Proving Vector Subspaces with Linear Transformations | Homework Help

In summary, to prove that the kernel of a linear transformation T is a vector subspace, it is necessary to show that the zero vector is in the kernel, and that for any u and v in the kernel, their sum and scalar multiples are also in the kernel. This follows from the basic properties of linear transformations. Therefore, the proof that ker(T) is a vector subspace of V is complete.
  • #1
boneill3
127
0

Homework Statement



Let V and W be vector spaces over [itex]F [/itex] and [itex]T:V \rightarrow W[/itex] a linear transformation. Prove that [itex]ker(T):=[/itex]{[itex]\epsilon V\mid T()=0_{v}[/itex]} is a vector subspace of [itex]V[/itex]

Homework Equations





The Attempt at a Solution



Is it all right just to state the trivial solution.

ie There exists the vector [itex]0v\epsilon V[/itex] such that
[itex]T(0v) \rightarrow W_{0}[/itex]

therfore the vector [itex]0v\epsilon V [/itex] is also [itex]0v\epsilon T [/itex]

or do I need more Axioms like

There exists the vectors [itex]-v\epsilon V[/itex] and [itex]v\epsilon V[/itex] such that
[itex]T(-v+v) = T(0v) \rightarrow W_{0}[/itex]


to prove that T() is a vector subspace of V
regards
Brendan
 
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  • #2
You're trying to prove that ker(T) is a subspace, so you have to show that 0 is in ker(T). Moreover, you have to show that if u and v are in ker(T), then so is u + v. Finally, if u is in ker(T) and c is a scalar, then you have to show that c*u is in ker(T). These all follow directly from the basic properties of linear transformations.
 
  • #3
Thanks for your help.

As a subspace always has the zero vector can I just say that for
{u,v} both elemants of V.
We have
0v = A
0u = B

Ax= 0 and Ay = 0, then A(x + y) = vx + vy = 0 + 0 = 0

Ax = 0 and c is a scalar, then A(cx) = cAx = c0 = 0

{Ax ,Bu} = 0 and c is a scalar, then Acx+Bcy = cAx+cBy = c0 + c0 = 0+0 = 0

Is that allright?



Ax = 0 and c is a scalar, then A(cx) = cAx = c0 = 0
 

FAQ: Proving Vector Subspaces with Linear Transformations | Homework Help

What is a linear subspace proof?

A linear subspace proof is a mathematical method used to show that a set of vectors in a vector space form a linear subspace. This involves demonstrating that the set satisfies the properties of a linear subspace, such as closure under vector addition and scalar multiplication.

What are the properties of a linear subspace?

A linear subspace must satisfy three properties: closure under vector addition (if two vectors are in the subspace, their sum must also be in the subspace), closure under scalar multiplication (if a vector is in the subspace, multiplying it by a scalar must also result in a vector in the subspace), and contain the zero vector (the vector with all zero components).

How is a linear subspace proof conducted?

A linear subspace proof involves using algebraic or geometric methods to show that the set of vectors satisfies the properties of a linear subspace. This may involve showing that the set is closed under vector addition and scalar multiplication, and contains the zero vector.

What is the purpose of a linear subspace proof?

The purpose of a linear subspace proof is to formally prove that a set of vectors forms a linear subspace. This is important in linear algebra, as it allows us to make conclusions about the behavior of the vectors in the subspace and perform calculations with them.

What are some practical applications of linear subspace proofs?

Linear subspace proofs are used in a variety of fields such as physics, engineering, and computer science. They are used to study systems of linear equations, analyze data in machine learning, and determine the stability of physical systems among other applications.

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