Matrices and their operations were discussed. Key points include:
1) A matrix is a rectangular array of numbers. The order of a m x n matrix refers to its m rows and n columns.
2) Common matrix types include row/column matrices (vectors), square matrices, diagonal matrices, scalar matrices, identity matrices, and zero matrices.
3) Basic matrix operations include addition, subtraction, multiplication by a scalar, transpose, and multiplication. Properties like commutativity, associativity, and distributivity apply.
A matrix is a rectangular arrangement of numbers organized in rows and columns. The order of a matrix refers to the number of rows and columns it contains. Entries are the individual numbers within the matrix. Basic matrix operations include addition, subtraction, scalar multiplication, and multiplication. To add or subtract matrices, they must be the same order, while scalar multiplication multiplies each entry of the matrix by the scalar.
A matrix is a rectangular array of numbers arranged in rows and columns. The dimensions of a matrix are written as the number of rows x the number of columns. Each individual entry in the matrix is named by its position, using the matrix name and row and column numbers. Matrices can represent systems of equations or points in a plane. Operations on matrices include addition, multiplication by scalars, and dilation of points represented by matrices.
It contains the basics of matrix which includes matrix definition,types of matrices,operations on matrices,transpose of matrix,symmetric and skew symmetric matrix,invertible matrix,
application of matrix.
The document defines and provides examples of different types of matrices, including:
- Square matrices, where the number of rows equals the number of columns.
- Rectangular matrices, where the number of rows does not equal the number of columns.
- Row matrices, with only one row.
- Column matrices, with only one column.
- Null or zero matrices, with all elements equal to zero.
- Diagonal matrices, with all elements equal to zero except those on the main diagonal.
The document also discusses transpose, adjoint, and addition of matrices.
The order of the given matrix is 2×3. So the maximum no. of elements is 2×3 = 6.
The correct option is B.
The element a32 belongs to 3rd row and 2nd column.
The correct option is B.
3. A matrix whose each diagonal element is unity and all other elements are zero is called
A) Identity matrix B) Unit matrix C) Scalar matrix D) Diagonal matrix
4. A matrix whose each row sums to unity is called
A) Row matrix B) Column matrix C) Unit matrix D) Stochastic matrix
5. The sum of all the elements on the principal diagonal of a square
This document discusses basic matrix operations including:
- Defining a matrix as a rectangular arrangement of numbers in rows and columns with an order specified by the number of rows and columns.
- Adding and subtracting matrices requires they have the same order and involves adding or subtracting corresponding entries.
- Multiplying a matrix by a scalar involves multiplying each entry in the matrix by the scalar value.
- Matrix multiplication is not commutative and can only be done if the number of columns in the first matrix equals the number of rows in the second matrix. It involves multiplying entries and summing the products based on their positions.
This document discusses inverse matrices and methods for calculating them. It defines an inverse matrix as a matrix M-1 such that MM-1=M-1M=I, where I is the identity matrix. Only square matrices can have inverses. Two common methods for calculating the inverse are Crammer's Method, which uses the adjoint and determinant of the matrix, and Gauss Method, which performs row operations. An example calculates the inverse of a 2x2 matrix using the property that the inverse is the transpose of the cofactor matrix divided by the determinant.
matrices
The beginnings of matrices goes back to the second century BC although traces can be seen back to the fourth century BC. However it was not until near the end of the 17th Century that the ideas reappeared and development really got underway.
It is not surprising that the beginnings of matrices and determinants should arise through the study of systems of linear equations. The Babylonians studied problems which lead to simultaneous linear equations and some of these are preserved in clay tablets which survive.
The document presents information on matrices, including:
- Definitions of matrices as rectangular arrangements of numbers arranged in rows and columns
- Common matrix operations such as addition, subtraction, scalar multiplication, and matrix multiplication
- Determinants and inverses of matrices
- How matrices can represent systems of linear equations
- Unique properties of matrices, such as the product of two non-zero matrices possibly being zero
- Applications of matrices in fields like geology, statistics, economics, and animation
The document presents information on matrices and their types. It defines a matrix as an arrangement of numbers, symbols or expressions in rows and columns. It discusses different types of matrices including row matrices, column matrices, square matrices, rectangular matrices, diagonal matrices, scalar matrices, unit/identity matrices, symmetric matrices, complex matrices, hermitian matrices, skew-hermitian matrices, orthogonal matrices, unitary matrices, and nilpotent matrices. It provides examples and definitions for hermitian matrices, orthogonal matrices, idempotent matrices, and nilpotent matrices. The presentation was given by Himanshu Negi on matrices and their types.
Here are the key steps to find the eigenvalues of the given matrix:
1) Write the characteristic equation: det(A - λI) = 0
2) Expand the determinant: (1-λ)(-2-λ) - 4 = 0
3) Simplify and factor: λ(λ + 1)(λ + 2) = 0
4) Find the roots: λ1 = 0, λ2 = -1, λ3 = -2
Therefore, the eigenvalues of the given matrix are -1 and -2.
Matrix and its operation (addition, subtraction, multiplication)NirnayMukharjee
Ìý
This document summarizes matrix operations including addition, subtraction, and multiplication. It defines a matrix as a rectangular arrangement of numbers in rows and columns. Matrix addition and subtraction can only be done on matrices with the same dimensions, by adding or subtracting the corresponding elements. Matrix multiplication involves multiplying the rows of the first matrix with the columns of the second matrix and summing the products to form the elements of the resulting matrix. Examples are provided to illustrate each operation.
The document provides an overview of a lecture covering matrices, matrix algebra, vectors, homogeneous coordinates, and transformations in homogeneous coordinates. Key points include: matrices are arrays of numbers; operations on matrices include addition, multiplication by a scalar, and multiplication; vectors can represent points in space as column or row matrices; homogeneous coordinates allow points to be represented by 4D vectors, enabling translations and other transformations to be described by 4x4 matrices. This provides a unified approach for combining multiple transformations.
This document provides an overview of matrices and matrix operations. It begins by stating the objectives of understanding matrix characteristics, applying basic matrix operations, knowing inverse matrices up to 3x3, and solving simultaneous linear equations up to 3 variables. It then defines what a matrix is, discusses matrix dimensions and types of matrices. The document outlines various matrix operations including addition, subtraction, multiplication and scalar multiplication. It provides examples of how to perform these operations. It also covers the transpose of a matrix and inverse matrices.
A matrix is a set of elements organized into rows and columns. Basic matrix operations include addition, subtraction, and multiplication. A matrix can be multiplied by another matrix if the number of columns of the first equals the number of rows of the second. The determinant of a matrix is a value that is used to determine properties of the matrix such as invertibility. Cramer's rule can be used to solve systems of linear equations involving matrices.
1. A set is a collection of distinct objects or elements that have some common property. Sets are represented using curly braces and capital letters. Elements within a set should not be repeated.
2. There are two main methods to define a set - the roster method which lists all elements, and the set builder method which describes the property defining membership.
3. There are several types of sets including finite sets with a set number of elements, infinite sets, singleton sets with one element, empty sets with no elements, equal sets with the same elements, and subsets which are sets within other sets.
The binomial theorem provides a formula for expanding binomial expressions of the form (a + b)^n. It states that the terms of the expansion are determined by binomial coefficients. Pascal's triangle is a mathematical arrangement that shows the binomial coefficients and can be used to determine the coefficients in a binomial expansion. The proof of the binomial theorem uses mathematical induction to show that the formula holds true for any positive integer value of n.
This document provides an overview of matrices including:
- How to describe matrices using m rows and n columns
- Common types of matrices such as row, column, zero, square, diagonal, and unit matrices
- Basic matrix operations including addition, subtraction, scalar multiplication
- Rules for matrix multiplication including that matrices must be conformable
- The transpose of a matrix which is obtained by interchanging rows and columns
- Properties of transposed matrices including (A+B)T = AT + BT and (AB)T = BTAT
This document defines and describes different types of matrices including:
- Upper and lower triangular matrices
- Determinants which are scalars obtained from products of matrix elements according to constraints
- Band matrices which are sparse matrices with nonzero elements confined to diagonals
- Transpose matrices which exchange the rows and columns of a matrix
- Inverse matrices which when multiplied by the original matrix produce the identity matrix
The document discusses the rules for matrix multiplication. It states that two matrices can only be multiplied if the number of columns of the first matrix is equal to the number of rows of the second matrix. It provides examples of multiplying different matrices and explains how to calculate each element of the resulting matrix by taking the dot product of the corresponding row and column. It also gives an example of using matrix multiplication to calculate total sales and revenue from sales data organized in matrices.
This document defines and provides examples of different types of matrices:
- Matrices are arrangements of elements in rows and columns represented by symbols.
- Types include row matrices, column matrices, square matrices, null matrices, identity matrices, diagonal matrices, scalar matrices, triangular matrices, transpose matrices, symmetric matrices, skew matrices, equal matrices, and algebraic matrices.
- Algebraic matrix operations include addition, subtraction, and multiplication where the matrices must be of the same order.
This document discusses different types of matrices including diagonal, scalar, identity, triangular, and complex matrices. It provides definitions and examples of each. Operations on matrices such as addition, subtraction, and multiplication are covered. Matrix multiplication is demonstrated using examples. Properties of matrix operations like addition and subtraction being commutative vs anti-commutative are explained.
The document describes the Gauss-Jordan method for finding the inverse of a matrix. It involves 3 steps: 1) Writing the original matrix next to an identity matrix of the same size. 2) Performing row operations on both matrices to transform the original matrix into the identity matrix. 3) The resulting matrix next to the identity matrix is then the inverse of the original matrix. An example is shown applying the method to find the inverse of a 3x3 matrix through a series of row operations.
This document provides information about determinants of square matrices:
- It defines the determinant of a matrix as a scalar value associated with the matrix. Determinants are computed using minors and cofactors.
- Properties of determinants are described, such as how determinants change with row/column operations or identical rows/columns.
- Examples are provided to demonstrate computing determinants by expanding along rows or columns and using cofactors and minors.
- Applications of determinants include finding the area of triangles and solving systems of linear equations.
This document discusses sequences and their limits. Some key points:
- A sequence is a list of numbers written in a definite order. It can be thought of as a function with domain the positive integers.
- The limit of a sequence is defined as the number L such that the terms of the sequence can be made arbitrarily close to L by choosing a sufficiently large term.
- A sequence converges if it has a finite limit, and diverges if its terms approach infinity. Bounded monotonic sequences are guaranteed to converge.
- Properties of sequence limits parallel those of limits of functions, including laws of limits and the ability to pass limits inside continuous functions.
A matrix is a rectangular array of numbers, symbols, or expressions arranged in rows and columns. It is commonly written with elements separated by commas and enclosed in brackets. A matrix can be classified based on its dimensions as row, column, square, or rectangular. Basic matrix operations include addition, subtraction, scalar multiplication, and multiplication of two matrices. Matrix multiplication is only defined when the inner dimensions are equal.
matrices
The beginnings of matrices goes back to the second century BC although traces can be seen back to the fourth century BC. However it was not until near the end of the 17th Century that the ideas reappeared and development really got underway.
It is not surprising that the beginnings of matrices and determinants should arise through the study of systems of linear equations. The Babylonians studied problems which lead to simultaneous linear equations and some of these are preserved in clay tablets which survive.
The document presents information on matrices, including:
- Definitions of matrices as rectangular arrangements of numbers arranged in rows and columns
- Common matrix operations such as addition, subtraction, scalar multiplication, and matrix multiplication
- Determinants and inverses of matrices
- How matrices can represent systems of linear equations
- Unique properties of matrices, such as the product of two non-zero matrices possibly being zero
- Applications of matrices in fields like geology, statistics, economics, and animation
The document presents information on matrices and their types. It defines a matrix as an arrangement of numbers, symbols or expressions in rows and columns. It discusses different types of matrices including row matrices, column matrices, square matrices, rectangular matrices, diagonal matrices, scalar matrices, unit/identity matrices, symmetric matrices, complex matrices, hermitian matrices, skew-hermitian matrices, orthogonal matrices, unitary matrices, and nilpotent matrices. It provides examples and definitions for hermitian matrices, orthogonal matrices, idempotent matrices, and nilpotent matrices. The presentation was given by Himanshu Negi on matrices and their types.
Here are the key steps to find the eigenvalues of the given matrix:
1) Write the characteristic equation: det(A - λI) = 0
2) Expand the determinant: (1-λ)(-2-λ) - 4 = 0
3) Simplify and factor: λ(λ + 1)(λ + 2) = 0
4) Find the roots: λ1 = 0, λ2 = -1, λ3 = -2
Therefore, the eigenvalues of the given matrix are -1 and -2.
Matrix and its operation (addition, subtraction, multiplication)NirnayMukharjee
Ìý
This document summarizes matrix operations including addition, subtraction, and multiplication. It defines a matrix as a rectangular arrangement of numbers in rows and columns. Matrix addition and subtraction can only be done on matrices with the same dimensions, by adding or subtracting the corresponding elements. Matrix multiplication involves multiplying the rows of the first matrix with the columns of the second matrix and summing the products to form the elements of the resulting matrix. Examples are provided to illustrate each operation.
The document provides an overview of a lecture covering matrices, matrix algebra, vectors, homogeneous coordinates, and transformations in homogeneous coordinates. Key points include: matrices are arrays of numbers; operations on matrices include addition, multiplication by a scalar, and multiplication; vectors can represent points in space as column or row matrices; homogeneous coordinates allow points to be represented by 4D vectors, enabling translations and other transformations to be described by 4x4 matrices. This provides a unified approach for combining multiple transformations.
This document provides an overview of matrices and matrix operations. It begins by stating the objectives of understanding matrix characteristics, applying basic matrix operations, knowing inverse matrices up to 3x3, and solving simultaneous linear equations up to 3 variables. It then defines what a matrix is, discusses matrix dimensions and types of matrices. The document outlines various matrix operations including addition, subtraction, multiplication and scalar multiplication. It provides examples of how to perform these operations. It also covers the transpose of a matrix and inverse matrices.
A matrix is a set of elements organized into rows and columns. Basic matrix operations include addition, subtraction, and multiplication. A matrix can be multiplied by another matrix if the number of columns of the first equals the number of rows of the second. The determinant of a matrix is a value that is used to determine properties of the matrix such as invertibility. Cramer's rule can be used to solve systems of linear equations involving matrices.
1. A set is a collection of distinct objects or elements that have some common property. Sets are represented using curly braces and capital letters. Elements within a set should not be repeated.
2. There are two main methods to define a set - the roster method which lists all elements, and the set builder method which describes the property defining membership.
3. There are several types of sets including finite sets with a set number of elements, infinite sets, singleton sets with one element, empty sets with no elements, equal sets with the same elements, and subsets which are sets within other sets.
The binomial theorem provides a formula for expanding binomial expressions of the form (a + b)^n. It states that the terms of the expansion are determined by binomial coefficients. Pascal's triangle is a mathematical arrangement that shows the binomial coefficients and can be used to determine the coefficients in a binomial expansion. The proof of the binomial theorem uses mathematical induction to show that the formula holds true for any positive integer value of n.
This document provides an overview of matrices including:
- How to describe matrices using m rows and n columns
- Common types of matrices such as row, column, zero, square, diagonal, and unit matrices
- Basic matrix operations including addition, subtraction, scalar multiplication
- Rules for matrix multiplication including that matrices must be conformable
- The transpose of a matrix which is obtained by interchanging rows and columns
- Properties of transposed matrices including (A+B)T = AT + BT and (AB)T = BTAT
This document defines and describes different types of matrices including:
- Upper and lower triangular matrices
- Determinants which are scalars obtained from products of matrix elements according to constraints
- Band matrices which are sparse matrices with nonzero elements confined to diagonals
- Transpose matrices which exchange the rows and columns of a matrix
- Inverse matrices which when multiplied by the original matrix produce the identity matrix
The document discusses the rules for matrix multiplication. It states that two matrices can only be multiplied if the number of columns of the first matrix is equal to the number of rows of the second matrix. It provides examples of multiplying different matrices and explains how to calculate each element of the resulting matrix by taking the dot product of the corresponding row and column. It also gives an example of using matrix multiplication to calculate total sales and revenue from sales data organized in matrices.
This document defines and provides examples of different types of matrices:
- Matrices are arrangements of elements in rows and columns represented by symbols.
- Types include row matrices, column matrices, square matrices, null matrices, identity matrices, diagonal matrices, scalar matrices, triangular matrices, transpose matrices, symmetric matrices, skew matrices, equal matrices, and algebraic matrices.
- Algebraic matrix operations include addition, subtraction, and multiplication where the matrices must be of the same order.
This document discusses different types of matrices including diagonal, scalar, identity, triangular, and complex matrices. It provides definitions and examples of each. Operations on matrices such as addition, subtraction, and multiplication are covered. Matrix multiplication is demonstrated using examples. Properties of matrix operations like addition and subtraction being commutative vs anti-commutative are explained.
The document describes the Gauss-Jordan method for finding the inverse of a matrix. It involves 3 steps: 1) Writing the original matrix next to an identity matrix of the same size. 2) Performing row operations on both matrices to transform the original matrix into the identity matrix. 3) The resulting matrix next to the identity matrix is then the inverse of the original matrix. An example is shown applying the method to find the inverse of a 3x3 matrix through a series of row operations.
This document provides information about determinants of square matrices:
- It defines the determinant of a matrix as a scalar value associated with the matrix. Determinants are computed using minors and cofactors.
- Properties of determinants are described, such as how determinants change with row/column operations or identical rows/columns.
- Examples are provided to demonstrate computing determinants by expanding along rows or columns and using cofactors and minors.
- Applications of determinants include finding the area of triangles and solving systems of linear equations.
This document discusses sequences and their limits. Some key points:
- A sequence is a list of numbers written in a definite order. It can be thought of as a function with domain the positive integers.
- The limit of a sequence is defined as the number L such that the terms of the sequence can be made arbitrarily close to L by choosing a sufficiently large term.
- A sequence converges if it has a finite limit, and diverges if its terms approach infinity. Bounded monotonic sequences are guaranteed to converge.
- Properties of sequence limits parallel those of limits of functions, including laws of limits and the ability to pass limits inside continuous functions.
A matrix is a rectangular array of numbers, symbols, or expressions arranged in rows and columns. It is commonly written with elements separated by commas and enclosed in brackets. A matrix can be classified based on its dimensions as row, column, square, or rectangular. Basic matrix operations include addition, subtraction, scalar multiplication, and multiplication of two matrices. Matrix multiplication is only defined when the inner dimensions are equal.
Matrix and its applications by mohammad imranMohammad Imran
Ìý
This document provides an overview of matrix mathematics concepts. It discusses how matrices are useful in engineering calculations for storing values, solving systems of equations, and coordinate transformations. The outline then reviews properties of matrices and covers various matrix operations like addition, multiplication, and transposition. It also defines different types of matrices and discusses determining the rank, inverse, eigenvalues and eigenvectors of matrices. Key matrix algebra topics like solving systems of equations and putting matrices in normal form are summarized.
This document provides definitions and explanations of various mathematical concepts. It defines linear and simultaneous equations, quadratic equations, matrices including types of matrices. It also discusses sequences and series, percentages, discounts, commission, and interest. Key terms are defined such as determinant, properties of addition and multiplication for matrices, and formulas for arithmetic and geometric progressions.
Brief review on matrix Algebra for mathematical economicsfelekephiliphos3
Ìý
This document provides an overview of matrix algebra concepts including:
- Definitions of matrices, vectors, scalars, and different types of matrices like identity, symmetric, diagonal, and triangular matrices
- Operations that can be performed on matrices like addition, multiplication, and determining the determinant
- Calculating minors and cofactors of a matrix elements and using them to find the adjoint and inverse of a matrix
- The process of transposing a matrix by swapping its rows and columns
This document provides an overview of matrices for a 12th grade math project. It defines what a matrix is and discusses different types of matrices including column matrices, row matrices, square matrices, diagonal matrices, scalar matrices, identity matrices, and zero matrices. It also covers matrix operations like addition, subtraction, and multiplication. Other topics include the transpose of a matrix, symmetric and skew-symmetric matrices, and invertible matrices. Elementary row operations on matrices are also introduced. Examples are provided to illustrate key matrix concepts and operations.
This document defines matrices and determinants, including examples and types of matrices. It describes how to add, subtract, and multiply matrices, and defines determinants and Cramer's rule. Cramer's rule is used to solve a 3x3 system of equations. The relationship between matrices and determinants is that determinants are uniquely related to square matrices but not vice versa, and determinants are used to calculate inverses.
This document defines and describes different types of matrices. It explains row matrices, column matrices, square matrices, transpose matrices, scalar matrices, diagonal matrices, singular matrices, non-singular matrices, zero matrices, identity matrices, and sub-matrices. Properties of matrices discussed include equality of matrices and determinants. Examples are provided to illustrate each matrix type.
Matrices are sets of numbers or expressions arranged in rows and columns. A matrix is defined by its order, or the number of rows and columns it contains. There are several types of matrices including square, zero, identity, and triangular matrices. Operations on matrices include finding the transpose, determinant, and reducing a matrix to row echelon form. Determinants are values that can be calculated for square matrices and have various properties when operating on matrices.
Matrix algebra deals with vector spaces between different dimensions. It involves operations like addition, subtraction, and multiplication on matrices. A matrix is an array of numbers arranged in rows and columns. The size of a matrix is described by the number of rows and columns. Common matrix operations include addition, subtraction, multiplication, transpose, scalar multiplication, and determining if matrices are equal.
This document provides an overview of matrices and determinants. It begins by defining a matrix and listing its key properties. It then describes 9 different types of matrices including square, diagonal, identity, and triangular matrices. The document outlines how to perform addition, subtraction, and multiplication of matrices. It also covers transposing matrices and calculating determinants. Finally, it discusses minors, cofactors, adjoints, inverses, and using Cramer's rule to solve systems of linear equations. Worked examples and practice problems are provided throughout.
This document provides an outline for a course on linear algebra. It covers topics such as matrices, vectors, determinants, systems of linear equations, eigenvalues and eigenvectors, and applications of linear algebra in economics. The key topics include definitions of matrices and vectors, addition and multiplication rules, inverses, Cramer's rule, Gaussian elimination, and using linear algebra to represent input-output models. It aims to introduce foundational concepts and techniques in linear algebra and illustrate how they can be applied, particularly in economics.
This document defines and provides examples of different types of matrices. It explains that a matrix is an arrangement of elements in rows and columns represented by symbols like parentheses and brackets. It then lists 13 types of matrices including row, column, square, null, identity, diagonal, scalar, triangular, transpose, symmetric, skew, equal, and algebraic matrices. Algebraic matrices can be added, subtracted, or multiplied following specific rules based on the matrices' orders and corresponding elements.
1) A matrix is a rectangular array of numbers arranged in rows and columns. The dimensions of a matrix are specified by the number of rows and columns.
2) The inverse of a square matrix A exists if and only if the determinant of A is not equal to 0. The inverse of A, denoted A^-1, is the matrix that satisfies AA^-1 = A^-1A = I, where I is the identity matrix.
3) For two matrices A and B to be inverses, their product must result in the identity matrix regardless of order, i.e. AB = BA = I. This shows that one matrix undoes the effect of the other.
The document provides an introduction to matrix operations including addition, subtraction, multiplication, and determinants. It defines what a matrix is and how they are represented and sized. It then explains how to perform addition and subtraction of matrices by adding or subtracting the corresponding entries. Matrix multiplication is defined as being possible only when the number of columns of the first matrix equals the rows of the second. Determinants are explained as being unique to square matrices, and formulas are given for finding the determinants of 2x2 and 3x3 matrices. Homework problems are assigned involving computing various matrix operations.
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APM event hosted by the South Wales and West of England Network (SWWE Network)
Speaker: Aalok Sonawala
The SWWE Regional Network were very pleased to welcome Aalok Sonawala, Head of PMO, National Programmes, Rider Levett Bucknall on 26 February, to BAWA for our first face to face event of 2025. Aalok is a member of APM’s Thames Valley Regional Network and also speaks to members of APM’s PMO Interest Network, which aims to facilitate collaboration and learning, offer unbiased advice and guidance.
Tonight, Aalok planned to discuss the importance of a PMO within project-based organisations, the different types of PMO and their key elements, PMO governance and centres of excellence.
PMO’s within an organisation can be centralised, hub and spoke with a central PMO with satellite PMOs globally, or embedded within projects. The appropriate structure will be determined by the specific business needs of the organisation. The PMO sits above PM delivery and the supply chain delivery teams.
For further information about the event please click here.
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2. • In 1985 Arthur caylay presented the system of matrices called Theory of matrices.
• It was the latest way to solve the systems of linear equations.
• For example x +2y=0
3x+4y=0
• As we know in equation system we deal with coefficients of variables to solve the problems, so Arthur
caylay plot these coefficients in this form.
• We denote a Matrix with capital letters A, B, C and so on.
• The numbers in a matrix is called its entries or elements.
• We define matrix as A collection of numbers in a rectangular array is called Matrix.
• We enclose the elements in [ ] Square brackets or in ( ) parenthesis.
• The elements are arranged in rows and columns. (1 2) is in Row 1 denoted by R1 and (3 4) are in R2.
While 1 and 2 are in Column form denoted by C1 and C2
3 4
• Or we can say that when we write numbers in a horizontal way this is called the row of the matrix and
when we write the numbers in vertical way we called it as columns of the matrix.
• We may have many rows and columns in a matrix.
3. Order of Matrix
• Order of matrix means how many rows and columns are their in a matrix.
• In order of matrix total number of rows are denoted by m and total number of columns
are denoted by n.
• We can say that order of a matrix is no of rows by no of columns such that m x n
(read as m-by-n).
• For example we have a matrix
• Now look at the matrix
a11 means 1st row and 1st column
a12 means 1st row and 2nd column
a13 means 1st row and 3rd column up to a1n means 1st row and columns
Similarly a21 means 2nd row and 1st column
a22 means 2nd row and 2nd column and a23 means 2nd row and 3rd
column up to a2n means 2nd row and n columns.
• We usually denote each element in matrix by amn means a number
Say 5 in 1st row m and 2nd column n.
• The order of matrix help us to perform the mathematical operations.
4. Types of Matrices
Equal Matrices
• Two matrices or more are said to be equal if their corresponding elements and order are the same. Such
that we have matrix A of order 2 x 2 and matrix B 2 x 2
• For example
• So for equal
Matrices we must
have 1. same order
2. same corresponding elements.
• We use equal matrices to find out the unknown variables by comparing
corresponding elements.
5. Row and Column Matrices
• A matrix with only one row such that by order 1 x n is said to be row matrix.
• In row matrix we have no concern with the number of columns n, we only look at the no of row which
should be one called row matrix.
• Fro example
Which have one row and 3 columns such that 1 x 3
• A matrix with only one column such that by order m x 1 is said to be column matrix.
• In column matrix we have no concern with the number of roes m, we only look at the no of column which
should be one called column matrix
• For example
• It is possible a matrix may be at the same be row and column matrix that is 1 x 1 for example [7].
6. Square, Rectangular and Zero Matrices
• A matrix is said to be square matrix if m = n means no of rows should be equal to number of columns.
• For example
Which have 2 rows
And 2 columns
• A matrix is said to be rectangular matrix if m ≠n means no of rows should not be equal to number of
columns.
• For example
Which have 2 rows
And 3 columns
• A matrix is said to be zero or null matrix whose have elements are zero 0. Irrespective of order of matrix.
• For example
• Remember 0 is a real number which is called the additive identity because whenever we add 0 with any
number it gives the same number as a answer. So we can say a matrix also contain zero matrix.
• A zero matrix may be row, column, square or rectangular matrix.
• It is denoted by o.
• It is used to find out the additive inverse .
7. Diagonal and Scalar Matrices
• For diagonal matrix the following conditions should be there.
1. It should be square matrix
2. It should contains at least 1 non zero element in its diagonal.
3. While other elements should be zero 0.
• A straight line inside a shape that goes from one corner to another is called diagonal.
• For example the line joining A to B is called diagonal
• Diagonal matrix in which its diagonal elements have at least 1 non zero element and other are zero
elements.
• A diagonal matrix which have the same diagonal and non zero elements called scalar matrix.
8. Identity Matrix
• A scalar matrix which have 1 in its diagonal.
• It is denoted by I.
• It is called identity matrix because when we
Multiply this with any matrix it gives the same matrix, the other matrix did not lost his identity.
• Identity matrix is always a square matrix.
9. Addition and Subtraction of Matrices
• Two matrices can be added if matrix A and B have the same order..
• The entries or elements are added with their corresponding entries.
• Two matrices can be subtracted if matrix A and B have the same order ..
• The entries or elements are subtracted with their corresponding entries.
• In subtraction matrices A – B ≠B – A .
10. Transpose and Negative of Matrix
• Let A be a matrix of order 2-by-3, then transpose of a matrix means interchanging rows by columns and
columns by rows.
• For example
• It is denoted by At
• For a matrix of order
Say 2 by 3 then its
transpose will change the
order by 3 by 2.
• Remember all real numbers have negative called additive inverse i.e 7 has -7. Similarly in matrices we
also have negative matrix.
11. Multiplication of Matrices
Scalar multiplication
• Two or more matrices can be multiplied if no of columns in first matrix equals to no of rows in second matrix.
Such that (no of columns in first matrix) n = m (no of rows in second matrix)
• The 1st row is multiplied by 1st column of other
matrix, then same row is multiplied by 2nd column of
other matrix and their product are added.
• The product we get from multiplication must have
the order of no of rows of 1st matrix and no of columns
of 2nd matrix. From their product we get matrix of order
2 by 2
• Scalar multiplication is the multiplication with the
real number whether positive or negative.