Invariant subspaces under linear operators

In summary, the statement is true. If U is a subspace of V that is invariant under every operator on V, then U = {0} or U = V. This means that if U is not equal to {0} and not equal to V, then it is not invariant under every operator on V. So your counterexample is correct in showing that the contrapositive of the statement is true. This means that the original statement is also true.
  • #1
jimmypoopins
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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.

Homework Equations


U is invariant under a linear operator T if u in U implies T(u) is in U.

The Attempt at a Solution


Assume {0} does not equal U does not equal V. Let {u1,...,un} be a basis for U. Extend to a basis for V: {u1,...,un,v1,...,vm}. Since V does not equal {0}, m is greater than or equal to 1. Define a linear operator by T=v1, i=1,...n and T(vi)=v1, i=1,...,m. Then U is not invariant under T.

I think this is a counterexample to the contrapositive of the statement. does it work? (the contrapositive is If U does not equal {0} does not equal V, then U is not invariant under every operator on V, right?)
 
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  • #2
I think you have the right idea, but your write up has some typos in it so I'm not sure.
 

What are invariant subspaces under linear operators?

Invariant subspaces under linear operators are subspaces of a vector space that are mapped onto themselves by the given linear operator. This means that any vector in the subspace remains in the subspace after the linear transformation is applied.

Why are invariant subspaces important in linear algebra?

Invariant subspaces are important because they provide a way to break down a complex vector space into smaller, simpler subspaces that are easier to analyze. They also have important applications in areas such as physics and engineering where linear transformations are used to model real-world systems.

How do you determine if a subspace is invariant under a given linear operator?

To determine if a subspace is invariant under a given linear operator, you can use the concept of eigenvectors and eigenvalues. If the subspace contains all of the eigenvectors corresponding to a particular eigenvalue, then it is invariant under the linear operator.

Can a vector space have multiple invariant subspaces under the same linear operator?

Yes, a vector space can have multiple invariant subspaces under the same linear operator. In fact, every vector space has at least two trivial invariant subspaces: the whole space itself and the zero subspace. There can also be non-trivial invariant subspaces that are proper subspaces of the vector space.

What is the relationship between invariant subspaces and diagonalization?

Invariant subspaces and diagonalization are closely related concepts. Invariant subspaces can be thought of as a generalization of diagonalization, where a linear operator is represented by a diagonal matrix. In fact, a linear operator can be diagonalized if and only if its vector space has a basis consisting of invariant subspaces.

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