BUSINESS MATHEMATICS -Matrices and Determinants

Matrices and Determinants: Definition,Types,Algebra of Matrices 

Properties of Determinants; Calculation of Values of Determinants upto Third Order, Ad-Joint of

a Matrix, Elementary Row or Column Operations; Inverse of a Matrix. Solution of a System of

Linear Equations having Unique Solution and Involving not More Than Three Variables.

Matrices and Determinants: A Comprehensive Guide

Introduction to Matrices

A matrix is a rectangular array of numbers, symbols, or expressions, organized into rows and columns. Matrices have various applications in mathematics, science, and engineering. The dimensions of a matrix are described as "m x n," where "m" is the number of rows, and "n" is the number of columns.

Types of Matrices

1. Row Matrix: 

 A matrix with a single row and multiple columns.

   - Example: [ 2 4 6 ]

     

2. Column Matrix: 

A matrix with a single column and multiple rows.

   - Example:

      | 3 |

     | 1 |

     | 5 |

    

3. Square Matrix: 

A matrix with an equal number of rows and columns (m x m).

   - Example:

 

     [ 4 2 ]

     [ 1 3 ]

4. Zero Matrix:

All elements are zeros.

   - Example:

     

     [ 0 0 0 ]

     [ 0 0 0 ]

5. Identity Matrix: 

A square matrix with ones on the main diagonal and zeros elsewhere.

   - Example:

     [ 1 0 0 ]

     [ 0 1 0 ]

     [ 0 0 1 ]

     

6. Diagonal Matrix:

A square matrix with nonzero elements only on the main diagonal.

   - Example:

  

     [ 2 0 0 ]

     [ 0 5 0 ]

     [ 0 0 3 ]

     

7. Scalar Matrix:

 A diagonal matrix where all diagonal elements are equal.

   - Example:

 

     [ 4 0 0 ]

     [ 0 4 0 ]

     [ 0 0 4 ]

 

8. Symmetric Matrix:

 A square matrix that is equal to its transpose.

   - Example:

     [ 1 2 3 ]

     [ 2 4 5 ]

     [ 3 5 6 ]

     

9. Skew-Symmetric Matrix:

A square matrix where the transpose is equal to its negative.

   - Example:

  

     [ 0 -2 3 ]

     [ 2 0 -4 ]

     [ -3 4 0 ]

     

10. Hermitian Matrix:

A square matrix that is equal to its conjugate transpose (for complex numbers).

    - Example:

      [ 2 -1 + 3i ]

      [ -1 5 0 ]

      [ 3i 0 -7 ]

      

11. Orthogonal Matrix:

A square matrix whose transpose is its inverse.

    - Example:

      [ 0.6 -0.8 ]

      [ 0.8 0.6 ]

12. Sparse Matrix:

A matrix in which most of the elements are zero.

    - Example:

      [ 0 0 0 ]

      [ 0 7 0 ]

      [ 0 0 0 ]

Algebra of Matrices

Matrices can be added, subtracted, and multiplied according to specific rules. Some fundamental operations include:

Addition and Subtraction

Two matrices can be added or subtracted if they have the same dimensions. The addition or subtraction is performed element-wise.

 Example (Matrix Addition):

 

   [ 2 4 6 ]       [ 1 1 1 ]       [ 3 5 7 ]

   [ 1 3 5 ]  +    [ 2 2 2 ]   =   [ 3 5 7 ]

Scalar Multiplication

A matrix can be multiplied by a scalar, which means multiplying each element of the matrix by the scalar value.

 Example (Scalar Multiplication):

   2  [ 1 2 3 ] = [ 2 4 6 ]

   

Matrix Multiplication

Matrix multiplication is a more complex operation. To multiply two matrices, the number of columns in the first matrix must equal the number of rows in the second matrix. The resulting matrix's dimensions are determined by the dimensions of the two multiplied matrices.

Example (Matrix Multiplication):

   [ 2 4 ]      [ 1 3 ]      [ 2*1+4*2 2*3+4*4 ]

   [ 1 3 ]  *   [ 2 4 ]  =   [ 1*1+3*2 1*3+3*4 ]

 Result:

   [ 10 16 ]

   [ 7 13 ]

Properties of Determinants

Determinants are scalar values associated with square matrices. They have several properties:

1. Determinant of the Identity Matrix:

The determinant of the identity matrix is 1.

2. Effect of Row Operations:

 Performing elementary row operations on a matrix does not change the determinant.

3. Transpose and Determinant:

The determinant of a matrix and its transpose are equal.

4. Determinant of a Product:

The determinant of a product of matrices is the product of their determinants.

5. Determinant of the Inverse:

The determinant of the inverse of a matrix is the reciprocal of the determinant of the original matrix if the matrix is invertible (non-singular).

 Calculation of Determinants

The determinant of a square matrix can be calculated using various methods, such as the expansion by minors (Laplace expansion) and the use of properties like row operations to simplify the matrix.

Example (Calculation of Determinant):

Consider a 3x3 matrix A:

   | 2 3 1 |

   | 4 1 3 |

   | 2 2 1 |

Using the expansion by minors:

   det(A) = 2 * det(A11) - 3 * det(A12) + 1 * det(A13)

Where A11, A12, and A13 are 2x2 submatrices obtained by removing the first row and the corresponding column from A.

Calculate the determinants of these submatrices and plug them into the formula to find det(A).

Adjoint of a Matrix

The adjoint of a square matrix, also known as the adjugate or classical adjoint, is a matrix whose determinant is a power of the determinant of the original matrix. It is useful for finding the inverse of a matrix.

 Example (Adjoint of a Matrix):

Consider a 3x3 matrix A:

   | 2 3 1 |

   | 4 1 3 |

   | 2 2 1 |

To find the adjoint of A, calculate the matrix of cofactors, which involves finding the determinants of 2x2 submatrices. The adjoint matrix will have these determinants arranged in a specific way.

 Elementary Row or Column Operations

Elementary row or column operations include:

1. Scaling: Multiplying a row (or column) by a nonzero scalar.

2. Row (or Column) Interchange: Exchanging two rows (or columns).

3. Row Addition (or Subtraction): Adding (or subtracting) a multiple of one row to another row.

These operations are used in various matrix-related calculations, such as finding the inverse of a matrix.

Inverse of a Matrix

The inverse of a square matrix is another matrix that, when multiplied by the original matrix, yields the identity matrix. Not all matrices have inverses, and a matrix must be non-singular (its determinant is non-zero) to have an inverse. The inverse of a matrix A is denoted as A^(-1).

 Example (Inverse of a Matrix):

Consider a 2x2 matrix A:

   

   | 3 1 |

   | 2 4 |

To find the inverse of A, calculate the determinant of A and use it to determine if the matrix is invertible. If the determinant is non-zero, find the adjoint of A, and then multiply it by 1/det(A) to obtain the inverse.

Solution of a System of Linear Equations

A system of linear equations can be represented in matrix form as Ax = b, where A is the coefficient matrix, x is the vector of variables, and b is the constant vector. If A is invertible, the solution is given by x = A^(-1) * b.

Unique Solution

A system of linear equations has a unique solution if and only if the determinant of the coefficient matrix A is non-zero. In this case, a unique set of values for the variables x can be determined.

Example (Solution of a System of Linear Equations):

Consider the following system of linear equations:

   2x + 3y = 7

   4x - y = 9

This system can be represented in matrix form as Ax = b, where:

   A = | 2 3 |

       | 4 -1 |

   x = | x |

       | y |

   b = | 7 |

       | 9 |

   

To find the solution, calculate the determinant of matrix A. If det(A) is non-zero, proceed to find the inverse of A. Then, multiply A^(-1) by the constant vector b to find the values of x and y.

In this case, if det(A) is non-zero, the system has a unique solution.

Involving not More Than Three Variables

The solution of a system of linear equations involving not more than three variables can be found using matrix techniques, including Cramer's rule for 2x2 and 3x3 systems and matrix inversion for larger systems.

Conclusion

Matrices and determinants are fundamental concepts in linear algebra and mathematics, with wide-ranging applications in various fields. They serve as valuable tools for solving systems of linear equations, analyzing data, and understanding transformations and operations. Understanding the properties and operations of matrices and determinants is essential for advanced mathematical and scientific endeavors.