Understanding Endomorphisms and Eigenspaces in Linear Algebra

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An endomorphism phi satisfies phi^3=phi, leading to the conclusion that the vector space V can be decomposed into the direct sum of eigenspaces E(0), E(1), and E(-1). This decomposition highlights the relationship between the eigenvalues and the structure of the vector space. Additionally, for a square matrix A over a field k, if r1, r2, ..., rt are its eigenvalues, then applying a polynomial f from k[x] to A results in f(r1), f(r2), ..., f(rt) also being eigenvalues of f(A). The discussion emphasizes the importance of understanding eigenspaces and their properties in linear algebra. Overall, these concepts are crucial for deeper insights into linear transformations and their effects on vector spaces.
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1. Let phi be an endomorphism such that phi^3=phi. Prove that V=E(0) (+) E(1) (+) E(-1), where E(r) denotes the eigenspace associated to the eigenvalue r.

2. Let k be a field and A a square matrix in k. Prove that if r1, r2, ..., rt are eigenvalues of A and f is an element of k[x] then f(r1), f(r2), ..., f(rt) are also eigenvalues of f(A).
 
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