 # Chapter 2 Matrices Definition of a matrix.

## Presentation on theme: "Chapter 2 Matrices Definition of a matrix."— Presentation transcript:

Chapter 2 Matrices Definition of a matrix

A system of 3 equations: Represented by a matrix:

Types of Matrices Square matrix: # of rows = # of columns
upper triangular matrix strictly upper triangular matrix

lower triangular matrix strictly lower triangular matrix
diagonal matrix

banded matrix a square matrix with elements of zero except for the principal diagonal and values in the positions adjacent to the diagonal. tridiagonal matrix

unit matrix: 1 on the principal diagonal
null matrix: All elements are zero.

symmetric matrix: a square matrix in which skew-symmetric matrix: a square matrix in which for all i and j

transpose of matrix A: AT
(AT) T = A

Matrix Operations Matrix equality Matrix addition and subtraction
C = A + B = B + A (commutative) C = A - B

Matrix Multiplication
One example

Rules of Matrix Multiplication
# of columns in A = # of rows in B # of rows in C = # of rows in A # of columns in C = # of columns in B

5. Matrix multiplication is not commutative
6. Matrix multiplication is associative

Example: Matrix Multiplication

Matrix Multiplication by a Scalar
An example:

Matrix Inversion where A-1 is the inverse of A, and I is the unit matrix

Example: Matrix Inversion

Matrix Singularity If the inverse of a matrix A exists, then A is said to be nonsingular. If the inverse of a matrix A does not exist, then A is said to be singular. If matrix A is singular, then the linear system of simultaneous equations represented by A has no unique solution.

There are an infinite number of solutions if 2a = b.
There is no feasible solution if 2a  b. Thus matrix A is singular.

trace of a square matrix = sum of diagonal elements
matrix augmentation: addition of a column or columns to the initial matrix

matrix partition

Vectors Column vector Row vector Vectors of two ordinates

orthogonal vectors Two vectors are said to be orthogonal if their product is equal to zero. If two vector are orthogonal, they are perpendicular to each other in the n-dimensional space.

normalized vectors A vector is normalized by dividing each element by its length. A normalized vector has a length 1. Two vectors that are both normalized and orthogonal to each other are said to be orthonormal vectors.

Example: Vectors

Normalized vectors:

Determinants A determinant of a matrix A is denoted by |A|.
The determinant of a 22 matrix: The determinant of a 33 matrix:

The determinant of an nn matrix:
The minor of aij, denoted by Aij, is the matrix after removing row i and column j. The determinant of an nn matrix: The general expression for the determinant of an nn matrix:

Example: Matrix Determinant
with the first row and their minors:

with the second column and their minors:
Since |A|=0, A is a singular matrix; that is the inverse of A doest not exist.

Properties of Determinants
1. If the values in any row (column) are proportional to the corresponding values in another row(column), the determinant equals zero

2. If all the elements in any row(column) equal zero, the determinant equals zero.
3. If all the elements of any row(column) are multiplied by a constant c, the value of the determinant is multiplied by c.

4. The value of the determinant is not changed by adding any row (column) multiplied by a constant c to another row (column). 5. If any two rows (columns) are interchanged, the sign of the determinant is changed.

6. The determinant of a matrix equals that of its transpose; that is, |A| = |AT|.
7. If a matrix A is placed in diagonal form, then the product of the elements on the diagonal equals the determinant of A.

8. If a matrix A has a zero determinant, then A is a singular matrix; that is, the inverse of A does not exist.

Rank of A Matrix A matrix of r rows and c columns is said to be of order r by c. If it is a square matrix, r by r, then the matrix is of order r. The rank of a matrix equals the order of highest-order nonsingular submatrix.

Example 1: Rank of Matrix
3 square submatrices: Each of these has a determinant of 0, so the rank is less than 2. Thus the rank of R is 1.

Example 2: Rank of Matrix
Since |A|=0, the rank is not 3. The following submatrix has a nonzero determinant: Thus, the rank of A is 2.