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Let $V$ be an n dimensional vector space.

Let $T_1^1$ be the set of bilinear functions $F: V^* \times V \rightarrow \mathbb{R}$ and $End(V)$ be all the set of all linear functions $A: V \rightarrow V$. Suppose that we create an orthonormal basis $(e_1,...e_n)$ for $V$ and that we denote the dual basis on $V^*$ by $(E_1,....E_n)$.

Then we can write every $v \in V$ as $v=a_1e_1+...+a_ne_n$ and we can write every $w \in V^*$ as $w = b_1E_1+....+b_nE_n$ Since $F$ is a bilinear function we can write it as a matrix.. How do we write the matrix representation of this bilnear function??

In general, this ismorphism is confusing to me for some reason. Can somebody help me understand it??

Also, say we are in the vector space $\mathbb{R}^2$ and take it's dual space.. I'm imagining vectors coming out of $\mathbb{R}^2$ as blue vectors starting at the origin, and then i'm imagining it's dual space as red vectors coming out of the origin. How does the red vector act on the blue vector if they are at the same point in the plane? A dot product? IDK I know this question is sort of vague and these are just some thoughts but I hope somebody out there has something to say!! Usually I get some good answers to these soft questions that end up being quite useful :P.

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    Are you aware of the basis-free construction of the isomorphism? – peek-a-boo May 05 '20 at 23:28
  • Yeah, but once given a basis I think i'm able to see that $T^2 \cong T_1^1 \cong T^2 \cong End(V)$ are all ismorphic.. and now I'm trying to understand all these isomorphisms in a basis-free way. –  May 06 '20 at 00:30
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    I will be answering here, in order, the array of questions the you had posed 1) The matrix for $F$ follows from the evaluations in the basis elements, that is $$[F(E_i,e_j)]$$ – janmarqz May 06 '20 at 01:01
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    Remark: The vector space $\mathbb R^2$ and its dual are different, the dual, written as ${\mathbb R^2}^$ is the vector space of linear maps (a.k.a. covectors) $\mathbb R^2\to\mathbb R$. So you can't allocate vectors and covectors in the same set. There is however, an isomorphism $\mathbb R^2\to{\mathbb R^2}^$ that allows you to represent any covector, say $f$, with a unique vector $a$ thru the formula $f(v)=a\bullet v$. This mechanism is called Reisz Representation Lemma – janmarqz May 06 '20 at 01:13
  • But if we choose a basis $(e_i)$ for $V$ and give $V$ the dual basis, then for every vector in $v$ we can represent it as $v = a_1e_1+...+a_ne_n$, a point in $n$ dimensional space. If we give $V^$ the dual space then what is the harm in visualizing an element $w \in V^$ as a point in $n$ dimensional space? I mean, we can write $w = b_1E_1+...b_nE_n \in V$ just for some $b_i \in \mathbb{R}$ or whatever field it's over...

    So yeah, I don't understand why we can just visualize elements of the dual space as points in an $n$ dimensional space :-(

    –  May 06 '20 at 01:22
  • One last comment, a better notational habit is (the clever) indexing technique: we index $b_i$ for basis vectors in $V$ and $\beta^j$ for (dual) basis covectors of $V^$. An generic element $v$ of $V$ would be expressed as $v=\sum_sv^sb_s$ or simply $v=v^sb_s$, a linear combination, and for a covector $f=f_s\beta^s$ a linear combination in $V^$. – janmarqz May 06 '20 at 01:32
  • The harm of confusing $V$ with $V^*$ is that you would be extra lost on this matters – janmarqz May 06 '20 at 01:36

2 Answers2

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By bilinearity you have

$$F(w,v) = F\left(\sum_i b_i E_i, \sum_j a_j e_j\right) = \sum_{i,j} b_i a_j F(E_i, e_j),$$

hence the matrix representing $F$ has entries $M_{ij} = F(E_i, e_j)$. (Notice that the identity matrix represents precisely the "standard" bilinear map $(w,v) \mapsto w(v)$.)

If you think of the matrix $M_{ij}$ as representing an endomorphism of $V$, it would be the endomorphism $$v \mapsto \left(\sum_j a_j F(E_i, e_j)\right)_{i=1}^n = \left(F\left(E_i, \sum_j a_j e_j\right)\right)_{i=1}^n = (F(E_i, v))_{i=1}^n.$$

For a basis-free description, see the answer of janmarqz, which uses the identifications

$$ \mathrm{Bil}(V^*,V; \mathbb{R}) \cong \mathrm{Hom}(V^* \otimes V , \mathbb{R}) \cong (V^* \otimes V)^* \cong V^{**} \otimes V^* \cong V \otimes V^*.$$

Edit: A few remarks following from the discussion below. Everything is for finite-dimensional vector spaces (since in the infinite case, everything breaks down). I can give you the following reason why the isomorphism $T^1_1(V) \cong \mathrm{End}(V)$ is "more special" than an isomorphism to $T^2(V)$ or $T_2(V)$: for all finite-dimensional vector spaces $V$ and $W$, there are canonical isomorphisms $$ \mathrm{Bil}(W^*,V; \mathbb{R}) \cong \mathrm{Hom}(W^* \otimes V , \mathbb{R}) \cong (W^* \otimes V)^* \cong W^{**} \otimes V^* \cong W \otimes V^* \cong \mathrm{Hom}(W,V).$$

You can write them all down (at least in one direction) without choosing a basis. This is not true for $\mathrm{Bil}(W,V; \mathbb{R}) \cong \mathrm{Hom}(W,V)$, for instance: there you need to choose a basis. Now, for vector spaces this might not make a huge difference, but once you work in a different setting (for instance, in representation theory, where only equivariant maps are allowed), then these isomorphisms still hold, whereas the ones obtained choosing bases do not work anymore. The bottom line is that one should just be aware that canonical isomorphisms, whenever they are given, are usually better than choosing a basis, because they might work also in more general settings.

As for blue and red vectors: yes, given a finite-dimensional vector space $V$, you can choose a basis (which is equivalent to giving an isomorphism $V \to \mathbb{R}^n$), endow $V^*$ with the dual basis (which gives an isomorphism $V^* \to \mathbb{R}^n$), and then represent both bases on $\mathbb{R}^n$, in blue and red, respectively: blue and red vectors will all agree with the standard basis of $\mathbb{R}^n$. However, if you want to keep the same isomorphisms $V \to \mathbb{R}^n$ and $V^* \to \mathbb{R}^n$ but to change the basis of $V$, funny things will happen when you accordingly also change the basis of $V^*$, as the two bases will not vary in the same way and therefore blue and red vectors will not coincide anymore. This is because the isomorphism $V \to \mathbb{R}^n$ depends covariantly on the basis you choose in $V$, whereas the isomorphism $V^* \to \mathbb{R}^n$ depends contravariantly on it. This is all a bit messy, I know. There are probably much better ways to explain it (just try to look it up, for instance here). What I really want to say is the following: if you pick a vector $v \in V$, there is no canonical choice of a "dual covector" in $V^*$. If you choose a basis $e_i$ and take its dual basis $E_i$, then you can map $v=\sum_i a_i e_i$ to $w=\sum_i a_i E_i$ in $V^*$. But if you then take another basis $e_i'$ and its dual basis $E_i'$, and $v$ is now written as $v=\sum_i a_i e_i = \sum_i a_i' e_i'$ in the new basis, you will be disappointed to find out that $w \neq \sum_i a_i' E_i'$.

57Jimmy
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It is well known that taking $f\otimes v\mapsto L_{f\otimes v}$, where $$L_{f\otimes v}:V\to V,$$ is defined by $$L_{f\otimes v}(w)=f(w)v,$$ gives us $T^1_1V\cong{\rm End}V$ of vector spaces, which details aren't difficult.

janmarqz
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  • Cool, yeah that makes sense. But do you understand what I was asking about, how if we give a vector space an orthogonal basis, then take the dual basis of that basis as a basis for the dual space, then we can visualize the covectors as vectors?? Then would the action of the covector on the vector just be a dot product? –  May 06 '20 at 00:15
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    So, in case of $e_j$ are an orthonormal basis, then the dual basis $E_j$ can be represented (a la Riesz) with the same basis $e_i$, that is $E_j:\mathbb R^2\to\mathbb R$ maps via $E_j(v)=e_j\bullet v$. – janmarqz May 06 '20 at 01:23
  • Yeah, I get that, but given $w=b_1E_1+...+b_nE_n$ in the dual space, this is indeed a vector, since the dual space is a vector space, and thus we can imagine it as an arrow. If $(E_i)$ is the dual basis to $(e_i)$ for $V$, then I'm just imagining the covectors to be blue and vectors to be red in the same coordinate space, and I don't see what the harm in that is. –  May 06 '20 at 01:28
  • Again, for any space $V$ of dimension $n$ and its dual $V^$ are both vector spaces, of the same dimension $n$ but different!* and you can not put their elements in the same set. – janmarqz May 06 '20 at 01:55
  • "The harm of confusing $V$ with $V^$ is that you would be extra lost on this matters*" – janmarqz May 06 '20 at 01:57
  • I feel like I'm not expressing myself clearly here... If we had a basis for 2 dimensional space $e_1$ and $e_2$, and took the dual basis for 2 dimensional vector space thus spanned by $E_1$ and $E_2$, and then we placed those two coordinate axes ontop of eachother, , it seems that you could consider the covector acting on the vector as the dot product. –  May 06 '20 at 01:59
  • You should read about the Riesz Representation Theorem for this what you are proposing, considering orthonormal basis. – janmarqz May 06 '20 at 02:09
  • this old answer might help you I think https://math.stackexchange.com/questions/2763290/is-there-a-fundamental-problem-with-extending-matrix-concepts-to-tensors/2770971#2770971 – janmarqz May 06 '20 at 02:17