Is this a valid proof for the invertibility of $\Phi$?

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In summary, the theorem states that if the dimension of the domain equals the dimension of the range, then the linear mapping is bijective. However, if the dimension of the range is greater than the dimension of the domain, then the linear mapping is not bijective.
  • #1
rputra
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I am working on this problem: Show that $\Phi =I +\Delta^2 + \Delta^3$ is invertible, if $\Delta$ is a derivative linear mapping from $P_n$ to itself, $I$ is the identity mapping, where $P_n$ is vector space of one-variable polynomials with complex coefficients.

I would like to solve this problem by using the theorem that says if (a) the dimension of $\Phi$'s domain equals to the dimension of the range, and if (b) $\Phi$ is one-to-one, then $\Phi$ is invertible.

The (a) is obvious, $dim (P_n) = n+1$. For the (b), I would like to find out the kernel of $\Phi$, and the to show that $Ker (\Phi) = \{0\}$ therefore proving the $\Phi$ is indeed one-to-one. But unfortunately I do not know how to get the $Ker(\Phi)$. My understanding is that $\Delta^3$ and $\Delta^2$ are respectively the third second and the second derivative of a polynomial, does this mean I have to resort to differential equation?

Any hint or guidance would be very much appreciated. Thank you for your time and effort.
 
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  • #2
Hi Tarrant

You haven't actually proved (a), since you haven't proven that the image of $\Phi$ has dimension $n + 1$. Also, since $P_n$ is finite dimensional, it suffices to prove either (a) or (b). This is because a linear transformation on a finite dimensional vector space is one-to-one if and only if it is onto.

Since $\{1,z,z^2,\ldots, z^n\}$ is a basis for $P_n$, it suffices to consider the rank (or nullity) of the matrix $[\Phi]$ of $\Phi$ relative to this basis. Since $\Phi(z^m) = z^m + m(m - 1)z^{m-2} + m(m-1)(m-2)z^{m-3}$ for all $m \ge 3$, it follows that $[\Phi]$ is upper triangular with ones along the diagonal. Therefore, $[\Phi]$ has full rank $n + 1$, and $\Phi$ is invertible.
 
  • #3
Euge said:
Hi Tarrant

You haven't actually proved (a), since you haven't proven that the image of $\Phi$ has dimension $n + 1$. Also, since $P_n$ is finite dimensional, it suffices to prove either (a) or (b). This is because a linear transformation on a finite dimensional vector space is one-to-one if and only if it is onto.

Since $\{1,z,z^2,\ldots, z^n\}$ is a basis for $P_n$, it suffices to consider the rank (or nullity) of the matrix $[\Phi]$ of $\Phi$ relative to this basis. Since $\Phi(z^m) = z^m + m(m - 1)z^{m-2} + m(m-1)(m-2)z^{m-3}$ for all $m \ge 3$, it follows that $[\Phi]$ is upper triangular with ones along the diagonal. Therefore, $[\Phi]$ has full rank $n + 1$, and $\Phi$ is invertible.

Thank you for your swift response. But unfortunately we are still in the young early weeks of this semester therefore we have not gone through the concepts of upper triangle, rank or nullity. The most we have covered are only bases, matrix associated with linear mapping, etc. Is there anyway you can modify your guidance along these lines?

Again thank you very much for your time and effort, am looking forward to hear from you again.
 
  • #4
Here's a more elementary way to prove (a), using the basis $\{1,z,z^2,\ldots,z^n\}$ of $P_n.$ First, can you see that $1$ must be in the range of $\Phi$? Then what about $z,\,z^2,\,\ldots$? Use induction if necessary to show that $z^k$ is in the range of $\Phi$ for all $k \leqslant n.$
 
  • #5
Tarrant said:
I am working on this problem: Show that $\Phi =I +\Delta^2 + \Delta^3$ is invertible, if $\Delta$ is a derivative linear mapping from $P_n$ to itself, $I$ is the identity mapping, where $P_n$ is vector space of one-variable polynomials with complex coefficients.

I would like to solve this problem by using the theorem that says if (a) the dimension of $\Phi$'s domain equals to the dimension of the range, and if (b) $\Phi$ is one-to-one, then $\Phi$ is invertible.

The (a) is obvious, $dim (P_n) = n+1$. For the (b), I would like to find out the kernel of $\Phi$, and the to show that $Ker (\Phi) = \{0\}$ therefore proving the $\Phi$ is indeed one-to-one. But unfortunately I do not know how to get the $Ker(\Phi)$. My understanding is that $\Delta^3$ and $\Delta^2$ are respectively the third second and the second derivative of a polynomial, does this mean I have to resort to differential equation?

Any hint or guidance would be very much appreciated. Thank you for your time and effort.

The above is the original posting that started this thread. However, in hindsight, I think I misquoted the theorem. (For that I sincerely apologize!) The complete theorem from Serge Lange's textbook should be as follow:

Let $L: V \longrightarrow W$ be a linear map. Assume that $dim (V) = dim (W)$. If $Ker (L) = \{0\}$ or if $Im(L) = W$, then $L$ is bijective.

Here the theorem says that "if the dimension of the domain equals the dimension of the co-domain..." and not the dimension of range. I suspect this nuance makes lots of difference. Therefore here are my question: Can I still use this theorem to solve the problem? Or perhaps there is other simpler, easier-to-understand approach? Keep in mind that we are still in the early young period of this semester, therefore we have not gone through any sophisticated theorems yet.

Thank you again for your time and effort.
 
Last edited:
  • #6
Tarrant said:
Problem: Show that $\Phi =I +\Delta^2 + \Delta^3$ is invertible, if $\Delta$ is a derivative linear mapping from $P_n$ to itself, $I$ is the identity mapping, where $P_n$ is vector space of one-variable polynomials with complex coefficients.

Do you think this following solution has merit? First of all, I would like to solve this problem with the theorem that I quoted earlier from Serge Lange's textbook:

Tarrant said:
Let $L: V \longrightarrow W$ be a linear map. Assume that $dim (V) = dim (W)$. If $Ker (L) = \{0\}$ or if $Im(L) = W$, then $L$ is bijective.

(a) It is obvious that the dimension of domain equals to the dimension of co-domain, as $dim(P_n) = (n+1) = dim(P_n).$
(b) Consider $\rho^n \in P_n,$ therefore
$$\begin{align}
\Delta (\rho^n) &= n\rho^{n-1}\\
\Delta^2 (\rho^n) &= n(n-1)\rho^{n-2}\\
\Delta^3 (\rho^n) &= n(n-1)(n-2)\rho^{n-3}\\
\end{align}$$
and
$$\Phi(\rho^n) = \rho^{n} + n(n-1)\rho^{n-2} + n(n-1)(n-2)\rho^{n-3}. $$

(c) In trying to find the $Ker (\Phi)$, I observe the followings:
*In order to make the last term zero, anyone of the $\rho^n, n, (n-1), (n-2)$ has to be zero.
*For the second term, anyone of the $\rho^n, n, (n-1)$ has to be zero.
*For the first term, $\rho^n$ has to be zero.
*In order to make all terms zero, $\rho^n$ has to be zero.
Hence, $Ker(\Phi) = \{0\}$, implying that $\Phi$ is one-to-one. Therefore $\Phi$ is invertible based on the theorem above.

Please let me know if the solution is acceptable. Thank you for your time and effort.
 

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

For a matrix to be invertible, it means that it has an inverse matrix that, when multiplied together, result in the identity matrix. In other words, the inverse matrix "undoes" the operations of the original matrix.

2. How can I prove a matrix is invertible?

There are a few ways to prove a matrix is invertible. One way is to show that the determinant of the matrix is non-zero. Another way is to use the Gauss-Jordan elimination method to transform the matrix into reduced row-echelon form, resulting in an identity matrix.

3. Can a non-square matrix be invertible?

No, a non-square matrix cannot be invertible. In order for a matrix to be invertible, it must have the same number of rows and columns, meaning it must be a square matrix.

4. What is the significance of an invertible matrix in linear algebra?

An invertible matrix is significant in linear algebra because it allows us to solve linear equations and systems of equations. It also allows us to find the solutions to certain problems, such as finding the inverse of a transformation.

5. Are all invertible matrices unique?

Yes, all invertible matrices are unique. This is because if a matrix has an inverse, it must be unique in order for the inverse operations to result in the identity matrix. Therefore, there can only be one inverse matrix for any given invertible matrix.

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