Does there exists a matrix who's product is itself?

Note that the 'dagger' sign means transposition and conjugation.)In summary, the conversation revolves around finding a matrix A that satisfies AA=A, similar to the identity matrix. The speaker recalls the existence of a matrix with all nonzero entries that has this property, possibly related to rotations. It is suggested to try an orthogonal projection or reflection to find such a matrix. The conversation then focuses on constructing a matrix for the orthogonal projection on the line y=x, which ends up being A=[1/2 1/2; 1/2 1/2]. The speaker expresses gratitude for the explanation and asks for further clarification on the mathematical reasoning behind it.
  • #1
mscudder3
29
0
To simplify this inquiry let us deal with R2. I know that if you have the identity matrix (let that be A), then AA=A. However, I recall the existence of a matrix with all nonzero entries that had the same property. Thinking of rotations, I cannot think of one, (since rotating by 0, or 2pi is equivalently the identity matrix). I'm sure that there must exist some type of orthogonal projection or reflection, but I'm unsure.

Thanks in advance!
 
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  • #2
mscudder3 said:
I'm sure that there must exist some type of orthogonal projection or reflection, but I'm unsure.
So instead of wondering, why not actually try an orthogonal projection or a reflection?


(aside -- you could always reduce the problem to an ordinary algebra problem with real numbers)
 
  • #3
Hurkyl said:
So instead of wondering, why not actually try an orthogonal projection or a reflection?


(aside -- you could always reduce the problem to an ordinary algebra problem with real numbers)

... I was wondering as in I cannot recall or figure it out.
Reducing it to algebra in terms of some a1 a2 a3 a4 (forming a 2x2), all that resolves is trivial solutions.

Would it be that difficult to show me one?
 
  • #4
mscudder3 said:
Would it be that difficult to show me one?

No, but you would learn more if you construct one yourself.

Now, take the line y=x in [itex]\mathbb{R}^2[/itex]. Can you construct the matrix of the orthogonal projection on that line?
 
  • #5
Let A=[a1 a2 ; a3 a4]. since y=x the vector x=(x1, x2)^t=(1, 1)^t
From the definition of a projection= [u1^2, u1*u2 ; u1*u2 u2^2]
I can see that if a1=a2=a3=a4=1/2 then,AA=A. However, I'm not quite sure on how/why?

Edit:
From Ax=b let x=(1 1)^t and A=projection and solve for u1 and u2 (which yields 1/2 for each entry). Then by the def of projection, b must be equal to x?
 
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  • #6
mscudder3 said:
From the definition of a projection= [x1^2, x1*x2 ; x1*x2 x2^2]

Umm, I'm not sure where you got that formula... But the matrix

[tex]\left(\begin{array}{cc} \frac{1}{2} & \frac{1}{2}\\ \frac{1}{2} & \frac{1}{2}\\ \end{array}\right)[/tex]

Certainly satisfies A2=A. So you've found your matrix!
 
  • #7
Ya thanks! Now that you have me thinking I'd like to understand exactly how I arrived at this solution. I got that equation (or matrix) from my text: Linear Algebra 4e by Bretscher. Book is VERY heavy on application, thus my struggle with the general theory.
 
  • #8
mscudder3 said:
Ya thanks! Now that you have me thinking I'd like to understand exactly how I arrived at this solution. I got that equation (or matrix) from my text: Linear Algebra 4e by Bretscher. Book is VERY heavy on application, thus my struggle with the general theory.

It's actually very easy once you know it! :smile:

Just take the standard basis of [itex]\mathbb{R}^2[/itex], that is: (1,0) and (0,1). The key point is checking where they are projected. A little bit of drawing (or calculating) gives us that (1,0) is being sent to (1/2,1/2) and (0,1) is being sent to (1/2,1/2). Thus (1/2,1/2) and (1/2,1/2) make up the columns of our matrix.

Note that there was nothing special about projecting on the line y=x. You can pick any line through the origin, they will all give you a matrix such that A2=A.

For example, let me project on y=-x. Then (1,0) is being sent to (1/2,-1/2) and (0,1) is being sent to (-1/2,1/2). Thus the matrix becomes

[tex]\left(\begin{array}{cc}\frac{1}{2} & -\frac{1}{2}\\ -\frac{1}{2} & \frac{1}{2}\\ \end{array}\right)[/tex]

As you can see this matrix also satisfies A2=A.
 
  • #9
Great! Thanks so much.
To briefly expand on your explanation, when you say it sends the basis (by the linear transform), how exactly do these numbers come about mathematically. I know I may be overlooking something, but I want to be complete.

From what I see, the length of the basis is clearly 1 (hence unit vector). Thus if that is projected onto y=x the new basis would be 1/sqrt(2), not 1/2.
 
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  • #10
Hello mscudder3,
Take any unit column vector a (i.e., nx1 matrix with sum of square of moduli of its elements being 1, where n is the number of your choice). Then the matrix m=[tex]aa^\dagger[/tex] will satisfy [tex]m^2=m.[/tex]
 

1. What is a matrix?

A matrix is a rectangular array of numbers or variables, typically represented by rows and columns. It is commonly used in mathematics, physics, and computer science to represent data and perform calculations.

2. Can a matrix multiply by itself?

Yes, a matrix can multiply by itself if it is a square matrix, meaning it has an equal number of rows and columns. This operation is known as matrix multiplication.

3. What is the result of a matrix multiplied by itself?

The result of a matrix multiplied by itself is a new matrix, also known as the product matrix. Each element in the product matrix is calculated by taking the dot product of the corresponding row and column in the original matrices.

4. Does every matrix have a product that is itself?

No, not every matrix has a product that is itself. In fact, only certain matrices known as idempotent matrices have this property. An idempotent matrix is a square matrix that, when multiplied by itself, results in the original matrix.

5. What are some real-world applications of matrices that have products of themselves?

Some common real-world applications of matrices with a product of themselves include population growth models, Markov chains in finance and economics, and electrical circuits in engineering. These systems can be represented and analyzed using idempotent matrices to predict future outcomes or behavior.

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