Linear Algebra: Invariant Subspaces

In summary, the problem asks for a counterexample to the assertion that every subspace of V is invariant under every operator on V. There is no guarantee that a particular operator will not have an invariant subspace, but if the problem asks for a subspace that is invariant under EACH operator, then no such subspace exists.
  • #1
*melinda*
86
0
Homework Statement

Prove or give a counterexample: If U is a subspace of V that is invariant under every operator on V, then U = {0} or U = V. Assume that V is finite dimensional.

The attempt at a solution

I really think that I should be able to produce a counterexample, however even if I find an appropriate subspace, I have NO idea how to show that U is invariant under every operator on V. There are an arbitrary number of them!

One thing I was thinking was use a one dimensional subspace for my U, because then every transformation of a vector u in U would be a constant times u. In other words, T(u) = cu.

Does anyone know if I'm on the right track?
 
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  • #2
Hint #1: You're not going to be able to produce a counterexample.

Hint #2: Suppose U is a nontrivial subspace of V that's invariant under every operator on V, and let {u_1, ..., u_n} be a basis for U. A linear operator is completely determined by its action on the basis.
 
  • #3
My textbook states that for operators on complex vector spaces with dimension greater than one, and real vector spaces with dimension greater than two, that there will be invariant subspaces other than {0} and V.

Maybe the book means for a particular operator?
 
  • #4
It doesn't matter if a particular operator has other invariant subspaces. This problem is asking you to consider subspaces that are invariant under EACH operator.
 
  • #5
morphism's point: Given [any subspace U of V, of dimension m, 0< m< n= dim V, there exist a basis for V, [itex]v_1, v_2, ..., v_n}[/itex] such that the first m vectors, [itex]{v_1, v_2, ..., v_m} form a subspace for U. Now CONSTRUCT a linear transformation on V such that U is NOT invariant under that linear transformation.
 

1. What is a linear algebra invariant subspace?

A linear algebra invariant subspace is a subset of a vector space that is closed under the operations of the vector space and is left invariant by the action of a linear transformation. This means that any vector in the subspace remains in the subspace after being transformed by the linear transformation.

2. How are invariant subspaces related to eigenvectors and eigenvalues?

Invariant subspaces are closely related to eigenvectors and eigenvalues. An eigenvector is a vector that does not change direction when multiplied by a linear transformation, and its corresponding eigenvalue is the scalar that scales the eigenvector. Invariant subspaces are spanned by eigenvectors, and the eigenvalues correspond to the action of the linear transformation on those subspaces.

3. What is the significance of invariant subspaces in linear algebra?

Invariant subspaces play a crucial role in understanding the behavior of linear transformations. They provide a way to break down a complex transformation into simpler, easier-to-understand parts. Invariant subspaces also allow for the study of important properties, such as stability, controllability, and observability, in linear systems.

4. How can we determine if a subspace is invariant under a given linear transformation?

To determine if a subspace is invariant under a linear transformation, we can use the concept of the null space. If the null space of the subspace is also the null space of the linear transformation, then the subspace is invariant. This means that any vector in the subspace will not be affected by the transformation and will remain in the subspace.

5. Can an invariant subspace be a proper subset of another invariant subspace?

Yes, an invariant subspace can be a proper subset of another invariant subspace. This means that the larger subspace is also invariant under the linear transformation, but it contains additional vectors that are not in the smaller subspace. Invariant subspaces can have different sizes and dimensions, depending on the linear transformation and the vector space in question.

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