# Matrix Multiplication

## Exploring Matrix Multiplication

Curiosity strikes Clair as she ponders the effects of rotating and scaling vectors on a graph. She wonders what would happen if a vector starting at the origin and ending at (1, 1) was rotated 90 degrees counterclockwise and scaled by a factor of 3. The traditional method of physically tracing and rotating or using a ruler to enlarge the vector can be tedious. Fortunately, there is a more efficient and simpler way to achieve this using matrix multiplication!

To represent the vector (0, 0) to (1, 1) on a graph, a 2x1 matrix is used, with the x-component in the first row and the y-component in the second row. Therefore, the matrix for this vector would be [1, 1]. To rotate the vector 90 degrees counterclockwise, it can be multiplied by the rotation matrix [0, -1; 1, 0]. Similarly, to scale the vector by 3, it can be multiplied by the scalar 3. In this article, we will delve into the mechanics of matrix multiplication and learn how to multiply a matrix by a scalar.

## Multiplying a Scalar with a Matrix

In scalar multiplication, each element of a matrix is multiplied by a scalar. For instance, if a vector needs to be scaled by 3, the matrix [1, 1] is multiplied by the scalar 3, resulting in [3, 3]. Generally, if a matrix A=[a, b; c, d] is multiplied by a scalar u, the product would be uA=[ua, ub; uc, ud].

Examples:

• If the scalar 4 is multiplied by the matrix [3, 6; 3, 4, 0], the resulting matrix would be [12, 24; 12, 16, 0].
• The product of 2A, where A=[1, 2, 3; 4, 9, 4; 5, 6, 7], would be [2, 4, 6; 8, 18, 8; 10, 12, 14].

## Multiplying Two Matrices

Unlike addition and subtraction, the order of matrices matters in matrix multiplication. The matrices do not necessarily have to be of the same order, but the number of columns in the first matrix must equal the number of rows in the second matrix. Otherwise, the multiplication cannot be performed.

To multiply two matrices A and B, where A has a dimension of m x n and B has a dimension of p x q, the compatibility condition is that n = p. The resulting matrix AB has a dimension of m x q. The elements of AB can be found by multiplying the elements in each row of A with the corresponding elements in each column of B, then adding the results.

Example: For matrices A=[1, 2, 4; 8, 6, 0; 3, 4, 9] and B=[3; 5; 7], the product BA can be found since the number of columns in B is equal to the number of rows in A. The resulting matrix would be 3. However, the product AB cannot be found as the number of columns in A is 3 and the number of rows in B is 1.

Let us consider two matrices A=[2, 4] and B=[5, 7].

Step 1: Perform the compatibility test.

The product AB can be found as the number of columns in A (2) is equal to the number of rows in B (2).

Step 2: Find the order of the product matrix.

The order of AB would be the number of rows in A (1) x the number of columns in B (1).

Step 3: Find the elements of the product matrix.

AB=[23]=6.

This is why the compatibility condition is crucial. To find the product AB, the elements in the first row of A (2, 4) are multiplied by the corresponding elements in the first column of B (5), resulting in 6.

In conclusion, understanding matrix multiplication is crucial for performing operations such as rotation and scaling on vectors. It is also important to note that the order of matrices matters in multiplication and the compatibility condition must be met for it to be possible. With this understanding, Clair can confidently perform transformations on vectors using matrix multiplication.

## The Significance of Understanding the Order of Product Matrices

Matrix multiplication is a fundamental mathematical operation, and understanding the order of product matrices is crucial for obtaining correct results. This article provides a step-by-step explanation on finding the order of the product matrix, determining its elements, and a key takeaway on the importance of matrix multiplication.

## Finding the Order of the Product Matrix

To find the order of the product matrix, we simply multiply the number of rows in the first matrix with the number of columns in the second matrix. This gives us a clear understanding of the resulting matrix's dimensions.

## Determining the Elements of the Product Matrix

The elements of the product matrix are found by multiplying corresponding elements in the rows of the first matrix with the columns of the second matrix. We then add up the products to get the final element in the product matrix. For example, let us consider two matrices A = abcd and B = efgh. The resulting matrix AB can be obtained by multiplying (a x e) + (b x f) (a x g) + (b x h) (c x e) + (d x g) (c x f) + (d x h).

This shows the importance of understanding the compatibility between matrices – in this case, as A has 2 columns and B has 2 rows, we can perform multiplication. Further, the order of AB will be 2x2 since it has 2 rows and 2 columns.

## Matrix Multiplication – Key Takeaways

There are a few key points to keep in mind when it comes to matrix multiplication:

• To multiply a scalar with a matrix, we simply multiply every element in the matrix with the scalar.
• When multiplying two matrices A and B, we must first perform a compatibility test, then determine the order of the resulting matrix by multiplying the number of rows in A with the number of columns in B, and finally, find the elements by adding up the products of corresponding elements in the rows and columns.
• The order in which matrices are multiplied matters – AB may not always be equal to BA.

## Example: Finding the Product Matrix AB

Let's take an example to illustrate the process of finding the product matrix. Given A = 20131440-2 and B = 1230-214-10, we can perform matrix multiplication as A has 3 columns and B has 3 rows. This gives us a resulting matrix AB with an order of 3x3. We can then determine the elements of AB by multiplying the first row of A with the second column of B, resulting in AB = 182930-21-138.

Matrix multiplication is a powerful tool for representing geometric transformations and obtaining accurate results efficiently. It is essential to understand the order of the resulting matrix to ensure correct calculations and ultimately, solve complex problems with ease.