Proving Linear Algebra Concepts: A=A^-1, A^T=A^-1

In summary, the conversation is about proving linear algebra concepts and two theorems: "If A = A^-1, then det A = +1 or -1" and "If A^T = A^-1, then det A = +1 or -1". The summary includes a brief explanation of how the two proofs were done using properties of determinants and matrices. The conversation ends with a confirmation that the proofs provided are correct.
  • #1
franz32
133
0
Hello , it's me again.

It's about proving linear aalgebra concepts.

1. Show that if A = A^-1, then det (A) = 1 or -1.

2. Show that if A^T = A^-1, then det (A) = 1 or -1.
 
Physics news on Phys.org
  • #2
[tex]\begin{align}
\det(AB)&=\det(A)\det(B) \\
\det(A^t)&=\det(A)
\end{align}[/tex]

These are all you need.
 
  • #3
Hmmmmm

Hello again,

So the two are enough to prove the two theorems.

But, I got only +1, how about -1?
 
  • #4
How did you do it?
 
  • #5
Here's mine

For "If A = A^-1, then det A = +1 or -1."

1. det A = det (A^-1) \\ determinating both sides
2. det A = 1 / (det A) \\ proeprty: det (A^-1) = 1 / (det A)
3. (det A)(det A) = 1 \\ Multiplying det A both sides
4. (det A^2) = 1 \\ simplifying
5. det A = + or 1 (square root of 1 ) \\ extracting sq.root
6. Thus, det A = +1 or -1.

For "If A^t = A^-1, then det A = 1 or -1."

1. (A^T) A = (A^-1) A \\ If the inverse of A is said to be existing, then A itself must exist and the product of the two must be an identity matrix. I multiplied A both sides of equation.

2. (A^T) A = I \\ Identity matrix.
3. det [ (A^T) A ] = det I \\ determinating both sides.
4. det(A^T) det A = det I \\ Prop: det AB = det A det B
5. det A det A = det I \\ Prop: det (A^T) = det A.
6. det A^2 = 1 \\ determinant of identity matrix is 1.
\\ simplifying
7. det A = + or - square root of 1 \\ extracting a square root.
8. Thus det A = 1 or -1.
 
  • #6
The first proof can be done using only property (1) that master_coda mentioned.

1 = det(I) = det(A * A^-1) = det(A)det(A^-1) = det(A)det(A) = det(A)^2

det(A)^2 = 1
det(A) = +/- 1
 
  • #7
Thanks!

Hi!

Hmmm, that's correct. =) Thank you. =)

Well, was my proving to both of the two in the previous "replies" correct? (I want to know if I am doing fine in my proving).
 
  • #8
Hey Muzza...

Hello Muzza,

I have read your answer. It seems correct but I doubt on one point.

The det A^-1 is equal to [1 / (det A)], how come it turns out to be det A?
 
  • #9
Your proofs seem correct to me.

det(A) = 1/det(A) does hold, for det(A) = 1, or det(A) = -1 (1 = 1/1 and -1 = 1/(-1) are both true statements), which is well... what you showed that the possible values for det(A) could be :P
 

1. What is the significance of proving that A=A^-1?

The significance of proving that A=A^-1 is that it shows that the matrix A is its own inverse. This means that when A is multiplied by its inverse, the result is the identity matrix, which is a matrix with 1s on the diagonal and 0s everywhere else. This is an important concept in linear algebra as it allows for efficient calculations and solving of equations involving matrices.

2. Why is it important to prove that A^T=A^-1?

Proving that A^T=A^-1 is important because it shows that the transpose of A is also its inverse. This means that when A is multiplied by its transpose, the result is the identity matrix. This concept is crucial in many applications of linear algebra, such as in solving systems of linear equations and in finding the inverse of a matrix.

3. How is the proof for A=A^-1 different from the proof for A^T=A^-1?

The proof for A=A^-1 and A^T=A^-1 are different because they involve different properties of matrices. The proof for A=A^-1 typically involves using the definition of matrix multiplication and the properties of the identity matrix. On the other hand, the proof for A^T=A^-1 involves using the definition of transpose and the properties of orthogonal matrices.

4. What are some real-world applications of proving A=A^-1 and A^T=A^-1?

The concept of A=A^-1 and A^T=A^-1 has various real-world applications, particularly in computer graphics, engineering, and physics. In computer graphics, it is used in 3D transformations and in creating realistic simulations. In engineering, it is used in solving systems of equations and in designing efficient circuits. In physics, it is used in solving problems related to motion and forces.

5. Can A and A^-1 be any type of matrix?

No, A and A^-1 cannot be any type of matrix. For A to be equal to its inverse, it must be a square matrix with non-zero determinant. Additionally, for A^T to be equal to its inverse, it must be an orthogonal matrix. If these conditions are not met, then the statements A=A^-1 and A^T=A^-1 are not true.

Similar threads

  • Linear and Abstract Algebra
Replies
3
Views
1K
  • Linear and Abstract Algebra
Replies
7
Views
532
  • Linear and Abstract Algebra
Replies
12
Views
1K
  • Linear and Abstract Algebra
Replies
6
Views
1K
  • Linear and Abstract Algebra
Replies
10
Views
315
  • Linear and Abstract Algebra
Replies
5
Views
1K
  • Linear and Abstract Algebra
Replies
9
Views
1K
  • Linear and Abstract Algebra
Replies
5
Views
1K
  • Linear and Abstract Algebra
Replies
5
Views
1K
  • Linear and Abstract Algebra
Replies
8
Views
1K
Back
Top