Question involving a linearly independent set of vectors

In summary: that because x1 and x2 are not linearly independent (they are both solutions to c1x1+c2x2 = 0), the equation c1x1+c2x2 = 0 is not necessarily linearly independent.
  • #1
happy_hippo
1
0

Homework Statement



Show that if {a, b, c} is a linearly independent set of vectors, then so are {a, b}, {a, c}, {b, c}, {a}, {b}, and {c}.

Homework Equations



None.

The Attempt at a Solution



Well I was just thinking that if {a, b, c} is a linearly independent set of vectors, then {a, b, c} span R3. And so if {a, b, c} span R3, then {a, b}, {a, c}, and {b, c} must span R2 and be linearly independent, and {a}, {b}, and {c} span R1 and be linearly independent. That's all I got. How could I prove this mathematically?


Cheers!
 
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  • #3
happy_hippo said:

Homework Statement



Show that if {a, b, c} is a linearly independent set of vectors, then so are {a, b}, {a, c}, {b, c}, {a}, {b}, and {c}.

Homework Equations



None.

The Attempt at a Solution



Well I was just thinking that if {a, b, c} is a linearly independent set of vectors, then {a, b, c} span R3. And so if {a, b, c} span R3, then {a, b}, {a, c}, and {b, c} must span R2 and be linearly independent, and {a}, {b}, and {c} span R1 and be linearly independent. That's all I got. How could I prove this mathematically?


Cheers!

i always find it's best to go back to the definition:

a set of vectors {v1,v2,...vk} is linearly independent if:

c1v1+c2v2+...+ckvk implies:

c1 = c2 = ... ck = 0.

in this case, suppose that we have:

r1a + r2b = 0.

then we have r1a + r2b + r3c = 0

(by setting r3 = 0), and by the ____ _____ of _______...
 
  • #4
Had to add a couple of corrections.
Deveno said:
i always find it's best to go back to the definition:

a set of vectors {v1,v2,...vk} is linearly independent if:

c1v1+c2v2+...+ckvk = 0 implies:

c1 = c2 = ... ck = 0 , and there are no other solutions for these constants.

in this case, suppose that we have:

r1a + r2b = 0.

then we have r1a + r2b + r3c = 0

(by setting r3 = 0), and by the ____ _____ of _______...

The "and there are no other solutions" part is important, because the equation c1v1+c2v2+...+ckvk = 0 has solutions whether the vectors are linearly independent or linearly dependent. The only distinction between these classifications hinges on there being a single solution (lin. independent) or an infinite number of solutions (lin. dependent), and students almost always have a hard time understanding this.
 
  • #5
happy_hippo said:
Well I was just thinking that if {a, b, c} is a linearly independent set of vectors, then {a, b, c} span R3. And so if {a, b, c} span R3, then {a, b}, {a, c}, and {b, c} must span R2
No, this is absolutely not true. The vectors a, b, and c "live" in R3. It would be reasonable to say that {a, b} span a two-dimensional subspace of R3, but that is definitely not the same as R2.

Vectors in R3 always have 3 coordinates; vectors in R2 have only two coordinates.
happy_hippo said:
and be linearly independent, and {a}, {b}, and {c} span R1 and be linearly independent. That's all I got. How could I prove this mathematically?
 
  • #6
Mark44 said:
Had to add a couple of corrections.

The "and there are no other solutions" part is important, because the equation c1v1+c2v2+...+ckvk = 0 has solutions whether the vectors are linearly independent or linearly dependent. The only distinction between these classifications hinges on there being a single solution (lin. independent) or an infinite number of solutions (lin. dependent), and students almost always have a hard time understanding this.

uh...i thought that that was what the word "implies" meant. i thought about using the word "forces" instead, making it clearer that the c's have to be all 0. i think this is just a semantical hitch, the idea is to set a linear combination to 0, and prove the coefficients must all be 0.

(to say "p implies q" is the same thing as: p ONLY IF q).
 
  • #7
I agree that there's no need to add that there are no other solutions, and that this follows from the meaning of "implies".
 
  • #8
Deveno said:
uh...i thought that that was what the word "implies" meant. i thought about using the word "forces" instead, making it clearer that the c's have to be all 0. i think this is just a semantical hitch, the idea is to set a linear combination to 0, and prove the coefficients must all be 0.

(to say "p implies q" is the same thing as: p ONLY IF q).

Consider these vectors in R2: x1 = <1, 2> and x2 = <2, 4>.

I set up the equation c1x1 + c2x2 = 0.

I note that c1 = c2 = 0 satisfies the equation above, and (mistakenly) conclude that the two vectors are linearly independent.

With P being the first equation above, and Q being the equation involving the constants, we see that P ==> Q. IOW, assuming that c1x1 + c2x2 = 0 is true, we see that c1 = c2 = 0 is true, hence the implication is true.

This ignores the fact that there are nontrivial solutions for the constants, and this is what many students miss when they are working with the definitions of lin. independence and lin. dependence, and why I always emphasize the part that there is only one solution for the constants (the trivial solution) when the vectors are lin. independent.
 
  • #9
Deveno said:
uh...i thought that that was what the word "implies" meant. i thought about using the word "forces" instead, making it clearer that the c's have to be all 0. i think this is just a semantical hitch, the idea is to set a linear combination to 0, and prove the coefficients must all be 0.

(to say "p implies q" is the same thing as: p ONLY IF q).

You are absolutely correct, but can you be sure the OP understands this? Clarification cannot hurt, and may help.

RGV
 
  • #10
Mark44 said:
Consider these vectors in R2: x1 = <1, 2> and x2 = <2, 4>.

I set up the equation c1x1 + c2x2 = 0.

I note that c1 = c2 = 0 satisfies the equation above, and (mistakenly) conclude that the two vectors are linearly independent.

With P being the first equation above, and Q being the equation involving the constants, we see that P ==> Q. IOW, assuming that c1x1 + c2x2 = 0 is true, we see that c1 = c2 = 0 is true, hence the implication is true.
No, that's not true. Given that c1x1 + c2x2 = 0 we can see that c1= -2c2. c1= c2= 0 satisfy that but it does not follow that they must 0. The first equation does not imply that they are 0.

This ignores the fact that there are nontrivial solutions for the constants, and this is what many students miss when they are working with the definitions of lin. independence and lin. dependence, and why I always emphasize the part that there is only one solution for the constants (the trivial solution) when the vectors are lin. independent.
 
Last edited by a moderator:
  • #11
Mark 44 mistakenly started with an example of two linearly dependent vectors, not two linearly independent ones.
 
  • #12
Try these instead: For example: a=<1,0> and b=<4,3>. c1a+c2b=0 implies c1<1,0>+c2<4,3>=0 implies the system {c1+4c2=0 and 3c2=0} which clearly has no other solution besides c1=c2=0. Therefore, a and b are linearly independent.
 
  • #13
catherinenanc said:
Mark 44 mistakenly started with an example of two linearly dependent vectors, not two linearly independent ones.
No, not mistakenly. I meant to start with two linearly dependent vectors.
 
  • #14
My whole point, and maybe I didn't state it in the best possible way is this:

If you have an equation c1x + c2x2 + ... + cnxn = 0, then c1 = c2 = ... = cn = 0 is a solution whether or not the vectors form a linearly independent set. Over the course of many years teaching Linear Algebra, I've noticed that many students don't get this fine point, and that it helps them if you emphasize that for linearly independent vectors, the solution for the constants is unique.
 
  • #15
i understand your point Mark44, and it's well-taken. as Ray pointed out, clarification doesn't hurt, as the "goal" of understanding linear independence is not to be able to state an impeccable definition of it, but rather, to be able to actually determine if sets are linearly independent or not.

but the problem here (and i agree it IS a problem), does not lie with linear independence per se. it lies in a mistaken application of logic (which perhaps cuts deeper than the issue at hand).

as i pointed out before, p→q means (in English) "p only if q". what happens, is people make THIS mistake:

suppose p, instance where q is true, therefore q always true, so p→q always true.

it is common for people to confuse inductive reasoning with deductive reasoning. the proper order of things is:

general case implies specific case. but we often think of a specific case as being an example of the general case, and make the mistake of "over-generalizing".

i agree that p→q and p↔q often sound "very similar". they even LOOK similar. the problem, at heart (in this definition of linear independence, and many other areas where students make "common mistakes") is not how precisely we define our terms...but rather, a failure to teach the basics of logical reasoning before throwing math (which depends on logical reasoning) proofs at students ill-equipped to understand them.
 

1. What does it mean for a set of vectors to be linearly independent?

Linear independence refers to the property of a set of vectors where none of the vectors can be expressed as a linear combination of the other vectors in the set. In other words, no vector in the set can be written as a sum of scalar multiples of the other vectors.

2. How can I determine if a set of vectors is linearly independent?

A set of vectors is linearly independent if the only solution to the equation c1v1 + c2v2 + ... + cnvn = 0 is when all the coefficients (c1, c2, ..., cn) are equal to 0. In other words, if the only way to get a linear combination of the vectors to equal 0 is by using a coefficient of 0 for each vector, then the set is linearly independent.

3. Can a set of only two vectors be linearly independent?

Yes, a set of only two vectors can be linearly independent. This is possible if the two vectors are not scalar multiples of each other and are not collinear (lie on the same line).

4. What is the difference between linear independence and linear dependence?

Linear dependence refers to the opposite property of linear independence, where at least one vector in a set can be expressed as a linear combination of the other vectors in the set. In other words, the set is not linearly independent. Linear dependence is also sometimes referred to as linearly correlated or linearly related.

5. How does linear independence relate to the dimension of a vector space?

The dimension of a vector space is equal to the number of linearly independent vectors that can span the space. This means that if a set of vectors is linearly independent, then the number of vectors in the set is equal to the dimension of the vector space they span. Additionally, adding a linearly independent vector to a set will increase the dimension of the vector space by 1.

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