The basic mathematical operations like addition, subtraction, multiplication and division can be done on matrices. i.e., (AT) ij = A ji ∀ i,j. Subsection MMEE Matrix Multiplication, Entry-by-Entry. Multiplicative identity: For a square matrix A AI = IA = A where I is the identity matrix of the same order as A. Let’s look at them in detail We used these matrices If $$A$$ is an $$m\times p$$ matrix, $$B$$ is a $$p \times q$$ matrix, and $$C$$ is a $$q \times n$$ matrix, then $A(BC) = (AB)C.$ This important property makes simplification of many matrix expressions possible. 19 (2) We can have A 2 = 0 even though A ≠ 0. Example. A matrix consisting of only zero elements is called a zero matrix or null matrix. Multiplicative Identity: For every square matrix A, there exists an identity matrix of the same order such that IA = AI =A. The first element of row one is occupied by the number 1 … $$\begin{pmatrix} e & f \\ g & h \end{pmatrix} \cdot \begin{pmatrix} a & b \\ c & d \end{pmatrix} = \begin{pmatrix} ae + cf & be + df \\ ag + ch & bg + dh \end{pmatrix}$$ For the A above, we have A 2 = 0 1 0 0 0 1 0 0 = 0 0 0 0. (3) We can write linear systems of equations as matrix equations AX = B, where A is the m × n matrix of coefficients, X is the n × 1 column matrix of unknowns, and B is the m × 1 column matrix of constants. The following are other important properties of matrix multiplication. Matrix transpose AT = 15 33 52 −21 A = 135−2 532 1 Example Transpose operation can be viewed as ﬂipping entries about the diagonal. The number of rows and columns of a matrix are known as its dimensions, which is given by m x n where m and n represent the number of rows and columns respectively. Even though matrix multiplication is not commutative, it is associative in the following sense. The proof of Equation \ref{matrixproperties2} follows the same pattern and is … Selecting row 1 of this matrix will simplify the process because it contains a zero. MATRIX MULTIPLICATION. The last property is a consequence of Property 3 and the fact that matrix multiplication is associative; A diagonal matrix is called the identity matrix if the elements on its main diagonal are all equal to $$1.$$ (All other elements are zero). In the next subsection, we will state and prove the relevant theorems. Equality of matrices Notice that these properties hold only when the size of matrices are such that the products are defined. Distributive law: A (B + C) = AB + AC (A + B) C = AC + BC 5. While certain “natural” properties of multiplication do not hold, many more do. Deﬁnition The transpose of an m x n matrix A is the n x m matrix AT obtained by interchanging rows and columns of A, Deﬁnition A square matrix A is symmetric if AT = A. For sums we have. Given the matrix D we select any row or column. A square matrix is called diagonal if all its elements outside the main diagonal are equal to zero. Properties of transpose The determinant of a 4×4 matrix can be calculated by finding the determinants of a group of submatrices. The proof of this lemma is pretty obvious: The ith row of AT is clearly the ith column of A, but viewed as a row, etc. Example 1: Verify the associative property of matrix multiplication … But first, we need a theorem that provides an alternate means of multiplying two matrices. Associative law: (AB) C = A (BC) 4. Zero matrix on multiplication If AB = O, then A ≠ O, B ≠ O is possible 3. Proof of Properties: 1. proof of properties of trace of a matrix. Let us check linearity. A matrix is an array of numbers arranged in the form of rows and columns. : ( AB ) C = A ( B + C ) = AB + (... 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