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Ppt on matrices and Determinants

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NirmalaSolapur

Matrices can be added, subtracted, and multiplied under certain conditions. Addition and subtraction require matrices to be the same size. Matrix multiplication requires the number of columns of the first matrix to equal the number of rows of the second matrix. Matrices can also be multiplied by scalars.

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B.L.D.E.A’s Shree Sanganabasava Mahaswamiji Polytechnic, Vijayapur Department of Science Presentation on Matrices and Determinants By: Smt.N.D.Solapur M.SC, M.Phil Lecturer in Science Department
Matrices Introduction
Matrices - Introduction Matrix algebra has at least two advantages: •Reduces complicated systems of equations to simple expressions •Adaptable to systematic method of mathematical treatment and well suited to computers Definition: A matrix is a set or group of numbers arranged in a square or rectangular array enclosed by two brackets [ ]11 −       − 03 24       dc ba
Matrices - Introduction Properties: •A specified number of rows and a specified number of columns •Two numbers (rows x columns) describe the dimensions or size of the matrix. Examples: 3x3 matrix 2x4 matrix 1x2 matrix           − 333 514 421       − 2 3 3 3 0 1 0 1 [ ]11 −
Matrices - Introduction A matrix is denoted by a bold capital letter and the elements within the matrix are denoted by lower case letters e.g. matrix [A] with elements aij               mnijmm nij inij aaaa aaaa aaaa 21 22221 1211 ... ...  i goes from 1 to m j goes from 1 to n Amxn= mAn
Matrices - Introduction TYPES OF MATRICES 1. Column matrix or vector: The number of rows may be any integer but the number of columns is always 1           2 4 1       −3 1             1 21 11 ma a a 
Matrices - Introduction TYPES OF MATRICES 2. Row matrix or vector Any number of columns but only one row [ ]611 [ ]2530 [ ]naaaa 1131211 
Matrices - Introduction TYPES OF MATRICES 3. Rectangular matrix Contains more than one element and number of rows is not equal to the number of columns             − 67 77 73 11       03302 00111 nm ≠
Matrices - Introduction TYPES OF MATRICES 4. Square matrix The number of rows is equal to the number of columns (a square matrix A has an order of m)       03 11           166 099 111 m x m The principal or main diagonal of a square matrix is composed of all elements aij for which i=j
Matrices - Introduction TYPES OF MATRICES 5. Diagonal matrix A square matrix where all the elements are zero except those on the main diagonal           100 020 001             9000 0500 0030 0003 i.e. aij =0 for all i = j aij = 0 for some or all i = j
Matrices - Introduction TYPES OF MATRICES 6. Unit or Identity matrix - I A diagonal matrix with ones on the main diagonal             1000 0100 0010 0001       10 01 i.e. aij =0 for all i = j a = 1 for some or all i = j       ij ij a a 0 0
Matrices - Introduction TYPES OF MATRICES 7. Null (zero) matrix - 0 All elements in the matrix are zero           0 0 0           000 000 000 0=ija For all i,j
Matrices - Introduction TYPES OF MATRICES 8. Triangular matrix A square matrix whose elements above or below the main diagonal are all zero           325 012 001           325 012 001           300 610 981
Matrices - Introduction TYPES OF MATRICES 8a. Upper triangular matrix A square matrix whose elements below the main diagonal are all zero i.e. aij = 0 for all i > j           300 810 781             3000 8700 4710 4471           ij ijij ijijij a aa aaa 00 0
Matrices - Introduction TYPES OF MATRICES A square matrix whose elements above the main diagonal are all zero 8b. Lower triangular matrix i.e. aij = 0 for all i < j           325 012 001           ijijij ijij ij aaa aa a 0 00
Matrices – Introduction TYPES OF MATRICES 9. Scalar matrix A diagonal matrix whose main diagonal elements are equal to the same scalar A scalar is defined as a single number or constant           100 010 001             6000 0600 0060 0006 i.e. aij = 0 for all i = j aij = a for all i = j           ij ij ij a a a 00 00 00
Matrices Matrix Operations
Matrices - Operations EQUALITY OF MATRICES Two matrices are said to be equal only when all corresponding elements are equal Therefore their size or dimensions are equal as well           325 012 001           325 012 001 A = B = A = B
Matrices - Operations Some properties of equality: •IIf A = B, then B = A for all A and B •IIf A = B, and B = C, then A = C for all A, B and C           325 012 001 A = B =           333231 232221 131211 bbb bbb bbb If A = B then ijij ba =
Matrices - Operations ADDITION AND SUBTRACTION OF MATRICES The sum or difference of two matrices, A and B of the same size yields a matrix C of the same size ijijij bac += Matrices of different sizes cannot be added or subtracted
Matrices - Operations Commutative Law: A + B = B + A Associative Law: A + (B + C) = (A + B) + C = A + B + C       −− =      −− +      − − 972 588 324 651 652 137 A 2x3 B 2x3 C 2x3
Matrices - Operations A + 0 = 0 + A = A A + (-A) = 0 (where –A is the matrix composed of –aij as elements)       − =      −      122 225 801 021 723 246
Matrices - Operations SCALAR MULTIPLICATION OF MATRICES Matrices can be multiplied by a scalar (constant or single element) Let k be a scalar quantity; then kA = Ak Ex. If k=4 and             − − = 14 32 12 13 A
Matrices - Operations             − − =×             − − =             − − × 416 128 48 412 4 14 32 12 13 14 32 12 13 4 Properties: • k (A + B) = kA + kB • (k + g)A = kA + gA • k(AB) = (kA)B = A(k)B • k(gA) = (kg)A
Matrices - Operations MULTIPLICATION OF MATRICES The product of two matrices is another matrix Two matrices A and B must be conformable for multiplication to be possible i.e. the number of columns of A must equal the number of rows of B Example. A x B = C (1x3) (3x1) (1x1)
Matrices - Operations B x A = Not possible! (2x1) (4x2) A x B = Not possible! (6x2) (6x3) Example A x B = C (2x3) (3x2) (2x2)
Matrices - Operations       =                 2221 1211 3231 2221 1211 232221 131211 cc cc bb bb bb aaa aaa 22322322221221 21312321221121 12321322121211 11311321121111 )()()( )()()( )()()( )()()( cbababa cbababa cbababa cbababa =×+×+× =×+×+× =×+×+× =×+×+× Successive multiplication of row i of A with column j of B – row by column multiplication
Matrices - Operations       ×+×+××+×+× ×+×+××+×+× =                 )37()22()84()57()62()44( )33()22()81()53()62()41( 35 26 84 724 321       = 5763 2131 Remember also: IA = A       10 01       5763 2131       = 5763 2131
Matrices - Operations Assuming that matrices A, B and C are conformable for the operations indicated, the following are true: 1. AI = IA = A 2. A(BC) = (AB)C = ABC - (associative law) 3. A(B+C) = AB + AC - (first distributive law) 4. (A+B)C = AC + BC - (second distributive law) Caution! 1. AB not generally equal to BA, BA may not be conformable 2. If AB = 0, neither A nor B necessarily = 0 3. If AB = AC, B not necessarily = C
Matrices - Operations AB not generally equal to BA, BA may not be conformable       =            =       =            =       =       = 010 623 05 21 20 43 2015 83 20 43 05 21 20 43 05 21 ST TS S T
Matrices - Operations If AB = 0, neither A nor B necessarily = 0       =      −−      00 00 32 32 00 11
Matrices - Operations TRANSPOSE OF A MATRIX If :       == 135 7423 2 AA 2x3           == 17 34 52 3 2 TT AA Then transpose of A, denoted AT is: T jiij aa = For all i and j
Matrices - Operations To transpose: Interchange rows and columns The dimensions of AT are the reverse of the dimensions of A       == 135 7423 2 AA           == 17 34 52 2 3 TT AA 2 x 3 3 x 2
Matrices - Operations Properties of transposed matrices: 1. (A+B)T = AT + BT 2. (AB)T = BT AT 3. (kA)T = kAT 4. (AT )T = A
Matrices - Operations 1. (A+B)T = AT + BT       −− =      −− +      − − 972 588 324 651 652 137           − − 95 78 28           − − =           − − +           − − 95 78 28 36 25 41 61 53 27
Matrices - Operations (AB)T = BT AT [ ] [ ] [ ]82 30 21 01 211 82 8 2 2 1 1 320 011 =           ⇒      =                
Matrices - Operations SYMMETRIC MATRICES A Square matrix is symmetric if it is equal to its transpose: A = AT       =       = db ba A db ba A T
Matrices - Operations When the original matrix is square, transposition does not affect the elements of the main diagonal       =       = db ca A dc ba A T The identity matrix, I, a diagonal matrix D, and a scalar matrix, K, are equal to their transpose since the diagonal is unaffected.
Matrices - Operations INVERSE OF A MATRIX Consider a scalar k. The inverse is the reciprocal or division of 1 by the scalar. Example: k=7 the inverse of k or k-1 = 1/k = 1/7 Division of matrices is not defined since there may be AB = AC while B = C Instead matrix inversion is used. The inverse of a square matrix, A, if it exists, is the unique matrix A-1 where: AA-1 = A-1 A = I
Matrices - Operations Example:       − − =       == − 32 11 12 13 1 2 2 A AA       =      − −             =            − − 10 01 32 11 12 13 10 01 12 13 32 11 Because:
Matrices - Operations Properties of the inverse: 11 11 11 111 1 )( )()( )( )( −− −− −− −−− = = = = A k kA AA AA ABAB TT 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
Matrices - Operations DETERMINANT OF A MATRIX To compute the inverse of a matrix, the determinant is required Each square matrix A has a unit scalar value called the determinant of A, denoted by det A or |A| 56 21 56 21 =       = A AIf then
Matrices - Operations If A = [A] is a single element (1x1), then the determinant is defined as the value of the element Then |A| =det A = a11 If A is (n x n), its determinant may be defined in terms of order (n-1) or less.
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.
Matrices - Operations           = 333231 232221 131211 aaa aaa aaa A 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 eg. 3332 2322 11 aa aa m =
Matrices - Operations Therefore the minor of a12 is: And the minor for a13 is: 3331 2321 12 aa aa m = 3231 2221 13 aa aa m =
Matrices - Operations COFACTORS The cofactor Cij of an element aij is defined as: ij ji ij mC + −= )1( When the sum of a row number i and column j is even, cij = mij and when i+j is odd, cij =-mij 1313 31 13 1212 21 12 1111 11 11 )1()3,1( )1()2,1( )1()1,1( mmjic mmjic mmjic +=−=== −=−=== +=−=== + + +
Matrices - Operations DETERMINANTS CONTINUED The determinant of an n x n matrix A can now be defined as nncacacaAA 1112121111det +++==  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)
Matrices - Operations Therefore the 2 x 2 matrix :       = 2221 1211 aa aa A Has cofactors : 22221111 aamc === And: 21211212 aamc −=−=−= And the determinant of A is: 2112221112121111 aaaacacaA −=+=
Matrices - Operations Example 1:       = 21 13 A 5)1)(1()2)(3( =−=A
Matrices - Operations For a 3 x 3 matrix:           = 333231 232221 131211 aaa aaa aaa A The cofactors of the first row are: 31223221 3231 2221 13 31233321 3331 2321 12 32233322 3332 2322 11 )( aaaa aa aa c aaaa aa aa c aaaa aa aa c −== −−=−= −==
Matrices - Operations The determinant of a matrix A is: 2112221112121111 aaaacacaA −=+= Which by substituting for the cofactors in this case is: )()()( 312232211331233321123223332211 aaaaaaaaaaaaaaaA −+−−−=
Matrices - Operations Example 2:           − = 101 320 101 A 4)20)(1()30)(0()02)(1( =+++−−=A )()()( 312232211331233321123223332211 aaaaaaaaaaaaaaaA −+−−−=
Matrices - Operations ADJOINT MATRICES A cofactor matrix C of a matrix A is the square matrix of the same order as A in which each element aij is replaced by its cofactor cij . Example:       − = 43 21 A       − = 12 34 C If The cofactor C of A is
Matrices - Operations The adjoint matrix of A, denoted by adj A, is the transpose of its cofactor matrix T CadjA = It can be shown that: A(adj A) = (adjA) A = |A| I Example:       − == =−−=       − = 13 24 10)3)(2()4)(1( 43 21 T CadjA A A
Matrices - Operations IadjAA 10 100 010 13 24 43 21 )( =      =      −       − = IAadjA 10 100 010 43 21 13 24 )( =      =      −      − =
Matrices - Operations USING THE ADJOINT MATRIX IN MATRIX INVERSION A adjA A =−1 Since AA-1 = A-1 A = I and A(adj A) = (adjA) A = |A| I then
Matrices - Operations Example       − =      − =− 1.03.0 2.04.0 13 24 10 11 A       − 43 21 A = To check AA-1 = A-1 A = I IAA IAA =      =      −      − = =      =      −       − = − − 10 01 43 21 1.03.0 2.04.0 10 01 1.03.0 2.04.0 43 21 1 1
Matrices - Operations Example 2           − − = 121 012 113 A |A| = (3)(-1-0)-(-1)(-2-0)+(1)(4-1) = -2 ),1( ),1( ),1( 31 21 11 −+= −−= −+= c c c The determinant of A is The elements of the cofactor matrix are ),2( ),4( ),2( 32 22 12 −−= −+= −−= c c c ),5( ),7( ),3( 33 23 13 += −= += c c c
Matrices - Operations           − −− − = 521 741 321 C The cofactor matrix is therefore so           − − −− == 573 242 111 T CadjA and           −− −− − =           − − −− − ==− 5.25.35.1 0.10.20.1 5.05.05.0 573 242 111 2 11 A adjA A
Matrices - Operations The result can be checked using AA-1 = A-1 A = I The determinant of a matrix must not be zero for the inverse to exist as there will not be a solution Nonsingular matrices have non-zero determinants Singular matrices have zero determinants
Matrix Inversion Simple 2 x 2 case
Simple 2 x 2 case Let       = dc ba A and       =− zy xw A 1 Since it is known that A A-1 = I then       =            10 01 zy xw dc ba
Simple 2 x 2 case Multiplying gives 1 0 0 1 =+ =+ =+ =+ dzcx dycw bzax byaw bcadA −= It can simply be shown that
Simple 2 x 2 case thus A d bcda d w d cw b aw d cw y b aw y = − = − = − − = − = 1 1
Simple 2 x 2 case A b bcda b x d cx b ax d cx z b ax z −= +− = − = − − = − = 1 1
Simple 2 x 2 case A c cbad c y c dy a by c dy w a by w −= +− = − = − − = − = 1 1
Simple 2 x 2 case A a bcad a z c dz a bz c dz x a bz x = − = − = − − = − = 1 1
Simple 2 x 2 case So that for a 2 x 2 matrix the inverse can be constructed in a simple fashion as       − − =             − ac bd A A a A c A b A d 1 •Exchange elements of main diagonal •Change sign in elements off main diagonal •Divide resulting matrix by the determinant =      =− zy xw A 1
Simple 2 x 2 case Example       − − =      − − −=       = − 2.04.0 3.01.0 24 31 10 1 14 32 1 A A Check inverse A-1 A=I I=      =            − − − 10 01 14 32 24 31 10 1
Matrices and Linear Equations Linear Equations
Linear Equations Linear equations are common and important for survey problems Matrices can be used to express these linear equations and aid in the computation of unknown values Example n equations in n unknowns, the aij are numerical coefficients, the bi are constants and the xj are unknowns nnnnnn nn nn bxaxaxa bxaxaxa bxaxaxa =+++ =+++ =+++     2211 22222121 11212111
Linear Equations The equations may be expressed in the form AX = B where ,, 2 1 11 22221 11211             =             = nnnnn n n x x x X aaa aaa aaa A       and             = nb b b B  2 1 n x n n x 1 n x 1 Number of unknowns = number of equations = n
Linear Equations If the determinant is nonzero, the equation can be solved to produce n numerical values for x that satisfy all the simultaneous equations To solve, premultiply both sides of the equation by A-1 which exists because |A| = 0 A-1 AX = A-1 B Now since A-1 A = I We get X = A-1 B So if the inverse of the coefficient matrix is found, the unknowns, X would be determined
Linear Equations Example 32 12 23 321 21 321 =−+ =+ =+− xxx xx xxx The equations can be expressed as           =                     − − 3 1 2 121 012 113 3 2 1 x x x
Linear Equations When A-1 is computed the equation becomes           −=                     −− −− − == − 7 3 2 3 1 2 5.25.35.1 0.10.20.1 5.05.05.0 1 BAX Therefore 7 ,3 ,2 3 2 1 −= −= = x x x
Linear Equations The values for the unknowns should be checked by substitution back into the initial equations 32 12 23 321 21 321 =−+ =+ =+− xxx xx xxx 3)7()3(2)2( 1)3()2(2 2)7()3()2(3 =−−−×+ =−+× =−+−−× 7 ,3 ,2 3 2 1 −= −= = x x x
Ppt on matrices and Determinants

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Ppt on matrices and Determinants

  • 1. B.L.D.E.A’s Shree Sanganabasava Mahaswamiji Polytechnic, Vijayapur Department of Science Presentation on Matrices and Determinants By: Smt.N.D.Solapur M.SC, M.Phil Lecturer in Science Department
  • 3. Matrices - Introduction Matrix algebra has at least two advantages: •Reduces complicated systems of equations to simple expressions •Adaptable to systematic method of mathematical treatment and well suited to computers Definition: A matrix is a set or group of numbers arranged in a square or rectangular array enclosed by two brackets [ ]11 −       − 03 24       dc ba
  • 4. Matrices - Introduction Properties: •A specified number of rows and a specified number of columns •Two numbers (rows x columns) describe the dimensions or size of the matrix. Examples: 3x3 matrix 2x4 matrix 1x2 matrix           − 333 514 421       − 2 3 3 3 0 1 0 1 [ ]11 −
  • 5. Matrices - Introduction A matrix is denoted by a bold capital letter and the elements within the matrix are denoted by lower case letters e.g. matrix [A] with elements aij               mnijmm nij inij aaaa aaaa aaaa 21 22221 1211 ... ...  i goes from 1 to m j goes from 1 to n Amxn= mAn
  • 6. Matrices - Introduction TYPES OF MATRICES 1. Column matrix or vector: The number of rows may be any integer but the number of columns is always 1           2 4 1       −3 1             1 21 11 ma a a 
  • 7. Matrices - Introduction TYPES OF MATRICES 2. Row matrix or vector Any number of columns but only one row [ ]611 [ ]2530 [ ]naaaa 1131211 
  • 8. Matrices - Introduction TYPES OF MATRICES 3. Rectangular matrix Contains more than one element and number of rows is not equal to the number of columns             − 67 77 73 11       03302 00111 nm ≠
  • 9. Matrices - Introduction TYPES OF MATRICES 4. Square matrix The number of rows is equal to the number of columns (a square matrix A has an order of m)       03 11           166 099 111 m x m The principal or main diagonal of a square matrix is composed of all elements aij for which i=j
  • 10. Matrices - Introduction TYPES OF MATRICES 5. Diagonal matrix A square matrix where all the elements are zero except those on the main diagonal           100 020 001             9000 0500 0030 0003 i.e. aij =0 for all i = j aij = 0 for some or all i = j
  • 11. Matrices - Introduction TYPES OF MATRICES 6. Unit or Identity matrix - I A diagonal matrix with ones on the main diagonal             1000 0100 0010 0001       10 01 i.e. aij =0 for all i = j a = 1 for some or all i = j       ij ij a a 0 0
  • 12. Matrices - Introduction TYPES OF MATRICES 7. Null (zero) matrix - 0 All elements in the matrix are zero           0 0 0           000 000 000 0=ija For all i,j
  • 13. Matrices - Introduction TYPES OF MATRICES 8. Triangular matrix A square matrix whose elements above or below the main diagonal are all zero           325 012 001           325 012 001           300 610 981
  • 14. Matrices - Introduction TYPES OF MATRICES 8a. Upper triangular matrix A square matrix whose elements below the main diagonal are all zero i.e. aij = 0 for all i > j           300 810 781             3000 8700 4710 4471           ij ijij ijijij a aa aaa 00 0
  • 15. Matrices - Introduction TYPES OF MATRICES A square matrix whose elements above the main diagonal are all zero 8b. Lower triangular matrix i.e. aij = 0 for all i < j           325 012 001           ijijij ijij ij aaa aa a 0 00
  • 16. Matrices – Introduction TYPES OF MATRICES 9. Scalar matrix A diagonal matrix whose main diagonal elements are equal to the same scalar A scalar is defined as a single number or constant           100 010 001             6000 0600 0060 0006 i.e. aij = 0 for all i = j aij = a for all i = j           ij ij ij a a a 00 00 00
  • 18. Matrices - Operations EQUALITY OF MATRICES Two matrices are said to be equal only when all corresponding elements are equal Therefore their size or dimensions are equal as well           325 012 001           325 012 001 A = B = A = B
  • 19. Matrices - Operations Some properties of equality: •IIf A = B, then B = A for all A and B •IIf A = B, and B = C, then A = C for all A, B and C           325 012 001 A = B =           333231 232221 131211 bbb bbb bbb If A = B then ijij ba =
  • 20. Matrices - Operations ADDITION AND SUBTRACTION OF MATRICES The sum or difference of two matrices, A and B of the same size yields a matrix C of the same size ijijij bac += Matrices of different sizes cannot be added or subtracted
  • 21. Matrices - Operations Commutative Law: A + B = B + A Associative Law: A + (B + C) = (A + B) + C = A + B + C       −− =      −− +      − − 972 588 324 651 652 137 A 2x3 B 2x3 C 2x3
  • 22. Matrices - Operations A + 0 = 0 + A = A A + (-A) = 0 (where –A is the matrix composed of –aij as elements)       − =      −      122 225 801 021 723 246
  • 23. Matrices - Operations SCALAR MULTIPLICATION OF MATRICES Matrices can be multiplied by a scalar (constant or single element) Let k be a scalar quantity; then kA = Ak Ex. If k=4 and             − − = 14 32 12 13 A
  • 25. Matrices - Operations MULTIPLICATION OF MATRICES The product of two matrices is another matrix Two matrices A and B must be conformable for multiplication to be possible i.e. the number of columns of A must equal the number of rows of B Example. A x B = C (1x3) (3x1) (1x1)
  • 26. Matrices - Operations B x A = Not possible! (2x1) (4x2) A x B = Not possible! (6x2) (6x3) Example A x B = C (2x3) (3x2) (2x2)
  • 29. Matrices - Operations Assuming that matrices A, B and C are conformable for the operations indicated, the following are true: 1. AI = IA = A 2. A(BC) = (AB)C = ABC - (associative law) 3. A(B+C) = AB + AC - (first distributive law) 4. (A+B)C = AC + BC - (second distributive law) Caution! 1. AB not generally equal to BA, BA may not be conformable 2. If AB = 0, neither A nor B necessarily = 0 3. If AB = AC, B not necessarily = C
  • 30. Matrices - Operations AB not generally equal to BA, BA may not be conformable       =            =       =            =       =       = 010 623 05 21 20 43 2015 83 20 43 05 21 20 43 05 21 ST TS S T
  • 31. Matrices - Operations If AB = 0, neither A nor B necessarily = 0       =      −−      00 00 32 32 00 11
  • 32. Matrices - Operations TRANSPOSE OF A MATRIX If :       == 135 7423 2 AA 2x3           == 17 34 52 3 2 TT AA Then transpose of A, denoted AT is: T jiij aa = For all i and j
  • 33. Matrices - Operations To transpose: Interchange rows and columns The dimensions of AT are the reverse of the dimensions of A       == 135 7423 2 AA           == 17 34 52 2 3 TT AA 2 x 3 3 x 2
  • 34. Matrices - Operations Properties of transposed matrices: 1. (A+B)T = AT + BT 2. (AB)T = BT AT 3. (kA)T = kAT 4. (AT )T = A
  • 35. Matrices - Operations 1. (A+B)T = AT + BT       −− =      −− +      − − 972 588 324 651 652 137           − − 95 78 28           − − =           − − +           − − 95 78 28 36 25 41 61 53 27
  • 36. Matrices - Operations (AB)T = BT AT [ ] [ ] [ ]82 30 21 01 211 82 8 2 2 1 1 320 011 =           ⇒      =                
  • 37. Matrices - Operations SYMMETRIC MATRICES A Square matrix is symmetric if it is equal to its transpose: A = AT       =       = db ba A db ba A T
  • 38. Matrices - Operations When the original matrix is square, transposition does not affect the elements of the main diagonal       =       = db ca A dc ba A T The identity matrix, I, a diagonal matrix D, and a scalar matrix, K, are equal to their transpose since the diagonal is unaffected.
  • 39. Matrices - Operations INVERSE OF A MATRIX Consider a scalar k. The inverse is the reciprocal or division of 1 by the scalar. Example: k=7 the inverse of k or k-1 = 1/k = 1/7 Division of matrices is not defined since there may be AB = AC while B = C Instead matrix inversion is used. The inverse of a square matrix, A, if it exists, is the unique matrix A-1 where: AA-1 = A-1 A = I
  • 41. Matrices - Operations Properties of the inverse: 11 11 11 111 1 )( )()( )( )( −− −− −− −−− = = = = A k kA AA AA ABAB TT 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
  • 42. Matrices - Operations DETERMINANT OF A MATRIX To compute the inverse of a matrix, the determinant is required Each square matrix A has a unit scalar value called the determinant of A, denoted by det A or |A| 56 21 56 21 =       = A AIf then
  • 43. Matrices - Operations If A = [A] is a single element (1x1), then the determinant is defined as the value of the element Then |A| =det A = a11 If A is (n x n), its determinant may be defined in terms of order (n-1) or less.
  • 44. 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.
  • 45. Matrices - Operations           = 333231 232221 131211 aaa aaa aaa A 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 eg. 3332 2322 11 aa aa m =
  • 46. Matrices - Operations Therefore the minor of a12 is: And the minor for a13 is: 3331 2321 12 aa aa m = 3231 2221 13 aa aa m =
  • 47. Matrices - Operations COFACTORS The cofactor Cij of an element aij is defined as: ij ji ij mC + −= )1( When the sum of a row number i and column j is even, cij = mij and when i+j is odd, cij =-mij 1313 31 13 1212 21 12 1111 11 11 )1()3,1( )1()2,1( )1()1,1( mmjic mmjic mmjic +=−=== −=−=== +=−=== + + +
  • 48. Matrices - Operations DETERMINANTS CONTINUED The determinant of an n x n matrix A can now be defined as nncacacaAA 1112121111det +++==  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)
  • 49. Matrices - Operations Therefore the 2 x 2 matrix :       = 2221 1211 aa aa A Has cofactors : 22221111 aamc === And: 21211212 aamc −=−=−= And the determinant of A is: 2112221112121111 aaaacacaA −=+=
  • 50. Matrices - Operations Example 1:       = 21 13 A 5)1)(1()2)(3( =−=A
  • 51. Matrices - Operations For a 3 x 3 matrix:           = 333231 232221 131211 aaa aaa aaa A The cofactors of the first row are: 31223221 3231 2221 13 31233321 3331 2321 12 32233322 3332 2322 11 )( aaaa aa aa c aaaa aa aa c aaaa aa aa c −== −−=−= −==
  • 52. Matrices - Operations The determinant of a matrix A is: 2112221112121111 aaaacacaA −=+= Which by substituting for the cofactors in this case is: )()()( 312232211331233321123223332211 aaaaaaaaaaaaaaaA −+−−−=
  • 53. Matrices - Operations Example 2:           − = 101 320 101 A 4)20)(1()30)(0()02)(1( =+++−−=A )()()( 312232211331233321123223332211 aaaaaaaaaaaaaaaA −+−−−=
  • 54. Matrices - Operations ADJOINT MATRICES A cofactor matrix C of a matrix A is the square matrix of the same order as A in which each element aij is replaced by its cofactor cij . Example:       − = 43 21 A       − = 12 34 C If The cofactor C of A is
  • 55. Matrices - Operations The adjoint matrix of A, denoted by adj A, is the transpose of its cofactor matrix T CadjA = It can be shown that: A(adj A) = (adjA) A = |A| I Example:       − == =−−=       − = 13 24 10)3)(2()4)(1( 43 21 T CadjA A A
  • 56. Matrices - Operations IadjAA 10 100 010 13 24 43 21 )( =      =      −       − = IAadjA 10 100 010 43 21 13 24 )( =      =      −      − =
  • 57. Matrices - Operations USING THE ADJOINT MATRIX IN MATRIX INVERSION A adjA A =−1 Since AA-1 = A-1 A = I and A(adj A) = (adjA) A = |A| I then
  • 58. Matrices - Operations Example       − =      − =− 1.03.0 2.04.0 13 24 10 11 A       − 43 21 A = To check AA-1 = A-1 A = I IAA IAA =      =      −      − = =      =      −       − = − − 10 01 43 21 1.03.0 2.04.0 10 01 1.03.0 2.04.0 43 21 1 1
  • 59. Matrices - Operations Example 2           − − = 121 012 113 A |A| = (3)(-1-0)-(-1)(-2-0)+(1)(4-1) = -2 ),1( ),1( ),1( 31 21 11 −+= −−= −+= c c c The determinant of A is The elements of the cofactor matrix are ),2( ),4( ),2( 32 22 12 −−= −+= −−= c c c ),5( ),7( ),3( 33 23 13 += −= += c c c
  • 60. Matrices - Operations           − −− − = 521 741 321 C The cofactor matrix is therefore so           − − −− == 573 242 111 T CadjA and           −− −− − =           − − −− − ==− 5.25.35.1 0.10.20.1 5.05.05.0 573 242 111 2 11 A adjA A
  • 61. Matrices - Operations The result can be checked using AA-1 = A-1 A = I The determinant of a matrix must not be zero for the inverse to exist as there will not be a solution Nonsingular matrices have non-zero determinants Singular matrices have zero determinants
  • 63. Simple 2 x 2 case Let       = dc ba A and       =− zy xw A 1 Since it is known that A A-1 = I then       =            10 01 zy xw dc ba
  • 64. Simple 2 x 2 case Multiplying gives 1 0 0 1 =+ =+ =+ =+ dzcx dycw bzax byaw bcadA −= It can simply be shown that
  • 65. Simple 2 x 2 case thus A d bcda d w d cw b aw d cw y b aw y = − = − = − − = − = 1 1
  • 66. Simple 2 x 2 case A b bcda b x d cx b ax d cx z b ax z −= +− = − = − − = − = 1 1
  • 67. Simple 2 x 2 case A c cbad c y c dy a by c dy w a by w −= +− = − = − − = − = 1 1
  • 68. Simple 2 x 2 case A a bcad a z c dz a bz c dz x a bz x = − = − = − − = − = 1 1
  • 69. Simple 2 x 2 case So that for a 2 x 2 matrix the inverse can be constructed in a simple fashion as       − − =             − ac bd A A a A c A b A d 1 •Exchange elements of main diagonal •Change sign in elements off main diagonal •Divide resulting matrix by the determinant =      =− zy xw A 1
  • 70. Simple 2 x 2 case Example       − − =      − − −=       = − 2.04.0 3.01.0 24 31 10 1 14 32 1 A A Check inverse A-1 A=I I=      =            − − − 10 01 14 32 24 31 10 1
  • 71. Matrices and Linear Equations Linear Equations
  • 72. Linear Equations Linear equations are common and important for survey problems Matrices can be used to express these linear equations and aid in the computation of unknown values Example n equations in n unknowns, the aij are numerical coefficients, the bi are constants and the xj are unknowns nnnnnn nn nn bxaxaxa bxaxaxa bxaxaxa =+++ =+++ =+++     2211 22222121 11212111
  • 73. Linear Equations The equations may be expressed in the form AX = B where ,, 2 1 11 22221 11211             =             = nnnnn n n x x x X aaa aaa aaa A       and             = nb b b B  2 1 n x n n x 1 n x 1 Number of unknowns = number of equations = n
  • 74. Linear Equations If the determinant is nonzero, the equation can be solved to produce n numerical values for x that satisfy all the simultaneous equations To solve, premultiply both sides of the equation by A-1 which exists because |A| = 0 A-1 AX = A-1 B Now since A-1 A = I We get X = A-1 B So if the inverse of the coefficient matrix is found, the unknowns, X would be determined
  • 75. Linear Equations Example 32 12 23 321 21 321 =−+ =+ =+− xxx xx xxx The equations can be expressed as           =                     − − 3 1 2 121 012 113 3 2 1 x x x
  • 76. Linear Equations When A-1 is computed the equation becomes           −=                     −− −− − == − 7 3 2 3 1 2 5.25.35.1 0.10.20.1 5.05.05.0 1 BAX Therefore 7 ,3 ,2 3 2 1 −= −= = x x x
  • 77. Linear Equations The values for the unknowns should be checked by substitution back into the initial equations 32 12 23 321 21 321 =−+ =+ =+− xxx xx xxx 3)7()3(2)2( 1)3()2(2 2)7()3()2(3 =−−−×+ =−+× =−+−−× 7 ,3 ,2 3 2 1 −= −= = x x x