Linear Transformation and Proving Norms

In summary, T is a linear transformation and one-to-one. If ||.|| is a norm on W, then ||x||=||T(x)|| is a norm on V.
  • #1
cassiew
6
0

Homework Statement



Suppose T : V --> W is a linear transformation and one-to-one. Show, if ||.|| is a norm on W, then ||x|| =||T(x)|| is a norm on V.
(V and W are vector spaces)

Homework Equations



T is linear, so T(x+y)= T(x) + T(y) and T(ax)= aT(x)
T is one-to-one, so T(x)=T(y) implies that x=y.
||.|| is a norm, so ||v||=0 iff v=0 and is always greater than or equal to 0;
||cv||=c||v||
||v+w|| is less than or equal to ||v||+||w||

The Attempt at a Solution



I know since T is one-one, then ker(T)={0} and since T is linear, then T(0)=0. I tried using the properties of linear transformations to prove the three properties (listed above) of a norm, and I think it can be solved in this way, but I haven't been able to figure it out.
 
Physics news on Phys.org
  • #2
I think it can be solved that way as well. Why don't you try it? To be clear, I'd suggest you write ||x|| to indicate the given norm on W and ||x||'=||T(x)|| to indicate the other norm. So just start with the first property you need to prove. Is ||x||'>=0 with ||x||'=0 only if x=0?
 
Last edited:
  • #3
Go through each one and apply the properties that you are given.
For the last one, the triangle inequality, take ||x+y|| = ||T(x+y)|| = ||T(x) + T(y)|| (by linearity). ||.|| is a norm on w, so apply the triangle inequality here.
 
  • #4
So, for the first property, I know that ker(T)={0} so then
||x||' = ||Tx|| = 0 iff x=0, but how do you know that ||Tx||>=0? Is it just because ||x|| is a norm?

For the second, I know
||cx||' = ||T(cx)|| = ||cT(x)|| = |c|*||Tx|| = |c|*||x||', so this holds.

For the triangle inequality, I know that
||x+y||' = ||T(x+y)|| = ||Tx+Ty||, but how is this <= to ||Tx||+||Ty||? Can you use the fact that ||x+y||<=||x||+||y||? I mean, I know ||x|| is a norm, which is given, but does that mean I can use the triangle inequality from that to show that ||x||' is a norm?
 
  • #5
I think the answers to both your questions are, yes, we know these things because we are given that ||x|| is a norm on W. The only property where there is really much of anything to prove is the first one, ||x||'=0 means ||T(x)||=0 which is only true if T(x)=0, which is only true if x=0 since T is one to one. Since ker(T)={0} as you've already said.
 
  • #6
Okay, thanks!
 

FAQ: Linear Transformation and Proving Norms

What is a linear transformation?

A linear transformation is a function between two vector spaces that preserves the operations of vector addition and scalar multiplication. In other words, if we have two vectors x and y, and a scalar c, the transformation T must satisfy the following properties:

  • T(x + y) = T(x) + T(y)
  • T(cx) = cT(x)

How can we prove that a transformation is linear?

To prove that a transformation is linear, we can show that it satisfies the properties mentioned in the answer to the first question. This can be done by plugging in arbitrary vectors and scalars and showing that the properties hold true. Alternatively, we can also use the definition of a linear transformation and show that it maps the zero vector to the zero vector and preserves linear combinations.

What is the purpose of proving norms?

Proving norms is important in linear algebra because it allows us to measure the size or length of a vector. This is useful in various applications, such as optimization problems, where we want to find the vector that minimizes or maximizes a certain objective function. Norms also help us to define distance between vectors, which is useful in applications such as machine learning and data analysis.

How do we prove that a function is a norm?

To prove that a function is a norm, we need to show that it satisfies the following properties:

  • Non-negativity: the norm of a vector is always equal to or greater than zero.
  • Definiteness: the norm of a vector is equal to zero if and only if the vector itself is the zero vector.
  • Homogeneity: the norm of a scalar multiple of a vector is equal to the absolute value of the scalar multiplied by the norm of the vector.
  • Triangle inequality: the norm of the sum of two vectors is less than or equal to the sum of the norms of the individual vectors.

Can we prove that different norms are equivalent?

Yes, it is possible to prove that different norms are equivalent. Two norms are considered equivalent if they define the same topology on a vector space. This means that the open sets in one norm are also open sets in the other norm. Proving equivalence of norms can be useful in certain applications where we can choose to work with one norm over another depending on convenience or computational efficiency.

Back
Top