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Angle between vector and its transposeby newphysist
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#1
Nov2112, 12:53 PM

P: 12

Hi
What is the angle between a vector (e.g. a row vector A) and it's transpose (a column vector) ? I know what transpose means mathematically but what is the intuition? Thanks guys 


#2
Nov2112, 01:29 PM

P: 150

In general case there is a distinction between row vectors and column vectors however in such cases it's hard to give concise meaning to the word "angle". 


#3
Nov2112, 01:49 PM

P: 828

I don't understand the question. Usually in vector spaces like this (well Euclidean Vector Spaces), we define "angle" to be the cosine of the inner product of two vectors. However,
a vector and its transpose are not in the same space. (In the sense that one is a 1xn matrix and one is a nx1 matrix.) 


#4
Nov2112, 01:51 PM

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Angle between vector and its transpose
I agree with Robert.
The question is meaningless. 


#5
Nov2112, 01:55 PM

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P: 21,311

Consider
##u = (1, 2, 3)## and $$u^T = \begin{pmatrix} 1 \\ 2 \\ 3\end{pmatrix}$$ Clearly the angle between the two vectors is 90° 


#6
Nov2112, 01:55 PM

P: 150

I don't think it's meaningless. In introductory classes (where vectors aren't even defined properly) rows and columns are often just different notations for components of vectors.



#7
Nov2112, 02:13 PM

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#8
Nov2112, 02:14 PM

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#9
Nov2112, 02:32 PM

P: 12

Assuming that question is meaningless, what is the intuition behind taking transpose?



#10
Nov2112, 02:46 PM

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It is a welldefined matrix operation. 


#11
Nov2112, 03:25 PM

P: 828

Additionally, there are important classes of matricies that are defined using transpose (or complex conjugate transpose which is where you do the transpose and take the complex conjugates of the entries of your matrix, if your matrix has real entries then obviously the complex conjugate transpose is just the transpose.) For example, if [itex]Q^\top=Q^{1}[/itex] the matrix is called orthogonal. These matrices preserve angles and lengths of vectors. These are good for numerical applications for that reason, but also it is MUCH easier to compute the transpose than it is to compute the inverse (in a sense, you don't need to "compute" the transpose, your code just needs to "iterate backward"  if you don't understand that part, its OK.) Another special class is Symmetric Matrices where [itex]M[/itex] is symmetric if [itex]M=M^\top[/itex]. These are really nice for several reasons. First, they are diagonalisable by an orthogonal matrix. Since they are diagonalisable, there is an eigenbasis and so you can do a "spectral resolution." Also, the eigenvalues are all real, even if the entries are complex. This is just *very light* scratching the surface, but there are MANY important topics that involve transposes of matrices and vectors. 


#12
Dec512, 05:49 PM

P: 12

Thanks 


#13
Dec512, 08:50 PM

P: 439

It's two different examples. The first is an alternative definition for the dot product (that I believe only applies to column vectors, someone correct me if I'm wrong), the second is an orthogonal projection onto a line. To verify this, take two unit vectors and perform the operation described and draw them!



#14
Dec612, 04:36 AM

P: 828

The projection of [itex]w[/itex] onto [itex]v[/itex] is given by [itex](v\circ w)v[/itex] which is equal to [itex](v^\top w)v[/itex] which is equal to [itex](vv^\top)w[/itex]. That is, [itex]vv^\top[/itex] is the projection matrix. Note that I have assumed that [itex]v[/itex] has unit length.



#15
Dec612, 09:10 AM

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#16
Dec612, 11:18 AM

P: 350

Meaningless is a strong word that probably does not help the OP much. To most nonmathematicians who use vectors, the the dual space is implicitly identified with the original space via the dot product. "Vectors" are written either as rows or columns depending on convenience. Why do so many people want to "ban" people from using reasonable notation just because they aren't versed with abstract mathematics? It's like being against differentials, or implicit functions, or Leibniz notation.



#17
Dec612, 12:46 PM

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Using that interpretation, you are saying that a "vector" and its "transpose" are just different ways of writing the same vector so the "angle between them" is necessarily 0.
(Of course "identifying the dual space with the original space via the dot product" depends on the choice of basis so still doesn't have any meaning as a property of the vector space itself. Oh, and "differentials", "implicit functions", and "Leibniz notation" all have very specific, rigorous, mathematical definitions.) 


#18
Dec612, 12:53 PM

Mentor
P: 18,333

Also, notation that is "reasonable" for one person might not be understandable for another person. We should teach standard notation that can be understood by everybody and not just by the person who invented it. 


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