Talk:Tensor product

From Wikipedia, the free encyclopedia

Jump to: navigation, search

WikiProject Mathematics (Rated Start-Class)

Mathematics portal

This article is within the scope of WikiProject Mathematics, which collaborates on articles related to mathematics.

Mathematics rating:

Start Class

High Priority

Field: Algebra

Please update this rating as the article progresses, or if the rating is inaccurate. Click to show/hide comments.
Please add to or update the comments to suggest improvements to the article.
Geometry guy 22:28, 17 June 2007 (UTC)

/Archive1

In the first example shown at the top I think there might be a mistake. It looks as though you take the tensor product of two rank two tensors only to get a matrix. It looks as though the end result of the tensor product of two rank two tensors is another rank two tensor and not a rank 4 tensor. There might be some confusion between the rank of a tensor and the rank of a matrix. Possibly a more knowledgeable mathematician should rewrite this. —Preceding unsigned comment added by 128.104.2.98 (talk) 01:22, 27 October 2008 (UTC)

On a different point, the top of the article mentions "degrees of freedom"... what is that?!? Can a function of 7 real variables have 12 degrees of freedom? How, and why, please explain this mystery!

No. A function of 7 real variables has at most 7 degrees of freedom. if, by "function of 7 variables", you mean z = f(x1,x2,x3,x4,x5,x6,x7), and not 0 (or constant) = f(x1,x2,x3,x4,x5,x6,x7) or x7 = f(x1,x2,x3,x4,x5,x6), which have a maximum of 6 degrees of freedom. if x1 and x2 are neccessarily correlated then the function really has one less degree of freedom than the number of variables. the number of degrees of freedom another name for the number of "linearly independant" variables. Kevin Baas^{talk: new} 03:30, July 31, 2005 (UTC)

Fair enough... why then does the article state "...resultant dimension = 12" when the displayed tensor product has 7 variables (see http://en.wikipedia.org/wiki/Tensor_product ; the left hand side has variables a1,a2,a3,b1,b2,b3,b4) ?!?

It lies in a space of arrays having 12 components. Charles Matthews 07:17, 2 August 2005 (UTC)

Something needs to be adjusted... as things stand (i.e., as the page is actually written):

   12 = dimension of ambient space = resultant dimension = count of degrees of freedom = number of variables = 7

The page is OK, really. The question of what the image of the tensor product is, which is what this discussion is circling round, is really something different (comes under Segre embedding, for example) and non-linear. Sticking with the linear theory, there are 12 dimensions in the 'array' space, into which 7 linear dimensions are mapped. Since the image is not closed under addition, there is no 'paradox'. Charles Matthews 15:28, 2 August 2005 (UTC)

Connect the dots ... on one side is the number 12, and on the other side is the number 7... I don't think 12 = 7, so which equality in the chain is wrong?!?

The third = sign is questionable; there are constraints. Charles Matthews 21:03, 13 October 2005 (UTC)

1 Left exact?
2 Programming
3 Tensor product of two tensors?
4 Tensor product of vector spaces
5 Why B times A?
6 Tensor product of elements of Hilbert spaces
7 Splitting the Hilbert spaces section
8 Main example is misleading
9 Most general bilinear operation
10 Please do not use phyics slang in math articles
11 Split?
12 Main example incorrect?
13 Tensor product of multilinear maps
14 ToDo: topology

[edit] Left exact?

The section on abstract construction states that tensor product is left exact. That seems a bit strange. The functor "- \otimes_R N" is _right_ exact (indeed, if I have a surjection M -> M' then the comment on generating sets implies that M \otimes_R N surjects onto M' \otimes_R N). The fact that this functor is not in general exact might imply what we want here, though it might be easier to see it directly: Z/22 \otimes_Z Z/3Z = 0.

Correct: see Tor functor for confirmation. Charles Matthews 21:00, 13 October 2005 (UTC)

[edit] Programming

I don't see why we need so much on the programming. For array programming, OK: these things are arrays, though not just arrays. But why does this page have to teach lower-level stuff like filling up arrays with numbers from other arrays? Charles Matthews 09:25, 25 October 2005 (UTC)

I agree. Anyone who knows C could implement it based on the opening example. The SQL example is interesting because it relates OUTER JOIN with the tensor product, but it doesn't need two examples. The article could also do with mention of the tensor product of functions. —BenFrantzDale 12:48, 25 October 2005 (UTC)

I'm pulling one of the two SQL examples. RaulMiller 13:01, 25 October 2005 (UTC)

[edit] Tensor product of two tensors?

I'm a bit dubious about this line:

$\mathrm{dim}( U \otimes V )=\mathrm{dim}(U) \cdot \mathrm{dim}(V)$

In the traditions I'm familiar with, dimensions are represented as vectors, and \cdot represents the inner product between two vectors. However, the dimension of a tensor product is the concatenation of the dimensions of its arguments. I think either (a) better notation should be used, or (b) an explicitly labeled link to a description of this notation should be used. RaulMiller 13:01, 25 October 2005 (UTC)

No, dimensions are numbers and this is just the dot standing for ordinary product of integers. Charles Matthews 13:08, 25 October 2005 (UTC)

Ok, then this is an ambiguity. A tensor of rank 5 could be said to have five numbers describing its dimension -- perhaps <2,3,5,7,11> or it could be said to have a single number describing its dimension -- perhaps the product of (2)(3)(5)(7)(11). I don't want to belabor this point, but I think the entry could use some phrase indicating the latter usage of the term. (I'll update the page if I think up something good before someone else does.) RaulMiller 15:53, 25 October 2005 (UTC)

Right, I see that the initial example was in unhelpful notation and I've clarified that. I've taken out the\cdot also. Charles Matthews 16:37, 25 October 2005 (UTC)

[edit] Tensor product of vector spaces

I'm not sure about the formulation "take the vector space generated by V x W and factor out the subspace generated by the following relations". Maybe it's just that the word "generated" is ambiguous, but when I think of V x W as a vector space, I usually have an addition like (v1,w1)+(v2,w2)=(v1+v2,w1+w2) in mind. In this space, you surely can't factor out a subspace to get the tensor product, which is in genral bigger than V x W. Should the addition in this case be defined in a different way? I think this should be mentioned. SaschaR 15:28, 4 June 2006 (UTC)

It means the vector space with basis V x W. I've modified the article to state this explicitly. --Zundark 16:40, 4 June 2006 (UTC)

Hm. Let V = W = R^2. Now V x W [b] as a vector space[/b] can be isomorphically represented has R^4 with the usual basis { (1, 0,0,0), (0,1,0,0) ...}.

The representation by the Kronecker Product sends all these base vectors to 0 Thus the "factoring out" can not be the vector-homeomorphic factoring but only the factoring of sets (the grouping of elements within the same set theoretic equivalence relaction.

Or have I misunderstood the term "with basis V x W"? 84.160.205.130 07:20, 24 October 2006 (UTC)

I think you've misunderstood it. V x W = R^2 x R^2 is the basis of the vector space, and the elements of the vector space are formal linear combinations of basis elements. You should ignore the structure on R^2 when forming this vector space - the basis elements are all linearly independent, by definition. --Zundark 08:24, 24 October 2006 (UTC)

So the whole of V x W is a basis where all elements (uncountably many) are all linearly independent by definition? That's to say the set V x W realy is an index set for the base vectors of the pre-factored vector space? If so, wouldn't it be clearer to start with the space Hom(VxW, R)? 84.160.205.130 18:18, 24 October 2006 (UTC)

Yes, you can think of V x W as an index set for the basis vectors. In fact, I would define the vector space as $\bigoplus_{p\in V\times W}\!\!\!K_p$ , where each

K p

is a copy of the base field. This is the set of functions with finite support from V x W to the base field (with the obvious operations). It's not the same as Hom(VxW, R). --Zundark 19:52, 24 October 2006 (UTC)

I see Hom(VxW, K) is obviously not the same as $\bigoplus_{p\in V\times W}\!\!\!K_p$ . Further, for finite dimensions, Hom(VxW, K) has the same dimension as VxW and $V\otimes W$ . So, on the way to $V\otimes W$ , there is nothing much left to be factored out. Trivially, Hom(VxW, K) is isomorphic to $V\otimes W$ the way any two vector spaces with the same dimensions are isomorphic.

Now all useful information for a human brain is packed into the way in which those isomorphisms can be chosen.

Let v_1 ... v_N be a basis of V and w_1 ... w_M be a basis of W. Would $\{\varphi_{kl} | \varphi_{kl}((v_\kappa,w_\lambda))=\delta_{k\kappa}\delta_{l\lambda}\}$ be a basis of $V\otimes W$ in a canonical way? (Note the braces to mark

(v κ, w λ)

a single element of VxW).

84.160.237.162 20:32, 26 October 2006 (UTC)

If you are considering V x W as a vector space, then its dimension is the sum of the dimensions of V and W. But the dimension of $V\otimes W$ is the product of the dimensions of V and W. So they are not usually isomorphic when the dimensions are finite. --Zundark 08:51, 27 October 2006 (UTC)

Ouch! Shame on me! I should have seen that. 84.160.238.49 18:33, 27 October 2006 (UTC)

[edit] Why B times A?

It is unclear why you write

$\mathbf{b} \otimes \mathbf{a} \rightarrow \begin{bmatrix}b_1 \\ b_2 \\ b_3 \\ b_4\end{bmatrix} \begin{bmatrix}a_1 & a_2 & a_3\end{bmatrix} = \begin{bmatrix}a_1b_1 & a_2b_1 & a_3b_1 \\ a_1b_2 & a_2b_2 & a_3b_2 \\ a_1b_3 & a_2b_3 & a_3b_3 \\ a_1b_4 & a_2b_4 & a_3b_4\end{bmatrix}$

rather than

$\mathbf{a} \otimes \mathbf{b} \rightarrow \begin{bmatrix}a_1 \\ a_2 \\ a_3 \\ a_4\end{bmatrix} \begin{bmatrix}b_1 & b_2 & b_3\end{bmatrix} =$ ...

Paolo.dL 12:02, 26 June 2007 (UTC)

[edit] Tensor product of elements of Hilbert spaces

In the definition

$\langle\phi_1\otimes\phi_2,\psi_1\otimes\psi_2\rangle = \langle\phi_1,\psi_1\rangle_1 \, \langle\phi_2,\psi_2\rangle_2 \quad \mbox{for all } \phi_i,\psi_i \in H_i$

I wondered what could be meant by φ⊗ψ for elements φ and ψ in a (general) Hilbert space. Maybe it should be mentioned that the above line defines both an inner and a tensor product (of elements in a Hilbert space). —Preceding unsigned comment added by Paux (talk • contribs) 08:20, 24 September 2007 (UTC)

[edit] Splitting the Hilbert spaces section

I propose splitting the section on the tensor product of Hilbert spaces, and merging it with the existing article Tensor product of Hilbert spaces. Currently the main article is shorter than the section here. The other option is to delete the main article (Tensor product of Hilbert spaces) and merge its contents here. Either way, the way the current content fork is currently set up is not a good idea. Are there any objections or suggestions? Silly rabbit (talk) 15:55, 8 January 2008 (UTC)

Question for editors: Should this be merged with Topological tensor product (currently Tensor product of Hilbert spaces redirects there), or should a new article be created? silly rabbit (talk) 19:47, 25 April 2008 (UTC)

[edit] Main example is misleading

It looks too much like matrix multiplication. I'm gonna go ahead and replace it with one of the examples from Kronecker product in a few hours/days if there are no complaints. —Preceding unsigned comment added by Thric3 (talk • contribs) 09:04, 7 February 2008 (UTC)

k edited. -Thric3 (talk) —Preceding comment was added at 06:16, 8 February 2008 (UTC)

[edit] Most general bilinear operation

The lede says the tensor product is always the most general bilinear operation. While I think I know what is meant by that (something close to being a universal object), it is a fairly cryptic formulation, and I think it is more likely to be confusing then helpful at this point. Besides, the word "product" mostly refers to the result of an operation, rather than to the operation itself. I'm tempted to take the phrase out, does anyone object? – Marc van Leeuwen (talk) 10:02, 24 April 2008 (UTC)

well, the result of the operation is the universal object, so the operation itself is the most general one... Mct mht (talk) 08:29, 25 April 2008 (UTC)

[edit] Please do not use phyics slang in math articles

This article has obviously been written by physicists and is should be rewritten by a mathematician. It is generally not a good idea to us slang such as "degree of freedom" in a math article. —Preceding unsigned comment added by 213.39.148.211 (talk) 10:39, 9 July 2008 (UTC)

I disagree if the paragraph is intended to convey the basic idea so that anyone can understand it. Nearly all of the remaining parts of the article, however, are quite mathematically precise. siℓℓy rabbit (talk) 12:08, 9 July 2008 (UTC)

I agree with the point, but not with the example. I am a pure mathematician, and I use the phrase "degrees of freedom". For example a straight line has two degrees of freedom, a circle has three, and a conic has five. Besides, our statistician friends use it very often too. Dharma6662000 (talk) 17:05, 12 August 2008 (UTC)

...our statistician friends use it very often too: Remind me never to come to one of your parties. </joking> siℓℓy rabbit (talk) 17:27, 12 August 2008 (UTC)

[edit] Split?

It seems a good idea to split the material on Array programming languages off, or to merge it with another article. Are there any volunteers who know about such things? I have some rather limitted exposure, and would prefer it if an array programming enthusiast would do it. siℓℓy rabbit (talk) 22:39, 6 August 2008 (UTC)

[edit] Main example incorrect?

Presuming that the main example is meant to represent the tensor product of tensor a with tensor b I believe that in the definition the b component should be transposed, as otherwise if one reduces a and b to rank 1 tensors by removing the second column of both (that is changing them to 1 by 2 vectors) the result is a 1 by 4 vector not a 2 by 2 2nd rank tensor as it should be, that is the formula should read:

$\begin{bmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \\ \end{bmatrix} \otimes \begin{bmatrix} b_{11} & b_{12} \\ b_{21} & b_{22} \\ \end{bmatrix} \rightarrow \begin{bmatrix} a_{11} \begin{bmatrix} b_{11} & b_{21} \\ b_{12} & b_{22} \\ \end{bmatrix} & a_{12} \begin{bmatrix} b_{11} & b_{21} \\ b_{12} & b_{22} \\ \end{bmatrix} \\ & \\ a_{21} \begin{bmatrix} b_{11} & b_{21} \\ b_{12} & b_{22} \\ \end{bmatrix} & a_{22} \begin{bmatrix} b_{11} & b_{21} \\ b_{12} & b_{22} \\ \end{bmatrix} \\ \end{bmatrix} = \begin{bmatrix} a_{11} b_{11} & a_{11} b_{21} & a_{12} b_{11} & a_{12} b_{21} \\ a_{11} b_{12} & a_{11} b_{22} & a_{12} b_{12} & a_{12} b_{22} \\ a_{21} b_{11} & a_{21} b_{21} & a_{22} b_{11} & a_{22} b_{21} \\ a_{21} b_{12} & a_{21} b_{22} & a_{22} b_{12} & a_{22} b_{22} \\ \end{bmatrix}$

rather than

$\begin{bmatrix} a_{11} & a_{12} \\ a_{21} & a_{22} \\ \end{bmatrix} \otimes \begin{bmatrix} b_{11} & b_{12} \\ b_{21} & b_{22} \\ \end{bmatrix} = \begin{bmatrix} a_{11} \begin{bmatrix} b_{11} & b_{12} \\ b_{21} & b_{22} \\ \end{bmatrix} & a_{12} \begin{bmatrix} b_{11} & b_{12} \\ b_{21} & b_{22} \\ \end{bmatrix} \\ & \\ a_{21} \begin{bmatrix} b_{11} & b_{12} \\ b_{21} & b_{22} \\ \end{bmatrix} & a_{22} \begin{bmatrix} b_{11} & b_{12} \\ b_{21} & b_{22} \\ \end{bmatrix} \\ \end{bmatrix} = \begin{bmatrix} a_{11} b_{11} & a_{11} b_{12} & a_{12} b_{11} & a_{12} b_{12} \\ a_{11} b_{21} & a_{11} b_{22} & a_{12} b_{21} & a_{12} b_{22} \\ a_{21} b_{11} & a_{21} b_{12} & a_{22} b_{11} & a_{22} b_{12} \\ a_{21} b_{21} & a_{21} b_{22} & a_{22} b_{21} & a_{22} b_{22} \\ \end{bmatrix}$

as it does currently --Physdragon (talk) 15:52, 1 March 2009 (UTC)

Actually having said that I'm not sure if my suggested revision is correct either since that is just a 4 by 4 Rank 2 tensor, the tensor product of two rank two tensors (that is they are represented in component representation using two indices) should give a rank 4 tensor (that is one with 4 indices). So it should be

$A_{ab}\otimes B_{cd} = C_{abcd}$ --Physdragon (talk) 14:14, 2 March 2009 (UTC)

The example is a bit confusing/poorly explained. You are thinking Cartesian product; but the tensor product is *not* the cartesian product... you should be thinking Kronecker product instead. It is 4 by 4 rank two because the tensor space has a 4-dimensional basis: $(a_x \otimes b_x, a_x \otimes b_y,a_y \otimes b_x,a_y \otimes b_y)$ Right? linas (talk) 03:58, 8 June 2009 (UTC)

I agree that the example is confusing, one of the primary problems being that you are quite correct in saying that it has a 4 dimensional basis, but this means that the tensor, if one wanted to represent it properly in the matrix like form, should be a 4-dimensional array of values with each 'side' of the array being two values wide. Quite obviously however it is not possible to represent this physically never mind on the two dimensions of a wikipedia page, and exactly how one would go about attempting to represent it in two dimensions I'm not sure. Nonetheless I believe the elements of the tensor are now correct at least whereas I'm fairly certain they weren't before even though I'm not sure how to represent it. To be honest given it's potential for confusion I'm not sure whether the graphical example should even be included, something akin to:

$A_{ab}\otimes B_{cd} = C_{abcd}$

Would probably have less potential for confusion but I felt that removing the example in it's entirety was rather a major edit I wasn't comfortable to make without agreement from other editors. Physdragon (talk) 14:52, 16 June 2009 (UTC)

I just had a little look at the French version of the tensor product page and the initial example with the tensor product of 2 vectors there seems much better constructed and more informative, as and when I have time I shall probably replace the main example here with an Anglicised version of the French one. Should anyone else feel like doing this before I get around to it, feel free. Physdragon (talk) 17:55, 16 June 2009 (UTC)

[edit] Tensor product of multilinear maps

I think the author of the section "Tensor product of multilinear maps" meant to say "Tensor product of multilinear forms", since if the maps take values somewhere else than in the underlying ring or field, the product on the r.h.s. is a priori not defined (or in turn assumes the tensor product of the two "arrival spaces" ?). — MFH:Talk 13:55, 12 May 2009 (UTC)

[edit] ToDo: topology

It would be nice to have a section devoted to the projective topology for locally convex spaces. The article on initial topology gives a one-sentence nod to this concept -- expanding that section into a sentence would seem to clutter things up. Similarly adding a section here would also seem to result in clutter. What to do? linas (talk) 04:06, 8 June 2009 (UTC)

Never mind, I just discovered Topological tensor product. Which seems to have numerous faults e.g. doesn't manage to ever wikilink to [[initial topology] Oh well. linas (talk) 04:10, 8 June 2009 (UTC)

Talk:Tensor product

From Wikipedia, the free encyclopedia

Contents