Matrix proof - augmented matrix - row reduction - column operation - proof

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Homework Help Overview

The discussion revolves around proving the equivalence of two statements regarding a system of linear equations represented by an augmented matrix. The original poster presents a scenario involving an m x (n + 1) matrix and seeks to understand the implications of linear dependence among its columns.

Discussion Character

  • Exploratory, Conceptual clarification, Problem interpretation

Approaches and Questions Raised

  • Participants suggest starting with examples to explore linear dependence among the columns of the matrix. There is a focus on expressing the relationship between the columns and deriving a system of equations from the assumption of linear dependence.

Discussion Status

Participants are actively engaging with the problem, exploring the implications of linear dependence and attempting to derive equations from the matrix representation. Some guidance has been offered regarding the structure of the equations, and there is an ongoing exploration of the relationship between the two statements.

Contextual Notes

There are indications of confusion regarding the representation of the columns as matrices and the implications of linear dependence. The discussion also touches on the concept of rank in relation to the matrices involved.

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Homework Statement



If we let A be the augmented m x (n + 1) matrix of a system of m linear equations
with n unknowns

Let B be the m x n matrix obtained from A by removing the last
column.

Let C be the matrix in row reduced form obtained from A by elementary
row operations.

Prove the the following statements are equivalent:

(i) The linear equations have no solutions

(ii) If [tex]c_1[/tex],..., [tex]c_{n+1}[/tex] are the columns of A, then [tex]c_{n+1}[/tex] is not a linear combination of
[tex]c_1[/tex],..., [tex]c_n[/tex]


Homework Equations





The Attempt at a Solution



I'm not sure were to start, some good advice would help and then I can use it - comment back

thank you
 
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You could start by finding some examples for yourself, this should give you a hint about what's going on.

Now, assume that [tex]c_1,...,c_{n+1}[/tex] are linearly dependent. This means that

[tex]\alpha_1 c_1+...+\alpha_n c_n=c_{n+1}[/tex]

for some [tex]\alpha_1,...,\alpha_n[/tex]. This determines a system of m equations. Can you tell me what that system is?
 
micromass said:
You could start by finding some examples for yourself, this should give you a hint about what's going on.

Now, assume that [tex]c_1,...,c_{n+1}[/tex] are linearly dependent. This means that

[tex]\alpha_1 c_1+...+\alpha_n c_n=c_{n+1}[/tex]

for some [tex]\alpha_1,...,\alpha_n[/tex]. This determines a system of m equations. Can you tell me what that system is?

Okay I think perhaps from this information I could get

[tex]a_1[/tex][tex]c_1[/tex] = [tex]c_{n+1}[/tex] - [tex]a_2[/tex][tex]c_2[/tex] - [tex]a_3[/tex][tex]c_3[/tex] - ... - [tex]a_n[/tex][tex]c_n[/tex]

and then do this for [tex]c_2[/tex] ... [tex]c_n[/tex] and have n equations

but I'm not sure how we would get a system of m equations from this,

unles we made a matrix with m rows. Also I see the value of [tex]a_{n+1}[/tex] = -1 ?
 
Note that [tex]c_1,...,c_{n+1}[/tex] are columns of a matrix. This means that they are just row matrices with m rows.
So, what you should do, is to write [tex]c_1,...,c_{n+1}[/tex] explicitly as row matrices and to regard the equation

[tex]\alpha_1 c_1+...+\alpha_n c_n=c_{n+1}[/tex]

if you exchange every ci by its actual form as a row matrix. You will see m equations popping up...
 
micromass said:
Note that [tex]c_1,...,c_{n+1}[/tex] are columns of a matrix. This means that they are just row matrices with m rows.
So, what you should do, is to write [tex]c_1,...,c_{n+1}[/tex] explicitly as row matrices and to regard the equation

[tex]\alpha_1 c_1+...+\alpha_n c_n=c_{n+1}[/tex]

if you exchange every ci by its actual form as a row matrix. You will see m equations popping up...

okay I think I understand a bit better, so considering c1 this will form the
first colum of A and it will have m rows, so we will have these equations, for the first row
of A it will be

c11c1 + c21c2 + ... + cn1cn = c(n+1)1

were cij s.t. i=column j=row

and the second will be

c12c1 + c22c2 + ... + cn2cn = c(n+1)2


we will get a series of m of there since the matrix A has m rows?
 
What is probably troubling you is that [tex]c_1[/tex] is a matrix. Thus, in fact:

[tex]c_i=\left(\begin{array}{c}c_{1,i}\\ c_{2,i}\\ \vdots\\ c_{m,i}\end{array}\right)[/tex]

So if you want to calculate the following:

[tex]\alpha_1c_1+...+\alpha_nc_n=c_{n+1}[/tex]

then you are actually calculating

[tex]\alpha_1 \left(\begin{array}{c}c_{1,1}\\ c_{2,1}\\ \vdots\\ c_{m,1}\end{array}\right)+\alpha_2 \left(\begin{array}{c}c_{1,2}\\ c_{2,2}\\ \vdots\\ c_{m,2}\end{array}\right)+ \hdots + \alpha_n \left(\begin{array}{c}c_{1,n}\\ c_{2,n}\\ \vdots\\ c_{m,n}\end{array}\right)=\left(\begin{array}{c}c_{1,n+1}\\ c_{2,n+1}\\ \vdots\\ c_{m,n+1}\end{array}\right)[/tex]

So, what happens if you make this addition?
 
micromass said:
What is probably troubling you is that [tex]c_1[/tex] is a matrix. Thus, in fact:

[tex]c_i=\left(\begin{array}{c}c_{1,i}\\ c_{2,i}\\ \vdots\\ c_{m,i}\end{array}\right)[/tex]

So if you want to calculate the following:

[tex]\alpha_1c_1+...+\alpha_nc_n=c_{n+1}[/tex]

then you are actually calculating

[tex]\alpha_1 \left(\begin{array}{c}c_{1,1}\\ c_{2,1}\\ \vdots\\ c_{m,1}\end{array}\right)+\alpha_2 \left(\begin{array}{c}c_{1,2}\\ c_{2,2}\\ \vdots\\ c_{m,2}\end{array}\right)+ \hdots + \alpha_n \left(\begin{array}{c}c_{1,n}\\ c_{2,n}\\ \vdots\\ c_{m,n}\end{array}\right)=\left(\begin{array}{c}c_{1,n+1}\\ c_{2,n+1}\\ \vdots\\ c_{m,n+1}\end{array}\right)[/tex]




So, what happens if you make this addition?

okay thanks, that is a lot more clear now


we will have m equations:

[tex]\alpha_1c_{1,1}+...+\alpha_nc_{1,n}=c_{1,(n+1)}[/tex]

.
.
.
.

[tex]\alpha_1c_{m,1}+...+\alpha_nc_{m,n}=c_{m,(n+1)}[/tex]

we assumed linear dependence, so I think we are trying to prove by counterexample
that linear dependence is not possible which imples the statement is true. So perhaps we must show next that these
[tex]a_1,a_2,...,a_n[/tex] do not exist such that this system holds?
 
ok I get it now, we got the system of equations and we showed that (ii) holds

is the same as saying there is no solutions of the linear equations which is exactly what one says (i) ?
 
if this is correct, could you possibly explain how Rank (A) > Rank (B)

where B is the m x n matrix we get from A by removing the last column .
 

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