Proof of ##M^n## (matrix multiplication problem)

In summary: In other words, the order in the proof does not matter.Homework Statement: Please see belowRelevant Equations: Please see below
  • #1
member 731016
Homework Statement
Please see below
Relevant Equations
Please see below
For,
1683666942440.png

Does anybody please know why they did not change the order in the second line of the proof? For example, why did they not rearrange the order to be ##M^n = (DP^{-1}P)(DP^{-1}P)(DP^{-1}P)(DP^{-1}P)---(DP^{-1}P)## for to get ##M^n = (DI)(DI)(DI)(DI)---(DI) = D^n##

Many thanks!
 
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  • #2
ChiralSuperfields said:
Homework Statement: Please see below
Relevant Equations: Please see below

For,
View attachment 326261
Does anybody please know why they did not change the order in the second line of the proof? For example, why did they not rearrange the order to be ##M^n = (DP^{-1}P)(DP^{-1}P)(DP^{-1}P)(DP^{-1}P)---(DP^{-1}P)## for to get ##M^n = (DI)(DI)(DI)(DI)---(DI) = D^n##

Many thanks!
They did exactly what you proposed only with one more step that shows how the associativity law is necessary here. We first need ##(AB)B^{-1}=A(BB^{-1})=AI=A## before we are allowed to write ##A##. It is important to recognize which laws of matrix multiplication are actually used. Here it's the law of associativity ##(AB)B^{-1}=A(BB^{-1})##, the definition of an inverse ##BB^{-1}=I## and the definition of the neutral element ##AI=A.## So every single property of matrix multiplication, except for the distributive law since there is no addition here, has actually been used.
 
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  • #3
ChiralSuperfields said:
Homework Statement: Please see below
Relevant Equations: Please see below

For,
View attachment 326261
Does anybody please know why they did not change the order in the second line of the proof? For example, why did they not rearrange the order to be ##M^n = (DP^{-1}P)(DP^{-1}P)(DP^{-1}P)(DP^{-1}P)---(DP^{-1}P)## for to get ##M^n = (DI)(DI)(DI)(DI)---(DI) = D^n##

Many thanks!
Because that would not be correct. Matrix multiplication is not commutative, and it is definitely not true that a matrix, ##M^n## that is probably non-diagonal, is equal to the diagonal matrix ##D^n##.
 
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  • #4
@FactChecker has a good point here. I didn't see that you switched the order of ##P## and ##D.##

The only matrices that can be swapped are the diagonal matrices with the same value on the entire diagonal.
$$
A\cdot B = B\cdot A \text{ for all matrices } A \Longleftrightarrow D=\operatorname{diag}(d,d,\ldots,d)
$$

So if we do not have any specific information about ##A,## we must treat it like an arbitrary matrix. And that leaves us with ##\begin{pmatrix}d&0&\ldots&0\\0&d&\ldots&0\\ \vdots&\vdots&\ddots&\vdots\\ 0&0&\ldots&d\\ \end{pmatrix}## as the only matrix that commutes with ##A.## If we consider a specific matrix ##A,## then there are possibly more matrices that commute with ##A##. (Commute means ##A\cdot B=B\cdot A.##) However, if all these matrices are as before, then ##PD\neq DP.##
 
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FAQ: Proof of ##M^n## (matrix multiplication problem)

What is the matrix multiplication problem?

The matrix multiplication problem involves finding the product of two matrices. Given two matrices A and B, the problem is to compute their product C such that C = A * B. The elements of the resulting matrix C are computed as the dot product of the rows of A with the columns of B.

Why is the matrix multiplication problem important?

Matrix multiplication is a fundamental operation in various fields such as computer graphics, scientific computing, machine learning, and engineering. Efficient algorithms for matrix multiplication can significantly impact the performance of numerous applications, making it a crucial area of study.

What are the common algorithms for matrix multiplication?

The most common algorithms for matrix multiplication include the naive algorithm (O(n^3) complexity), Strassen's algorithm (O(n^2.81) complexity), and the Coppersmith-Winograd algorithm (O(n^2.376) complexity). Researchers are continually working on developing more efficient algorithms to reduce the computational complexity of matrix multiplication.

What is the current state of research on matrix multiplication?

Research on matrix multiplication is ongoing, with the goal of finding more efficient algorithms. Recent advancements include optimizing existing algorithms and exploring new approaches such as recursive algorithms, parallel computing techniques, and leveraging hardware accelerations like GPUs and specialized processors.

How does matrix multiplication relate to other areas of mathematics and computer science?

Matrix multiplication is closely related to linear algebra, as it is a fundamental operation on matrices. It also plays a critical role in computer science, particularly in areas like algorithms, complexity theory, and data structures. Efficient matrix multiplication algorithms are essential for large-scale data processing, machine learning models, and solving systems of linear equations.

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