 
MATRICES
MATRICESMATRICES
MATRICES
 
Matrix Algebra : Mathematics for Business
Matrix Algebra : Mathematics for BusinessMatrix Algebra : Mathematics for Business
Matrix Algebra : Mathematics for Business
 
MATRICES
MATRICESMATRICES
MATRICES
 
Set concepts
Set conceptsSet concepts
Set concepts
 
Matrices ppt
Matrices pptMatrices ppt
Matrices ppt
 

Similar to Matrix2 english

001 matrices and_determinants
001 matrices and_determinants001 matrices and_determinants
001 matrices and_determinants
physics101
 
Matrices and determinants
Matrices and determinantsMatrices and determinants
Matrices and determinants
oscar
 
chap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.pptchap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.ppt
adonyasdd
 

Similar to Matrix2 english (20)

Week3
Week3Week3
Week3
 
APM.pdf
APM.pdfAPM.pdf
APM.pdf
 
Matrices
MatricesMatrices
Matrices
 
Week2
Week2Week2
Week2
 
Mat 223_Ch3-Determinants.ppt
Mat 223_Ch3-Determinants.pptMat 223_Ch3-Determinants.ppt
Mat 223_Ch3-Determinants.ppt
 
001 matrices and_determinants
001 matrices and_determinants001 matrices and_determinants
001 matrices and_determinants
 
Business mathametics and statistics b.com ii semester (2)
Business mathametics and statistics b.com ii semester (2)Business mathametics and statistics b.com ii semester (2)
Business mathametics and statistics b.com ii semester (2)
 
Matrices and determinants
Matrices and determinantsMatrices and determinants
Matrices and determinants
 
Engg maths k notes(4)
Engg maths k notes(4)Engg maths k notes(4)
Engg maths k notes(4)
 
Determinants - Mathematics
Determinants - MathematicsDeterminants - Mathematics
Determinants - Mathematics
 
M1 PART-A
M1 PART-AM1 PART-A
M1 PART-A
 
Chapter 3: Linear Systems and Matrices - Part 3/Slides
Chapter 3: Linear Systems and Matrices - Part 3/SlidesChapter 3: Linear Systems and Matrices - Part 3/Slides
Chapter 3: Linear Systems and Matrices - Part 3/Slides
 
Determinants
DeterminantsDeterminants
Determinants
 
Determinants
DeterminantsDeterminants
Determinants
 
Ee107 sp 06_mock_test1_q_s_ok_3p_
Ee107 sp 06_mock_test1_q_s_ok_3p_Ee107 sp 06_mock_test1_q_s_ok_3p_
Ee107 sp 06_mock_test1_q_s_ok_3p_
 
Determinants, crammers law, Inverse by adjoint and the applications
Determinants, crammers law,  Inverse by adjoint and the applicationsDeterminants, crammers law,  Inverse by adjoint and the applications
Determinants, crammers law, Inverse by adjoint and the applications
 
Matrices & Determinants
Matrices & DeterminantsMatrices & Determinants
Matrices & Determinants
 
chap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.pptchap01987654etghujh76687976jgtfhhhgve.ppt
chap01987654etghujh76687976jgtfhhhgve.ppt
 
Debojyoit
Debojyoit Debojyoit
Debojyoit
 
Matrices
MatricesMatrices
Matrices
 

Recently uploaded

Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Victor Rentea
 

Recently uploaded (20)

Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data DiscoveryTrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
TrustArc Webinar - Unlock the Power of AI-Driven Data Discovery
 
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
Emergent Methods: Multi-lingual narrative tracking in the news - real-time ex...
 
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
Biography Of Angeliki Cooney | Senior Vice President Life Sciences | Albany, ...
 
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
Connector Corner: Accelerate revenue generation using UiPath API-centric busi...
 
Corporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptxCorporate and higher education May webinar.pptx
Corporate and higher education May webinar.pptx
 
ICT role in 21st century education and its challenges
ICT role in 21st century education and its challengesICT role in 21st century education and its challenges
ICT role in 21st century education and its challenges
 
DBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor PresentationDBX First Quarter 2024 Investor Presentation
DBX First Quarter 2024 Investor Presentation
 
MS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectorsMS Copilot expands with MS Graph connectors
MS Copilot expands with MS Graph connectors
 
Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024Manulife - Insurer Transformation Award 2024
Manulife - Insurer Transformation Award 2024
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
Artificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : UncertaintyArtificial Intelligence Chap.5 : Uncertainty
Artificial Intelligence Chap.5 : Uncertainty
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Ransomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdfRansomware_Q4_2023. The report. [EN].pdf
Ransomware_Q4_2023. The report. [EN].pdf
 
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUKSpring Boot vs Quarkus the ultimate battle - DevoxxUK
Spring Boot vs Quarkus the ultimate battle - DevoxxUK
 
Exploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with MilvusExploring Multimodal Embeddings with Milvus
Exploring Multimodal Embeddings with Milvus
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
Apidays New York 2024 - Accelerating FinTech Innovation by Vasa Krishnan, Fin...
 
FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024FWD Group - Insurer Innovation Award 2024
FWD Group - Insurer Innovation Award 2024
 
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024Finding Java's Hidden Performance Traps @ DevoxxUK 2024
Finding Java's Hidden Performance Traps @ DevoxxUK 2024
 

Matrix2 english

  • 1. MATRICES BY ALFIA MAGFIRONA D100102004 CIVIL ENGINEERING DEPARTEMENT ENGINEERING FACULTY MUHAMMADIYAH UNIVERSITY OF SURAKARTA
  • 2. MATRICES - OPERATIONS MINORS If A is an n x n matrix and one row and one column are deleted, the resulting matrix is an (n-1) x (n-1) submatrix of A. The determinant of such a submatrix is called a minor of A and is designated by mij , where i and j correspond to the deleted row and column, respectively. mij is the minor of the element aij in A.
  • 3. eg. a11 a12 a13 A a21 a22 a23 a31 a32 a33 Each element in A has a minor Delete first row and column from A . The determinant of the remaining 2 x 2 submatrix is the minor of a11 a22 a23 m11 a32 a33
  • 4. Therefore the minor of a12 is: a21 a23 m12 a31 a33 And the minor for a13 is: a21 a22 m13 a31 a32
  • 5. E. COFACTOR OF MATRIX If A is a square matrix, then the minor of its entry aij, also known as the i,j, or (i,j), or (i,j)th minor of A, is denoted by Mij and is defined to be the determinant of the submatrix obtained by removing from A its i-th row and j-th column. It follows: Cij ( 1)i j mij When the sum of a row number i and column j is even, cij = mij and when i+j is odd, cij =-mij c11 (i 1, j 1) ( 1)1 1 m11 m11 c12 (i 1, j 2) ( 1)1 2 m12 m12 1 3 c13 (i 1, j 3) ( 1) m13 m13
  • 6. The Formula : C11 C12 C13 M 11 M 12 M 13 C21 C22 C23 M 21 M 22 M 23 C31 C32 C33 M 31 M 32 M 33
  • 7. DETERMINANTS CONTINUED The determinant of an n x n matrix A can now be defined as A det A a11c11 a12c12  a1nc1n The determinant of A is therefore the sum of the products of the elements of the first row of A and their corresponding cofactors. (It is possible to define |A| in terms of any other row or column but for simplicity, the first row only is used)
  • 8. Therefore the 2 x 2 matrix : a11 a12 A a21 a22 Has cofactors : c11 m11 a22 a22 And: c12 m12 a21 a21
  • 9. For a 3 x 3 matrix: a11 a12 a13 A a21 a22 a23 a31 a32 a33 The cofactors of the first row are: a22 a23 c11 a22 a33 a23a32 a32 a33 a21 a23 c12 (a21a33 a23a31 ) a31 a33 a21 a22 c13 a21a32 a22 a31 a31 a32
  • 10. F. ADJOINT OF MATRIX  The adjoint matrix for 2 x 2 square matrix A= , so Adjoint of matrix A is Elements in the first diagonal of matrix is exchanged, and the second diagonal of matrix is just changed mark.
  • 11. A= second diagonal of matrix first diagonal of matrix Adj A =
  • 12. PROBLEM Find Adjoint of matrix We can use the formula of The adjoint matrix for 2 x 2 square matrix. So, Adj
  • 13. The adjoint matrix for 3 x 3 square matrix OR
  • 14. To determine the adjoint matrix for 3 x 3 square matrix is used cofactor matrix in each elements in the square of matrix.
  • 15. It uses cofactor of matrix A1.1 to fill in fisrt rows of A and for the others we must use others cofactor. Don’t forget to obseve the mark : (+) or (-)
  • 16. PROBLEM Find Adjoint of matrix Solution : OR
  • 17. Adj or Adj
  • 18. G. INVERSE OF MATRIX It is easy to show that the inverse of matrix is uniqe and the inverse of the inverse of A is A-1 but there is also many properties inverse matix; that is, ������������������ ������ a. ������−������ = the inverse of matrix ������ = (������������������ ) ������ b. ������������−������ = ������ ������ = ������ (������������������������������������������������) −������ For any nonsingular matrix A c. ������������������������ ������ = ������ ������������������������ = ������ ������ For any square matrix A ������ d. ������−������ = If A is nonsingular ������ e. ������������ = ������, ������ = ������−������ ������ If A is an m x n nonsingular matrix, ������������ = ������, ������ = ������������−������ If B is an n x m matrix, and there exist matrix X f. ������������ −������ = ������−������ ������−������ For any two nonsingular matrices A and B
  • 19. A square matrix that has an inverse is called a nonsingular matrix  A matrix that does not have an inverse is called a singular matrix  Square matrices have inverses except when the determinant is zero  When the determinant of a matrix is zero the matrix is singular
  • 20. EXAMPLE 1 2 A= 3 4 1 1 4 2 0.4 0.2 A 10 3 1 0.3 0.1 To check AA-1 = A-1 A = I 1 1 2 0.4 0.2 1 0 AA I 3 4 0.3 0.1 0 1 1 0.4 0.2 1 2 1 0 A A I 0.3 0.1 3 4 0 1
  • 21. Example 2 3 1 1 A 2 1 0 1 2 1 The determinant of A is |A| = (3)(-1-0)-(-1)(-2-0)+(1)(4-1) = -2 The elements of the cofactor matrix are c11 ( 1), c12 ( 2), c13 (3), c21 ( 1), c22 ( 4), c23 (7), c31 ( 1), c32 ( 2), c33 (5),
  • 22. The cofactor matrix is therefore 1 2 3 C 1 4 7 1 2 5 so 1 1 1 adjA C T 2 4 2 3 7 5 and 1 1 1 0.5 0.5 0.5 1 adjA 1 A 2 4 2 1.0 2.0 1.0 A 2 3 7 5 1.5 3.5 2.5