Decomposition of a complex vector space into 2 T-invariant subspaces

In summary, decomposition of a complex vector space into 2 T-invariant subspaces refers to breaking down a complex vector space into two smaller subspaces that are invariant under a linear transformation T. This is useful for understanding the structure of a vector space and the behavior of T on that space. The key components of this decomposition are the two subspaces, U and V, satisfying certain conditions. It is closely related to the concept of eigenspaces, where U and V can be thought of as eigenspaces corresponding to different eigenvalues of T. This decomposition can also be generalized to more than 2 subspaces, but the 2 T-invariant subspaces decomposition is the most common and useful in many applications.
  • #1
winter85
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Homework Statement


Suppose V is a complex vector space and [tex]T \in L(V)[/tex]. Prove that
there does not exist a direct sum decomposition of V into two
proper subspaces invariant under T if and only if the minimal
polynomial of T is of the form [tex](z - \lambda)^{dim V}[/tex] for some [tex]\lambda \in C[/tex].

Homework Equations


The Attempt at a Solution



First suppose that the minimal polynomial of T is [tex]p(z) = (z - \lambda)^{n}[/tex] where n = dim V. Suppose further that [tex]V = U \oplus W[/tex] where U and W are T-invariant proper subspaces of V. So [tex]dim U \geq 1[/tex] and [tex]dim W \geq 1[/tex]. Since n = dim U + dim W, we have that [tex]dim U \leq n-1[/tex] and [tex]dim W \leq n-1[/tex].
Now the minimal polynomial of T is [tex]p(z) = (z - \lambda)^{n}[/tex], therefore [tex](T - \lambda I)^{n}v = 0[/tex] for all v in V but there is at least one v in V such that [tex](T - \lambda I)^{n-1}v \neq 0[/tex]. Because of the decomposition of V, we can write: v = u + w for some u in U and some w in W. Applying [tex](T - \lambda I)^{n-1} [/tex] to both sides we get:

[tex](T - \lambda I)^{n-1}v = (T - \lambda I)^{n-1}u + (T - \lambda I)^{n-1}w \neq 0[/tex]

since both U and W are invariant under T, we have Tu in U and and [tex] -\lambda u \in U [/tex] so [tex](T - \lambda I)u \in U [/tex]. Thefore U (and similarly W) are both invariant under [tex](T-\lambda I) [/tex], so invariant under [tex](T-\lambda I)^{n-1} [/tex]. [tex](T - \lambda I)^{n-1}v \neq 0 [/tex], so either one of [tex](T - \lambda I)^{n-1}u [/tex] or [tex](T - \lambda I)^{n-1}w [/tex] is not zero, because they're in different subspaces. Assume it's the first one. So there exists u in U such that

[tex](T - \lambda I)^{n-1}u \neq 0[/tex]. But [tex](T - \lambda I)^{n}u = 0 [/tex] (for all u in U). Constraining T to U, we see that the minimal polynomial of T on U is [tex](z - \lambda I)^{n} [/tex]. But [tex] dim U \leq n-1 < n [/tex], a contradiction.

I hope the reasoning above is correct; if it is, it proves that minimal polyomial of T is [tex](z-\lambda I)^{n} [/tex] implies that T cannot be decomposed into the direct sum of two proper T-invariant subspaces.

However I'm stumped about how to prove the other direction.
Also, I would like to know what's the mistake in the following reasoning:

Since V is a complex vector space, then T in L(V) has an eigenvalue [tex]\lambda[/tex], so V has an T-invariant subspace of dimension 1. Then there exists a subspace W of V such that [tex]V = U \oplus W[/tex]. It follows that W is T-invariant and of dimension dim V - 1[/tex]. So it's always possible to decompose V into two proper T-invariant proper subspaces :S

Thank you :)
 
Last edited:
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  • #2


To prove the other direction, we can use the contrapositive statement. That is, if there exists a direct sum decomposition of V into two proper subspaces invariant under T, then the minimal polynomial of T cannot be of the form (z-\lambda)^{dim V} for some \lambda \in C.

Assume there exists a direct sum decomposition of V into two proper subspaces U and W invariant under T. Then we can write V = U \oplus W and dim U = m, dim W = n, where m + n = dim V. Since U and W are T-invariant, we have (T-\lambda I)U \subseteq U and (T-\lambda I)W \subseteq W for all \lambda \in C.

Now, consider the minimal polynomial of T, p(z) = (z-\lambda)^{dim V}. Since p(T) = 0, we have (T-\lambda I)^{dim V} = 0. But since U and W are T-invariant, we have (T-\lambda I)^{dim V} = (T-\lambda I)^{m}(T-\lambda I)^{n}. This implies that (T-\lambda I)^{m} = 0 and (T-\lambda I)^{n} = 0. But this means that (T-\lambda I)U = 0 and (T-\lambda I)W = 0, which contradicts the fact that U and W are proper subspaces of V. Therefore, the minimal polynomial of T cannot be of the form (z-\lambda)^{dim V} for some \lambda \in C.

As for your reasoning, the mistake lies in assuming that there exists a T-invariant subspace of dimension 1. This is not always the case, as there are examples where a T-invariant subspace does not exist. Also, even if we assume that there exists a T-invariant subspace of dimension 1, it does not guarantee that we can always decompose V into two proper T-invariant subspaces. This only holds true if the minimal polynomial of T is of the form (z-\lambda)^{dim V} for some \lambda \in C, which is what we are trying to prove.
 

1. What is the definition of "decomposition of a complex vector space into 2 T-invariant subspaces"?

The decomposition of a complex vector space into 2 T-invariant subspaces refers to the process of breaking down a complex vector space into two smaller subspaces that are both invariant under a linear transformation T. This means that the subspaces remain unchanged when acted upon by the transformation T.

2. How is the decomposition of a complex vector space into 2 T-invariant subspaces useful?

This decomposition is useful in understanding the structure of a complex vector space and the behavior of a linear transformation T on that space. It allows us to analyze the properties and relationships between the two subspaces and how they interact with each other under the transformation T.

3. What are the key components of this decomposition?

The key components of this decomposition are the two T-invariant subspaces, which are typically denoted as U and V. These subspaces must satisfy two conditions: 1) the sum of U and V must equal the original vector space, and 2) the intersection of U and V is just the zero vector.

4. How is this decomposition related to the concept of eigenspaces?

The decomposition of a complex vector space into 2 T-invariant subspaces is closely related to the concept of eigenspaces. In fact, the two subspaces U and V can be thought of as the eigenspaces of T corresponding to different eigenvalues. This decomposition allows us to understand the eigenvectors and eigenvalues of T in a more structured way.

5. Can this decomposition be generalized to more than 2 subspaces?

Yes, this decomposition can be extended to more than 2 subspaces. In general, a complex vector space can be decomposed into n T-invariant subspaces, where n is the number of distinct eigenvalues of T. However, the 2 T-invariant subspaces decomposition is the most common and useful in many applications.

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