How to define this linear transformation

In summary, we are given a linear space $V$ over the real numbers and two subspaces $U$ and $W$ of $V$. $S: U \rightarrow Y$ and $T: W \rightarrow Y$ are linear transformations that satisfy $(\forall x \in U \cap W)$ $S(x)=T(x)$. We need to define a linear transformation $F: U+W \rightarrow Y$ that matches with $S$ for values in $U$ and matches with $T$ for values in $W$. The natural way to define $F$ is $F(x) = S(u) + T(w)$, where $x = u+w$, $u \in U$, and $
  • #1
Granger
168
7
> Admit that $V$ is a linear space about $\mathbb{R}$ and that $U$ and $W$ are subspaces of $V$. Suppose that $S: U \rightarrow Y$ and $T: W \rightarrow Y$ are two linear transformations that satisfy the property:

> $(\forall x \in U \cap W)$ $S(x)=T(x)$

> Define a linear transformation $F$: $ U+W \rightarrow Y$ that matches with S for values in U and matches with T with values in W.

My thought is to choose the linear transformation $F=S+T$ because it will be the union of both transformation, right? But now I know this is incorrect...

Can someone give an hint on how to approach the problem (without using matrices...)?

I know that the function might be equal to S for the vectors that belong to U and equal to T for vector that belong to V... But how do I get there and write a linear transformation?
 
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  • #2
GrangerObliviat said:
> Admit that $V$ is a linear space about $\mathbb{R}$ and that $U$ and $W$ are subspaces of $V$. Suppose that $S: U \rightarrow Y$ and $T: W \rightarrow Y$ are two linear transformations that satisfy the property:

> $(\forall x \in U \cap W)$ $S(x)=T(x)$

> Define a linear transformation $F$: $ U+W \rightarrow Y$ that matches with S for values in U and matches with T with values in W.

My thought is to choose the linear transformation $F=S+T$ because it will be the union of both transformation, right? But now I know this is incorrect...

Can someone give an hint on how to approach the problem (without using matrices...)?

I know that the function might be equal to S for the vectors that belong to U and equal to T for vector that belong to V... But how do I get there and write a linear transformation?
A vector $x$ in $U+W$ has to be of the form $x = u+w$, where $u\in U$ and $w\in W$. The natural way to define $F$ is $F(x) = S(u) + T(w)$. The thing you have to be careful about is to show that this $F$ is well-defined. In other words, suppose that $x$ can be expressed as an element of $U+W$ in more than one way, say $x = u_1 + w_1$ and also $x = u_2 + w_2$. In order for the definition of $F$ to make sense, it should be true that both expressions for $x$ give rise to the same value for $F(x)$. So you have to show that $S(u_1) + T(w_1) = S(u_2) + T(w_2).$ Can you do that?
 
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1.

What is a linear transformation?

A linear transformation is a mathematical function that maps one vector space to another, while preserving the structure of the original vector space. In simpler terms, it is a transformation that preserves lines and planes, and is often represented by a matrix.

2.

How is a linear transformation defined?

A linear transformation is defined by its action on the basis vectors of the vector space. This means that the transformation is completely determined by its effect on the basis vectors, which are usually denoted by e1, e2, e3, and so on.

3.

What is the difference between a linear transformation and a non-linear transformation?

A linear transformation is a transformation that follows the rules of linearity, meaning that it preserves addition and scalar multiplication. A non-linear transformation, on the other hand, does not follow these rules and often results in curved or distorted shapes.

4.

How do you represent a linear transformation?

A linear transformation can be represented by a matrix, where the columns of the matrix correspond to the images of the basis vectors. Additionally, a linear transformation can also be represented by a system of linear equations.

5.

What are some real-life examples of linear transformations?

Linear transformations can be found in various fields such as physics, engineering, computer graphics, and economics. Some examples include rotation, scaling, translation, and reflection in computer graphics, and the transformation of coordinates in mapping and navigation systems.

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