[Linear Algebra] Help with Linear Transformations part 2

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Homework Help Overview

The discussion revolves around linear transformations in the context of linear algebra, specifically focusing on a linear transformation \( j \) that satisfies \( j \circ j = id_V \). The original poster is tasked with proving that certain sets, \( S \) and \( A \), are subspaces and that the vector space \( V \) can be expressed as a direct sum of these subspaces.

Discussion Character

  • Exploratory, Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • The original poster attempts to verify that \( S \) and \( A \) are subspaces, noting that the intersection \( S \cap A \) is trivial. They express uncertainty about formalizing the proof that \( V = S \oplus A \). They also question how to apply their findings to deduce relationships between symmetric and skew-symmetric matrices.

Discussion Status

Participants are exploring various interpretations of the problem, with some suggesting references to external resources that may provide insights into the decomposition of the vector space. The original poster is seeking clarification on their methodology and the implications of the linear transformation properties.

Contextual Notes

There is mention of a potential confusion regarding the application of the linear transformation \( j \) and its relationship to the decomposition of matrices. The original poster also notes a language barrier in translating the problem statement.

iJake
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Homework Statement



Homework Statement



(a) Let ##V## be an ##\mathbb R##-vector space and ##j : V \rightarrow V## a linear transformation such that ##j \circ j = id_V##. Now, let

##S = \{v \in V : j(v) = v\}## and ##A = \{v \in V : j(v) = -v\}##

Prove that ##S## and ##A## are subspaces and that ##V = S \oplus A##.

(b) Deduce that the decomposition of the matrices in direct sum from the symmetric and skew-symmetric matrices from part (a) (finding a convenient linear transformation ##j##)

[apologies if that last part is a bit weird sounding, I'm translating from Spanish]

Homework Equations


---

The Attempt at a Solution



a) The test for ##S## and ##A## being subspaces is fairly trivial so I don't include it. Now, to determine that ##V = S \oplus A## I'm finding it a little trickier. I observe clearly that ##S \cap A = \{0\}## but how do I formalize that and lead into it proving that V is the direct sum of S and A?

b) is also confusing me. I found this and it looks remarkably similar to my problem, but I do not know how to apply it here.

Thank you Physics Forums for any help.
 
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I apologize for taking so long to reply to this thread! I was away.

I'm still finding this one a bit tricky. At first I took ##u \in S## and ##w \in A## but these were not working for me I don't think. Now I've reached

##u \in A = j(v) + v = -v + v##
##w \in S = j(v) - v = v - v##

##v = u + w = (j(v) + v) + (j(v) - v) = (-v + v) + (v - v) = 0 + 0 = 0##

Is this the idea I'm meant to apply? I'm dubious as I don't think I really used the fact that ##j \circ j = id_V##.

I suppose once I'm sure about the methodology for this first part I'll be better equipped to deduce the relationship between symmetric and skew-symmetric matrices. What does my problem mean when it says to find the convenient linear map ##j##?

Thanks again for taking the time to assist me.
 
check out the relatively prime decomposition theorem on page 26 of these notes;

http://alpha.math.uga.edu/%7Eroy/4050sum08.pdfit says that since your map satisfies the polynomial X^2-1 = (X-1)(X+1), then your space is a direct sum of subspaces on which your map satisfies ether X-1 or X+1.

it is not entirely trivial since the proof uses the euclidean algorithm as i recall.

or look at the equivalent decomposition lemma at the beginning of chapter 4 of these notes:

http://alpha.math.uga.edu/%7Eroy/laprimexp.pdf
 

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