# Definition 12 An alternating bilinear form on a vector space V is a map B : V V F such that

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1 4 Exterior algebra 4.1 Lines and 2-vectors The time has come now to develop some new linear algebra in order to handle the space of lines in a projective space P (V ). In the projective plane we have seen that duality can deal with this but lines in higher dimensional spaces behave differently. From the point of view of linear algebra we are looking at 2-dimensional vector subspaces U V. To motivate what we shall do, consider how in Euclidean geometry we describe a 2-dimensional subspace of R 3. We could describe it through its unit normal n, which is also parallel to u v where u and v are linearly independent vectors in the space and u v is the vector cross product. The vector product has the following properties: u v = v u (λ 1 u 1 + λ 2 u 2 ) v = λ 1 u 1 v + λ 2 u 2 v We shall generalize these properties to vectors in any vector space V the difference is that the product will not be a vector in V, but will lie in another associated vector space. Definition 12 An alternating bilinear form on a vector space V is a map B : V V F such that B(v, w) = B(w, v) B(λ 1 v 1 + λ 2 v 2, w) = λ 1 B(v 1, w) + λ 2 B(v 2, w) This is the skew-symmetric version of the symmetric bilinear forms we used to define quadrics. Given a basis {v 1,..., v n }, B is uniquely determined by the skew symmetric matrix B(v i, v j ). We can add alternating forms and multiply by scalars so they form a vector space, isomorphic to the space of skew-symmetric n n matrices. This has dimension n(n 1)/2, spanned by the basis elements E ab for a < b where E ab ij {a, b} {i, j} and E ab ab = Eab ba = 1. = 0 if Definition 13 The second exterior power Λ 2 V of a finite-dimensional vector space is the dual space of the vector space of alternating bilinear forms on V. Elements of Λ 2 V are called 2-vectors. 41

2 This definition is a convenience there are other ways of defining Λ 2 V, and for most purposes it is only its characteristic properties which one needs rather than what its objects are. A lot of mathematics is like that just think of the real numbers. Given this space we can now define our generalization of the cross-product, called the exterior product or wedge product of two vectors. Definition 14 Given u, v V the exterior product u v Λ 2 V is the linear map to F which, on an alternating bilinear form B, takes the value (u v)(b) = B(u, v). From this definition follows some basic properties: (u v)(b) = B(u, v) = B(v, u) = (v u)(b) so that and in particular u u = 0. v u = u v ((λ 1 u 1 + λ 2 u 2 ) v)(b) = B(λ 1 u 1 + λ 2 u 2, v) = λ 1 B(u 1, v) + λ 2 B(u 2, v) which implies (λ 1 u 1 + λ 2 u 2 ) v = λ 1 u 1 v + λ 2 u 2 v. if {v 1,..., v n } is a basis for V then v i v j for i < j is a basis for Λ 2 V. This last property holds because v i v j (E ab ) = E ab ij and in facts shows that {v i v j } is the dual basis to the basis {E ab }. Another important property is: Proposition 15 Let u V be a non-zero vector. Then u v = 0 if and only if v = λu for some scalar λ. Proof: If v = λu, then u v = u (λu) = λ(u u) = 0. Conversely, if v λu, u and v are linearly independent and can be extended to a basis, but then u v is a basis vector and so is non-zero. 42

3 It is the elements of Λ 2 V of the form u v which will concern us, for suppose U V is a 2-dimensional vector subspace, and {u, v} is a basis of U. Then any other basis is of the form {au + bv, cu + dv}, so, using u u = v v = 0, we get (au + bv) (cu + dv) = (ad bc)u v and since the matrix ( a ) b c d is invertible ad bc 0. It follows that the 1-dimensional subspace of Λ 2 V spanned by u v for a basis of U is well-defined by U itself and is independent of the choice of basis. To each line in P (V ) we can therefore associate a point in P (Λ 2 V ). The problem is, not every vector in Λ 2 V can be written as u v for vectors u, v V. In general it is a linear combination of such expressions. The task, in order to describe the space of lines, is to characterize such decomposable 2-vectors. Example: Consider v 1 v 2 + v 3 v 4 in a 4-dimensional vector space V. Suppose we can write this as v 1 v 2 + v 3 v 4 = (a 1 v 1 + a 2 v 2 + a 3 v 3 + a 4 v 4 ) (b 1 v 1 + b 2 v 2 + b 3 v 3 + b 4 v 4 ). Equating the coefficient of v 1 v 2 gives a 1 b 2 a 2 b 1 = 1 and so (a 1, b 1 ) is non-zero. On the other hand the coefficients of v 1 v 3 and v 1 v 4 give a 1 b 3 a 3 b 1 = 0 a 1 b 4 a 4 b 1 = 0 and since (a 1, b 1 ) 0, b 3 a 4 a 3 b 4 = 0. But the coefficient of v 3 v 4 gives a 4 b 3 a 3 b 4 = 1 which is a contradiction. This 2-vector is not therefore decomposable. We shall find an easier method of seeing this by working with p-vectors and exterior products. 4.2 Higher exterior powers Definition 15 An alternating multilinear form of degree p on a vector space V is a map M : V... V F such that 43

4 M(u 1,..., u i,..., u j,..., u p ) = M(u 1,..., u j,..., u i,..., u p ) M(λ 1 v 1 + λ 2 v 2, u 2,..., u p ) = λ 1 M(v 1, u 2,..., u p ) + λ 2 M(v 2, u 2,..., u p ) Example: Let u 1,..., u n be column vectors in R n. Then M(u 1,..., u n ) = det(u 1 u 2... u n ) is an alternating multilinear form of degree n. The set of all alternating multilinear forms on V is a vector space, and M is uniquely determined by the values M(v i1, v i2,..., v ip ) for a basis {v 1,..., v n }. But the alternating property allows us to change the order so long as we multiply by 1 for each transposition of variables. This means that M is uniquely determined by the values of indices for i 1 < i 2 <... < i p. The number of these is the number of p-element subsets of n, i.e. ( n p), so this is the dimension of the space of such forms. In particular if p > n this space is zero. We define analogous constructions to those above for a pair of vectors: Definition 16 The p-th exterior power Λ p V of a finite-dimensional vector space is the dual space of the vector space of alternating multilinear forms of degree p on V. Elements of Λ p V are called p-vectors. and Definition 17 Given u 1,..., u p V the exterior product u 1 u 2... u p Λ p V is the linear map to F which, on an alternating multilinear form M takes the value (u 1 u 2... u p )(M) = M(u 1, u 2,..., u p ). The exterior product u 1 u 2... u p has two defining properties it is linear in each variable u i separately interchanging two variables changes the sign of the product 44

5 if two variables are the same the exterior product vanishes. We have a useful generalization of Proposition 15: Proposition 16 The exterior product u 1 u 2... u p of p vectors u i V vanishes if and only if the vectors are linearly dependent. Proof: If there exists a linear relation λ 1 u λ p u p = 0 with λ i 0, then u i is a linear combination of the other vectors u i = j i µ j u j but then u 1 u 2... u p = u 1... ( j i µ j u j ) u i+1... u p and expand this out by linearity, each term has a repeated variable u j and so vanishes. Conversely, if u 1,..., u p are linearly independent they can be extended to a basis and u 1 u 2... u p is a basis vector for Λ p V and is thus non-zero. The exterior powers Λ p V have natural properties with respect to linear transformations: given a linear transformation T : V W, and an alternating multilinear form M on W we can define an induced one T M on V by and this defines a dual linear map with the property that T M(v 1,..., v p ) = M(T v 1,..., T v p ) Λ p T : Λ p V Λ p W Λ p T (v 1 v 2... v p ) = T v 1 T v 2... T v p. One such map is very familiar: take p = n, so that Λ n V is one-dimensional and spanned by v 1 v 2... v n for a basis {v 1,..., v n }. A linear transformation from a 45

6 1-dimensional vector space to itself is just multiplication by a scalar, so Λ n T is some scalar in the field. In fact it is the determinant of T. To see this, observe that Λ n T (v 1... v n ) = T v 1... T v n and the right hand side can be written using the matrix T ij of T as T i1 1v i1... T innv in = T i T innv i1... v in. i 1,...,i n i 1,...,i n Each of the terms vanishes if any two of i 1,..., i n are equal by the property of the exterior product, so we need only consider the case where (i 1,..., i n ) is a permutation of (1,..., n). Any permutation is a product of transpositions, and any transposition changes the sign of the exterior product, so Λ n T (v 1... v n ) = σ S n sgn(σ)t σ(1)1 T σ(2)2... T σ(n)n v 1... v n which is the definition of the determinant of T ij. From our point of view the determinant is naturally defined for a linear transformation T : V V, and what we just did was to see how to calculate it from the matrix of T. We now have vector spaces Λ p V of dimension ( n p) naturally associated to V. The space Λ 1 V is by definition the dual space of the space of linear functions on V, so Λ 1 V = V = V and by convention we set Λ 0 V = F. Given p vectors v 1,..., v p V we also have a corresponding vector v 1 v 2... v p Λ p V and the notation suggests that there should be a product so that we can remove the brackets: (u 1... u p ) (v 1... v q ) = u 1... u p v 1... v q and indeed there is. So suppose a Λ p V, b Λ q V, we want to define a b Λ p+q V. Now for fixed vectors u 1,..., u p V, M(u 1, u 2,..., u p, v 1, v 2,..., v q ) is an alternating multilinear function of v 1,..., v q, so if b = λ j1...j q v j1... v jq j 1 <...<j q then j 1 <...<j q λ j1...j q M(u 1,..., u p, v j1,..., v jq ) 46

7 only depends on b and not on the particular way it is written in terms of a basis {v 1,..., v n }. Similarly if a = µ i1...i p u i1... u ip i 1 <...<i p then i 1 <...<i p µ i1...i p M(u i1,..., u ip, v 1,..., v q ) only depends on a. We can therefore unambiguously define a b by its value on an alternating p + q-form M as (a b)(m) = µ i1...i p λ j1...j q M(u i1,..., u ip, v j1,..., v jq ). i 1 <..<i p;j i,..<j q The product just involves linearity and removing the brackets. Example: Suppose a = v 1 + v 2, b = v 1 v 3 v 3 v 2, with v 1, v 2, v 3 V then a b = (v 1 + v 2 ) (v 1 v 3 v 3 v 2 ) = v 1 v 1 v 3 v 1 v 3 v 2 + v 2 v 1 v 3 v 2 v 3 v 2 = v 1 v 3 v 2 + v 2 v 1 v 3 = v 1 v 2 v 3 v 1 v 2 v 3 = 0 where we have used the basic rules that a repeated vector from V in an exterior product gives zero, and the interchange of two vectors changes the sign. Note that u 1 u 2... u p v 1... v q = ( 1) p v 1 u 1 u 2... u p v 2... v q because we have to interchange v 1 with each of the p u i s to bring it to the front, and then repeating u 1 u 2... u p v 1... v q = ( 1) pq v 1... v q u 1 u 2... u p. This extends by linearity to all a Λ p V, b Λ q V. We then have the basic properties of the exterior product; a (b + c) = a b + a c 47

8 (a b) c = a (b c) a b = ( 1) pq b a if a Λ p V, b Λ q V What we have done may seem rather formal, but it has many concrete applications. For example if a = x y then a a = x y x y = 0 because x V is repeated. So it is much easier to determine that a = v 1 v 2 + v 3 v 4 from the Exercise above is not decomposable: (v 1 v 2 + v 3 v 4 ) (v 1 v 2 + v 3 v 4 ) = 2v 1 v 2 v 3 v Decomposable 2-vectors A line in P (V ) defines a point in P (Λ 2 V ) defined by a decomposable 2-vector a = x y. We need to characterize algebraically this decomposability, and the following theorem does just that: Theorem 17 Let a Λ 2 V be a non-zero element. Then a is decomposable if and only if a a = 0 Λ 4 V. Proof: If a = x y for two vectors x and y then because of the repeated factor x (or y). a a = x y x y = 0 We prove the converse by induction on the dimension of V. If dim V = 0, 1 then Λ 2 V = 0, so the first case is dim V = 2. In this case dim Λ 2 V = 1 and v 1 v 2 is a non-zero element if v 1, v 2 is a basis for V, so any a is decomposable. We consider the case dim V = 3 separately now. Given a non-zero a Λ 2 V, define A : V Λ 3 V by A(v) = a v. Since dim Λ 3 V = 1, dim ker A 2, so let u 1, u 2 be linearly independent vectors in the kernel and extend to a basis u 1, u 2, u 3 of V. We can then write a = λ 1 u 2 u 3 + λ 2 u 3 u 1 + λ 3 u 1 u 2. 48

9 Now by definition 0 = a u 1 = λ 1 u 2 u 3 u 1 so λ 1 = 0 and similarly 0 = a u 2 implies λ 2 = 0. It follows that a = λ 3 u 1 u 2, which is decomposable. Now assume inductively that the theorem is true for dim V n 1 and consider the case dim V = n. Using a basis v 1,..., v n, write a = n 1 i<j i=1 a ij v i v j n 1 = ( a in v i ) v n + = u v n + a n 1 1 i<j a ij v i v j where u U and a v 1,..., v n 1. Λ 2 U and U is the (n 1)-dimensional space spanned by Now 0 = a a = (u v n + a ) (u v n + a ) = 2u a v n + a a. But v n doesn t appear in the expansion of u a or a a so we separately obtain u a = 0, a a = 0. By induction a a = 0 implies a = u 1 u 2 and so the first equation reads u u 1 u 2 = 0 which from Proposition 16 says that there is a linear relation λu + µ 1 u 1 + µ 2 u 2 = 0. If λ = 0, then u 1 and u 2 are linearly dependent so a = u 1 u 2 = 0. This means that u = u v n and is therefore decomposable. If λ 0, u = λ 1 u 1 + λ 2 u 2, so a = λ 1 u 1 v n + λ 2 u 2 v n + u 1 u 2 and this is the 3-dimensional case which is always decomposable as we showed above. We conclude that a, in each case, is decomposable. 49

10 4.4 The Klein quadric The first case where we can apply Theorem 17 is when dim V = 4, to describe the projective lines in the 3-dimensional space P (V ). In this case dim Λ 4 V = 1 with a basis vector v 0 v 1 v 2 v 3 if V is given the basis v 0,..., v 3. For a Λ 2 V we write a = λ 1 v 0 v 1 + λ 2 v 0 v 2 + λ 3 v 0 v 3 + µ 1 v 2 v 3 + µ 2 v 3 v 1 + µ 3 v 1 v 2 and then a a = B(a, a)v 0 v 1 v 2 v 3 where B(a, a) = 2(λ 1 µ 1 + λ 2 µ 2 + λ 3 µ 3 ) (8) This is a non-degenerate quadratic form, and so B(a, a) = 0 defines a nonsingular quadric Q P (Λ 2 V ). Moreover, any other choice of basis rescales B by a non-zero constant and so Q is well defined in projective space. We see then that a line l P (V ) defines a decomposable 2-vector a = x y, unique up to a scalar and since a a = 0, it defines a point L Q P (Λ 2 V ). Conversely, Theorem 17 tells us that every point in Q is represented by a decomposable 2-vector. Hence Proposition 18 There is a one-to-one correspondence l L between lines l in a 3-dimensional projective space P (V ) and points L in the 4-dimensional quadric Q P (Λ 2 V ). It was Felix Klein ( ), building on the work of his supervisor Julius Plücker, who first described this in detail and Q is usually called the Klein quadric. The equation of the quadric in the form (8) shows that there are linear subspaces inside it of maximal dimension 2 whatever the field. The linear subspaces all relate to intersection properties of lines in P (V ). For example: Proposition 19 Two lines l 1, l 2 P (V ) intersect if and only if the line joining the two corresponding points L 1, L 2 Q lies entirely in Q. Proof: Let U 1, U 2 V be the two-dimensional subspaces of V defined by l 1, l 2. Suppose the lines intersect in X, with representative vector u V. Then extend to bases {u, u 1 } for U 1 and {u, u 2 } for U 2. The line in P (Λ 2 V ) joining L 1 and L 2 is then P (W ) where W is spanned by u u 1 and u u 2. 50

11 Any 2-vector in W is thus of the form λ 1 u u 1 + λ 2 u u 2 = u (λ 1 u 1 + λ 2 u 2 ) which is decomposable and so represents a point in Q. Conversely, if the lines do not intersect, U 1 U 2 = {0} so V = U 1 U 2. In this case choose bases {u 1, v 1 } of U 1 and {u 2, v 2 } of U 2. Then {u 1, v 1, u 2, v 2 } is a basis of V and in particular u 1 v 1 u 2 v 2 0. A point on the line joining L 1, L 2 is now represented by a = λ 1 u 1 v 1 + λ 2 u 2 v 2 so that a a = 2λ 1 λ 2 u 1 v 1 u 2 v 2 which vanishes only if λ 1 or λ 2 are zero. Thus the line only meets Q in the points L 1 and L 2. Now fix a point X P (V ) and look at the set of lines passing through this point: Proposition 20 The set of lines l P (V ) passing through a fixed point X P (V ) corresponds to the set of points L Q which lie in a fixed plane contained in Q. Proof: Let x be a representative vector for X. The line P (U) passes through X if and only if x U, so P (U) is represented in the Klein quadric by a 2-vector of the form x u. Extend x to a basis {x, v 1, v 2, v 3 } of V, then any decomposable 2-vector of the form x y can be written as x (µx + λ 1 v 1 + λ 2 v 2 + λ 3 v 3 ) = λ 1 x v 1 + λ 2 x v 2 + λ 3 x v 3. Thus any line passing through X is represented by a 2-vector in the 3-dimensional space of decomposables spanned by x v 1, x v 2, x v 3, which is a projective plane in Q. Conversely any point in this plane defines a line in P (V ) through X. A plane in Q defined by a point X P (V ) like this is called an α-plane. There are other planes in Q: Proposition 21 Let P (W ) P (V ) be a plane. The set of lines l P (W ) corresponds to the set of points L Q which lie in a fixed plane contained in Q. 51

12 A plane of this type contained in Q is called a β-plane. Proof: We just use duality here: if U V is 2-dimensional, then its annihilator U 0 V is 4 2 = 2-dimensional, so there is a one-to-one correspondence between lines in P (V ) and lines in P (V ). A point in Q therefore defines a line in either the projective space or its dual. Now the dual of the set of lines passing through a point is the set of lines lying in a (hyper)-plane. So applying Proposition 20 to P (V ) gives the result. In fact there are no more planes: Proposition 22 Any plane in the Klein quadric Q is either an α-plane or a β-plane. Proof: Take a plane in Q and three non-collinear points L 1, L 2, L 3 on it. We get three lines l 1, l 2, l 3 in P (V ). Since the line joining L 1 to L 2 lies in the plane and hence in Q, it follows from Proposition 19 that each pair of l 1, l 2, l 3 intersect. There are two possibilities: the three lines are concurrent: the three lines meet in three distinct points: In the first case the three lines pass through a single point and so L 1, L 2, L 3 lie in an α-plane. But this must be the original plane since the three representative vectors for L 1, L 2, L 3 are linearly independent as the points are not collinear. 52

13 In the second case, if u 1, u 2, u 3 are representative vectors for the three points of intersection of l 1, l 2, l 3, then L 1, L 2, L 3 are represented by u 2 u 3, u 3 u 1, u 1 u 2. A general point on the plane is then given by λ 1 u 2 u 3 + λ 2 u 3 u 1 + λ 1 u 3 u 1 which is a general element of Λ 2 U where U is spanned by u 1, u 2, u 3. Thus l 1, l 2, l 3 all lie in the plane P (U) P (V ). The existence of these two families of linear subspaces of maximal dimension is characteristic of even-dimensional quadrics it is the generalization of the two families of lines we saw on the cooling tower quadric surface. In the case of the Klein quadric, two different α-planes intersect in a point, since there is a unique line joining two points. Similarly (and by duality) two β planes meet in a point. An α-plane and a β plane in general have empty intersection if X is a point and π a plane with X π, there is no line in π which passes through X. If X π, then the intersection is a line. 4.5 Exercises 1. If a Λ p V and p is odd, show that a a = Calculate a b in the following cases: a = b = v 1 v 2 + v 2 v 3 + v 3 v 1 a = v 1 v 2 + v 3 v 1, b = v 2 v 3 v 4 a = v 1 + v 2 + v 3, b = v 1 v 2 + v 2 v 3 + v 3 v 1. [v 1, v 2, v 3, v 4 are linearly independent] 3. Which of the following 2-vectors is decomposable? v 1 v 2 + v 2 v 3 v 1 v 2 + v 2 v 3 + v 3 v 4 v 1 v 2 + v 2 v 3 + v 3 v 4 + v 4 v 1. 53

14 [v 1, v 2, v 3, v 4 are linearly independent] 4. If dim V = n shown that every a Λ n 1 V is decomposable. 5. Let l P (V ) be a line and m another such that the corresponding point M Q lies on the polar hyperplane to L Q. Show that l and m intersect. 54

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