Change of Basis and Unitary Transformations

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Discussion Overview

The discussion centers on the relationship between two orthonormal basis sets in a vector space, specifically exploring how to express the transformation between these bases using unitary matrices. Participants examine the mathematical representation of this transformation and the implications of expressing basis vectors in terms of one another.

Discussion Character

  • Technical explanation
  • Mathematical reasoning
  • Debate/contested

Main Points Raised

  • Some participants propose that the transformation between the old basis ##\{v_i\}## and the new basis ##\{w_i\}## can be expressed as ##v_i = \sum_j S_{ij} w_j##, where ##S_{ij}## forms a matrix.
  • Others argue that the unitary matrix ##U## is the inverse of the matrix formed by ##S_{ij}##, suggesting a relationship between these matrices.
  • A participant questions how to explicitly express the transformation in matrix form, drawing parallels to standard forms of simultaneous equations.
  • Some participants express confusion about the representation of vectors in the equations and how to interpret the transformation in terms of matrix multiplication.
  • One participant suggests that the operator ##U## can be defined in terms of outer products of the basis vectors, while another raises concerns about the clarity of definitions being used.

Areas of Agreement / Disagreement

Participants generally agree on the existence of a relationship between the matrices ##S_{ij}## and ##U##, but there is no consensus on the explicit formulation of these relationships or the interpretation of the equations involved. Multiple competing views remain regarding the definitions and representations of the basis vectors and transformations.

Contextual Notes

There are limitations in the discussion regarding the assumptions made about the representation of vectors as column vectors and the conventions used in defining the transformations. Some mathematical steps remain unresolved, particularly in how to consistently express the relationships between the basis vectors and the matrices.

devd
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Say, we have two orthonormal basis sets ##\{v_i\}## and ##\{w_i\}## for a vector space A.

Now, the first (old) basis, in terms of the second(new) basis, is given by, say,

$$v_i=\Sigma_jS_{ij}w_j,~~~~\text{for all i.}$$

How do I explicitly (in some basis) write the relation, ##Uv_i=w_i##, for some unitary matrix, ##U##?

What is the relation between the matrix formed by the numbers ##S_{ij}## and ##U##?
 
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The S_{ij} form a matrix. U is its inverse.
 
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mathman said:
The S_{ij} form a matrix. U is its inverse.

How do I see this explicitly?
 
devd said:
How do I see this explicitly?

Can you see how the simultaneous equations

$$v_i=\Sigma_jS_{ij}w_j,~~~~\text{for all i.}$$

can be expressed as an equation involving matrices?

It's the same idea as expressing

1) ##ax + by = c##
2) ##dx + ey = f##

as

##\begin{pmatrix} a & b \\ c & d \end{pmatrix} \begin{pmatrix} x \\ y \end{pmatrix} = \begin{pmatrix} c \\f \end{pmatrix}##
 
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Stephen Tashi said:
Can you see how the simultaneous equations
can be expressed as an equation involving matrices?

It's the same idea as expressing

1) ##ax + by = c##
2) ##dx + ey = f##

as

##\begin{pmatrix} a & b \\ c & d \end{pmatrix} \begin{pmatrix} x \\ y \end{pmatrix} = \begin{pmatrix} c \\f \end{pmatrix}##

Yes, but where I got confused was that the entries of the column vectors themselves are vectors.
$$v_j=\sum_jS_{ij}w_i,~~~~\text{for all}~ j.$$
The ##v_j~'s ## and ##w_i~'s## in the equation above are not numbers but vectors.

But, I think, I have figured it out.

Let, U be a linear transformation on the vector space A, and ##\{v_j\}## be a basis. Then,
$$Uv_j=\sum_iU_{ij}v_i$$
Now, we define
$$\sum_iU_{ij}v_i=w_j\\ \implies v_j=U^{-1}w_j$$

Now, if we write the old basis vectors ##\{v_j\}## in terms of the new basis,
$$v_j=\sum_iS_{ij}w_i=Sw_j$$
Comparing the two equations for ##v_j##, we see that ##S=U^{-1}.##

In fact, we can write the the operators, ##U## and ##S## as outer products of the basis vectors,
$$U=\sum_j v_j\otimes v_j\\ S=\sum_i w_i \otimes w_i.$$

Do you think this makes sense? Or are there still some lacunae in my understanding?
 
devd said:
Yes, but where I got confused was that the entries of the column vectors themselves are vectors.
$$v_j=\sum_jS_{ij}w_i,~~~~\text{for all}~ j.$$
You need to have a "##v_i##" instead of a "##v_j##" on the left hand side of that equation.

Yes, the terms involved in that sum , ##S_{i,j} w_i##, are vectors, but we aren't yet dealing with any representation of ##w_i## as a vector of numbers. So, effectively, ##w_i## plays the role of a variable, just like the typical uses of "##x##" and "##y##".

Let, U be a linear transformation on the vector space A, and ##\{v_j\}## be a basis. Then,
$$Uv_j=\sum_iU_{ij}v_i$$
Yes, those equations ( considering all ##i##) define a linear transformation ##U## expressed in the ##v##-basis.

To interpret each equation as involving a matrix multiplication ##U v_j = b_j##, we must adopt some convention about how ##v_j## is represented as a column vector. Using "##v_j##" as ambiguous notation, we could say that ##v_j## represents a column vector of "variables" that are all zeroes except for the variable "##v_j##" in its ##j##th entry. Or we could say that ##v_j## ( in ##v##-basis) will be a column vector with a 1 in the ##j##th entry of the column and zeroes elsewhere. So interpreting the left hand side of the equation as matrix multiplication does involve a some convention about how ##v_j## is represented as a column vector.

We must also adopt a similar convention about the column vector on the right hand side. We might say it represents a linear combination of vectors from the ##v##-basis and that the ##k##th entry of the column vector on the right side the equation represents the coefficient of ##v_k## in the linear combination.
Now, we define
$$\sum_iU_{ij}v_i=w_j$$

It isn't clear what you are defining. Are you defining ##U## or are you defining the ##w_j## ?

If we take the ##w##-basis as given, you are defining ##U## as a matrix whose ##j##th column consists of the coefficients needed to express ##w_j## as a linear combination of vectors in the ##v##-basis.

However, the usual way to express the ##w##-basis in terms of the ##v##-basis in matrix form would be to consider the column vector ##w = (w_1,w_2,w_3,..)## as the ##w##-basis expressed as a vector of "variables", and the column vector ##w = (v_1,v_2,v_3,...)## as the ##v##-basis expressed a vector of variables, and to define the matrix ##U## to satisfy ##w = Uv##.

This definition would imply ##w_i = \sum_j U_{i,j} v_j ##, so the summation is over the column index "##j##" instead of the row index "##i##".
 

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