Why is the matrix representation of a linear map unique?

In summary, Friedberg's first theorem states that for any finite-dimensional vector space V and a basis of n vectors, there exists a unique linear transformation that maps the basis vectors to given vectors in a second vector space W. He uses this theorem to prove that for any linear map T from V to W, there exists a matrix representation with scalars a_{ij} such that T(x_j) can be expressed as a linear combination of the basis vectors of W. This does not require the first theorem, but the converse does.
  • #1
Bipolarity
776
2
Friedberg proves the following theorem:
Let V and W be vector spaces over a common field F, and suppose that V is finite-dimensional with a basis [itex] \{ x_{1}...x_{n} \}. [/itex] For any vectors [itex] y_{1}...y_{n} [/itex] in W, there exists exactly one linear transformation [itex] T: V → W [/itex] such that [itex] T(x_{i}) = y_{i} [/itex] for i = 1,...,n.

He uses this theorem to assert the following:
Suppose that V and W are finite-dimensional vector spaces with ordered bases [itex] = \{ x_{1}...x_{n} \} [/itex] and [itex] = \{ y_{1}...y_{m} \} [/itex], respectively. Let [itex] T: V →W [/itex] be a linear map. Then there exist scalars [itex] a_{ij} \in F \mbox{ (i = 1,..., m and j = 1,...,n) } [/itex] such that

[tex] T(x_{j}) = \sum^{m}_{i=1}a_{ij}y_{i} \mbox{ for } 1 \leq j \leq n [/tex]

He doesn't really prove the assertion he makes regarding the matrix representation, and it is not obvious to me. Obviously the first theorem (regarding the action of linear maps upon bases) is necessary to show that the matrix representation exists and is the unique linear map satisfying the action of the linear map upon bases. The problem is that in the first theorem, he uses n vectors from W. In the second theorem, the basis he uses for W has m vectors which may or may not be equal to n. If it is not equal to n, why is he allowed to use the theorem?

I apologize if I'm not clear enough. Please let me know which part is not clear and I will clarify further.

BiP
 
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  • #2
The assertion doesn't really have anything to do with the theorem you mention. All you need in the assertion is the fact that ##(y_1,...,y_m)## form a basis. By definition of a basis, we know that for any ##y\in W##, there exists ##\alpha_1,...,\alpha_m\in F## such that

[tex]y = \sum_{i=1}^m \alpha_i y_i[/tex]

This is all you're using. Indeed, just apply this observation to ##y = T(x_j)##.

So simply the existence of the ##\alpha_{ij}## doesn't need the theorem you mention. However, the converse (that the ##\alpha_{ij}## determine some linear map) does need the theorem.
 

1. What is a linear map?

A linear map, also known as a linear transformation, is a function between two vector spaces that preserves the operations of vector addition and scalar multiplication. In simpler terms, it is a mathematical function that maps one vector space to another in a way that maintains the linear structure of the original space.

2. How are linear maps unique?

Linear maps are unique in the sense that they have a specific set of properties that distinguish them from other types of functions. These properties include: preserving vector addition and scalar multiplication, maintaining the zero vector, and preserving linear combinations. Additionally, linear maps can be represented by matrices, which further adds to their uniqueness.

3. What is the difference between a linear map and a nonlinear map?

The main difference between a linear map and a nonlinear map is that a linear map preserves the operations of vector addition and scalar multiplication, while a nonlinear map does not. In other words, a linear map maintains the linear structure of a vector space, while a nonlinear map does not necessarily do so.

4. Can linear maps be invertible?

Yes, linear maps can be invertible. In fact, a linear map is invertible if and only if its associated matrix is invertible. This means that for every linear map, there exists a unique inverse map that can undo the effects of the original map.

5. How are linear maps used in real life?

Linear maps have many practical applications in fields such as engineering, physics, economics, and computer science. Some examples of their use include image and signal processing, data compression, and optimization problems. They are also used in the study of differential equations and in creating mathematical models for various real-world phenomena.

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