Q: What is the maximum dimension of [D - λ]^-1(W) for a subspace W of V?

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In summary, [D - λ]^-1(W) refers to the inverse of the linear transformation D - λ applied to the subspace W, and the maximum dimension of [D - λ]^-1(W) can be calculated by finding the number of linearly independent vectors in the subspace W. This dimension represents the largest possible number of linearly independent vectors that can be obtained when applying the inverse of D - λ to the subspace W. The maximum dimension of [D - λ]^-1(W) is a subset of the dimension of V and cannot be greater than the dimension of W.
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Chris L T521
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Here's this week's problem!

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Problem
: It is a basic result in analysis that for any $f\in V$, and any $\lambda\in\mathbb{C}$, $Df=\lambda\cdot f$ if and only if $f(x)=C\cdot e^{\lambda x}$ for some $C\in\mathbb{C}$. Now assume that $W$ is a subspace of $V$. Define $$[D-\lambda]^{-1}(W)=\{f\in V:Df-\lambda f\in W\}.$$

Prove that $\dim([D-\lambda]^{-1}W)\leq\dim(W)+1$.

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Remember to read the http://www.mathhelpboards.com/showthread.php?772-Problem-of-the-Week-%28POTW%29-Procedure-and-Guidelines to find out how to http://www.mathhelpboards.com/forms.php?do=form&fid=2!
 
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This week's problem was correctly answered by Deveno. You can find his solution below.

[sp]Let $U = [D - \lambda]^{-1}(W)$ and let $T$ be the restriction of $D-\lambda$ to $U$.

Then we have:

$\dim(U) = \dim(\text{ker }T) + \dim(\text{im }T)$

But $\text{im }T = T(U) = (D-\lambda)(U) = (D-\lambda)([D - \lambda]^{-1}(W)) = W$.

Moreover, we have that $\text{ker }T \subseteq \text{ker }(D-\lambda)$, which has dimension 1, since it is spanned by $\{e^{\lambda x}\}$.

Since $\text{ker }T$ is clearly a subspace of $\text{ker }(D - \lambda)$ (namely: $U \cap \text{ker }(D - \lambda)), \dim(\text{ker }T) \leq 1$.

Thus:

$\dim([D - \lambda]^{-1}(W)) = \dim(U) = \dim(\text{ker }T) + \dim(\text{im }T) = \dim(\text{ker }T) + \dim(W) \leq 1 + \dim(W)$[/sp]
 

FAQ: Q: What is the maximum dimension of [D - λ]^-1(W) for a subspace W of V?

1. What is the meaning of [D - λ]^-1(W) in this context?

In this context, [D - λ]^-1(W) refers to the inverse of the linear transformation D - λ applied to the subspace W. This means that the result of applying [D - λ]^-1(W) to any vector in W will be the unique vector in V that, when transformed by D - λ, results in the original vector from W.

2. How can the maximum dimension of [D - λ]^-1(W) be calculated?

The maximum dimension of [D - λ]^-1(W) can be calculated by finding the number of linearly independent vectors in the subspace W. This can be done by determining the size of the basis for W or by using the rank-nullity theorem to find the number of linearly independent vectors in W.

3. What does the maximum dimension of [D - λ]^-1(W) represent?

The maximum dimension of [D - λ]^-1(W) represents the largest possible number of linearly independent vectors that can be obtained when applying the inverse of D - λ to the subspace W. This dimension is important in understanding the properties of the transformation D - λ and its relationship to the subspace W.

4. How does the maximum dimension of [D - λ]^-1(W) affect the overall dimension of V?

The maximum dimension of [D - λ]^-1(W) is a subset of the dimension of V. Therefore, it cannot be larger than the dimension of V. However, if the maximum dimension of [D - λ]^-1(W) is equal to the dimension of V, this means that the subspace W is a critical subspace for the linear transformation D - λ and has a significant impact on the overall dimension of V.

5. Can the maximum dimension of [D - λ]^-1(W) be greater than the dimension of W?

No, the maximum dimension of [D - λ]^-1(W) cannot be greater than the dimension of W. This is because the inverse of D - λ is only defined for vectors in W, so the maximum number of linearly independent vectors in [D - λ]^-1(W) cannot exceed the dimension of W.

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