Proving AI_n = A in Linear Algebra Class: Understanding the Final Step

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In summary, the proof for AI_n = A uses the following equation: AI_n = (a_{st}) + (a_{st}-\delta_{st}) where ## 1 \leq s \leq m ## and ## 1 \leq t \leq n ## most of ## \delta_{st} = 0 ## is already known, and we use the definition of matrix multiplication to get the final equation.
  • #1
phospho
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I'm trying to prove AI_n = A for my linear algebra class:

## A = (a_{st}) ##
where ## 1 \leq s \leq m ## and ## 1 \leq t \leq n ##

then ## (AI_n)_{sk} = \displaystyle\sum_{t=1}^n a_{st}(I_n)_{tk} = \displaystyle\sum_{t=1}^n a_{st}\delta_{tk} ##

we know that most of ## \delta_{tk} = 0 ## from the definition,

therefore

##\displaystyle\sum_{t=1}^n a_{st}\delta_{tk} = a_{sk}\delta_{kk} + \displaystyle\sum_{t\not=k} a_{st}\delta_{tk} = a_{sk}(1) + 0 = a_{sk} ##
I don't understand the final bit of the proof, we start with defining A = a_{st}, and we end up with a_{sk}, how is this equivalent?
 
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  • #2
phospho said:
##\displaystyle\sum_{t=1}^n a_{st}\delta_{tk} = a_{sk}\delta_{kk} + \displaystyle\sum_{t\not=k} a_{st}\delta_{tk} = a_{sk}(1) + 0 = a_{sk} ##
I don't understand the final bit of the proof, we start with defining A = a_{st}, and we end up with a_{sk}, how is this equivalent?
Which part don't you understand?
  1. ##\sum_t a_{st}\delta_{tk} = a_{sk}\delta_{kk} + \sum_{t\ne k} a_{st}\delta_{tk}##
  2. ##a_{sk}\delta_{kk} + \sum_{t\ne k} a_{st}\delta_{tk} = a_{sk}(1) + 0##
  3. ##a_{sk}(1) + 0 = a_{sk}##
 
  • #3
D H said:
Which part don't you understand?
  1. ##\sum_t a_{st}\delta_{tk} = a_{sk}\delta_{kk} + \sum_{t\ne k} a_{st}\delta_{tk}##
  2. ##a_{sk}\delta_{kk} + \sum_{t\ne k} a_{st}\delta_{tk} = a_{sk}(1) + 0##
  3. ##a_{sk}(1) + 0 = a_{sk}##

the second one, i.e. how you get from 1. to ##a_{sk}\delta_{kk} ## how does the t change to a k? for a and delta, am I righ tin thinking it's because of the deifnition of matrix multiplication?
 
  • #4
The first step is just taking one specific term out of the sum, the term for which t=k. This results in ##\sum_t a_{st}\delta_{tk} = a_{sk}\delta_{kk} + \sum_{t\ne k} a_{st}\delta_{tk}##.

The second step merely replaces the value of the Kronecker delta with its value. The value of ##\delta_{kk}## in first term, ##a_{sk}\delta_{kk}##, is just one. The second term is the sum ##\sum_{t\ne k} a_{st}\delta_{tk}##, and here ##\delta_{tk}## is identically zero since ##\delta_{tk}=0## for all t ≠ k.
 
  • #6
anyone?
 

Related to Proving AI_n = A in Linear Algebra Class: Understanding the Final Step

1. What is the significance of proving AI_n = A in linear algebra class?

Proving AI_n = A is an important step in understanding the properties of matrices and their operations. It shows that the identity matrix (I) multiplied by any matrix (A) will result in the same matrix (A). This is a fundamental concept in linear algebra and is used in various applications such as computer graphics, physics, and engineering.

2. How do you prove AI_n = A in linear algebra class?

To prove AI_n = A, you need to use the properties of matrix multiplication. First, you need to show that the dimensions of I and A are compatible for multiplication (I has to have the same number of columns as A has rows). Then, you can use the definition of matrix multiplication to show that the resulting matrix is equal to A. It is important to show all the steps and explain each one clearly.

3. What is the difference between AI_n = A and IA_n = A in linear algebra class?

AI_n = A and IA_n = A may seem similar, but they are different in terms of the order of multiplication. In AI_n = A, the identity matrix is on the left side and A is on the right, while in IA_n = A, the identity matrix is on the right side and A is on the left. However, both equations ultimately lead to the same result, which is A.

4. Why is proving AI_n = A considered the final step in linear algebra class?

Proving AI_n = A is considered the final step because it ties together many important concepts and properties in linear algebra, such as matrix multiplication, identity matrices, and the properties of the identity matrix. It also serves as a building block for more complex operations and applications of matrices.

5. Can AI_n = A be applied to matrices of any size in linear algebra class?

Yes, AI_n = A can be applied to matrices of any size as long as the dimensions are compatible for multiplication. This means that the number of columns in the identity matrix must be equal to the number of rows in matrix A. This concept holds true for matrices of any size, from 2x2 to 100x100 or even larger.

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