How Do Ranks of Composite Linear Transformations Compare?

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Homework Help Overview

The discussion revolves around the ranks of composite linear transformations defined by linear maps S and T between vector spaces U, V, and W over the same field K. Participants are tasked with proving several inequalities and equalities related to the ranks of these transformations.

Discussion Character

  • Conceptual clarification, Assumption checking, Mixed

Approaches and Questions Raised

  • Participants explore the implications of the rank of composite transformations TS in relation to the ranks of T and S. There are attempts to clarify the definitions and relationships between the mappings and their images.

Discussion Status

Some participants have provided insights into the relationships between the ranks and the implications of nonsingularity. However, there remains uncertainty regarding specific proofs and the interpretation of terms like "entries" in the context of vector spaces.

Contextual Notes

There is a discussion about the dimensions of the vector spaces involved and whether they can be considered to have the same number of entries. Additionally, the implications of nonsingularity on the ranks of the transformations are under examination.

mathmathmad
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Homework Statement


Let S(U)=V and T(V)=W be linear maps where U,V, W are vector spaces over the same field K. Prove :

Homework Equations


a) Rank (TS) <= Rank (T)
b) Rank (TS) <= Rank (S)
c) if U=V and S is nonsingular then Rank (TS) = Rank (T)
d) if V=W and T is nonsingular then Rank (TS) = Rank (S)

The Attempt at a Solution


a) TS maps to W, so is T
b) TS maps to W, but S to V, but how do I show the ranks for (a) and (b)?
c) d) So inverse of S and T exists, and err...

U,V,W are vector space over the SAME field, does that mean they have the same number of entries, say R2, R3, etc etc
 
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mathmathmad said:

Homework Statement


Let S(U)=V and T(V)=W be linear maps where U,V, W are vector spaces over the same field K. Prove :

Homework Equations


a) Rank (TS) <= Rank (T)[/math]
The rank of a linear map is the dimension of its image space. Let w be in TS(U). Then there exist u in U such that TS(u)= w. Let v= S(u). Then T(v)= TS(u)= w. That is, if w is in TS(U) then it is in T(V). TS(U) is a subset of T(U) and, since they are both subspaces of W, TS(U) is a subspace of T(U).

b) Rank (TS) <= Rank (S)
Let \{u_1, u_2,\cdot\cdot\cdot, u_n\} be a basis for U. Then \{S(u_1), S(u_2), \cdot\cdot\cdot, S(u_n)\} spans S(U) (but is not neccessarily independent) so the dimension of S(U) is less than or equal to the dimension of U. Further \{TS(u_1), TS(u_2), \cdot\cdot\cdot, TS(u_n)\} spans TS(U) but is not necessarily independent so dimension of TS(U) is less than or equal to the dimension of S(U).

c) if U=V and S is nonsingular then Rank (TS) = Rank (T)
d) if V=W and T is nonsingular then Rank (TS) = Rank (S)

The Attempt at a Solution


a) TS maps to W, so is T
I have no idea what you mean by this. What "is T"? Are you saying TS= T? That's obviously not true.

b) TS maps to W, but S to V, but how do I show the ranks for (a) and (b)?
c) d) So inverse of S and T exists, and err...

U,V,W are vector space over the SAME field, does that mean they have the same number of entries, say R2, R3, etc etc
I have no idea what you mean by "entries". If you mean dimension, no. R2, R3, etc. are all "over the SAME field", the real numbers, but do NOT have the same "number of entries".
 
"TS maps to W, so is T"

sorry, what I meant was T also maps to W

for (c) and (d)
what does the the nonsingularity of the matrix imply?
how does that show that the ranks are both equal?
 
mathmathmad said:
"TS maps to W, so is T"

sorry, what I meant was T also maps to W

for (c) and (d)
what does the the nonsingularity of the matrix imply?
how does that show that the ranks are both equal?

I think the best way to show that the ranks are equal is by considering the surjectivity and injectivity of S and T respectively (since S and T are non-singular). From there, it should be pretty much straightforward.
 

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