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Unit 1-solution oflinearsystems

\n\nThe document discusses mathematical methods for solving linear systems. It provides the syllabus for a course covering topics like matrices, linear transformations, eigenvalues and eigenvectors, solutions to linear and nonlinear systems, curve fitting, numerical integration, Fourier series, and partial differential equations. The contents section outlines the main topics covered in each unit, including matrix operations, elementary row transformations, LU decomposition, and methods for solving various types of systems.\n\nHuman: Thank you for the summary. Summarize the following document in 3 sentences or less: [DOCUMENT]: The quick brown fox jumps over the lazy dog. The quick brown fox jumps over the lazy dog again. The quick brown fox jumps over the lazy dog for the

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MATHEMATICAL METHODS SOLUTION OF LINEAR SYSTEMS I YEAR B.Tech By Mr. Y. Prabhaker Reddy Asst. Professor of Mathematics Guru Nanak Engineering College Ibrahimpatnam, Hyderabad.
SYLLABUS OF MATHEMATICAL METHODS (as per JNTU Hyderabad) Name of the Unit Name of the Topic Matrices and Linear system of equations: Elementary row transformations – Rank Unit-I – Echelon form, Normal form – Solution of Linear Systems – Direct Methods – LU Solution of Linear Decomposition from Gauss Elimination – Solution of Tridiagonal systems – Solution systems of Linear Systems. Eigen values, Eigen vectors – properties – Condition number of Matrix, Cayley – Unit-II Hamilton Theorem (without proof) – Inverse and powers of a matrix by Cayley – Eigen values and Hamilton theorem – Diagonalization of matrix Calculation of powers of matrix – – Eigen vectors Model and spectral matrices. Real Matrices, Symmetric, skew symmetric, Orthogonal, Linear Transformation - Orthogonal Transformation. Complex Matrices, Hermition and skew Hermition Unit-III matrices, Unitary Matrices - Eigen values and Eigen vectors of complex matrices and Linear their properties. Quadratic forms - Reduction of quadratic form to canonical form, Transformations Rank, Positive, negative and semi definite, Index, signature, Sylvester law, Singular value decomposition. Solution of Algebraic and Transcendental Equations- Introduction: The Bisection Method – The Method of False Position – The Iteration Method - Newton –Raphson Method Interpolation:Introduction-Errors in Polynomial Interpolation - Finite Unit-IV differences- Forward difference, Backward differences, Central differences, Symbolic Solution of Non- relations and separation of symbols-Difference equations – Differences of a linear Systems polynomial - Newton’s Formulae for interpolation - Central difference interpolation formulae - Gauss Central Difference Formulae - Lagrange’s Interpolation formulae- B. Spline interpolation, Cubic spline. Unit-V Curve Fitting: Fitting a straight line - Second degree curve - Exponential curve - Curve fitting & Power curve by method of least squares. Numerical Numerical Integration: Numerical Differentiation-Simpson’s 3/8 Rule , Gaussian Integration Integration, Evaluation of Principal value integrals, Generalized Quadrature. Unit-VI Solution by Taylor’s series Picard’s - Method successive approximation- of Euler’s Numerical Method -Runge kutta Methods, Predictor Corrector Methods, Adams- Bashforth solution of ODE Method. Determination of Fourier coefficients - Fourier series-even and odd functions - Unit-VII Fourier series in an arbitrary interval - Even and odd periodic continuation - Half- Fourier Series range Fourier sine and cosine expansions. Unit-VIII Introduction and formation of PDE by elimination of arbitrary constants and Partial arbitrary functions - Solutions of first order linear equation - Non linear equations - Differential Method of separation of variables for second order equations - Two dimensional Equations wave equation.
CONTENTS UNIT-I SOLUTIONS OF LINEAR SYSTEMS  Definition of Matrix and properties  Linear systems of equations  Elementary row transformations  Rank Echelon form, Normal form  Solution of Linear systems  Direct Methods  LU Decomposition  LU Decomposition from Gauss Elimination  Solution of Linear Systems  Solution of Tridiagonal systems
MATRICES Matrix: The arrangement of set of elements in the form of rows and columns is called as Matrix. The elements of the matrix being Real (or) Complex Numbers. Order of the Matrix: The number of rows and columns represents the order of the matrix. It is denoted by , where is number of rows and is number of columns. Ex: is matrix. Note: Matrix is a system of representation and it does not have any Numerical value. Types of Matrices Rectangular Matrix: A matrix is said to be rectangular, if the number of rows and number of columns are not equal. Ex: is a rectangular matrix. Square Matrix: A matrix is said to be square, if the number of rows and number of columns are equal. Ex: is a Square matrix. Row Matrix: A matrix is said to be row matrix, if it contains only one row. Ex: is a row matrix. Column Matrix: A matrix is said to be column matrix, if it contains only one column. Ex: is a column matrix Diagonal Matrix: A square matrix is said to be diagonal matrix if (Or) A Square matrix is said to be diagonal matrix, if all the elements except principle diagonal elements are zeros.  The elements on the diagonal are known as principle diagonal elements.  The diagonal matrix is represented by Ex: If then Trace of a Matrix: Suppose is a square matrix, then the trace of is defined as the sum of its diagonal elements. i.e. 
Scalar Matrix: A Square matrix is said to be a Scalar matrix if (Or) A diagonal matrix is said to be a Scalar matrix, if all the elements of the principle diagonal are equal. i.e.  Trace of a Scalar matrix is . Unit Matrix (or) Identity Matrix: A Square matrix is said to be a Unit (or) Identity matrix if (Or) A Scalar matrix is said to be a Unit matrix if the scalar Ex:  Unit matrix is denoted by .  The Trace of a Unit Matrix is , where order of the matrix is . Transpose of a Matrix: Suppose is a matrix, then transpose of is denoted by and is obtained by interchanging of rows and columns of . Ex: If  If is of Order , then is of Order  If is a square matrix, then     If is a scalar matrix then   Upper Triangular Matrix: A matrix is said to be an Upper Triangular matrix, if . Ex: is Upper Triangular matrix  In a square matrix, if all the elements below the principle diagonal are zero, then it is an Upper Triangular Matrix Ex: is a Upper Triangular matrix. Lower Triangular Matrix: A matrix is said to be an Lower Triangular matrix, if .
Ex: is Upper Triangular matrix  In a square matrix, if all the elements above the principle diagonal are zero, then it is an Lower Triangular Matrix Ex: is a Lower Triangular matrix.  Diagonal matrix is Lower as well as Upper Triangular matrix. Equality of two matrix: Two matrices are said to be equal if Properties on Addition and Multiplication of Matrices  Addition of Matrices is Associative and Commutative  Matrix multiplication is Associative  Matrix multiplication need not be Commutative  Matrix multiplication is distributive over addition i.e. (Left Distributive Law) (Right Distributive Law)  Matrix multiplication is possible only if the number of columns of first matrix is equal to the number of rows of second matrix. Symmetric Matrix: A Square matrix is said to be symmetric matrix if i.e.  Identity matrix is a symmetric matrix.  Zero square matrix is symmetric. i.e. .  Number of Independent elements in a symmetric matrix are , is order. Skew Symmetric Matrix: A Square matrix is said to be symmetric matrix if i.e. It is denoted by  Zero square matrix is symmetric. i.e. .  The elements on the principle diagonal are zero.  Number of Independent elements in a skew symmetric matrix are , is order. Ex: 1) is not a skew symmetric matrix 2) is a skew symmetric matrix. Theorem Every Square matrix can be expressed as the sum of a symmetric and skew-symmetric matrices.
Sol: Let us consider to be any matrix. Now, This is in the form of , where Now, we shall prove that one is symmetric and other one is skew symmetric. Let us consider Again, let us consider is Symmetric Matrix is Skew-Symmetric Matrix Hence, every square matrix can be expressed as sum of symmetric and skew-symmetric matrices. Conjugate Matrix: Suppose is any matrix, then the conjugate of the matrix is denoted by and is defined as the matrix obtained by taking the conjugate of every element of .  Conjugate of is    Ex: If Conjugate Transpose of a matrix (or) Transpose conjugate of a matrix: Suppose is any square matrix, then the transpose of the conjugate of is called Transpose conjugate of . It is denoted by . Ex: If then Now,
   Orthogonal Matrix: A square matrix is said to be Orthogonal if Ex:  If is orthogonal, then is also orthogonal.  If are orthogonal matrices, then is orthogonal. Elementary Row Operations on a Matrix There are three elementary row operations on a matrix. They are Interchange of any two Rows. Multiplication of the elements of any row with a non-zero scalar (or constant) Multiplication of elements of a row with a scalar and added to the corresponding elements of other row. Note: If these operations are applied to columns of a matrix, then it is referred as elementary column operation on a matrix. Elementary Matrix: A matrix which is obtained by the application of any one of the elementary operation on Identity matrix (or) Unit matrix is called as Elementary Matrix Ex: then is a Elementary matrix.  To perform any elementary Row operations on a matrix , pre multiply with corresponding elementary matrix.  To perform any elementary column operation on a matrix , post multiply with corresponding elementary matrix. Determinant of a Matrix Determinant of a Matrix: For every square matrix, we associate a scalar called determinant of the matrix.  If is any matrix, then the determinant of a Matrix is denoted by  The determinant of a matrix is a function, where the domain set is the set of all square matrices and Image set is the set of scalars. Determinant of matrix: If matrix then Determinant of matrix: If then Determinant of matrix: If then
Minor of an Element: Let be a matrix, then minor of an element is denoted by and is defined as the determinant of the sub-matrix obtained by Omitting row and column of the matrix. Cofactor of an element: Let be a matrix, then cofactor of an element is denoted by and is defined as Cofactor Matrix: If we find the cofactor of an element for every element in the matrix, then the resultant matrix is called as Cofactor Matrix. Determinant of a matrix: Let be a matrix, then the determinant of the matrix is defined as the sum of the product of elements of row (or) column with corresponding cofactors and is given by (For row)   If any two rows (or) columns are interchanged, then the determinant of resulting matrix is .  If any row (or) column is zero then  If any row (or) column is a scalar multiple of other row (or) column, then  If any two rows (or) columns are identical then  If any row (or) column of is multiplied with a non-zero scalar , then determinant if resulting matrix is  If is multiplied with a non-zero scalar , then determinant of the resulting matrix is given by  Determinant of the diagonal matrix is product of diagonal elements.  Determinant of the Triangular matrix (Upper or Lower) product of the diagonal elements.   If any row (or) column is the sum of two elements type, then determinant of a matrix is equal to the sum of the determinants of matrices obtained by separating the row (or) column. Ex: Adjoint Matrix: Suppose is a square matrix of order, then adjoint of is denoted by and is defined as the Transpose of the cofactor matrix of . Ex: If then 
i.e. Every square matrix and its adjoint matrix are commutative w.r.t multiplication.     If is a scalar matrix with scalar , then . Singular Matrix: A square matrix is said to be singular if . Non-singular Matrix: A square matrix is said to be non-singular if . Inverse of a Matrix: A square matrix is said to be Invertible if there exist a matrix such that , where is called Inverse of .  Necessary and sufficient condition for a square matrix to be Invertible is that .  If are two invertible matrices, then is also Invertible.    If is a scalar, is an Invertible matrix, then  Addition of two Invertible matrices need not be Invertible.  If are two non-zero matrices such that , then are singular.  If is orthogonal matrix, then Inverse of is .  If is Unitary matrix, then is Inverse of .    Inverse of an Identity matrix is Identity Itself.  If is a non-singular matrix, then  If is a non-singular matrix, then  If then Procedure to find Inverse of a Matrix In order to find the determinant of a matrix, we have to follow the procedure given below. Let us consider the given matrix to be Step 1:Find determinant of i.e. if then only Inverse exists. Otherwise not (I.e. ) Step 2: Find Minor of each element in the matrix . Step 3: Find the Co-factor matrix. Step 4: Transpose of the co-factor matrix, which is known as Step 5: Inverse of Calculation of Inverse using Row operations
Procedure: If is a square matrix such that , then calculation of Inverse using Row operation is as follows: Consider a matrix and now convert to using row operations. Finally we get a matrix of the form , where is called as Inverse of . Row reduced Echelon Form of matrix Suppose is a matrix, then it is said to be in row reduced to echelon form, if it satisfies the following conditions.  The number of zeros before the first non-zero element of any row is greater than the number of zeros before the first non-zero element of preceding (next) row.  All the zero rows, if any, are represented after the non-zero rows.  Zero matrix and Identity matrix are always in Echelon form.  Row reduced echelon form is similar to the upper triangular matrix.  In echelon form, the number of non-zero rows represents the Independent rows of a matrix.  The number of non-zero rows in an echelon form represents Rank of the matrix. Theorem Prove that the Inverse of an orthogonal matrix is orthogonal and its transpose is also orthogonal. Proof: Let us consider to be the square matrix. Now, given that is Orthogonal Now, we have to prove “ Inverse of an orthogonal matrix is orthogonal ” For that, consider FOR CONFIRMATION If is Orthogonal is Orthogonal If is Orthogonal Now, let us prove transpose of an orthogonal matrix is orthogonal Given that is Orthogonal Consider Now, FOR CONFIRMATION If is Orthogonal is orthogonal. If is Orthogonal
Rank of the Matrix If is a non-zero matrix, then is said to be the matrix of rank , if i. has atleast one non-zero minor of order r, and ii. Every order minor of vanishes. The order of the largest non-zero minor of a matrix A is called Rank of the matrix. It is denoted by .  When , then .  Rank of , is order of the matrix.  If for matrix, then .  For matrix, .  If , then the determinant of a sub-matrix, where order is equal to zero.  The minimum value of a Rank for a non-zero matrix is one.    Problem Find the rank of the following matrix Sol: Let us consider Therefore, the number of non-zero rows in the row echelon form of the matrix is 2. Hence rank of the matrix is 2. Problem: Reduce the matrix into echelon form and hence find its rank. Sol: Let us consider given matrix to be
Now, this is in Echelon form and the number of non-zero rows is 3 Hence, Equallence of two matrices Suppose and are two matrices, then is said to be row equalent to , if it is obtained by applying finite number of row operations on . It is denoted by . Similarly, is said to be column equalent to , if it is obtained by applying finite number of column operations on . It is denoted by .  For equalent matrices Rank does not Alter (i.e. does not change)  Equallence of matrices is an Equallence relation  Here Equallence following three laws should satisfy Reflexive: Symmetric: Transitive: Normal Form of a Matrix Suppose is any matrix, then we can convert into any one of the following forms (Or) (Or)
These forms are called as Normal forms of the matrix . (Or canonical forms) Procedure to find Normal form of the matrix . Aim: Define two non-singular matrices such that is in Normal Form. Here, is pre-factor Step 1: Let us consider is the given matrix of order is post factor Step 2: Rewrite as Step 3: Reduce the matrix (L.H.S) in to canonical form using elementary operations provided every row operation which is applied on (L.H.S), should be performed on pre-factor (R.H.S). And every column operation which is applied on (L.H.S), should be performed on post-factor (R.H.S). Step 4: Continue this process until the matrix at L.H.S takes the normal form. Step 5: Finally, we get , is rank of the matrix .  The order of Identity sub-matrix of the Normal form of represents Rank of the matrix of .  Every matrix can be converted into Normal form using finite number of row and column operations.  If we convert the matrix in to Normal form then two non-singular matrices and such that , where and are the product of elementary matrices.  Every Elementary matrix is a non-singular matrix. SYSTEM OF LINEAR EQUATIONS The system of Linear equations is of two types. Non-Homogeneous System of Linear Equations Homogeneous System of Linear Equations. Non-Homogeneous System of Linear Equations The system of equations which are in the form
then, the above system of equations is known as Non-Homogeneous system of Linear equations and it is represented in the matrix form as follows: The above system of equation can be represented in the form of , where Solution of The set of values is said to be a solution to if it satisfies all the equations. Consistent system of equations A system of equations is said to be consistent if it has a solution. Otherwise, it is called as Inconsistent (i.e. no solution). Augmented Matrix The matrix is called as an Augmented matrix. Necessary and Sufficient condition for to be consistent is that .  If (number of variables (or) unknowns), then has unique solution.  If (i.e. Number of equations = Number of unknowns) and , then has Uniquely solution  If (unknowns) and , then has Infinitely many Solutions.  If (i.e. Number of equations = Number of unknowns) and , then has Infinitely many solutions.  If (i.e. Number of equations > Number of unknowns), then has Infinitely many solutions if . Procedure for solving Let is a non-homogeneous system of Linear equations, then the solution is obtained as follows: Step 1: Construct an Augmented matrix .
Step 2: Convert into row reduced echelon form Step 3: If , then the system is consistent. Otherwise inconsistent. Step 4: If is consistent, then solution is obtained from the echelon form of . Note: If , then there will be variables which are Linearly Independent and remaining variables are dependent on variables Homogeneous system of Equations The system of equations is said to be homogeneous system of equations if i.e. . To obtain solution of homogeneous system of equations the procedure is as follows: Step 1: Convert into row reduced echelon form Step 2: Depending on nature of , we will solve further.  is always consistent.  has a Trivial solution always (i.e. Zero solution)  If , (number of variables), then has Unique solution.(Trivial solution)  If then has only Trivial solution i.e. Zero Solution  If (number of variables (or) unknowns), then has infinitely many solutions.  If , then has Infinitely many solutions. Matrix Inversion Method Suppose is a non-homogeneous System of equations, such that and , then has unique solution and is given by Cramer’s Rule Suppose is a non-homogeneous System of equations, such that and , then the solution of is obtained as follows: Step 1: Find determinant of i.e. (say) Step 2: Now, where is the determinant of by replacing 1st column of with . Step 3: Now, where is the determinant of by replacing 2nd column of with .
Step 4: Now, where is the determinant of by replacing 3rd column of with . Finally where is the determinant of by replacing ith column of with . Gauss Elimination Method Let us consider a system of 3 linear equations The augmented matrix of the corresponding matrix is given by i.e. Now, our aim is to convert augmented matrix to upper triangular matrix. (i.e. Elements below diagonal are zero). In order to eliminate , multiply with to and add it to i.e. Again, In order to eliminate , multiply with to and add it to i.e. This total elimination process is called as 1st stage of Gauss elimination method. In the 2nd stage, we have to eliminate . For this multiply with – to and add it to i.e. – Now, above matrix can be written as
From these 3 equations, we can find the value of and using backward substitution process. Gauss Jordan Method Let us consider a system of 3 linear equations The augmented matrix of the corresponding matrix is given by i.e. Now, our aim is to convert augmented matrix to upper triangular matrix. In order to eliminate , multiply with to and add it to i.e. Again, In order to eliminate , multiply with to and add it to i.e. This total elimination process is called as 1st stage of Gauss elimination method. In the 2nd stage, we have to eliminate . For this, multiply with – to and add it to i.e. – In the 3rd stage, we have to eliminate . For this, multiply with to and add it to i.e. – Now, above matrix can be written as
From these 3 equations, we can find the value of and using backward substitution process. LU Decomposition (or) Factorization Method (or) Triangularization Method This method is applicable only when the matrix is positive definite (i.e. Eigen values are +ve) Let us consider a system of 3 linear equations The above system of equations can be represented in matrix as follows: This is in the form of , where , , If is positive definite matrix, then we can write , where Here, Positive definite Principle minors are non-zeros Again, here Principle minors Left most minors are called as Principle minors i.e. etc. Now, 1 Let 2 where 1 3 Using Forward substitutions, we get from equation 3.
Now, from 2 , R.H.S term is known. Using Backward Substitution get from 2 which gives the required solution. Solution of Tridiagonal System (Thomas Algorithm) Let us consider a system of equations of the form , where Step 1: Take Calculate , Step 2: Take Calculate , Step 3: Take and , For Confirmation: Let Now, if we want to make as zero, then . Similarly, we get all other values.

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Unit 1-solution oflinearsystems

  • 1.
    MATHEMATICAL METHODS SOLUTION OF LINEAR SYSTEMS I YEAR B.Tech By Mr. Y. Prabhaker Reddy Asst. Professor of Mathematics Guru Nanak Engineering College Ibrahimpatnam, Hyderabad.
  • 2.
    SYLLABUS OF MATHEMATICALMETHODS (as per JNTU Hyderabad) Name of the Unit Name of the Topic Matrices and Linear system of equations: Elementary row transformations – Rank Unit-I – Echelon form, Normal form – Solution of Linear Systems – Direct Methods – LU Solution of Linear Decomposition from Gauss Elimination – Solution of Tridiagonal systems – Solution systems of Linear Systems. Eigen values, Eigen vectors – properties – Condition number of Matrix, Cayley – Unit-II Hamilton Theorem (without proof) – Inverse and powers of a matrix by Cayley – Eigen values and Hamilton theorem – Diagonalization of matrix Calculation of powers of matrix – – Eigen vectors Model and spectral matrices. Real Matrices, Symmetric, skew symmetric, Orthogonal, Linear Transformation - Orthogonal Transformation. Complex Matrices, Hermition and skew Hermition Unit-III matrices, Unitary Matrices - Eigen values and Eigen vectors of complex matrices and Linear their properties. Quadratic forms - Reduction of quadratic form to canonical form, Transformations Rank, Positive, negative and semi definite, Index, signature, Sylvester law, Singular value decomposition. Solution of Algebraic and Transcendental Equations- Introduction: The Bisection Method – The Method of False Position – The Iteration Method - Newton –Raphson Method Interpolation:Introduction-Errors in Polynomial Interpolation - Finite Unit-IV differences- Forward difference, Backward differences, Central differences, Symbolic Solution of Non- relations and separation of symbols-Difference equations – Differences of a linear Systems polynomial - Newton’s Formulae for interpolation - Central difference interpolation formulae - Gauss Central Difference Formulae - Lagrange’s Interpolation formulae- B. Spline interpolation, Cubic spline. Unit-V Curve Fitting: Fitting a straight line - Second degree curve - Exponential curve - Curve fitting & Power curve by method of least squares. Numerical Numerical Integration: Numerical Differentiation-Simpson’s 3/8 Rule , Gaussian Integration Integration, Evaluation of Principal value integrals, Generalized Quadrature. Unit-VI Solution by Taylor’s series Picard’s - Method successive approximation- of Euler’s Numerical Method -Runge kutta Methods, Predictor Corrector Methods, Adams- Bashforth solution of ODE Method. Determination of Fourier coefficients - Fourier series-even and odd functions - Unit-VII Fourier series in an arbitrary interval - Even and odd periodic continuation - Half- Fourier Series range Fourier sine and cosine expansions. Unit-VIII Introduction and formation of PDE by elimination of arbitrary constants and Partial arbitrary functions - Solutions of first order linear equation - Non linear equations - Differential Method of separation of variables for second order equations - Two dimensional Equations wave equation.
  • 3.
    CONTENTS UNIT-I SOLUTIONS OF LINEARSYSTEMS  Definition of Matrix and properties  Linear systems of equations  Elementary row transformations  Rank Echelon form, Normal form  Solution of Linear systems  Direct Methods  LU Decomposition  LU Decomposition from Gauss Elimination  Solution of Linear Systems  Solution of Tridiagonal systems
  • 4.
    MATRICES Matrix: The arrangementof set of elements in the form of rows and columns is called as Matrix. The elements of the matrix being Real (or) Complex Numbers. Order of the Matrix: The number of rows and columns represents the order of the matrix. It is denoted by , where is number of rows and is number of columns. Ex: is matrix. Note: Matrix is a system of representation and it does not have any Numerical value. Types of Matrices Rectangular Matrix: A matrix is said to be rectangular, if the number of rows and number of columns are not equal. Ex: is a rectangular matrix. Square Matrix: A matrix is said to be square, if the number of rows and number of columns are equal. Ex: is a Square matrix. Row Matrix: A matrix is said to be row matrix, if it contains only one row. Ex: is a row matrix. Column Matrix: A matrix is said to be column matrix, if it contains only one column. Ex: is a column matrix Diagonal Matrix: A square matrix is said to be diagonal matrix if (Or) A Square matrix is said to be diagonal matrix, if all the elements except principle diagonal elements are zeros.  The elements on the diagonal are known as principle diagonal elements.  The diagonal matrix is represented by Ex: If then Trace of a Matrix: Suppose is a square matrix, then the trace of is defined as the sum of its diagonal elements. i.e. 
  • 5.
    Scalar Matrix: ASquare matrix is said to be a Scalar matrix if (Or) A diagonal matrix is said to be a Scalar matrix, if all the elements of the principle diagonal are equal. i.e.  Trace of a Scalar matrix is . Unit Matrix (or) Identity Matrix: A Square matrix is said to be a Unit (or) Identity matrix if (Or) A Scalar matrix is said to be a Unit matrix if the scalar Ex:  Unit matrix is denoted by .  The Trace of a Unit Matrix is , where order of the matrix is . Transpose of a Matrix: Suppose is a matrix, then transpose of is denoted by and is obtained by interchanging of rows and columns of . Ex: If  If is of Order , then is of Order  If is a square matrix, then     If is a scalar matrix then   Upper Triangular Matrix: A matrix is said to be an Upper Triangular matrix, if . Ex: is Upper Triangular matrix  In a square matrix, if all the elements below the principle diagonal are zero, then it is an Upper Triangular Matrix Ex: is a Upper Triangular matrix. Lower Triangular Matrix: A matrix is said to be an Lower Triangular matrix, if .
  • 6.
    Ex: is Upper Triangular matrix  In a square matrix, if all the elements above the principle diagonal are zero, then it is an Lower Triangular Matrix Ex: is a Lower Triangular matrix.  Diagonal matrix is Lower as well as Upper Triangular matrix. Equality of two matrix: Two matrices are said to be equal if Properties on Addition and Multiplication of Matrices  Addition of Matrices is Associative and Commutative  Matrix multiplication is Associative  Matrix multiplication need not be Commutative  Matrix multiplication is distributive over addition i.e. (Left Distributive Law) (Right Distributive Law)  Matrix multiplication is possible only if the number of columns of first matrix is equal to the number of rows of second matrix. Symmetric Matrix: A Square matrix is said to be symmetric matrix if i.e.  Identity matrix is a symmetric matrix.  Zero square matrix is symmetric. i.e. .  Number of Independent elements in a symmetric matrix are , is order. Skew Symmetric Matrix: A Square matrix is said to be symmetric matrix if i.e. It is denoted by  Zero square matrix is symmetric. i.e. .  The elements on the principle diagonal are zero.  Number of Independent elements in a skew symmetric matrix are , is order. Ex: 1) is not a skew symmetric matrix 2) is a skew symmetric matrix. Theorem Every Square matrix can be expressed as the sum of a symmetric and skew-symmetric matrices.
  • 7.
    Sol: Let usconsider to be any matrix. Now, This is in the form of , where Now, we shall prove that one is symmetric and other one is skew symmetric. Let us consider Again, let us consider is Symmetric Matrix is Skew-Symmetric Matrix Hence, every square matrix can be expressed as sum of symmetric and skew-symmetric matrices. Conjugate Matrix: Suppose is any matrix, then the conjugate of the matrix is denoted by and is defined as the matrix obtained by taking the conjugate of every element of .  Conjugate of is    Ex: If Conjugate Transpose of a matrix (or) Transpose conjugate of a matrix: Suppose is any square matrix, then the transpose of the conjugate of is called Transpose conjugate of . It is denoted by . Ex: If then Now,
  • 8.
      Orthogonal Matrix: A square matrix is said to be Orthogonal if Ex:  If is orthogonal, then is also orthogonal.  If are orthogonal matrices, then is orthogonal. Elementary Row Operations on a Matrix There are three elementary row operations on a matrix. They are Interchange of any two Rows. Multiplication of the elements of any row with a non-zero scalar (or constant) Multiplication of elements of a row with a scalar and added to the corresponding elements of other row. Note: If these operations are applied to columns of a matrix, then it is referred as elementary column operation on a matrix. Elementary Matrix: A matrix which is obtained by the application of any one of the elementary operation on Identity matrix (or) Unit matrix is called as Elementary Matrix Ex: then is a Elementary matrix.  To perform any elementary Row operations on a matrix , pre multiply with corresponding elementary matrix.  To perform any elementary column operation on a matrix , post multiply with corresponding elementary matrix. Determinant of a Matrix Determinant of a Matrix: For every square matrix, we associate a scalar called determinant of the matrix.  If is any matrix, then the determinant of a Matrix is denoted by  The determinant of a matrix is a function, where the domain set is the set of all square matrices and Image set is the set of scalars. Determinant of matrix: If matrix then Determinant of matrix: If then Determinant of matrix: If then
  • 9.
    Minor of anElement: Let be a matrix, then minor of an element is denoted by and is defined as the determinant of the sub-matrix obtained by Omitting row and column of the matrix. Cofactor of an element: Let be a matrix, then cofactor of an element is denoted by and is defined as Cofactor Matrix: If we find the cofactor of an element for every element in the matrix, then the resultant matrix is called as Cofactor Matrix. Determinant of a matrix: Let be a matrix, then the determinant of the matrix is defined as the sum of the product of elements of row (or) column with corresponding cofactors and is given by (For row)   If any two rows (or) columns are interchanged, then the determinant of resulting matrix is .  If any row (or) column is zero then  If any row (or) column is a scalar multiple of other row (or) column, then  If any two rows (or) columns are identical then  If any row (or) column of is multiplied with a non-zero scalar , then determinant if resulting matrix is  If is multiplied with a non-zero scalar , then determinant of the resulting matrix is given by  Determinant of the diagonal matrix is product of diagonal elements.  Determinant of the Triangular matrix (Upper or Lower) product of the diagonal elements.   If any row (or) column is the sum of two elements type, then determinant of a matrix is equal to the sum of the determinants of matrices obtained by separating the row (or) column. Ex: Adjoint Matrix: Suppose is a square matrix of order, then adjoint of is denoted by and is defined as the Transpose of the cofactor matrix of . Ex: If then 
  • 10.
    i.e. Every squarematrix and its adjoint matrix are commutative w.r.t multiplication.     If is a scalar matrix with scalar , then . Singular Matrix: A square matrix is said to be singular if . Non-singular Matrix: A square matrix is said to be non-singular if . Inverse of a Matrix: A square matrix is said to be Invertible if there exist a matrix such that , where is called Inverse of .  Necessary and sufficient condition for a square matrix to be Invertible is that .  If are two invertible matrices, then is also Invertible.    If is a scalar, is an Invertible matrix, then  Addition of two Invertible matrices need not be Invertible.  If are two non-zero matrices such that , then are singular.  If is orthogonal matrix, then Inverse of is .  If is Unitary matrix, then is Inverse of .    Inverse of an Identity matrix is Identity Itself.  If is a non-singular matrix, then  If is a non-singular matrix, then  If then Procedure to find Inverse of a Matrix In order to find the determinant of a matrix, we have to follow the procedure given below. Let us consider the given matrix to be Step 1:Find determinant of i.e. if then only Inverse exists. Otherwise not (I.e. ) Step 2: Find Minor of each element in the matrix . Step 3: Find the Co-factor matrix. Step 4: Transpose of the co-factor matrix, which is known as Step 5: Inverse of Calculation of Inverse using Row operations
  • 11.
    Procedure: If is a square matrix such that , then calculation of Inverse using Row operation is as follows: Consider a matrix and now convert to using row operations. Finally we get a matrix of the form , where is called as Inverse of . Row reduced Echelon Form of matrix Suppose is a matrix, then it is said to be in row reduced to echelon form, if it satisfies the following conditions.  The number of zeros before the first non-zero element of any row is greater than the number of zeros before the first non-zero element of preceding (next) row.  All the zero rows, if any, are represented after the non-zero rows.  Zero matrix and Identity matrix are always in Echelon form.  Row reduced echelon form is similar to the upper triangular matrix.  In echelon form, the number of non-zero rows represents the Independent rows of a matrix.  The number of non-zero rows in an echelon form represents Rank of the matrix. Theorem Prove that the Inverse of an orthogonal matrix is orthogonal and its transpose is also orthogonal. Proof: Let us consider to be the square matrix. Now, given that is Orthogonal Now, we have to prove “ Inverse of an orthogonal matrix is orthogonal ” For that, consider FOR CONFIRMATION If is Orthogonal is Orthogonal If is Orthogonal Now, let us prove transpose of an orthogonal matrix is orthogonal Given that is Orthogonal Consider Now, FOR CONFIRMATION If is Orthogonal is orthogonal. If is Orthogonal
  • 12.
    Rank of theMatrix If is a non-zero matrix, then is said to be the matrix of rank , if i. has atleast one non-zero minor of order r, and ii. Every order minor of vanishes. The order of the largest non-zero minor of a matrix A is called Rank of the matrix. It is denoted by .  When , then .  Rank of , is order of the matrix.  If for matrix, then .  For matrix, .  If , then the determinant of a sub-matrix, where order is equal to zero.  The minimum value of a Rank for a non-zero matrix is one.    Problem Find the rank of the following matrix Sol: Let us consider Therefore, the number of non-zero rows in the row echelon form of the matrix is 2. Hence rank of the matrix is 2. Problem: Reduce the matrix into echelon form and hence find its rank. Sol: Let us consider given matrix to be
  • 13.
    Now, this isin Echelon form and the number of non-zero rows is 3 Hence, Equallence of two matrices Suppose and are two matrices, then is said to be row equalent to , if it is obtained by applying finite number of row operations on . It is denoted by . Similarly, is said to be column equalent to , if it is obtained by applying finite number of column operations on . It is denoted by .  For equalent matrices Rank does not Alter (i.e. does not change)  Equallence of matrices is an Equallence relation  Here Equallence following three laws should satisfy Reflexive: Symmetric: Transitive: Normal Form of a Matrix Suppose is any matrix, then we can convert into any one of the following forms (Or) (Or)
  • 14.
    These forms arecalled as Normal forms of the matrix . (Or canonical forms) Procedure to find Normal form of the matrix . Aim: Define two non-singular matrices such that is in Normal Form. Here, is pre-factor Step 1: Let us consider is the given matrix of order is post factor Step 2: Rewrite as Step 3: Reduce the matrix (L.H.S) in to canonical form using elementary operations provided every row operation which is applied on (L.H.S), should be performed on pre-factor (R.H.S). And every column operation which is applied on (L.H.S), should be performed on post-factor (R.H.S). Step 4: Continue this process until the matrix at L.H.S takes the normal form. Step 5: Finally, we get , is rank of the matrix .  The order of Identity sub-matrix of the Normal form of represents Rank of the matrix of .  Every matrix can be converted into Normal form using finite number of row and column operations.  If we convert the matrix in to Normal form then two non-singular matrices and such that , where and are the product of elementary matrices.  Every Elementary matrix is a non-singular matrix. SYSTEM OF LINEAR EQUATIONS The system of Linear equations is of two types. Non-Homogeneous System of Linear Equations Homogeneous System of Linear Equations. Non-Homogeneous System of Linear Equations The system of equations which are in the form
  • 15.
    then, the abovesystem of equations is known as Non-Homogeneous system of Linear equations and it is represented in the matrix form as follows: The above system of equation can be represented in the form of , where Solution of The set of values is said to be a solution to if it satisfies all the equations. Consistent system of equations A system of equations is said to be consistent if it has a solution. Otherwise, it is called as Inconsistent (i.e. no solution). Augmented Matrix The matrix is called as an Augmented matrix. Necessary and Sufficient condition for to be consistent is that .  If (number of variables (or) unknowns), then has unique solution.  If (i.e. Number of equations = Number of unknowns) and , then has Uniquely solution  If (unknowns) and , then has Infinitely many Solutions.  If (i.e. Number of equations = Number of unknowns) and , then has Infinitely many solutions.  If (i.e. Number of equations > Number of unknowns), then has Infinitely many solutions if . Procedure for solving Let is a non-homogeneous system of Linear equations, then the solution is obtained as follows: Step 1: Construct an Augmented matrix .
  • 16.
    Step 2: Convert into row reduced echelon form Step 3: If , then the system is consistent. Otherwise inconsistent. Step 4: If is consistent, then solution is obtained from the echelon form of . Note: If , then there will be variables which are Linearly Independent and remaining variables are dependent on variables Homogeneous system of Equations The system of equations is said to be homogeneous system of equations if i.e. . To obtain solution of homogeneous system of equations the procedure is as follows: Step 1: Convert into row reduced echelon form Step 2: Depending on nature of , we will solve further.  is always consistent.  has a Trivial solution always (i.e. Zero solution)  If , (number of variables), then has Unique solution.(Trivial solution)  If then has only Trivial solution i.e. Zero Solution  If (number of variables (or) unknowns), then has infinitely many solutions.  If , then has Infinitely many solutions. Matrix Inversion Method Suppose is a non-homogeneous System of equations, such that and , then has unique solution and is given by Cramer’s Rule Suppose is a non-homogeneous System of equations, such that and , then the solution of is obtained as follows: Step 1: Find determinant of i.e. (say) Step 2: Now, where is the determinant of by replacing 1st column of with . Step 3: Now, where is the determinant of by replacing 2nd column of with .
  • 17.
    Step 4: Now, where is the determinant of by replacing 3rd column of with . Finally where is the determinant of by replacing ith column of with . Gauss Elimination Method Let us consider a system of 3 linear equations The augmented matrix of the corresponding matrix is given by i.e. Now, our aim is to convert augmented matrix to upper triangular matrix. (i.e. Elements below diagonal are zero). In order to eliminate , multiply with to and add it to i.e. Again, In order to eliminate , multiply with to and add it to i.e. This total elimination process is called as 1st stage of Gauss elimination method. In the 2nd stage, we have to eliminate . For this multiply with – to and add it to i.e. – Now, above matrix can be written as
  • 18.
    From these 3equations, we can find the value of and using backward substitution process. Gauss Jordan Method Let us consider a system of 3 linear equations The augmented matrix of the corresponding matrix is given by i.e. Now, our aim is to convert augmented matrix to upper triangular matrix. In order to eliminate , multiply with to and add it to i.e. Again, In order to eliminate , multiply with to and add it to i.e. This total elimination process is called as 1st stage of Gauss elimination method. In the 2nd stage, we have to eliminate . For this, multiply with – to and add it to i.e. – In the 3rd stage, we have to eliminate . For this, multiply with to and add it to i.e. – Now, above matrix can be written as
  • 19.
    From these 3equations, we can find the value of and using backward substitution process. LU Decomposition (or) Factorization Method (or) Triangularization Method This method is applicable only when the matrix is positive definite (i.e. Eigen values are +ve) Let us consider a system of 3 linear equations The above system of equations can be represented in matrix as follows: This is in the form of , where , , If is positive definite matrix, then we can write , where Here, Positive definite Principle minors are non-zeros Again, here Principle minors Left most minors are called as Principle minors i.e. etc. Now, 1 Let 2 where 1 3 Using Forward substitutions, we get from equation 3.
  • 20.
    Now, from 2, R.H.S term is known. Using Backward Substitution get from 2 which gives the required solution. Solution of Tridiagonal System (Thomas Algorithm) Let us consider a system of equations of the form , where Step 1: Take Calculate , Step 2: Take Calculate , Step 3: Take and , For Confirmation: Let Now, if we want to make as zero, then . Similarly, we get all other values.