Understanding Singular Linear Maps: R^m -> R^n

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Homework Help Overview

The discussion revolves around the concept of singular linear maps, specifically in the context of linear transformations from R^m to R^n. Participants are exploring definitions and implications of singularity in linear maps, as well as the relationship between linear maps and their matrix representations.

Discussion Character

  • Conceptual clarification, Assumption checking

Approaches and Questions Raised

  • Participants are attempting to clarify the definition of a singular linear map and its implications based on the dimensions m and n. There are questions regarding the matrix representation of linear maps and the conditions under which a linear map is considered singular.

Discussion Status

Some participants have provided definitions and insights into the nature of singular linear maps, while others are seeking further clarification on related topics in linear algebra. There is an ongoing exploration of the implications of dimensionality on the properties of linear maps.

Contextual Notes

There are mentions of incomplete information regarding the original question posed by the original poster, as well as a lack of clarity in lecture notes about the concept of singularity in linear maps.

annoymage
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Homework Statement



the question here said

is L, linear transformation/mapping is singular?

i'm still googling the definition singular linear map,

can anyone give me the definition please T_T

p/s; i thought it L maybe the matrix representation, but the question

L : R^m -> R^n

but aren't matrix representation are define on L: M(m,1) -> M(n,1)

should i take transpose of R^m and R^n ?
 
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If L is a linear map between finite-dimensional vector spaces (such as \mathbb{R}^m and \mathbb{R}^n), then it can be represented by a matrix. In fact, it can be represented by many different matrices, each corresponding to different choices of basis. But sometimes the answer to a question can be clearer if you don't focus on the matrix representation. Unfortunately, you did not fully state the question! (i.e., how is L defined?)

In any case, a singular linear map is simply one that does not have an inverse. It can fail to have an inverse in one (or both) of two ways: (1) it is not injective, meaning there exist a \neq b \in \mathbb{R}^m such that L(a) = L(b); or (2) it is not surjective, meaning that there exists some b \in \mathbb{R}^n such that L(a) \neq b for all a \in \mathbb{R}^m.

If m \neq n, then L is guaranteed to be singular. (Why?)
 
thanks for the definition.

And your question, I can see If m<n then not surjective means singular

if m>n then not injective means singular. But still working on that. Tomorrow i'll try to post, help check it.

Anyway, i don't understand this,
jbunniii said:
If L is a linear map between finite-dimensional vector spaces (such as \mathbb{R}^m and \mathbb{R}^n), then it can be represented by a matrix. In fact, it can be represented by many different matrices, each corresponding to different choices of basis.

what topic should i study for this thing?
 
annoymage said:
And your question, I can see If m<n then not surjective means singular

Correct, surjectivity is impossible when m &lt; n, and injectivity is impossible when m &gt; n.
Anyway, i don't understand this

what topic should i study for this thing?

Well, the topic is linear algebra, but you can study it at several levels of sophistication. Are you taking a course in it? If so, have you seen the definition of a linear map between two vector spaces? It should look something like this:

If V and W are vector spaces over the same field K and L : V \rightarrow W, then L is a linear map if the following are true for all v, v_1, v_2 \in V, and k \in K:

(1) L(v_1 + v_2) = L(v_1) + L(v_2)

(2) L(kv) = kL(v)
 
annoymage said:
what topic should i study for this thing?

doesn't matter, i think i found it, i'll read it forthwith
 
jbunniii said:
Well, the topic is linear algebra, but you can study it at several levels of sophistication. Are you taking a course in it? If so, have you seen the definition of a linear map between two vector spaces? It should look something like this:

If V and W are vector spaces over the same field K and L : V \rightarrow W, then L is a linear map if the following are true for all v, v_1, v_2 \in V, and k \in K:

(1) L(v_1 + v_2) = L(v_1) + L(v_2)

(2) L(kv) = kL(v)

yea i did pass that topic, only my lecture note doesn't define what singularity of linear map is, anyway thank you so much
 

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