Restriction of a Linear Transformation

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dward1996
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Given a linear tansformation T of a vector space V (over a field K) with eigenbasis {v_{1},...,v_{n}}, and a (non-trivial) subspace W of V such that T(W) is a subset of W, a lecturer keeps using the result that W will contain an eignvector for T. I can see why this would be the case if the field K were algebraically closed, but how do we know that W will have any eigenvectors for T if K is an arbitrary field?
 
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It follows from the fact that the operator has an eigenbasis. Here's a proof:

W is a nontrivial subspace so it contains some nonzero vector [itex]v=a_{i_1}v_{i_1}+\cdots +a_{i_k}v_{i_k}[/itex], where the [itex]v_{i_k}[/itex]s are some subset of the eigenbasis, all of the [itex]a_{i_k}[/itex]s are nonzero, and [itex]1\leq k\leq n[/itex]. Since [itex]T(W)\subseteq T[/itex], we have [itex]T(v)\in W[/itex]. If all the eigenvalues of the [itex]v_{i_k}[/itex]s are equal, we've found an eigenvector so we're done. If not, we can use the fact that W is a subspace to construct a linear combination of v and T(v), in W, that eliminates one of the [itex]v_{i_k}[/itex]s (concretely, [itex]\lambda_{i_1}v-T(v)[/itex], where [itex]\lambda_{i}[/itex] is the eigenvalue of [itex]v_{i}[/itex]. It's nonzero if the eigenvalues aren't all equal.). So we construct another nonzero vector in W, but with k reduced by 1.

We can now repeat the process. It must eventually terminate, at k=1 if not before. So we've constructed an eigenvector for T in W.
 
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Thanks for that. It makes perfect sense now!