Prove the following; (vector spaces and linear operators)

In summary, the given conversation discusses a vector space V and a linear operator T on V, where T^2 = I. It is proven that the only possible eigenvalues for T are 1 and -1. It is also shown that V can be decomposed into the direct sum of two subspaces, U_1 and U_-1, where U_1 consists of vectors that are mapped to themselves by T, and U_-1 consists of vectors that are mapped to their negatives by T.
  • #1
toni07
25
0
a) V = U_1 ⊕ U_-1 where U_λ = {v in V | T(v) = λv}
b) if V = M_nn(R) and T(A) = A^t then what are U_1 and U_-1

When V is a vector space over R, and T : V -> V is a linear operator for which
T^2 = IV .
 
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  • #2
Re: Prove the following;

Given $T^2 = I$, it's clear the only possible eigenvalues are 1 and -1, since the minimal polynomial for $T$ divides $x^2 - 1 = (x + 1)(x - 1)$.

Note that:

$\frac{1}{2}(T + I) - \frac{1}{2}(T - I) = I$

Thus:

$v = Iv = \frac{1}{2}(T + I)(v) - \frac{1}{2}(T - I)(v)$

I claim $u = \frac{1}{2}(T + I)(v) \in U_1$ and

$w = -\frac{1}{2}(T - I)(v) \in U_{-1}$.

To see this, observe that:

$T(u) = T(\frac{1}{2}(T + I)(v)) = \frac{1}{2}T(T + I)(v) = \frac{1}{2}(T^2 + T)(v)$

$= \frac{1}{2}(T + I)(v) = u$, while:

$T(w) = T(-\frac{1}{2}(T - I)(v)) = -\frac{1}{2}T(T - I)(v) = -\frac{1}{2}(T^2 - T)(v)$

$= \frac{1}{2}(T - I)(v) = -w$.

Thus $V = U_1 + U_{-1}$.

Your turn (prove this sum is direct).
 

Related to Prove the following; (vector spaces and linear operators)

1. What is a vector space?

A vector space is a mathematical structure that consists of a set of vectors and a set of operations that can be performed on those vectors. The operations include addition and scalar multiplication, and they must follow certain rules, such as closure and associativity, for the set to be considered a vector space.

2. Can you provide an example of a vector space?

Yes, the set of real numbers (R) is a common example of a vector space. In this case, the vectors are simply the single numbers, and the operations of addition and scalar multiplication follow the usual rules for real numbers.

3. What is a linear operator?

A linear operator is a function that maps one vector space to another, and it preserves the structure of the vector space. This means that the operation of addition and scalar multiplication in the output vector space must follow the same rules as in the input vector space.

4. How do you prove that a function is a linear operator?

To prove that a function is a linear operator, you must show that it satisfies two properties: additivity and homogeneity. Additivity means that the function preserves addition, so f(u + v) = f(u) + f(v). Homogeneity means that the function preserves scalar multiplication, so f(αv) = αf(v) for any scalar α.

5. What are the implications of a function being a linear operator?

If a function is a linear operator, it means that it follows the fundamental properties of vector spaces, and it can be used to perform various mathematical operations on vectors. This is important in many areas of science, such as physics, engineering, and data analysis, where vector spaces and linear operators are used extensively.

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