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**8**Comments December 16, 2009

# MIT Linear Algebra, Lecture 3: Matrix Multiplication and Inverse Matrices

This is the third post in an article series about MIT's course "Linear Algebra". In this post I will review lecture three on five ways to **multiply matrices**, **inverse matrices** and an algorithm for finding inverse matrices called **Gauss-Jordan elimination**.

The first lecture covered the geometry of linear equations and the second lecture covered the matrix elimination.

Here is lecture three.

## Lecture 3: Matrix Multiplication and Inverse Matrices

Lecture three starts with **five ways to multiply matrices**.

The first way is the classical way. Suppose we are given a matrix **A** of size mxn with elements a_{ij} and a matrix **B** of size nxp with elements b_{jk}, and we want to find the product **A**·**B**. Multiplying matrices **A** and **B** will produce matrix **C** of size mxp with elements .

Here is how this sum works. To find the first element c_{11} of matrix **C**, we sum over the 1st row of **A** and the 1st column of **B**. The sum expands to c_{11} = a_{11}·b_{11} + a_{12}·b_{21} + a_{13}·b_{31} + ... + a_{1n}·b_{n1}. Here is a visualization of the summation:

We continue this way until we find all the elements of matrix **C**. Here is another visualization of finding c_{23}:

The second way is to take each column in **B**, multiply it by the whole matrix **A** and put the resulting column in the matrix **C**. The columns of **C** are combinations of columns of **A**. (Remember from previous lecture that a matrix times a column is a column.)

For example, to get column 1 of matrix **C**, we multiply **A**·(column 1 of matrix **B**):

The third way is to take each row in **A**, multiply it by the whole matrix **B** and put the resulting row in the matrix **C**. The rows of **C** are combinations of rows of **B**. (Again, remember from previous lecture that a row times a matrix is a row.)

For example, to get row 1 of matrix **C**, we multiply row 1 of matrix **A** with the whole matrix **B**:

The fourth way is to look at the product of **A**·**B** as a sum of (columns of **A**) times (rows of **B**).

Here is an example:

The fifth way is to chop matrices in blocks and multiply blocks by any of the previous methods.

Here is an example. Matrix **A** gets subdivided in four submatrices A_{1} A_{2} A_{3} A_{4}, matrix **B** gets divided in four submatrices B_{1} B_{2} B_{3} B_{4} and the blocks get treated like simple matrix elements.

Here is the visualization:

Element **C**_{1}, for example, is obtained by multiplying **A**_{1}·**B**_{1} + **A**_{2}·**B**_{3}.

Next the lecture proceeds to finding the **inverse matrices**. An inverse of a matrix **A** is another matrix, such that **A ^{-1}**·

**A**=

**I**, where I is the identity matrix. In fact if

**A**is the inverse matrix of a square matrix

^{-1}**A**, then it's both the left-inverse and the right inverse, i.e.,

**A**·

^{-1}**A**=

**A**·

**A**=

^{-1}**I**.

If a matrix **A** has an inverse then it is said to be **invertible** or **non-singular**.

Matrix A is singular if we can find a non-zero vector **x** such that A·**x** = **0**. The proof is easy. Suppose A is not singular, i.e., there exists matrix A^{-1}. Then A^{-1}·A·**x** = **0**·A^{-1}, which leads to a false statement that **x** = **0**. Therefore A must be singular.

Another way of saying that matrix **A** is singular is to say that columns of matrix **A** are linearly dependent (one ore more columns can be expressed as a linear combination of others).

Finally, the lecture shows a deterministic method for finding the inverse matrix. This method is called the **Gauss-Jordan elimination**. In short, Gauss-Jordan elimination transforms augmented matrix (**A**|**I**) into (**I**|**A**^{-1}) by using only row eliminations.

Please watch the lecture to find out how it works in all the details:

Direct link: http://www.youtube.com/watch?v=FX4C-JpTFgY

Topics covered in lecture three:

- [00:51] The first way to multiply matrices.
- [04:50] When are we allowed to multiply matrices?
- [06:45] The second way to multiply matrices.
- [10:10] The third way to multiply matrices.
- [12:30] What is the result of multiplying a column of A and a row of B?
- [15:30] The fourth way to multiply matrices.
- [18:35] The fifth way to multiply matrices by blocks.
- [21:30] Inverses for square matrices.
- [24:55] Singular matrices (no inverse matrix exists).
- [30:00] Why singular matrices can't have inverse?
- [36:20] Gauss-Jordan elimination.
- [41:20] Gauss-Jordan idea A·I -> I·A
^{-1}.

Here are my notes of lecture three:

Have fun with this lecture! The next post is going to be about the A=LU matrix decomposition (also known as factorization).

PS. This course is taught from Introduction to Linear Algebra textbook. Get it here:

## Comments

For c_23 the result is 20, not 30.

Przemek, oops, thanks for catching that. :)

Hi Peter,

can you please consistently tag the series - would make it easier to find.

Max, okay, I will try to be more consistent!

>submatrices A11 A12 A21 A22

Should be A1, A2, A3, A4.

That was really interesting in university. It`s a pity that i can`t use this for my work and will forget it soon.

thx for this post.

@christopher: yes, it`s a pity...

Hi,

Thanks for the notes. I having hard time to understand 3rd way. row vector multiply by a matrix. Could you please give an example using 3x3 matrix? I have checked on internet but unable to find specific example for the same.

Br..

Tushar Goel

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