Notes on Products and the Exterior For Math 245

K. Purbhoo July 16, 2012

1 Tensor Products

1.1 Axiomatic deﬁnition of the tensor In we have many types of products. For example,

• The product: V × F → V • The : Rn × Rn → • The : R3 × R3 → R3

products: Mm×k × Mk×n → Mm×n Note that the three vector spaces involved aren’t necessarily the same. What these examples have in common is that in each case, the product is a bilinear . The is just another example of a product like this. If V1 and V2 are any two vector spaces over a ﬁeld F, the tensor product is a :

V1 × V2 → V1 ⊗ V2 , where V1 ⊗ V2 is a vector over F. The tricky part is that in order to deﬁne this map, we ﬁrst need to construct this V1 ⊗ V2. We give two deﬁnitions. The ﬁrst is an axiomatic deﬁnition, in which we specify the properties that V1 ⊗ V2 and the bilinear map must have. In some sense, this is all we need to with tensor products in a practical way. Later we’ll show that such a space actually exists, by constructing it.

Deﬁnition 1.1. Let V1,V2 be vector spaces over a ﬁeld F. A pair (Y, µ), where Y is a vector space over F and µ : V1 × V2 → Y is a bilinear map, is called the tensor product of V1 and V2 if the following condition holds:

(*) Whenever β1 is a for V1 and β2 is a basis for V2, then

µ(β1 × β2) := {µ(x1, x2) | x1 ∈ β1, x2 ∈ β2} is a basis for Y .

1 Notation: We write V1 ⊗ V2 for the vector space Y , and x1 ⊗ x2 for µ(x1, x2). The condition (*) does not actually need to be checked for every possible pair of bases β1, β2: it is enough to check it for any single pair of bases.

Theorem 1.2. Let Y be a vector space, and µ : V1 × V2 → Y a bilinear map. Suppose there exist bases γ1 for V1, γ2 for V2 such that µ(γ1 × γ2) is a basis for Y . Then condition (*) holds (for any choice of basis).

The proof is replete with quadruply-indexed and represents every sensible person’s worst idea of what tensor products are all about. Luckily, we only need to do this once. You may wish to skip over this proof, particularly if this is your ﬁrst time through these notes.

Proof. Let β1, β2 be bases for V1 and V2 respectively. We ﬁrst show that µ(β1 × β2) spans Y . Let y ∈ Y . Since µ(γ1 × γ2) spans Y , we can write X y = ajkµ(z1j, z2k) j,k P where z1j ∈ γ1, z2k ∈ γ2. But since β1 is a basis for V1 z1j = bjlx1l, where x1l ∈ β1, and P l similarly z2k = m ckmx2m, where x2m ∈ β2. Thus X X X y = ajkµ( bjlx1l , ckmx2m) j,k l m X = ajkbjlckmµ(x1l, x2j) j,k,l,m so y ∈ span(µ(β1 × β2)). Now we need to show that µ(β1 × β2) is linearly independent. If V1 and V2 are both ﬁnite dimensional, this follows from the fact that |µ(β1 × β2)| = |µ(γ1 × γ2)| and both span Y . For inﬁnite , a more sophisticated argument is needed. Suppose X dlmµ(x1l, x2m) = 0 , l,m P where x1l ∈ β1, x2m ∈ β2. Then by change of basis, x1l = eljz1j, where z1j ∈ γ1, and P j x2m = fmkz2k, where z2k ∈ γ2. Note that the elj form an inverse “matrix” to the bjl k P P above, in that j eljbjl0 = δll0 , and similarly k fmkckm0 = δmm0 . Thus we have X 0 = dlmµ(x1l, x2m) = 0 l,m X X X = dlmµ( eljz1j, fmkz2k) l,m j k X X = dlmeljfmkµ(z1j, z2k) j,k l,m

2 P Since µ(γ1 × γ2) is linearly independent, l,m dlmeljfmk = 0 for all j, k. But now X dl0m0 = dlmδll0 δmm0 l,m ! ! X X X = dlm bjl0 elj ckm0 fmk l,m j k ! X X = bjl0 ckm0 dlmeljfmk j,k l,m = 0 for all l0, m0. The tensor product can also be deﬁned for more than two vector spaces.

Deﬁnition 1.3. Let V1,V2,...Vk be vector spaces over a ﬁeld F. A pair (Y, µ), where Y is a vector space over F and µ : V1 × V2 × · · · × Vk → Y is a k-, is called the tensor product of V1,V2,...,Vk if the following condition holds:

(*) Whenever βi is a basis for Vi, i = 1, . . . , k,

{µ(x1, x2, . . . , xk) | xi ∈ βi} is a basis for Y .

We write V1 ⊗V2 ⊗· · ·⊗Vk for the vector space Y , and x1 ⊗x2 ⊗· · ·⊗xk for µ(x1, x2, . . . , xk).

1.2 Examples: working with the tensor product

Let V,W be vector spaces over a ﬁeld F. There are two ways to work with the tensor product. One way is to think of the space V ⊗ W abstractly, and to use the to manipulate the objects. In this context, the elements of V ⊗ W just look like expressions of the form X aivi ⊗ wi , i where ai ∈ F, vi ∈ V , wi ∈ W . Example 1.4. Show that (1, 1) ⊗ (1, 4) + (1, −2) ⊗ (−1, 2) = 6(1, 0) ⊗ (0, 1) + 3(0, 1) ⊗ (1, 0) in R2 ⊗ R2. Solution: Let x = (1, 0), y = (0, 1). We rewrite the left hand side in terms of x and y, and use bilinearity to expand. (1, 1) ⊗ (1, 4) + (1, −2) ⊗ (−1, 2) = (x + y) ⊗ (x + 4y) + (x − 2y) ⊗ (−x + 2y) = (x ⊗ x + 4x ⊗ y + y ⊗ x + 4y ⊗ y) + (−x ⊗ x + 2x ⊗ y − 2y ⊗ x − 4y ⊗ y) = 6x ⊗ y + 3y ⊗ x = 6(1, 0) ⊗ (0, 1) + 3(0, 1) ⊗ (1, 0)

3 The other way is to actually identify the space V1 ⊗ V2 and the map V1 × V2 → V1 ⊗ V2 with some familiar object. There are many examples in which it is possible to make such an identiﬁcation naturally. Note, when doing this, it is crucial that we not only specify the vector space we are identifying as V1 ⊗ V2, but also the product (bilinear map) that we are using to make the identiﬁcation.

n m Example 1.5. Let V = Frow, and W = Fcol. Then V ⊗ W = Mm×n(F), with the product deﬁned to be v ⊗ w = wv, the matrix product of a column and a row vector. To see this, we need to check condition (*). Let {e1, . . . , em} be the for W , and {f1, . . . , fn} be the standard basis for V . Then {fj ⊗ ei} is the standard basis for Mm×n(F). So condition (*) checks out. Note: we can also say that W ⊗V = Mm×n(F), with the product deﬁned to be w⊗v = wv. From this, it looks like W ⊗ V and V ⊗ W are the same space. Actually there is a subtle but important diﬀerence: to deﬁne the product, we had to switch the two factors. The relationship between V ⊗ W and W ⊗ V is very closely analogous to that of the Cartesian products A × B and B × A, where A and B are sets. There’s an obvious between them and from a practical point of view, they carry the same information. But if we started conﬂating the two, and writing (b, a) and (a, b) interchangeably it would cause a lot of problems. Similarly V ⊗ W and W ⊗ V are naturally isomorphic to each other, so in that sense they are the same, but we would never write v ⊗ w when we mean w ⊗ v.

Example 1.6. Let V = F[x], vector space of in one . Then V ⊗ V = F[x1, x2] is the space of polynomials in two variables, the product is deﬁned to be f(x) ⊗ g(x) = f(x1)g(x2). To check condition (*), note that β = {1, x, x2, x3,...} is a basis for V , and that

β ⊗ β = {xi ⊗ xj | i, j = 0, 1, 2, 3,... } i j = {x1x2 | i, j = 0, 1, 2, 3,... } is a basis for F[x1, x2]. Note: this is not a commutative product, because in general

f(x) ⊗ g(x) = f(x1)g(x2) 6= g(x1)f(x2) = g(x) ⊗ f(x) .

Example 1.7. If V is any vector space over F, then V ⊗ F = V . In this case, ⊗ is just scalar .

Example 1.8. Let V,W be ﬁnite dimensional vector spaces over F. Let V ∗ = L(V, F) be the of V . Then V ∗ ⊗ W = L(V,W ), with multiplication deﬁned as f ⊗ w ∈ L(V,W ) is the linear transformation (f ⊗ w)(v) = f(v) · w, for f ∈ V ∗, w ∈ W . n m This is just the abstract version of Example 1.5. If V = Fcol and W = Fcol, then ∗ n V = Frow. Using Example 1.5, V ⊗W is identiﬁed with Mm×n(F), which is in turn identiﬁed with L(V,W ). Exercise: Prove that condition (*) holds. Note: If V and W are both inﬁnite dimensional then V ∗ ⊗ W is a subspace of L(V,W ) but not equal to it. Speciﬁcally, V ∗ ⊗W = {T ∈ L(V,W ) | dim R(T ) < ∞} is the set of ﬁnite

4 linear transformations in L(V,W ). This follows from the fact that f ⊗ w has rank 1, for any f ∈ V ∗, w ∈ W , and a of such transformations must have ﬁnite rank.

Example 1.9. Let V be the space of vectors in Newtonian 3-space. Let T be the vector space of time measurements. Then V ⊗ T is the space of vectors in Newtonian 3-space. This is because velocity times time equals displacement. The point of this example is that physical quantities have units associated with them. Velocity is not a vector in R3. It’s an element of a totally diﬀerent 3-dimensional vector space over R. To perform calculations, we identify it with R3 by choosing coordinate directions such as up forward and right and units such as m/s, but these are artiﬁcial constructions. Displacement again lives in a diﬀerent vector space, and the tensor product allows us to relate elements in these diﬀerent physical spaces.

Example 1.10. Consider Qn and R as vector spaces over Q. Then Qn ⊗ R = Rn as vector spaces over Q, where the multiplication is just on Rn. n Proof that condition (*) holds. Let β = e1, . . . , en be the standard basis for Q , and let n n γ be a basis for R over Q. First we show that β ⊗ γ is spans R . Let (a1, . . . an) ∈ R . We P P can write ai = bijxj, where xj ∈ γ. Thus (a1, . . . , an) = bijei ⊗ xj. Next we show P i,j that β ⊗ γ is linearly independent. Suppose i,j cijei ⊗ xj = 0. Since X X X cijei ⊗ xj = ( c1jxj,..., cnjxk) , i,j j j P we have j cijxj = 0 for all i. Since {xj} are linearly independent, cij = 0, as required.

1.3 Constructive deﬁnition of the tensor product To give a construction of the tensor product, we need the notion of a free vector space.

Deﬁnition 1.11. Let A be a , and F a ﬁeld. The free vector space over F generated by A is the vector space Free(A) consisting of all formal ﬁnite linear combinations of elements of A.

Thus, A is always a basis of Free(A).

Example 1.12. Let A = {♠, ♥, ♣, ♦}, and F = R. Then the Free(A) is the four-dimensional vector space consisting of all elements a♠ + b♥ + c♣ + d♦, where a, b, c, d ∈ R. and scalar multiplication are deﬁned in the obvious way:

(a♠ + b♥ + c♣ + d♦) + (a0♠ + b0♥ + c0♣ + d0♦) = (a + a0)♠ + (b + b0)♥ + (c + c0)♣ + (d + d0)♦

k(a♠ + b♥ + c♣ + d♦) = (ka)♠ + (kb)♥ + (kc)♣ + (kd)♦)

5 When the elements of the set A are or vectors, the notation gets tricky, because there is a danger of confusing the operations of addition and scalar multiplication and the zero-element in the vector space Free(A), and the operations of addition and multiplication and the in A (which are irrelevant in the deﬁnition of Free(A)). To help keep these straight in situations where there is a danger of confusion, we’ll write , and when we mean the operations of addition and scalar multiplication in Free(A). We’ll denote the zero vector of Free(A) by 0Free(A).

Example 1.13. Let N = {0, 1, 2, 3, 4,... }, and F = R. Then Free(N) is the inﬁnite dimen- sional vector space whose elements are of the form

(a0 0)  (a1 1)  ···  (am m) , for some m ∈ N. Note that the element 0 here is not the zero vector in Free(N). It’s called 0 because it happens to be the zero element in N, but this is completely irrelevant in the construction of the free vector space. If we wanted we could write this a little diﬀerently by putting xi in place of i ∈ N. In this new notation, the elements of Free(N) would look like

0 1 m a0x + a1x + ··· + amx , for some m ∈ N, in other words elements of the vector space of polynomials in a single variable.

Deﬁnition 1.14. Let V and W be vector spaces over a ﬁeld F. Let P := Free(V × W ) , the free vector space over F generated by the set V × W . Let R ⊂ P be the subspace spanned by all vectors of the form

(u + kv, w + lx)  ((−1) (u, w))  ((−k) (v, w))  ((−l) (u, x))  ((−kl) (v, x)) with k, l ∈ F, u, v ∈ V , and w, x ∈ W . Let πP/R : P → P/R be the map. Let µ : V × W → P/R be the map  µ(v, w) = πP/R (v, w) . The pair (P/R , µ) is the tensor product of V and W . We write V ⊗ W for P/R, and v ⊗ w for µ(v, w). We need to show that our two deﬁnitions agree, i.e.. that tensor product as deﬁned in Deﬁnition 1.14 satisﬁes the conditions of Deﬁnition 1.1. In particular, we need to show that µ is bilinear, and that the pair (P/R , µ) satisﬁes condition (*). We can show the bilinearity immediately. Essentially bilinearity is built into the deﬁni- tion. If the P is the space of all linear combinations of symbols (v, w), then R is the space of all those linear combinations that can be simpliﬁed to the zero vector using bilinearity. Thus P/R is the set of all expressions, where two expressions are equal iﬀ one can be simpliﬁed to the other using bilinearity.

6 Proposition 1.15. The map µ in Deﬁnition 1.14 is bilinear.

Proof. Let k, l ∈ F, u, v ∈ V , and w, x ∈ W . We must show that µ(u + kv, w + lx) = µ(u, w) + kµ(v, w) + lµ(u, x) + klµ(v, x) .

We know that πP/R(z) = 0 for all z ∈ R. In particular, we have 0 = πP/R (u + kv, w + lx)   ((−1) (u, w))  ((−k) (v, w))  ((−l) (u, x))  ((−kl) (v, x))  = πP/R (u + kv, w + lx)     − πP/R (u, w) − kπP/R (v, w) − lπP/R (u, x) − klπP/R (v, x) = µ(u + kv, w + lx) − µ(u, w) − kµ(v, w) − lµ(u, x) − klµ(v, x) ,

and the result follows. To prove that condition (*) holds we use the following important lemma from the of quotient spaces.

Lemma 1.16. Suppose V and W are vector spaces over a ﬁeld F and T : V → W is a linear transformation. Let S be a subspace of V . Then there exists a linear transformation T : V/S → W such that T (x + S) = T (x) for all x ∈ V if and only if T (s) = 0 for all s ∈ S. Moreover, if T exists it is unique, and very linear transformation V/S → W arises in this way from a unique T .

Proof. Suppose that T exists. Then for all s ∈ S we have T (s) = T (s + S) = T (0V/S) = 0. Now suppose that T (s) = 0 for all s ∈ S. We must show that T (x + S) = T (x) makes T well deﬁned. In other words, we must show that if v, v0 ∈ V are such that v + S = v0 + S, then T (v + S) = T (v) = T (v0) = T (v0 + S). Now, if v + S = v0 + S, then v − v0 ∈ S. Thus T (v) − T (v0) = T (v − v0) = 0 and so T (v) = T (v0) as required. The ﬁnal remarks are clear, since the statement T (x + S) = T (x) uniquely determines either of T , T from the other.

Theorem 1.17 ( of tensor products). Let V, W, M be vector spaces over a ﬁeld F. Let V ⊗ W = P/R be the tensor product, as deﬁned in Deﬁnition 1.14. For any bilinear map φ : V × W → M, there is a unique linear transformation φ : V ⊗ W → M, such that φ(v ⊗ w) = φ(v, w) for all v ∈ V , w ∈ W . Moreover, every linear transformation in L(V ⊗ W, M) arises in this way.

Proof. Since V ×W is a basis for P, we can extend any map φ : V ×W → M to a linear map φ : P → M. By a similar argument to Proposition 1.15, one can see that φ is bilinear if and only if φ(s) = 0 for all s ∈ R. Thus by Lemma 1.16, there exists a linear map φ : P/R → M if and only if φ is bilinear, and every such linear transformation arises from a bilinear map in this way.

More generally, every linear map V1 ⊗ V2 ⊗ · · · ⊗ Vk → M corresponds to a k-linear map V1 × V2 × · · · × Vk → M.

7 Theorem 1.18. Condition (*) holds for the tensor product as deﬁned in Deﬁnition 1.14. Proof. Let β = be a basis for V , and γ a basis for W . We must show that µ(β × γ) is a basis for P/R. First we show it is spanning. Let z ∈ P/R. Then we can write   z = a1πP/R (u1, x1) + ··· + amπP/R (um, xm)

= a1µ(u1, x1) + ··· + amµ(um, xm) P with a1, . . . am ∈ F, u1, . . . um ∈ V , and x1, . . . xm ∈ W . But now ui = bijvj with vj ∈ β P j and xi = k cijwk with wk ∈ γ. Hence

z = a1µ(u1, x1) + ··· + amµ(um, xm) m X X X = aiµ( bijvj , bikwk) i=1 j k m X X = aibijcikµ(vj, wk) . i=1 j,k Next we show . Suppose that X dijµ(vi, wj) = 0 i,j

∗ with vi ∈ β, wj ∈ γ. Let fk ∈ V be the linear for which fk(vk) = 1, and fk(v) = 0, for v ∈ β \{vk}. Deﬁne Fk : V × W → W to be Fk(v, w) = fk(v)w. Then Fk is bilinear, so by Theorem 1.17 there is a induced linear transformation Fk : V ⊗ W → W such that P Fk(µ(u, k)) = fk(u)x. Applying Fk to the equation i,j dijµ(vi, wj) = 0, we have ! X 0 = Fk dijµ(vi, wj) i,j X = dijfk(vi)wj i,j X = dkjwj , j

for all k. But {wj} are linearly independent, so dkj = 0. Exercise: Let V ⊗ W be the tensor product as deﬁned in Deﬁnition 1.14. Let (Y, µ) be a pair satisfying the deﬁnition of the tensor product of V and W as in Deﬁnition 1.1. Prove that there is a natural ϕ : V ⊗ W → Y such that ϕ(v ⊗ w) = µ(v, w) for all v ∈ V , w ∈ W . (In other words, show that the tensor product is essentially unique.)

1.4 The and linear transformations

Let V be a ﬁnite dimensional vector space over a ﬁeld F. Recall from Example 1.8 that V ∗ ⊗ V is identiﬁed with the space L(V ) of linear operators on V .

8 Proposition 1.19. There is a natural linear transformation tr : V ∗ ⊗ V → F, such that tr(f ⊗ v) = f(v) for all f ∈ V ∗, v ∈ V .

Proof. The map (f, v) 7→ f(v) is bilinear, so the result follows by Theorem 1.17.

The map tr is called the trace on V . We can also deﬁne the trace from V ⊗ V ∗ → F, using either the fact that V = V ∗∗, or by switching the factors in the deﬁnition (both give the same answer).

n Example 1.20. Let V = Fcol. Let {e1, . . . en} be the standard basis for V , and let {f1, . . . , fn} be the (standard basis for row vectors). ∗ From Example 1.5, we can identify V ⊗ V with Mn×n. A matrix A ∈ Mn×n can be written as X A = Aijejfi i,j X = Aijfi ⊗ ej . i,j

Therefore X tr(A) = Aij tr(fi ⊗ ej) i,j X = Aijfi(ej) i,j X = Aijδij i,j

= A11 + A22 + ··· + Ann .

Now, let V1,V2,...,Vk be ﬁnite dimensional vector spaces over F, and consider the tensor ∗ product space V1 ⊗ V2 ⊗ · · · ⊗ Vk. Suppose that Vi = Vj for some i, j. Using the trace, we can deﬁne a linear transformation

trij : V1 ⊗ · · · ⊗ Vk → V1 ⊗ · · · ⊗ Vi−1 ⊗ Vi+1 ⊗ · · · ⊗ Vj−1 ⊗ Vj+1 · · · ⊗ Vk

satisfying

trij(v1 ⊗ · · · ⊗ vk) = tr(vi ⊗ vj)(v1 ⊗ · · · ⊗ vi−1 ⊗ vi+1 ⊗ · · · ⊗ vj−1 ⊗ vj+1 · · · ⊗ vk) .

This transformation is sometimes called contraction. The most example of this is something familiar in disguise.

Example 1.21. Let V, W, X be ﬁnite dimensional vector spaces. From Example 1.8 we have V ∗ ⊗ W = L(V,W ), W ∗ ⊗ X = L(W, X) and V ∗ ⊗ X = L(V,X). The map

∗ ∗ ∗ tr23 : V ⊗ W ⊗ W ⊗ X → V ⊗ X

9 can therefore be identiﬁed with a map from

tr23 : L(V,W ) ⊗ L(W, X) → L(V,X) . By theorem 1.17, this arises from a bilinear map

L(V,W ) × L(W, X) → L(V,X) .

This map is nothing other than the composition of linear transformations: (T,U) 7→ U ◦ T . Proof. Since composition is bilinear, and the space of all linear transformations is spanned by rank 1 linear transformations, it is enough to prove this in the case where T ∈ L(V,W ), and U ∈ L(W, X) have rank 1. If T ∈ L(V,W ) has rank 1, then T (v) = f(v)w, where w ∈ W , f ∈ V ∗. As in Example 1.8 we identify this with f ⊗ w ∈ V ∗ ⊗ W . Similarly U(w) = g(w)x where x ∈ X, g ∈ W ∗, and U is identiﬁed with g ⊗ x ∈ W ∗ ⊗ X. Then

U ◦ T (v) = f(v)g(w)x = (tr(w ⊗ g))(f ⊗ x)(v) so U ◦ T is identiﬁed with

tr(w ⊗ g)(f ⊗ x) = tr23(f ⊗ w ⊗ g ⊗ x) , as required. Theorem 1.17 can also be used to deﬁne the tensor product of two linear transformations.

Proposition 1.22. Let V,V 0, W, W 0 be vector spaces over a ﬁeld F. If T ∈ L(V,V 0), and U ∈ L(W 0,W 0), there a linear unique transformation T ⊗ U ∈ L(V ⊗ W, V 0 ⊗ W 0) satisfying

T ⊗ U(v ⊗ w) = (T v) ⊗ (Uw) for all v ∈ V , w ∈ W . In particular, if T ∈ L(V ) and U ∈ L(W ) are linear operators, then T ⊗ U ∈ L(V ⊗ W ) is a linear on V ⊗ W . Proof. The map (v, w) 7→ (T v) ⊗ (Uw) is bilinear, so the result follows from Theorem 1.17.

2 The

The universal property for tensor products says that every bilinear map V × W → M is essentially the same as a linear map V ⊗ W → M. If that linear map is something reasonably natural and surjective, we may be able to view M as a quotient space of V ⊗ W . Most interesting products in algebra can be somehow regarded as quotient spaces of tensor products. One of the most familiar is the algebra. Let V be a ﬁnite dimensional vector space over a ﬁeld F.) Let

0 1 k ⊗k T (V ) = F , T (V ) = V, and T (V ) = V ⊗ V ⊗ · · · ⊗ V = V . | {z } k

10 Let Ck be the subspace of Tk(V ) spanned by all vectors of the form

(x1 ⊗ · · · ⊗ xi ⊗ · · · ⊗ xj ⊗ · · · ⊗ xk) − (x1 ⊗ · · · ⊗ xj ⊗ · · · ⊗ xi ⊗ · · · ⊗ xk) , with x1, . . . xk ∈ V , and i, j ∈ {1, . . . , k}. The tensor product is not commutative, but when we quotient by the space Ck, we impose the commutativity relation and thereby obtain a commutative product.

Deﬁnition 2.1. The kthsymmetric of V is the quotient space Symk(V ) = Tk(V )/Ck.

We write x1 · x2 ··· xk (or x1x2 ··· xk) for πTk(V )/Ck (x1 ⊗ x2 ⊗ · · · ⊗ xk). Note the following facts.

k k 1. The map V → Sym (V ), deﬁned by (x1, x2, . . . , xk) 7→ x1 · x2 ··· xk is k-linear.

2. x1 ··· xi ··· xj ··· xk = x1 ··· xj ··· xi ··· xk for all i, j ∈ {1, . . . , k} and all x1, . . . xk ∈ V . (In other words “ · ” is commutative.)

k 3. If {v1, v2, . . . , vn} is a basis for V , then any element of Sym (V ) can be written as a polynomial of degree k in v1, v2, . . . , vn. 4. There is a bilinear map Symk(V ) × Syml(V ) → Symk+l(V ) such that

(x1 · x2 ··· xk, y1 · y2 ··· yl) 7→ x1 · x2 ··· xk · y1 · y2 ··· yl .

When expressed in terms of a basis, this map is just polynomial multiplication.

In other words, Symk(V ) can be thought of as the space of polynomials of degree k in the elements of V , and the “ · ” is just ordinary commutative polynomial multiplication. When the elements are expressed in terms of a basis, these look exactly like polynomials.

3 Example 2.2. Consider the product (1, 1, 1) · (−1, 1, 1) · (0, 1, −1) ∈ Sym (R3) Let x = (1, 0, 0), y = (0, 1, 0), z = (0, 0, 1). When we expand this product in terms of x, y, z we obtain:

(1, 1, 1) · (−1, 1, 1) · (0, 1, −1) = (x + y + z)(−x + y + z)(y − z) = x2y − x2z + y3 + y2z − yz2 − z3 .

If we want to consider polynomials of mixed degrees, we can look at the of all the symmetric products of V :

Sym•(V ) := Sym0(V ) ⊕ Sym1(V ) ⊕ Sym2(V ) ⊕ Sym3(V ) ⊕ · · ·

The multiplication Symk(V ) × Syml(V ) → Symk+l(V ) makes Sym•(V ) a commutative .

11 3 The

3.1 Deﬁnition and examples The exterior algebra is constructed similarly to the polynomial algebra. Its properties are analogous to those of the polynomial algebra, but the multiplication that arises is not com- mutative. As before V will be a vector space over a ﬁeld F. Let Ak be the subspace of Tk(V ) spanned by all vectors of the form

x1 ⊗ x2 ⊗ · · · ⊗ xk with x1, . . . xk ∈ V where xi = xj for some i 6= j.

Deﬁnition 3.1. The kthexterior power of V is the quotient space Vk(V ) = Tk(V )/Ak.

We write x1 ∧ x2 ∧ · · · ∧ xk πTk(V )/Ak (x1 ⊗ x2 ⊗ · · · ⊗ xk).

Example 3.2. If V is any vector space, then A0 = {0} and A1 = {0}. Therefore V0(V ) = 0 V1 1 T (V ) = F, and (V ) = T (V ) = V . We have the following properties:

k Vk 1. The map V → (V ), deﬁned by (x1, x2, . . . , xk) 7→ x1 ∧ x2 ∧ · · · ∧ xk is k-linear.

2. For all x1, . . . xk ∈ V , x1 ∧ x2 ∧ . . . xk = 0 if xi = xj for some i 6= j.

3. For all x1, . . . xk ∈ V and all i 6= j, we have

x1 ∧ · · · ∧ xi ∧ · · · ∧ xj ∧ · · · ∧ xk = −x1 ∧ · · · ∧ xj ∧ · · · ∧ xi ∧ · · · ∧ xk .

Proof.

0 = x1 ∧ · · · ∧ (xi + xj) ∧ · · · ∧ (xi + xj) ∧ · · · ∧ xk

= x1 ∧ · · · ∧ xi ∧ · · · ∧ xi ∧ · · · ∧ xk + x1 ∧ · · · ∧ xi ∧ · · · ∧ xj ∧ · · · ∧ xk

+ x1 ∧ · · · ∧ xj ∧ · · · ∧ xi ∧ · · · ∧ xk + x1 ∧ · · · ∧ xj ∧ · · · ∧ xj ∧ · · · ∧ xk

= x1 ∧ · · · ∧ xi ∧ · · · ∧ xj ∧ · · · ∧ xk + x1 ∧ · · · ∧ xj ∧ · · · ∧ xj ∧ · · · ∧ xk

We can use these properties to simplify expressions.

Example 3.3. Let V = R2, x = (1, 0), y = (0, 1). Then (a, b) ∧ (c, d) = (ax + by) ∧ (cx + dy) = ac(x ∧ x) + ad(x ∧ y) + bc(y ∧ x) + bd(y ∧ y) = 0 + ad(x ∧ y) + bc(y ∧ x) + 0 = ad(x ∧ y) − bc(y ∧ x) = (ad − bc)(x ∧ y)

a b Note that the coeﬃcient of x ∧ y is det ( c d ).

12 On the other hand, if we do a similar calculation with three terms,

(a, b) ∧ (c, d) ∧ (e, f) = (ax + by) ∧ (cx + dy) ∧ (ex + fy) = ace(x ∧ x ∧ x) + ade(x ∧ y ∧ x) + bce(y ∧ x ∧ x) + bde(y ∧ y ∧ x) + acf(x ∧ x ∧ y) + adf(x ∧ y ∧ y) + bcf(y ∧ x ∧ y) + bdf(y ∧ y ∧ y) = 0

Vk 2 we get exactly zero. It looks the the dimensions of dim (R ) are given by the table below. k 0 1 2 3 4 5 ··· Vk 2 dim (R ) 1 2 1 0 0 0 ··· Vk 2 basis of (R ) {1} {x, y} {x ∧ y} ∅ ∅ ∅ ··· However, at this point we haven’t completely proved this. We still need to show that x ∧ y V2 2 is not the zero vector in (R ). As with the symmetric products, there is a multiplication map between the exterior products, which allows us to think of “∧00 as a multiplication operator. This product is commonly known as the wedge product. It is associative, but not commutative. Proposition 3.4. There is a natural bilinear map Vk(V ) × Vl(V ) → Vk+l(V ) such that

(v1 ∧ v2 ∧ · · · ∧ vk, w1 ∧ w2 ∧ · · · ∧ wl) 7→ v1 ∧ v2 ∧ · · · ∧ vk ∧ w1 ∧ w2 ∧ · · · ∧ wl We will defer the proof until Section 3.2. Deﬁnition 3.5. The collection of the spaces Vk(V ), for k = 0, 1, 2,... , together with the operation ∧ is called the exterior algebra on V . More speciﬁcally, the space

V•(V ) := V0(V ) ⊕ V1(V ) ⊕ V2(V ) ⊕ V3(V ) ⊕ · · ·

is a ring, with multiplication deﬁned by the wedge product. A vector space over F which also has the structure of a ring is called an F-algebra. Example 3.6. Let V = R3, x = (1, 0, 0), y = (0, 1, 0), z = (0, 0, 1). We have (a, b, c) ∧ (d, e, f) = (ax + by + cz) ∧ (dx + ey + fz) = ae(x ∧ y) + bd(y ∧ x)af(x ∧ z) + cd(z ∧ x)bf(y ∧ z) + ce(z ∧ y) = (ae − bd)(x ∧ y) + (af − cd)(x ∧ z) + (bf − ce)(y ∧ z) .

To compute (a, b, c) ∧ (d, e, f) ∧ (g, h, i) we just multiply this resulting expression by gx + hy + iz.

(a, b, c) ∧ (d, e, f) ∧ (g, h, i) = ((ae − bd)(x ∧ y) + (af − cd)(x ∧ z) + (bf − ce)(y ∧ z)) ∧ (gx + hy + iz) = (ae − bd)i(x ∧ y ∧ z) + (af − cd)h(x ∧ z ∧ y) + (bf − ce)z(y ∧ z ∧ x) = ((ae − bd)i − (af − cd)h + (bf − ce)z)(x ∧ y ∧ z)

13 Here we’ve used y ∧ z ∧ x = −x ∧ z ∧ y = x ∧ y ∧ z. Note that the coeﬃcient is equal to a b c det d e f g h i and moreover, the expression we obtained is exactly the cofactor expansion along the bottom row.

Example 3.7. Let V = R4 with basis {x, y, z, w}. Let α = x ∧ y + z ∧ w. We have α ∧ α = (x ∧ y + z ∧ w) ∧ (x ∧ y + z ∧ w) = x ∧ y ∧ x ∧ y + x ∧ y ∧ z ∧ w + z ∧ w ∧ x ∧ y + z ∧ w ∧ z ∧ w = x ∧ y ∧ z ∧ w − x ∧ w ∧ z ∧ y = x ∧ y ∧ z ∧ w + x ∧ y ∧ z ∧ w = 2x ∧ y ∧ z ∧ w

It may at ﬁrst be surprising that the answer we obtained is not zero, but this is no mistake. What this proves is that α = x ∧ y + z ∧ w is not a pure wedge product, i.e. α 6= u ∧ v for any u, v ∈ R4. Indeed, (u ∧ v) ∧ (u ∧ v) = 0 for all u, v. However, expressions that are not pure wedge products can be multiplied with themselves to give a non-zero vector. Proposition 3.8. If α ∈ Vk(V ), ω ∈ Vl(V ), then α ∧ ω = (−1)klω ∧ α. Thus although the wedge product is not commutative, it is reasonably close commutative. A product with this property said to supercommutative.

Proof. If α = v1 ∧ v2 ∧ · · · ∧ vk, ω = w1 ∧ w2 ∧ · · · ∧ wl, then we can get from v1 ∧ v2 ∧ · · · ∧ vk ∧ w1 ∧ w2 ∧ · · · ∧ wl, to ±w1 ∧ w2 ∧ · · · ∧ wl ∧ v1 ∧ v2 ∧ · · · ∧ vk by swapping adjacent v’s and w’s. Each swap contributes a factor of −1, and it takes exactly kl swaps.

3.2 Linear tranformations In order to prove many properties of the exterior algebra and wedge product, we need a better understanding of linear transformations with domain Vk(V ). The following theorem is analogous to the universal property for tensor products.

Theorem 3.9. Let V,M be vectors spaces over F. For any k-linear, alternating , k Vk φ : V → M, there exists a unique map φ : (V ) → M such that φ(v1 ∧ v2 ∧ · · · ∧ vk) = Vk φ(v1, v2, . . . , vk) for all v1, v2, . . . , vk ∈ V . Moreover every linear map (V ) → M arises in this way.

Proof. Since φ is k-linear, by Theorem 1.17, there is an equivalent linear map φe : Tk(V ) → k M. We must show that φe descends to a map from V (V ) = Tk(V )/Ak to M. By Lemma 1.16, such a map descends if and only if φe(s) = 0 for all s ∈ Ak, and moreover every such linear map arises in this way. But by the deﬁnition of Ak this is equivalent to the fact that φ is alternating, as required.

14 We are now in a to prove Proposition 3.4. Proof. Bilinearity means in each component, with the other held ﬁxed. First, ﬁx w1, . . . , wl ∈ V , and consider the map

(v1, . . . , vk) 7→ v1 ∧ v2 ∧ · · · ∧ vk ∧ w1 ∧ w2 ∧ · · · ∧ wl . This map is k-linear and alternating, so by Theorem 3.9 we obtain a linear map Vk(V ) → Vk+l(V ) satisfying a linear map satisfying

v1 ∧ v2 ∧ · · · ∧ vk 7→ v1 ∧ v2 ∧ · · · ∧ vk ∧ w1 ∧ w2 ∧ · · · ∧ wl .

Denote this linear map α 7→ α ∧ (w1 ∧ w2 ∧ · · · ∧ wl). Next we apply a similar argument to the second factor. Consider an arbitrary element α ∈ Vk(V ). The argument in the ﬁrst paragraph shows that we have a well-deﬁned map

(w1, w2, . . . , wl) 7→ α ∧ (w1 ∧ w2 · · · ∧ wl) P If we write α = i aivi1 ∧ vi2 ∧ . . . vik where ai ∈ F and vij ∈ V , we can rewrite this as X (w1, w2, . . . , wl) 7→ aivi1 ∧ vi2 ∧ . . . vik ∧ w1 ∧ w2 ∧ · · · ∧ wl , i from which we see that this map is is l-linear and alternating. By Theorem 3.9 we obtain a linear map Vl(V ) → Vk+l(V ) which we denote ω 7→ α ∧ ω. Finally, we need to check that the map (α, ω) 7→ α ∧ ω is bilinear. The argument of the second paragraph shows that it is linear in the second factor. It remains to show that it is P Vl linear in the ﬁrst. Fix ω = j bjwj1 ∧ wj2 ∧ · · · ∧ wjl ∈ (V ). Then X α 7→ α ∧ ω = bjα ∧ (wj1 ∧ wj2 ∧ · · · ∧ wjl) . j

From the ﬁrst paragraph, we know that each map α 7→ α ∧ (wj1 ∧ wj2 ∧ · · · ∧ wjl) is linear. Hence α 7→ α ∧ ω is a linear combination of linear maps, and therefore linear.

3.3 Linear independence As we have already seen, the exterior algebra has many -like properties. One of the key properties of the determinant is that the determinant of a matrix is non-zero if and only if its rows (or columns) are linearly independent. Our next goal is to prove the following theorem, which says that the wedge product generalizes this idea.

Theorem 3.10. Let V be a vector space over a ﬁeld F. A set of vectors {v1, v2, . . . , vk} is Vk linearly independent if and only if v1 ∧ v2 ∧ · · · ∧ vk 6= 0 ∈ (V ). The “if” direction is easy. Unfortunately, at the moment we don’t have any tools for proving that two vectors in Vk(V ) are not equal. One of the best ways to prove that a vector is non-zero is to apply a linear transformation to it. If the result is non-zero, the vector must be non-zero. This will be our strategy. We can use Theorem 3.9 to obtain linear functionals on Vk(V ).

15 ∗ ∗ Theorem 3.11. Let f1, f2, . . . , fk ∈ V , where V is the dual space to V . There is a well Vk deﬁned linear functional Ff1,f2,...,fk : (V ) → F, such that   f1(v1) f1(v2) ··· f1(vk) f2(v1) f2(v2) ··· f2(vk) F (v ∧ v ∧ · · · ∧ v ) = det   . f1,f2,...,fk 1 2 k  . . .   . . .  fk(v1) fk(v2) ··· fk(vk)

0 0 0 ∗ 0 0 0 Vk ∗ Moreover, if f1, f2, . . . , fk ∈ V , and f1 ∧ f2 ∧ · · · ∧ fk = f1 ∧ f2 ∧ · · · ∧ fk ∈ (V ), then F = F 0 0 0 . f1,f2,...,fk f1,f2,...,fk

In of this second part, we write f1 ∧ f2 ∧ · · · ∧ fk(v1 ∧ v2 ∧ · · · ∧ vk) for Ff1,f2,...,fk (v1 ∧ v2 ∧ · · · ∧ vk). Proof. The ﬁrst statement follows from Theorem 3.9 and the fact that determinant is k- linear alternating in the columns. The second statement is equivalent to saying that for Vk ∗ any given v1, v2, . . . vk, there is a well deﬁned linear map from G : (V ) → F such that

G(f1 ∧ f2 ∧ · · · ∧ fk) = Ff1,f2,...,fk (v1 ∧ v2 ∧ · · · ∧ vk). This follows from Theorem 3.9 and the fact that the determinant is k-linear and alternating in the rows.

Proof of Theorem 3.10. First, suppose that {v1, v2, . . . , vk} is linearly dependent. Then we can write one of these vectors as a linear combination of the others; without loss of generality, say vk = a1v1 + a2v2 + ··· + ak−1vk−1. Then

v1 ∧ v2 ∧ · · · ∧ vk = v1 ∧ v2 ∧ · · · ∧ vk−1 ∧ (a1v1 + a2v2 + ··· + ak−1vk−1)

= a1v1 ∧ v2 ∧ · · · ∧ vk−1 ∧ v1 + a2v1 ∧ v2 ∧ · · · ∧ vk−1 ∧ v2

+ ··· + ak−1v1 ∧ v2 ∧ · · · ∧ vk−1 ∧ vk−1 = 0

Now, suppose that {v1, v2, . . . , vk} is linearly independent. Since this set can be extended ∗ to a basis, we can deﬁne linear functionals f1, . . . , fk ∈ V such that fi(vj) = δij. But then by Theorem 3.11 we have

1 0 ··· 0 0 1 ··· 0   f1 ∧ f2 ∧ · · · ∧ fk(v1 ∧ v2 ∧ · · · ∧ vk) = det . . . = 1 , . . . 0 0 ··· 1 so v1 ∧ v2 ∧ · · · ∧ vk 6= 0. Corollary 3.12. If dim V < k then dim Vk(V ) = 0. Proof. If dim V < k then any k vectors will be linearly dependent, so any k-fold wedge product is zero. Using similar ideas, we can compute the dimensions of Vk(V ) when V is ﬁnite dimen- sional, for all k < dim V .

16 Theorem 3.13. Let V be an n-dimensional vector space over a ﬁeld F, and let β1 = {e1, . . . en} be a basis for V . For each k ≤ n, let

βk = {ei1 ∧ ei2 ∧ · · · ∧ eik | 1 ≤ i1 < i2 < ··· < ik ≤ n} . Vk Vk n Then βk is a basis for (V ). In particular, dim (V ) = k . Exercise: Prove Theorem 3.13.

3.4 Functoriality and the determinant Let T : V → W be a linear transformation. For any non-negative integer k, we can deﬁne a Vk Vk linear transformation T∗ : (V ) → (W ) satisfying

T∗(v1 ∧ v2 ∧ · · · ∧ vk) := T (v1) ∧ T (v2) ∧ · · · ∧ T (vk) ,

for all v1, v2, . . . , vk ∈ V . To see that T∗ exists, by Theorem 3.9 it is enough to check that the map (v1, v2, . . . , vk) 7→ T (v1) ∧ T (v2) ∧ · · · ∧ T (vk) is bilinear and alternating, which is straightforward.

Proposition 3.14. Let V and W be vector spaces over a ﬁeld F, and let T ∈ L(V,W ). For any non-negative integer k, the following are true. Vk Vk (i) If T is surjective then T∗ ∈ L( (V ), (W )) is surjective. Vk Vk (ii) If T is injective then T∗ ∈ L( (V ), (W )) is injective. Vk Vk (iii) If T is an isomorphism then T∗ ∈ L( (V ), (W )) is an isomorphism.

Proof. For (i) suppose T is surjective. It suﬃces to show that every vector w1 ∧w2 ∧. . . wk ∈ Vk (W ) is in the range of T∗, which is clear. For (ii) suppose T is injective. Let γ be a basis for V , and extend {T (w) | w ∈ γ} to a basis β for W . If ! X 0 = T∗ ai1i2...ik wi1 ∧ wi2 ∧ · · · ∧ wik

1≤i1

1≤i1

where {w1, w2, . . . , wn} is an ordered ﬁnite of γ then all ai1i2...ik must be equal to zero. (Exercise: Prove this.) Thus T∗ is injective. Finally (iii) follows from (i) and (ii). One consequence of this is that if W is a subspace of V , then Vk(W ) can be regarded as a subspace of Vk(V ). More formally, let i : W → V be the inclusion map. This is injective, Vk Vk Vk Vk so i∗ : (W ) → (V ) is injective. Thus we can identify (W ) with R(i∗) ⊂ (V ).

17 Proposition 3.15. Let V1,V2,V3 be vector spaces over F, and let T ∈ L(V1,V2), U ∈ L(V2,V3). For any non-negative integer k, we have: Vk Vk (i) (UT )∗ = U∗T∗ ∈ L( (V1), (V3)).

−1 −1 (ii) If T is an isomorphism, then (T∗) = (T )∗. Exercise: Prove Proposition 3.15.

n Vn Theorem 3.16. Let A ∈ Mn×n(F), and let V = Fcol. Then for all ω ∈ (V ), we have

(LA)∗(ω) = det(A)ω , where LA ∈ L(V ) is the linear transformation LA(x) = Ax.

Proof. Let {e1, e2, . . . , en} be the standard basis for V ; let {f1, f2, . . . , fn} be the dual basis. Vn Since dim (V ) = 1, it suﬃces to prove the result for ω = e1 ∧ e2 ∧ · · · ∧ en. We have

f1 ∧ f2 ∧ · · · ∧ fn((LA)∗(e1 ∧ e2 ∧ · · · ∧ en)) = f1 ∧ f2 ∧ · · · ∧ fn(Ae1 ∧ Ae2 ∧ · · · ∧ Aen)   f1(Ae1) f1(Ae2) ··· f1(Aen) f (Ae ) f (Ae ) ··· f (Ae )  2 1 2 2 2 n  = det  . . .  = det A  . . .  fn(Ae1) fn(Ae2) ··· fn(Aen)

Since f1 ∧ f2 ∧ · · · ∧ fn(e1 ∧ e2 ∧ · · · ∧ en) = 1, the result follows.

Corollary 3.17. Let V be an n-dimensional vector space over F, and T ∈ L(V ). Then for Vn all ω ∈ (V ), we have T∗(ω) = det(T )ω.

n −1 Proof. Let Φ : V → Fcol be any isomorphism. Then by deﬁnition, det T = det(ΦT Φ ).

−1 Φ∗(T∗(ω))) = (ΦT Φ )∗(Φ∗(ω)) = (det T )(Φ∗(ω)) = Φ∗((det T )ω) .

Since Φ∗ is an isomorphism, the result follows.

3.5 Pl¨ucker relations The exterior algebra is useful in studying ﬁnite dimensional subspaces of a vector space.

Theorem 3.18. Let V be an vector space over a ﬁeld F. Let W1, W2 be k-dimensional subspaces of V , with bases {x1, x2, . . . , xk} and {y1, y2, . . . , yk} respectively. Then

x1 ∧ x2 ∧ · · · ∧ xk = c y1 ∧ y2 ∧ · · · ∧ yk for some non-zero scalar c ∈ F if and only if W1 = W2.

18 Exercise: Prove Theorem 3.18. In other words, vectors in Vk(V ) which are pure wedges represent k-dimensional sub- spaces of V , up to a non-zero constant. It is therefore an important question to ask which vectors in Vk(V ) are pure wedges. Our ﬁrst criterion for determining when a vector ω ∈ Vk(V ) is a pure wedge product is the following. Vk Theorem 3.19. Let V vector space over F, and let ω ∈ (V ) be a non-zero vector. Let Wω = {x ∈ V | x ∧ ω = 0}. Then dim Wω ≤ k, and dim Wω = k if and only if ω is a pure wedge.

Before we can prove this, we need to develop some further results.

Lemma 3.20. Let V be a vector space over F. Let f ∈ V ∗. For each k, there is a map Vk Vk−1 χf : (V ) → (V ) such that for all v1, v2, . . . , vk ∈ V ,

χf (v1 ∧ v2 ∧ · · · ∧ vk) = f(v1)v2 ∧ v3 ∧ · · · ∧ vk − f(v2)v1 ∧ v3 ∧ · · · ∧ vk k−1 + ··· + (−1) f(vk)v1 ∧ v2 ∧ · · · ∧ vk−1 .

Proof. The right hand side is k-linear and alternating in (v1, v2, . . . vk), so this follows by Theorem 3.9. More generally, we have the following important theorem.

Theorem 3.21. Let V be a vector space over F, and let k ≥ l be a positive integers. There exists a bilinear map: χ : Vl(V ∗) × Vk(V ) → Vk−l(V ) such that

χ(f1 ∧ f2 ∧ · · · ∧ fl , v1 ∧ v2 ∧ · · · ∧ vk) = χfl ◦ χfl−1 ◦ · · · ◦ χf1 (v1 ∧ v2 ∧ · · · ∧ vk)

∗ for all f1, f2, . . . , fl ∈ V , v1, v2, . . . , vk ∈ V .

Proof. We need to show that for ﬁxed v1, v2, . . . , vk ∈ V , the map

(f1, . . . , fl) 7→ χfl ◦ χfl−1 ◦ · · · ◦ χf1 (v1 ∧ v2 ∧ · · · ∧ vk)

is l-linear and alternating. The l-linearity is clear. To see that this map is alternating, we need to check that the right hand side is 0 if fi = fj for some i 6= j. To prove this, ﬁrst one ∗ checks that χf ◦ χf = 0 for all f ∈ V . This implies all that χf ◦ χg = −χg ◦ χf , so we can swap adjacent pairs (picking up a factor of −1 with each swap) until fi and fj are next to each other. But then we have

χfl ◦ χfl−1 ◦ · · · ◦ χf1 = ±χfl ◦ · · · ◦ χfi ◦ χfj ◦ · · · ◦ χf1 = 0 ,

as required.

19 Exercise: If k = l, prove that

χ(f1 ∧ f2 ∧ · · · ∧ fk , v1 ∧ v2 ∧ · · · ∧ vk) = f1 ∧ f2 ∧ · · · ∧ fk(v1 ∧ v2 ∧ · · · ∧ vk) .

Lemma 3.22. Let V 0 be a subspace of V , and let x ∈ V \ V 0. There is a linear functional ∗ Vj 0 Vj f ∈ V such that χf (x ∧ θ) = θ for all θ ∈ (V ) ⊂ (V ). Proof. Let f ∈ V ∗ be a linear functional such that f(x) = 1, f(y) = 0 for all y ∈ V 0. Then we have 0 = χf (x ∧ θ) = f(x)θ + 0 = θ.

Lemma 3.23. Let V , ω and Wω be as in Theorem 3.19, and let x ∈ Wω be a non-zero vector. Then ω = x ∧ α for some α ∈ Vk−1(V 0), where V 0 is a codimension one subspace of V that does not contain x.

Proof. Let V 0 be any codimension one subspace of V which does not contain x. Let f ∈ V ∗ be the linear functional of Lemma 3.22. We can write ω = x ∧ α + β, uniquely, where Vk−1 0 Vk 0 α ∈ (V ), and β ∈ (V ). But 0 = χf (x ∧ ω) = χf (x ∧ β) = β, so ω = x ∧ α, as required.

Proof of Theorem 3.19. Suppose ω = v1 ∧ v2 ∧ · · · ∧ vk. By Theorem 3.10, since ω 6= 0, {v1, v2, . . . , vk} is linearly independent. Again by Theorem 3.10, Wω = span{v1, v2, . . . , vk}, hence dim Wω = k. Now suppose that dim Wω = k. We proceed by induction. The result is trivial for k = 1. Assume the result is true for all smaller values of k. Let x ∈ Wω be a non-zero vector. By Vk−1 0 0 Lemma 3.23 we can write ω = x1 ∧ α, where α ∈ (V ), and V is a codimension one 0 0 0 subspace of V that does not contain x. Let W = Wω ∩ V . We claim that x ∧ α = 0 for all x0 ∈ W 0. To see this let f ∈ V ∗ be the linear functional of Lemma 3.22. Then we have

0 0 0 0 = χf (x ∧ ω) = χf (−x ∧ x ∧ α) = −x ∧ α .

Since V 0 has codimension one, dim W 0 = dim W − 1 = k − 1. Thus the inductive hypothesis, α = v1 ∧ v2 ∧ · · · ∧ vk−1, so ω = x ∧ v1 ∧ v2 ∧ · · · ∧ vk−1. Finally, if dim Wω > k we could apply this second argument with two diﬀerent k- Vk Vk dimensional subspaces W1,W2 ⊂ Wω. But then we would have ω ∈ (W1)∩ (W2), which Vk contradicts Theorem 3.18. Therefore we must have dim Wω ≤ k for all ω ∈ (V ). For some purposes, Theorem 3.19 is not the best answer to the problem of determining which elements of Vk(V ) are pure wedges. The following criterion has the advantage that it can be used to give explicit necessary and suﬃcient equations that the coeﬃcients of a pure wedge product must satisfy (when everything is expanded in terms of a basis). These equations are called the Pl¨ucker relations.

Theorem 3.24 (Pl¨ucker relations). Let V be a vector space over F, and let k be a positive integer. A vector ω ∈ Vk(V ) is a pure wedge product if and only if, for all ξ ∈ Vk−1(V ∗) we have χ(ξ, ω) ∧ ω = 0 .

We need two lemmas.

20 Lemma 3.25. Let V be a vector space over F, and let k ≥ l be a positive integers. Let ∗ W = span{v1, . . . , vk}, where v1, v2, . . . vk ∈ V , and let f1, f2, . . . fl ∈ V . Then Vk−l χ(f1 ∧ f2 ∧ · · · ∧ fl , v1 ∧ v2 ∧ · · · ∧ vk) ∈ (W ) .

Proof. For l = 1 the statement is immediate from the deﬁnition of χf . Using

χ(f1 ∧ f2 ∧ · · · ∧ fl , v1 ∧ v2 ∧ · · · ∧ vk) = χfl ◦ χfl−1 ◦ · · · ◦ χf1 (v1 ∧ v2 ∧ · · · ∧ vk) , the general case follows by induction. Vk Lemma 3.26. Let V be a vector space over F, and let ω ∈ (V ) be a non-zero vector. Let Vk−1 ∗ Wfω = {χ(ξ, ω) | ξ ∈ (V )}. Then dim Wfω ≥ k. ∗ Proof. Let f1, f2, . . . fk ∈ V be linear functionals such that f1∧f2∧· · ·∧fk(ω) = 1. (Exercise: k−i Prove that these exist.) Let ξi = (−1) f1 ∧ · · · ∧ fi−1 ∧ fi+1 ∧ · · · ∧ fk . Then, k−i fj(χ(ξi, ω)) = (−1) fj ◦ χfk ◦ · · · ◦ χfi+1 ◦ χfk−i ◦ · · · ◦ χf1 (ω) k−i = (−1) f1 ∧ · · · ∧ fi−1 ∧ fi+1 ∧ · · · ∧ fk ∧ fj(ω)

= δij .

In particular, the set {χ(ξ1, ω), χ(ξ2, ω), . . . , χ(ξk, ω)} is linearly independent.

Proof of Theorem 3.24. Suppose that ω = v1 ∧ v2 ∧ · · · ∧ vk is a pure wedge. Let W = Vk V1 span{v1, . . . , vk}. Then ω ∈ (W ). By Lemma 3.25, χ(ξ, ω) ∈ (W ), so χ(ξ, ω) ∧ ω ∈ Vk+1(W ). Since dim(W ) ≤ k, by Corollary 3.12 this implies χ(ξ, ω) ∧ ω = 0. Vk−1 ∗ Suppose that for all ξ ∈ (V ) we have χ(ξ, ω)∧ω = 0. Let Wfω be as in Lemma 3.26, and let Wω be as in Theorem 3.19. Then x ∧ ω = 0 for all x ∈ Wfω. So Wfω ⊂ Wω. By Lemma 3.26 dim Wfω ≥ k, whereas dim Wω ≤ k. Thus dim Wω = k, which by Theorem 3.19 implies that ω is a pure wedge product. 5 Example 3.27. Let V = R , with basis {e1, e2, . . . , e5}. Let {f1, f2, . . . , f5} be the dual basis. Consider a general vector ω ∈ V2(V ). X ω = aijei ∧ ej . 1≤i

χ(fk, ω) ∧ ω = 0 , for k = 1, 2, 3, 4, 5 . Expanding this out, we obtain

0 = χ(fk, ω) ∧ ω X X  = χ(fk, aijei ∧ ej) ∧ almel ∧ em 1≤i

21 It is possible to simplify this further, but at the expense of messier notation. However, from here we can see quite explicitly what happens for any speciﬁc value of k. For example, if k = 1 we get.

5 X X 0 = a1jalm ej ∧ el ∧ em j=2 l

= (a12a34 e2 ∧ e3 ∧ e4 + a12a35 e2 ∧ e3 ∧ e5 + a12a45 e2 ∧ e4 ∧ e5)

+ (a13a24 e3 ∧ e2 ∧ e4 + a13a25 e3 ∧ e2 ∧ e5 + a13a45 e3 ∧ e4 ∧ e5)

+ (a14a23 e4 ∧ e2 ∧ e3 + a14a25 e4 ∧ e2 ∧ e5 + a14a35 e4 ∧ e3 ∧ e5)

+ (a15a23 e5 ∧ e2 ∧ e3 + a15a24 e5 ∧ e2 ∧ e4 + a15a34 e5 ∧ e3 ∧ e4)

= (a12a34 − a13a24 + a14a23)e2 ∧ e3 ∧ e4

+ (a12a35 − a13a25 + a15a23)e2 ∧ e3 ∧ e5

+ (a12a45 − a14a25 + a15a24)e2 ∧ e4 ∧ e5

+ (a13a45 − a14a35 + a15a34)e3 ∧ e4 ∧ e5

Thus, we must have

a12a34 − a13a24 + a14a23 = 0

a12a35 − a13a25 + a15a23 = 0

a12a45 − a14a25 + a15a24 = 0

a13a45 − a14a35 + a15a34 = 0 .

Each other value of k produces four similar looking equations, but in fact there are only 5 equations in total, because each equation comes up four times in this process. The last equation is (unsurprisingly):

a23a45 − a24a35 + a25a34 = 0 .

By Theorem 3.24, ω is a pure wedge if an only if its coeﬃcients satisfy these ﬁve equations.

3.6 Inner products and

Let F be either C or R, and Let V be an n-dimensional vector space over F, and let h·, ·i be an inner product on V . Recall that an inner product on a V satisﬁes three conditions:

1. For all x, y, z ∈ V , and c ∈ F, we have hx + cy, zi = hx, zi + chy, zi.

22 2. For x, y ∈ V , hx, yi = hy, xi.

3. For all non-zero vectors x ∈ V , hx, xi > 0.

If F is the ﬁeld of real numbers, then the complex conjugation is trivial. Vk Theorem 3.28. There is an inner product h·, ·ik on (V ) such that   hx1, y1i hx1, y2i · · · hx1, yki hx , y i hx , y i · · · hx , y i  2 1 2 2 2 k  hx1 ∧ x2 ∧ · · · ∧ xk , y1 ∧ y2, ∧ . . . ykik = det  . . .  .  . . .  hxk, y1i hxk, y2i · · · hxk, yki p As usual, we deﬁne the of a vector kωkk = hω, ωik. Proof. The existence of such a form satisfying condition 1 follows from Theorem 3.9. Condi- tion 2 follows from this holds for the inner product on V , and the fact that det A = det A. To prove condition 3, it is enough to show that there is a basis for Vk(V ) which is orthonormal. Vk Exercise: If {e1, . . . , en} is an for V , then the basis for (V ) constructed in Theorem 3.13 is orthonormal.

n Recall that if v1, v2, . . . vn ∈ R are linearly independent vectors, there is a determined by these vectors whose is | det A|, where v1, . . . , vn are the columns of A. This works more generally on any vector space V with an orthonormal basis {e1, . . . , en}. If vi = T (ei) for some linear transformation T , then the volume of the parallelepiped determined by v1, v2, . . . , vn is | det T |. Vk n This inner product on (R ) can be used to compute the volume of a parallelepiped in Rn, when k < n. n Theorem 3.29. Let v1, . . . vk ∈ R be linearly independent vectors. Then the k-dimensional volume of the parallelepiped determined by v1, v2, . . . , vk is kv1 ∧ v2 ∧ · · · ∧ vkkk.

Proof. Let W = span{v1, . . . , vk}. Let {w1, w2, . . . , wk} be an orthonormal basis for W . Note k that the linear transformation from W → R which takes wi to the standard basis vectors is volume preserving. Then vi = T wi for some linear transformation T ∈ L(W ). Then

v1 ∧ v2 ∧ · · · ∧ vk = T∗(w1 ∧ w2 ∧ · · · ∧ wk) = det(T )w1 ∧ w2 ∧ · · · ∧ wk .

Therefore, kv1 ∧ v2 ∧ · · · ∧ vkkk = | det T |, which equals the volume of the parallelepiped. We can also compute volumes of simplices.

Deﬁnition 3.30. A k- in Rn is a k-dimensional , with k + 1 vertices n v0, v1, . . . , vk ∈ R , where {v1 − v0, v2 − v0, . . . , vk − v0} is a linearly independent set. For example, a 1-simplex is a segment, a 2-simplex is a , a 3-simplex is a .

Theorem 3.31. Let S be the k-simplex with vertices 0, v1, v2, . . . , vk. Let P be the paral- 1 lelepiped determined by v1, v2, . . . , vk. Then vol(S) = k! vol(P ).

23 Proof. Let W be the subspace spanned by {v1, . . . , vk}. Since dim W = k, any linear trans- formation from W to Rk preserves the ratios of k-dimensional volumes. Thus it enough to prove this for the case where the vertices are n = k, and vi = e1 + e2 + ··· + ei, where k {e1, . . . , ek} is the basis for R . In this case, the volume of the parallelepiped is 1, and the volume of the simplex is

Z 1 Z x1 Z x2 Z xk−1 ··· 1 dxkdxk−1 . . . dx2dx1 , x1=0 x2=0 x3=0 xk=0

1 which one can easily show is equal to k! .

24
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