Linear Algebra: Rank of the Matrix (M1.3)

 Echelon form means that the matrix is in one of two states:

• Row echelon form.
• Reduced row echelon form

A matrix is in row echelon form if it meets the following requirements:

• The first non-zero number from the left (the “leading coefficient“) is always to the right of the first non-zero number in the row above.
• Rows consisting of all zeros are at the bottom of the matrix.

  

REF

https://www.statisticshowto.com/matrices-and-matrix-algebra/reduced-row-echelon-form-2/ 

Linear Algebra: Matrix Inverse (M1.2)

 

 

 

 

Linear Algebra: Matrix operations (M1.1)

Matrix operations mainly involve three algebraic operations which are addition of matrices, subtraction of matrices, and multiplication of matrices. Matrix is a rectangular array of numbers or expressions arranged in rows and columns.

As far as linear algebra is concerned, the two most important operations with vectors are vector addition [adding two (or more) vectors] and scalar multiplication (multiplying a vector by a scalar).

Operations

Addition, subtraction and multiplication are the basic operations on the matrix. To add or subtract matrices, these must be of identical order and for multiplication, the number of columns in the first matrix equals the number of rows in the second matrix.

• Addition of Matrices
• Subtraction of Matrices
• Scalar Multiplication of Matrices
• Multiplication of Matrices

Matrix Addition

If A[a_ij]_mxn and B[b_ij]_mxn are two matrices of the same order then their sum A + B is a matrix, and each element of that matrix is the sum of the corresponding elements. 

i.e. A + B = [a_ij + b_ij]_mxn

 For the Example:

Properties of Matrix Addition: If a, B and C are matrices of same order, then

(a) Commutative Law: A + B = B + A

(b) Associative Law:  (A + B) + C = A + (B + C)

(c) Identity of the Matrix: A + O =  O + A = A, where O is zero matrix which is additive identity of the matrix,

(d) Additive Inverse: A + (-A) = 0 = (-A) + A, where (-A) is obtained by changing the sign of every element of A which is additive inverse of the matrix,

(e) $\left. \begin{matrix} A+B=A+C \\ B+A=C+A \\ \end{matrix} \right\}\Rightarrow B=C$

(f) $tr\left( A\pm B \right)=tr\left( A \right)\pm tr\left( B \right)$

(g) If A + B = 0 = B + A, then B is called additive inverse of A and also A is called the additive inverse of A.

Subtraction of Matrices

If A and B are two matrices of the same order, then we define $A-B=A+\left( -B \right).$

We can subtract the matrices by subtracting each element of one matrix from the corresponding element of the second matrix. i.e. A – B = [a_ij – b_ij]_mxn

Scalar Multiplication of Matrices

If $A={{\left[ {{a}_{ij}} \right]}_{m\times n}}$ is a matrix and k any number, then the matrix which is obtained by multiplying the elements of A by k is called the scalar multiplication of A by k and it is denoted by k A thus if $A={{\left[ {{a}_{ij}} \right]}_{m\times n}}$

Then $k{{A}_{m\,\times n}}={{A}_{m\,\times \,n}}k=\left[ k{{a}_{i\times j}} \right]$

Properties of Scalar Multiplication: If A, B are matrices of the same order and λ and μ are any two scalars then;

(a) $\lambda \left( A+B \right)=\lambda A+\lambda B$

(b) $\left( \lambda +\mu \right)A=\lambda A+\mu A$

(c) $\lambda \left( \mu A \right)=\left( \lambda \,\mu A \right)=\mu \left( \lambda A \right)$

(d) $\left( -\lambda A \right)=-\left( \lambda A \right)=\lambda \left( -A \right)$

(e) $tr\left( kA \right)=k\,\,tr\,\,\left( A \right)$

 

Multiplication of Matrices

If A and B be any two matrices, then their product AB will be defined only when the number of columns in A is equal to the number of rows in B.

The first step in defining matrix multiplication is to recall the definition of the dot product of two vectors. Let r and c be two n‐vectors. Writing r as a 1 x n row matrix and c as an n x 1 column matrix, the dot product of r and c is

​

Note that in order for the dot product of r and c to be defined, both must contain the same number of entries. Also, the order in which these matrices are written in this product is important here: The row vector comes first, the column vector second. 

Properties of matrix multiplication

(a) Matrix multiplication is not commutative in general, i.e. in general $AB\ne BA.$

(b) Matrix multiplication is associative, i.e. (AB)C = A(BC).

(c) Matrix multiplication is distributive over matrix addition, i.e. A.(B + C) = A.B + A.C and (A + B)C = AC + BC.

(d) If A is an m × n matrix, then ${{I}_{m}}A=A=A{{I}_{n}}.$

(e) The product of two matrices can be a null matrix while neither of them is null, i.e. if AB = 0, it is not necessary that either A = 0 or B = 0.

(f) If A is an m × n matrix and O is a null matrix then ${{A}_{m\,\times n}}.{{O}_{n\,\times p}}={{O}_{m\,\times p}}.$ i.e. the product of the matrix with a null matrix is always a null matrix.

(g) If AB = 0 (It does not mean that A = 0 or B = 0, again the product of two non-zero matrices may be a zero matrix).

(h) If AB = AC , B ≠ C (Cancellation Law is not applicable).

(i) $tr\left( AB \right)=tr\left( BA \right).$

j) There exist a multiplicative identity for every square matrix such AI = IA = A

Matrix Transpose

If A is m × n, the transpose of A is the n × m matrix, denoted by A^T , whose columns are formed from the corresponding rows of A.

  

A square matrix has the same number of rows and columns. An identity matrix is a square matrix with ones on the diagonal from upper left to lower right and zeros elsewhere. For example:

   I = 
       1 0 0 
       0 1 0
       0 0 1

Such a matrix is often denoted I.

 

REF

https://www.cliffsnotes.com/study-guides/algebra/linear-algebra/matrix-algebra/operations-with-matrices 

https://byjus.com/jee/matrix-operations/