Proving 1. $\leftrightarrow$ 2.: A Matrix Invertibility Challenge

In summary, it is not true that Ker(A)= empty set, because 0 \in \textrm{ker}(A) . And why do you think it implies that it's square?Ax = 0 => x = 0 ==> rank(A) = n = rank(ATA)==> ATA is invertible.
  • #1
ImAnEngineer
209
1
I found on wikipedia that the following statements are equivalant:
1. Matrix A is left invertible
2. Ax=0 => x=0

I couldn't find the proof so I try to do it myself.

From 1. to 2. is easy. Assume A is left invertible. If Ax=0, then x=Ix=A-1Ax=A-10 = 0 .

I can't figure out how to do 2=>1. Any help is appreciated.

Things that might prove useful:
- A is injective
- dim(ker(A))=0
- rank(A)=n (if A is an m x n matrix)
- [tex]n\leq m[/tex]
 
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  • #2
If the solutions to [tex]Ax=0[/tex] are only the trivial one, then its RREF is the identity matrix, thus it is invertible.

And another:
[tex]Ax=0[/tex] are only the trivial solution, then [tex]\textrm{det}A\ne 0[/tex] thus it is invertible.
 
  • #3
Zorba said:
If the solutions to [tex]Ax=0[/tex] are only the trivial one, then its RREF is the identity matrix, thus it is invertible.

And another:
[tex]Ax=0[/tex] are only the trivial solution, then [tex]\textrm{det}A\ne 0[/tex] thus it is invertible.

Why and why?

Note that A is not (necessarily) a square matrix
 
  • #4
Only square matrices can be inverted, thus the statements only make sense for them.

All of the statements above I used can be shown to be equivalent, but I need to know what level you are at so I can know which equivalent statements to use.

[tex]Ax=0 \Rightarrow \textrm{RREF}(A)=I[/tex] follows from the rank-nullity theorem, have you seen this theorem?

[tex]Ax=0 \Rightarrow \textrm{det}A\ne0[/tex] follows from properties of the determinant and also properties of elementary matrices, are you familiar with these?
 
  • #5
Zorba said:
Only square matrices can be inverted, thus the statements only make sense for them.

All of the statements above I used can be shown to be equivalent, but I need to know what level you are at so I can know which equivalent statements to use.

[tex]Ax=0 \Rightarrow \textrm{RREF}(A)=I[/tex] follows from the rank-nullity theorem, have you seen this theorem?

[tex]Ax=0 \Rightarrow \textrm{det}A\ne0[/tex] follows from properties of the determinant and also properties of elementary matrices, are you familiar with these?
This is simply not true. I have a better source ("Linear algebra done wrong"), but I'll use wikipedia to quote:
Non-square matrices (m-by-n matrices for which m ≠ n) do not have an inverse. However, in some cases such a matrix may have a left inverse or right inverse. If A is m-by-n and the rank of A is equal to n, then A has a left inverse: an n-by-m matrix B such that BA = I. If A has rank m, then it has a right inverse: an n-by-m matrix B such that AB = I.

I underlined what I'm trying to proof.

I know the theorems but I don't see how they are applicable to a non-square matrix.
 
  • #6
ImAnEngineer said:
This is simply not true. I have a better source (Linear algebra done wrong), but I'll use wikipedia to quote:I underlined what I'm trying to proof.

Ah I see, just a left-inverse, in that case then you must avoid mentioning determinants although my first statement still holds.
 
  • #7
Zorba said:
Ah I see, just a left-inverse, in that case then you must avoid mentioning determinants although my first statement still holds.

That statement does hold indeed, and helped me to get:
rank(A) + dim(ker(A)) = rank(A) = n
rank(At) + dim(ker(At))= rank(A) + dim(ker(At)) = n + dim(ker(At)) = m
Hence: [tex]m\geq n[/tex]

But it doesn't get me any further.
 
  • #8
ImAnEngineer said:
That statement does hold indeed, and helped me to get:
rank(A) + dim(ker(A)) = rank(A) = n
rank(At) + dim(ker(At))= rank(A) + dim(ker(At)) = n + dim(ker(At)) = m
Hence: [tex]m\geq n[/tex]

But it doesn't get me any further.

But [tex]Ax=0[/tex] having only the trivial solution [tex]\Rightarrow \textrm{ker}A={ \emptyset }[/tex] which again seems to imply that its square... I don't know, I'll have to have a think about it.
 
  • #9
Zorba said:
But [tex]Ax=0[/tex] having only the trivial solution [tex]\Rightarrow \textrm{ker}A={ \emptyset }[/tex] which again seems to imply that its square... I don't know, I'll have to have a think about it.

It's not true that Ker(A)= empty set, because [tex]0 \in \textrm{ker}(A)[/tex] . And why do you think it implies that it's square?
 
  • #10
Ax = 0 => x = 0
==> rank(A) = n = rank(ATA)
==> ATA is invertible.

Using this you should get the ball rolling... Or I may be wrong.
 
  • #11
(ATA)-1ATA = I

That's it, thanks :)
 

1. What does it mean for a matrix to be invertible?

A matrix is considered invertible if it has an inverse matrix. This means that there exists another matrix that, when multiplied by the original matrix, results in the identity matrix. In other words, the inverse matrix "undoes" the original matrix.

2. How do you prove that a matrix is invertible?

To prove that a matrix is invertible, you can use various methods such as row reduction, determinants, or the inverse matrix formula. These methods involve manipulating the matrix and its elements to show that an inverse matrix exists.

3. Can a matrix be both invertible and non-invertible?

No, a matrix cannot be both invertible and non-invertible. A matrix is either one or the other. However, a matrix may be close to being non-invertible, meaning it has a very small determinant, but it is still considered invertible.

4. What is the importance of invertible matrices?

Invertible matrices are important because they allow us to solve systems of equations and perform other operations on matrices. They also have many applications in fields such as engineering, physics, and computer science.

5. Is it possible for a square matrix to not have an inverse?

Yes, it is possible for a square matrix to not have an inverse. This type of matrix is called a singular or non-invertible matrix. A matrix may not have an inverse if its determinant is equal to 0, or if the matrix is not square.

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