Finding invariant subspaces

In summary, for a finite dimensional, nonzero complex vector space V and a linear map T, it can be shown that V contains invariant subspaces of dimension j for j=1, ..., dim V by using the Schur decomposition or upper triangular matrix theorem. This is because, under certain bases, the matrix of T is upper triangular, allowing for the identification of invariant subspaces.
  • #1
samkolb
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Homework Statement



Let V be a finite dimensional, nonzero complex vector space. Let T be be a linear map on V. Show that V contains invariant subspaces of dimension j for j=1, ..., dim V.

Homework Equations


Since V is complex, V contains an invariant subspace of dimension 1.


The Attempt at a Solution


I started with dim V=3. Then V contains an invariant subspace of dimension 1.
Let U1=span{u1} denote this space, and extend this to a basis for V: V=span{u1,v2,v3}.

What I would like to do is show that span{v2,v3} contains an invariant subspace of dimension 1, span{u2}. Then form the invariant subspace U2=span{u1,u2}.

But I don't know how to show that span{v2,v3} contains an invariant subspace of dimension 1. I'm not sure that it's even true.
 
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  • #2
Did you prove the Schur decomposition? That any complex matrix is similar to an upper triangular matrix?
 
  • #3
I have this theorem:

If V is a complex vector space and T is a linear map on V, then T has an upper trianguler matrix with respect to some basis of V.

I think that this is equivalent to the Schur Theorem. I think I know how to proceed from here.

Choose a basis of V for which the matrix of T is upper triangular. Then the definition of the matrix of a linear map shows that V contains an invariant subspace of dimension j for j=1,...,dim V.

Is this right?

Thanks
 
  • #4
Sure. The matrix form does make it pretty easy to see the invariant subspaces.
 

1. What are invariant subspaces?

Invariant subspaces are vector subspaces that remain unchanged under a given transformation or operator. In other words, the vectors in an invariant subspace are mapped onto themselves when the transformation is applied.

2. Why are invariant subspaces important?

Invariant subspaces play a crucial role in understanding the behavior of linear systems. They can help us simplify complex systems and analyze the dynamics of a system by focusing on its invariant subspaces.

3. How can one find invariant subspaces?

One way to find invariant subspaces is by using eigenvalues and eigenvectors. Invariant subspaces correspond to the eigenspaces of a given transformation or operator. Another method is by using projection operators.

4. Can invariant subspaces change over time?

No, by definition, invariant subspaces remain unchanged under a given transformation or operator. However, the basis vectors of an invariant subspace may change over time if the transformation or operator itself changes.

5. What are some real-world applications of invariant subspaces?

Invariant subspaces have applications in various fields such as physics, engineering, and computer science. For example, they are used in quantum mechanics to study the energy levels of atoms and molecules, in control systems to analyze the stability of a system, and in image processing to identify patterns and features in images.

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