Shilov's Linear Algebra Subspace Question

In summary, Shilov says that a linear combination of vectors in a subset of space is independent if and only if the dimension of the subset is the same as the dimension of the space.
  • #1
Chacabucogod
56
0
Hi,

I'm reading Shilov's linear algebra and in part 2.44 he talks about linear independent vectors in a subspace L which is a subset of space K( he refers to it as K over L). I don't understand why he says that a linear combination of vectors of the subspace L and vectors of the subspace K over L is independent. Is it the same subspace or am I wrong? Shilov also says that the dimension of the subspace K over L n-l. Why?

LINK to the page:

http://books.google.com.mx/books?id=5U6loPxlvQkC&pg=PA44&lpg=PA44&dq=2.44+shilov+linear+algebra&source=bl&ots=bcYpdoyvx7&sig=3daYGhPuQKVDbO2nc1TGrXb75tA&hl=es&sa=X&ei=P1MaU9nrOYPj2AWKjYHQCw&ved=0CEkQ6AEwAw#v=onepage&q=2.44%20shilov%20linear%20algebra&f=false
 
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  • #2
Chacabucogod said:
Hi,

I'm reading Shilov's linear algebra and in part 2.44 he talks about linear independent vectors in a subspace L which is a subset of space K( he refers to it as K over L). I don't understand why he says that a linear combination of vectors of the subspace L and vectors of the subspace K over L is independent.

He proves it in the text. He takes a linear independent set ##\{f_1,...,f_l\}\subseteq L## and a set ##\{g_1,...,g_k\}\subseteq K## such that they are linear indepent over L. Then Shilov explicitely shows that

[tex]\alpha_1 f_1+...+\alpha_lf_l +\beta_1 g_1 +... + \beta_k g_k = 0[/tex]

implies that ##\alpha_1 = ... = \alpha_l = \beta_1 = ...=\beta_k = 0## which implies linear independence in K.

Shilov also says that the dimension of the subspace K over L n-l. Why?

He explicitely proves this in the book. What about the proof is unclear?
 
  • #3
So both f and g are bases of the subspace?
 
  • #4
Chacabucogod said:
So both f and g are bases of the subspace?

The set ##\{f_1,...,f_l\}## is a linear independent set of ##L##. And ##L## is assumed to have dimension ##l##, so ##\{f_1,...,f_l\}## is indeed a basis.

The ##\{g_1,...,g_k\}## do not (in general) form a basis of anything.

However, we do know that if ##k = n-l## (where ##K## has been assumed to have dimension ##n##), then ##\{f_1,...,f_l,g_1,...,g_k\}## is a basis of ##K##.
 
  • #5
I still don't get it :(. The set of g's are just random vectors, or what? Because he says that vectors g_1...g_k are part of L.
 
  • #6
Chacabucogod said:
I still don't get it :(. The set of g's are just random vectors, or what? Because he says that vectors g_1...g_k are part of L.

He never says that the ##g_1,...,g_k## are elements of ##L##.

The ##g_1,...,g_k## are just random elements of ##K##. And you only know that they are linearly independent over ##L##.
 
  • #7
Ok... What does he mean then with:

α1g1+...+αkgk belongs to L
 
  • #8
Chacabucogod said:
Ok... What does he mean then with:

α1g1+...+αkgk belongs to L

He means that if ##\alpha_1 g_1 + ... + \alpha_kg_k\in L##, then ##\alpha_1 = ...=\alpha_k = 0##.

So the only linear combination of the ##\{g_1,...,g_k\}## belonging to ##L## is the zero vector. And it's not because some linear combination belongs to ##L##, that the ##g_j## separately belong to ##L##.
 
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  • #9
Do you have an example? Because I still can't grasp the idea. Thank you
 
  • #10
Chacabucogod said:
Do you have an example? Because I still can't grasp the idea. Thank you

Take ##K = \mathbb{R}^2## and ##L## the ##x##-axis, thus ##L = \mathrm{span}\{(1,0)\}##.

Then ##(1,1)## and ##(1,-1)## are not independent over ##L## because

[tex]1\cdot (1,1) + 1\cdot (1,-1) = (2,0)\in L[/tex]

but the coefficients are not all ##0##.

Consider however ##K=\mathbb{R}^3## and ##L = \mathrm\{(1,0,0)\}## the ##x##-axis again. Then ##(0,1,0)## and ##(0,0,1)## are independent over ##L##. Indeed, take any linear combination

[tex]x:=\alpha(0,1,0) + \beta(0,0,1) = (0,\alpha,\beta)[/tex]

if this were in ##L##, then there would exist some scalar ##c## such that ##x = c(1,0,0)##. But then

[tex](c,0,0) = (0,\alpha,\beta)[/tex]

which is clearly only possible if ##c=\alpha=\beta=0##.
 
  • #11
Ok now I get it. Thanks a lot for taking the time to answer my questions and having the patience.
 
  • #12
I think that these things will make more sense to you once you get to quotient spaces. Right now, I think the concept is pulled a bit out of the air.
 

Related to Shilov's Linear Algebra Subspace Question

1. What is Shilov's Linear Algebra Subspace Question?

Shilov's Linear Algebra Subspace Question is a problem posed by the Russian mathematician Georgii Shilov. It asks whether every subspace of a finite-dimensional vector space can be expressed as the intersection of a finite number of one-dimensional subspaces.

2. Why is Shilov's Linear Algebra Subspace Question important?

The answer to Shilov's Linear Algebra Subspace Question has implications in various areas of mathematics, including functional analysis and topology. It also has practical applications in fields such as engineering and computer science.

3. Has Shilov's Linear Algebra Subspace Question been solved?

Yes, Shilov's Linear Algebra Subspace Question has been solved. It was first answered by mathematicians Alexander Grothendieck and David Hilbert, who proved that the answer is yes for subspaces of finite-dimensional vector spaces over any field.

4. Are there any related questions to Shilov's Linear Algebra Subspace Question?

Yes, there are related questions such as whether every subspace can be expressed as the sum of a finite number of one-dimensional subspaces, and whether every subspace can be expressed as the union of a finite number of one-dimensional subspaces.

5. How is Shilov's Linear Algebra Subspace Question relevant to other areas of mathematics?

Shilov's Linear Algebra Subspace Question is relevant to other areas of mathematics such as functional analysis, topology, and algebraic geometry. It is also related to the theory of linear transformations and the study of vector spaces.

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