Linear algebra proof (matrices and linear transformations)

In summary, the student is trying to solve a homework equation, but is stuck due to confusion about how to do matrix multiplication.
  • #1
Ryker
1,086
2

Homework Statement


Let T [tex]\in[/tex] L(V, W), where dim(V) = m and dim(W) = n. Let {v1, ..., vm} be a basis of V and {w1, ..., wn} a basis for W. Define the matrix A of T with respect to the pair of bases {vi} and {wj} to be the n-by-m matrix A = (aij), where

[tex]T(v_{i}) = \displaystyle\sum_{j=1}^{n}a_{ji}w_{j}, 1 \le i \le m, 1 \le j \le n.[/tex]

The vector spaces V and W are isomorphic via the bases {vi} and {wj} to the spaces Fm and Fn, respectively. Show that ifx [tex]\in[/tex] Fm is the column vector corresponding to the vector x [tex]\in[/tex] V via the isomorphism, then Ax is the column vector in Fn corresponding to T(x). In other words, the correspondence between linear transformations and matrices is such that the action of T on a vectorx is realized by the matrix multiplication Ax.

Homework Equations


The Attempt at a Solution


I can see this is true, and when we learned this in class, it was pretty clear to me. But now I'm going through some linear algebra book and the stuff is introduced in a different way that confuses the hell out of me.

What I've tried to do here is just show what T(x) and Ax get you, and that they are equal. But I just can't get to that, it seems.

For T(x), I get the following:

[tex]T(x) = T(\displaystyle\sum_{i=1}^{m}b_{i}v_{i}) = \displaystyle\sum_{i=1}^{m}b_{i} \displaystyle\sum_{j=1}^{n}a_{ji}w_{i},[/tex]

and for Ax:[tex]Ax = \displaystyle\sum_{j=1}^{n} (\displaystyle\sum_{i=1}^{m}a_{ji}x_{i}) = \displaystyle\sum_{j=1}^{n} (\displaystyle\sum_{i=1}^{m}a_{ji}b_{i}v_{i}).[/tex]

I don't know how to make the jump from vi to wj. Or have I gone completely in the wrong direction? Any help would be greatly appreciated.
 
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  • #2
I believe there is a problem with matrix multiplication in your solution. The first warning: how did you get [tex]v_i[/tex]'s into the result? It clearly shouldn't have happened, since the dimensions don't match. I'd say
[tex]Ax=\sum_{j=1}^n\left(\sum_{i=1}^m a_{ji}b_i w_j\right)[/tex]
because that transformed coordinates are for this other basis, not V's basis.
Good luck!
 
  • #3
Hmm, I got vi in my result, because I figured if x ϵ V, then xi = b1v1 + b2v2 + ... + bmvm. So that's where I get stuck, I don't know how to get to having it expressed in terms of wj, because A is defined in terms of aij and x as a linear combination of the basis vectors {vi}.

How did you get to that solution you offered? If I got to that, then clearly I'd be good to go, since it would match T(x), but I just can't get to that.
 
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1. What is a matrix?

A matrix is a rectangular array of numbers (or other mathematical objects) arranged in rows and columns. It is often used to represent a system of linear equations or to perform various mathematical operations.

2. What is a linear transformation?

A linear transformation is a function that maps one vector space to another in a way that preserves the linear structure of the original space. In other words, the output of a linear transformation is always a linear combination of the input vectors.

3. How do you prove that two matrices are equal?

To prove that two matrices are equal, you need to show that they have the same dimensions and that each corresponding element in the matrices is equal. This can be done by using the definition of matrix equality and performing algebraic operations on the matrices to show that they are identical.

4. What is the identity matrix and how is it used in proofs?

The identity matrix is a square matrix with 1's along the main diagonal and 0's everywhere else. When a matrix is multiplied by the identity matrix, the result is the original matrix. In proofs, the identity matrix is often used to show that a given matrix is invertible or to simplify calculations.

5. What is the difference between a row vector and a column vector?

A row vector is a 1-dimensional matrix with its elements arranged in a single row, while a column vector is a 1-dimensional matrix with its elements arranged in a single column. The main difference is in their dimensions, which affects how they can be multiplied and used in matrix operations.

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