# Linear Algebra/OLD/Matrix Operations

## Zero Matrix[edit]

A **zero matrix** is a matrix with all its entries being zero. An example of a zero matrix is

## Scalars[edit]

A scalar is a nonzero constant, which are used to scale the matrix.

If *r* is a scalar and *A* is matrix, then the **scalar multiple** *rA* is the matrix whose columns are *r* times the corresponding columns in *A*.

Here is an example,

A scalar is indirectly used when we subtract two matrices, because *-B* can be defined as (-1)*B*. This means when we subtract the matrix *B* from the matrix *A*, *A-B* is the same as *A*+(-1)*B*.

## Addition and subtraction[edit]

Two matrices can only be added or subtracted if they have the same size. Matrix addition and subtraction are done entry-wise, which means that each entry in *A*+*B* is the sum of the corresponding entries in *A* and *B*.

Here is an example of matrix addition

And an example of subtraction

Remember you can not add or subtract two matrices of different sizes.

The following rules applies to sums and scalar multiples of matrices.

Let *A*, *B*, and *C* be matrices of the same size, and let *r* and *s* be scalars.

*A*+*B*=*B*+*A*- (
*A*+*B*) +*C*=*A*+ (*B*+*C*) *A*+ 0 =*A**r*(*A*+*B*) =*rA*+*rB*- (
*r*+*s*)*A*=*rA*+*sA* *r*(*sA*) = (*rs*)*A*

## Matrix Multiplication[edit]

Matrix multiplication is slightly less intuitive for the beginning student of linear algebra than is scalar multiplication. It is, however, no more difficult.

**Definition**

If * A* is a

*1 by m*matrix and

*is an*

**B***m by 1*matrix, then the product

*is given as*

**AB**

This summation of terms can be expressed as a Riemann sum

We can use this knowledge to determine if matrix multiplication can occur. For example, a multiplied by a matrix will yield a matrix. If the number of columns of the first matrix is equal to the number of rows of the second matrix, multiplication can occur (as seen in the example mentioned).

Matrix multiplication is noncommutative, meaning *a * b* does not equal *b * a*. This is easy to see by looking at the example above.

## Powers[edit]

If *A* is an matrix and if *k* is a positive integer, then denotes the product of *k* copies of *A*

If *A* is nonzero and if **x** is in , then is the result of left-multiplying **x** by *A* repeatedly *k* times. If *k* = 0, then should be **x** itself. Thus is interpreted as the identity matrix.

## Transpose[edit]

Given the matrix *A*, the **transpose** of *A* is the , denoted , whose columns are formed from the corresponding rows of *A*.

For example

The following rules applied when working with transposing

- For any scalar
*r*,

The 4th rule can be generalize to products of more than two factors, as "The transpose of a product of matrices equals the product of their transposes in the *reverse* order." Meaning