High School Proving the uniqueness of eigenspaces

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SUMMARY

The discussion focuses on proving the uniqueness of eigenspaces for linear transformations represented by matrices. It establishes that if ##x\in\ker(T_\lambda^2)\cap\ker(T_\mu^2)##, then ##x## must equal zero under certain conditions. The proof utilizes polynomial identities and properties of linear transformations, specifically showing that if two transformations yield the same kernel, their eigenvectors must be identical. The discussion also explores the implications of singular matrices and the algebraic properties of polynomials in relation to eigenvalues.

PREREQUISITES
  • Understanding of linear algebra concepts, particularly eigenvalues and eigenspaces.
  • Familiarity with kernel and image of linear transformations.
  • Knowledge of polynomial rings and their properties in algebra.
  • Experience with matrix operations and row reduction techniques.
NEXT STEPS
  • Study the properties of kernels in linear transformations and their implications on eigenspaces.
  • Learn about polynomial identities and their applications in linear algebra.
  • Explore the uniqueness of eigenvectors in relation to distinct eigenvalues.
  • Investigate the role of singular matrices in linear transformations and their effect on solution sets.
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Mathematicians, students of linear algebra, and researchers focusing on eigenvalue problems and linear transformations will benefit from this discussion.

Eclair_de_XII
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TL;DR
Let ##A## be a linear transformation that maps some vector space to itself. Let ##\lambda,\mu## be two distinct scalars and define ##T_k:=A-k\cdot I##, where ##I## denotes the identity transformation. Show that the following hold:

\begin{eqnarray}
\ker(T_\lambda^2)\cap\ker(T_\mu^2)=0
\end{eqnarray}
Let ##x\in\ker(T_\lambda^2)\cap\ker(T_\mu^2)##. Then the following must hold:

\begin{eqnarray}
(A^2-2\lambda\cdot A+\lambda^2I)x=0\\
(A^2-2\mu\cdot A+\mu^2I)x=0
\end{eqnarray}

Subtracting the latter equation from the former gives us:

\begin{eqnarray}
0-0&=&0\\
&=&(-2\lambda\cdot Ax+\lambda^2x)-(-2\mu\cdot Ax+\mu^2x)\\
0&=&-2(\lambda-\mu)Ax+(\lambda-\mu)(\lambda+\mu)x
\end{eqnarray}

Bearing in mind that performing row operations on any system of linear equations does not change its solution set, we proceed by dividing both sides of the equality by ##\lambda-\mu##:

\begin{eqnarray}
0&=&-2Ax+(\lambda+\mu)x\\
&=&-2Ax+(\lambda+\lambda+(\mu-\lambda)x\\
&=&-2Ax+2\lambda\cdot x+(\mu-\lambda)x\\
&=&2(\lambda\cdot I-A)x+(\mu-\lambda)x\\
&=&2T_\lambda x+(\mu-\lambda)x
\end{eqnarray}

By definition, ##x\in\ker(T_\lambda+\frac{1}{2}(\lambda-\mu)I)## whenever ##I## is restricted to ##\ker(T_\lambda^2)\cap\ker(T_\mu^2)##. It can be shown through similar means that ##x\in\ker(T_\mu+\frac{1}{2}(-\lambda+\mu)I)##.

(I am still figuring out how to finish this proof, if it can be finished.)
 
Last edited:
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the problem is unnecessarily complicated. denote A-lambda.Id by B, and then denote A-mu.Id by B-c.Id, where c= mu-lambda ≠0. then try to show that if B^2 x=0 and also (B-c)^2 x = 0, where c≠0, then x = 0.
 
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Sure. I show first that ##(B-c)^2## can be expanded like a polynomial.

\begin{eqnarray}
(B-cI)^2&=&[B^2-B(cI)-(cI)B+c^2I]\\
&=&[B^2-c(BI)-c(BI)+c^2I]\\
&=&[B^2-2c(BI)+c^2I]\\
\end{eqnarray}

Let ##x\in\ker(B^2)\cap\ker[(B-c)^2]##.

\begin{eqnarray}
0&=&B^2x\\
&=&(B-cI)^2x\\
&=&0-0\\
&=&(2cB-c^2I)x\\
&=&(2B-cI)x\\
&=&(B+B-cI)x\\
-Bx&=&(B-cI)x
\end{eqnarray}

By uniqueness of the inverse, either ##B-cI=-B## or ##x=0##. The former fails to hold if ##B\neq\frac{1}{2}cI##, in which case, ##x=0##.

If this solution were correct, I am wondering how I would show this is true (if it is true) for higher powers. I don't like thinking about the amount of algebra I'd need to do to prove it in the general case like how I did it for the case when the power is just two.
 
Last edited:
or, applying B again at step 19 implies cBx= 0, so Bx = 0, so from step 18, also c^2 x = 0, so x = 0.

a basic algebra fact is that in the integers and in the polynomial ring over a field, and in any euclidean domain, the greatest common divisor of two elements can be written as a linear combination of those elements. Hence if P,Q are relatively prime polynomials, so their gcd is 1, there are polynomials f, g, such that fP+gQ = 1. Then for any map A, plugging A into both sides of this equation, gives f(A)P(A) + g(A)Q(A) = Id. Now assuming P(A)x = Q(A)x = 0, gives that 0 = x.

In your original problem, P = (X-lambda)^2, and Q = (X-mu)^2.
 
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Thank you. That is very helpful.

mathwonk said:
applying B again at step 19 implies cBx= 0
Will that not change the solution set, though, if for example, ##B## is singular? Wait, never mind. I think that the new solution set obtained from applying ##B## to both sides would just be a superset of the original solution set. And since the new solution set is trivial, it must follow that the original solution set is also trivial.
 
Last edited:
using this idea on your original problem directly, note that if P = X-a, and Q = X-b, with a ≠b, then P-Q = b-a ≠0, so we can take f = 1/(b-a) and g = -1/(b-a), to get fP+gQ = 1.

Then to do the case of P^2 and Q^2, just cube both sides of this equation. Every term on the left will be divisible by either P^2 or Q^2, and the right side will still equal 1. So we get some equation of form FP^2 + GQ^2 = 1. the same trick works for all higher powers. I.e. to deal with P^n and Q^n, raise the equation to the power 2n-1, I guess.
 
i don't understand your concern in post #5, since if B is not singular, you are done immediately, so we may assume if we wish that B is singular, but that is irrelevant to the argument. I am just trying to prove that x = 0. from step 19 we know that 2Bx = cx, so applying B again gives 2B^2x = cBx, but B^2 x = 0, by hypothesis, so cBx = 0. but c≠0 so Bx = 0. now either step 18 or 19 gives that 0 = cx or 0 = c^2x, either way x = 0.
 

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