Subspace theorem problem (matrix)

In summary, the subspace theorem is applied to show that S = {x \in ℝ : Ax = 0} is a subspace of R^n, where n is the number of columns in matrix A. This is proven by showing that the zero vector is in S, S is closed under addition and multiplication, and that for any u and v in S, their sum is also in S. Additionally, it is shown that the scalar multiple of any vector in S is also in S. Therefore, by the subspace theorem, S is a subspace of R^n.
  • #1
NewtonianAlch
453
0

Homework Statement


Suppose A is a fixed matrix in M. Apply the subspace theorem to show that

S = {x [itex]\in[/itex] ℝ : Ax = 0}

The Attempt at a Solution



Zero vector for x = <0,0,0>

A*<0,0,0> = 0

Therefore zero vector is in ℝ and S is non-empty.

Addition:

For u & v [itex]\in[/itex] S

u + v = 0 + 0 = 0

A*(u+v) = 0 => A(0) = 0

S is closed under addition

Multiplication:

λ [itex]\in[/itex] R

λ*A*u = λ*0 = 0

Therefore closed under multiplication and by subspace theorem, if a subspace of S.

Is this correct?
 
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  • #2
NewtonianAlch said:

Homework Statement


Suppose A is a fixed matrix in M. Apply the subspace theorem to show that

S = {x [itex]\in[/itex] ℝ : Ax = 0}
I can't tell what space x is in. It just shows up as a box in my browser. Is it R3?

Also, what is it you're supposed to show. All you have above is a definition for S. Are you supposed to show that S is a subspace of R3 (or whatever the space is)?
NewtonianAlch said:

The Attempt at a Solution



Zero vector for x = <0,0,0>

A*<0,0,0> = 0

Therefore zero vector is in ℝ and S is non-empty.

Addition:

For u & v [itex]\in[/itex] S

u + v = 0 + 0 = 0
No, you're not given any information that allows you to conclude that u and v are zero vectors or that they add to 0.
NewtonianAlch said:
A*(u+v) = 0 => A(0) = 0
No, for the reason given above. You need to use the properties of matrix multiplication.
NewtonianAlch said:
S is closed under addition

Multiplication:

λ [itex]\in[/itex] R

λ*A*u = λ*0 = 0
No, because you can't say that u is necessarily equal to 0.
NewtonianAlch said:
Therefore closed under multiplication and by subspace theorem, if a subspace of S.

Is this correct?
 
  • #3
I assume that instead of ℝ you mean Rn (Mark: it shows up in my browser as a blackboard [itex]\mathbb{R}[/itex]).

It looks almost correct to me, except for this line:
u + v = 0 + 0 = 0
You are taking u and v arbitrarily in S then they are not necessarily equal to zero. You may want to use the linearity of matrix multiplication here.

No, because you can't say that u is necessarily equal to 0.
Actually he used that Au = 0 there, so that is correct.
 
  • #4
CompuChip said:
I assume that instead of ℝ you mean Rn (Mark: it shows up in my browser as a blackboard [itex]\mathbb{R}[/itex]).
The OP is using vectors in R3, though.
CompuChip said:
It looks almost correct to me, except for this line:
u + v = 0 + 0 = 0
You are taking u and v arbitrarily in S then they are not necessarily equal to zero. You may want to use the linearity of matrix multiplication here.


Actually he used that Au = 0 there, so that is correct.
Right. I mistakenly thought the OP replaced u with 0, but it was Au being replaced. I didn't catch that.
 
  • #5
NewtonianAlch said:

Homework Statement


Suppose A is a fixed matrix in M. Apply the subspace theorem to show that

S = {x [itex]\in[/itex] ℝ : Ax = 0}
You mean "show that S is a subspace of"- I guess R3 although you said ℝ.


The Attempt at a Solution



Zero vector for x = <0,0,0>

A*<0,0,0> = 0

Therefore zero vector is in ℝ and S is non-empty.
Yes, that is correct.

Addition:

For u & v [itex]\in[/itex] S

u + v = 0 + 0 = 0
No. What you have written here is that u= v= 0 and that is not necessarily true.
However, what you may have meant to write may have been

A*(u+v) = 0 => A(0) = 0
Not quite. You need A(u+ v)= Au+ Av= 0+ 0= 0.

S is closed under addition

Multiplication:

λ [itex]\in[/itex] R

λ*A*u = λ*0 = 0
Again, not quite. You want [itex]A(\lambda u)= \lambda A(u)= \lambda(0)= 0[/itex]

Therefore closed under multiplication and by subspace theorem, if a subspace of S.

Is this correct?
 
  • #6
That should have been an R^n yes! I mistakenly left that out, sorry about the confusion!
 
  • #7
Thanks HallsofIvy, I guess I need to expand and be more explicit in each step of the way. I just assumed I didn't have to show that commutative, or was it associative law of multiplication. But I understand what you mean!
 
  • #8
I'm not too sure what's wrong with the addition aspect.

Given that I want to essentially prove that the matrix A multiplied by an addition of vectors would equal zero A*(u + v) = 0

So would it suffice to say that the above statement is true, and u + v = 0 but that neither u or v has to equal 0?
 
  • #9
But it isn't true that u+ v= 0. What is true is that Au= 0 and Av= 0. That's completely different.
 
  • #10
for emphasis, u in S does NOT mean u is 0. it just means that Au = 0.

of course, the 0-vector 0 is ONE of the vectors in S, but an arbitrary vector in S need not be the 0-vector (it's hard to say for sure without knowing more about the matrix (or linear map) A).

what you need to show, is that:

if u,v is in S, so Au = 0 and Av = 0,

then A(u+v) = 0.

next, you need to show that if c is in R, and u is in S, that cu is also in S

(which entails showing that A(cu) = 0).
 

FAQ: Subspace theorem problem (matrix)

What is the subspace theorem problem?

The subspace theorem problem refers to a mathematical concept in linear algebra that involves finding a subspace within a given vector space that is invariant under a linear transformation. This means that the subspace remains unchanged after the transformation is applied.

Why is the subspace theorem important?

The subspace theorem is important because it allows us to better understand the structure of vector spaces and how linear transformations affect them. It also has many practical applications in fields such as physics, engineering, and computer science.

How do you solve a subspace theorem problem?

To solve a subspace theorem problem, you first need to identify the vector space and the linear transformation involved. Then, you must find the basis for the subspace that remains invariant under the transformation. This can be done by finding the eigenvectors of the transformation or by using the null space and range of the transformation.

What are some common mistakes when solving a subspace theorem problem?

One common mistake when solving a subspace theorem problem is not properly identifying the vector space and the linear transformation involved. Another mistake is not finding the correct basis for the subspace. It is also important to check that the subspace is indeed invariant under the transformation, as it is possible to make an error in the calculations.

Can the subspace theorem be applied to real-world problems?

Yes, the subspace theorem has many real-world applications, such as in image processing, control systems, and data compression. It can also be used to study physical systems, such as quantum mechanics and fluid dynamics. Understanding the subspace theorem can help us analyze and solve complex problems in various fields.

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