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1 BILINEAR FORMS KEITH CONRAD The geometry of R n is controlled algebraically by the dot product. We will abstract the dot product on R n to a bilinear form on a vector space and study algebraic and geometric notions related to bilinear forms (especially the concept of orthogonality in all its manifestations: orthogonal vectors, orthogonal subspaces, and orthogonal bases). Section 1 defines a bilinear form on a vector space and offers examples of the two most common types of bilinear forms: symmetric and alternating bilinear forms. In Section 2 we will see how a bilinear form looks in coordinates. Section 3 describes the important condition of nondegeneracy for a bilinear form. Orthogonal bases for symmetric bilinear forms are the subject of Section 4. Symplectic bases for alternating bilinear forms are discussed in Section 5. Quadratic forms are in Section 6 (characteristic not 2) and Section 7 (characteristic 2). The tensor product viewpoint on bilinear forms is briefly discussed in Section 8. Vector spaces in Section 1 are arbitrary, but starting in Section 2 we will assume they are finite-dimensional. It is assumed that the reader is comfortable with abstract vector spaces and how to use bases of (finite-dimensional) vector spaces to turn elements of a vector space into column vectors and linear maps between vector spaces into matrices. It is also assumed that the reader is familiar with duality on finite-dimensional vector spaces: dual spaces, dual bases, the dual of a linear map, and the natural isomorphism of finite-dimensional vector spaces with their double duals (which identifies the double dual of a basis with itself and the double dual of a linear map with itself). For a vector space V we denote its dual space as V. The dual basis of a basis {e 1,..., e n } of V is denoted {e 1,..., e n}, so the e i s are the coordinate functions on V relative to that basis: e i (e j) is 1 for i = j and 0 for i j. Although V is naturally isomorphic to V, students are always cautioned against identifying V with V, since there is no natural isomorphism. In a nutshell, the subject of bilinear forms is about what happens if we make an identification of V with V and keep track of it. Different identifications have different geometric properties. 1. Definitions and examples Definition 1.1. Let F be a field and V be a vector space over F. A bilinear form on V is a function B : V V F that is linear in each variable when the other one is fixed. That is, B(v + v, w) = B(v, w) + B(v, w), B(cv, w) = cb(v, w) for all v, v, w V and c F, and for all v, w, w V and c F. We call B symmetric when B(v, w + w ) = B(v, w) + B(v, w ), B(v, w) = B(w, v) for all v, w V 1 B(v, cw) = cb(v, w),

2 2 KEITH CONRAD and skew-symmetric when B(v, w) = B(w, v) for all v, w V. We call B alternating when B(v, v) = 0 for all v V. A bilinear space is a vector space equipped with a specific choice of bilinear form. We call a bilinear space symmetric, skew-symmetric, or alternating when the chosen bilinear form has that corresponding property. A common synonym for skew-symmetric is anti-symmetric. Example 1.2. The dot product v w on R n is a symmetric bilinear form. Example 1.3. For a fixed matrix A M n (R), the function f(v, w) = v Aw on R n is a bilinear form, but not necessarily symmetric like the dot product. All later examples are essentially generalizations of this construction. Example 1.4. For any field F, viewed as a 1-dimensional vector space over itself, multiplication m: F F F is a symmetric bilinear form and not alternating. It is skew-symmetric when F has characteristic 2. Example 1.5. A skew-symmetric and alternating bilinear form on R 2 is ( ) B((x, y), (x, y )) := xy x x x y = det y y. For example, B((2, 1), (3, 4)) = 5 and B((2, 1), (2, 1)) = 0. Viewing R 2 as C by (x, y) x + iy, B(z, w) = Im(zw) = Im(zw) for complex numbers z and w. Among the three types of bilinear forms we have defined (symmetric, skew-symmetric, alternating), the first and third types are more basic than the second. In fact, we now show that a skew-symmetric bilinear form is just another name for a symmetric or an alternating bilinear form, depending on whether or not the characteristic of the field is 2. Theorem 1.6. In all characteristics, an alternating bilinear form is skew-symmetric. In characteristic not 2, a bilinear form is skew-symmetric if and only if it is alternating. In characteristic 2, a bilinear form is skew-symmetric if and only if it is symmetric. Proof. When B is alternating and v, w V, expanding the right side of the equation 0 = B(v + w, v + w) shows 0 = B(v, v) + B(v, w) + B(w, v) + B(w, w) = B(v, w) + B(w, v), so B(v, w) = B(w, v). Therefore alternating bilinear forms are skew-symmetric in all characteristics (even in characteristic 2). Outside of characteristic 2, a skew-symmetric bilinear form is alternating since B(v, v) = B(v, v) = 2B(v, v) = 0 = B(v, v) = 0. That skew-symmetric and symmetric bilinear forms coincide in characteristic 2 is immediate since 1 = 1 in characteristic 2. Despite Theorem 1.6, the label skew-symmetric is still needed. One reason is that it is used in preference to alternating by many geometers who work over R, where the two notions coincide. Another reason is that the concept of bilinear form makes sense on modules, not just vector spaces, and there are skew-symmetric bilinear forms on modules

3 BILINEAR FORMS 3 that are neither symmetric nor alternating (Exercise 2.8). However, we will only deal with bilinear forms on vector spaces. Theorem 1.7. In characteristic not 2, every bilinear form B is uniquely expressible as a sum B 1 + B 2, where B 1 is symmetric and B 2 is alternating (equivalently, skew-symmetric). In characteristic 2, the alternating bilinear forms are a subset of the symmetric bilinear forms. Proof. The last part is immediate from Theorem 1.6. Now we work in characteristic not 2. For a bilinear form B, suppose we can write B = B 1 + B 2 with symmetric B 1 and alternating (so skew-symmetric) B 2. Then for vectors v and w, (1.1) B(v, w) = B 1 (v, w) + B 2 (v, w) and (1.2) B(w, v) = B 1 (w, v) + B 2 (w, v) = B 1 (v, w) B 2 (v, w). Adding and subtracting (1.1) and (1.2), we get formulas for B 1 and B 2 in terms of B: B(v, w) + B(w, v) B(v, w) B(w, v) (1.3) B 1 (v, w) =, B 2 (v, w) =. 2 2 Turning this reasoning around, the bilinear forms B 1 and B 2 defined by (1.3) are symmetric and alternating respectively, so we have established the existence and uniqueness of B 1 and B 2. Theorem 1.8. In characteristic not 2, a symmetric bilinear form B(v, w) is completely determined by its values B(v, v) on the diagonal. Proof. For any v and w, 1 2 (B(v + w, v + w) B(v, v) B(w, w)) = 1 (B(v, w) + B(w, v)) = B(v, w). 2 Note we used symmetry of B in the last equation. The fact that, for symmetric B, we can recover the 2-variable function B(v, w) from the 1-variable function B(v, v) outside of characteristic 2 is called polarization. For instance, it shows us that a symmetric bilinear form B is identically 0 if and only if B(v, v) = 0 for all v (not just B(e i, e i ) = 0 on a basis; see Example 1.10). Polarization will play an important role when we treat quadratic forms later. Let s look at some more examples of bilinear forms. Example 1.9. On R 2, B((x, y), (x, y )) = xx yy is symmetric. How is this formula different from the one in Example 1.5? Example On R 2, B((x, y), (x, y )) = xy +yx is symmetric. Since B((x, y), (x, y)) = 2xy, B(e i, e i ) = 0 where {e 1, e 2 } is the standard basis of R 2. Example Fix a vector u in R 3. For v and w in R 3, let B u (v, w) = u (v w), where is the cross product. This is alternating. Example Let V be a finite-dimensional vector space over F. On the vector space End F (V, V ), set B(L, L ) = Tr(LL ). This is called the trace form on End F (V, V ). It is bilinear since the trace is linear. It is symmetric since Tr(LL ) = Tr(L L).

4 4 KEITH CONRAD Example Let V be a finite-dimensional vector space over F with dual space V. On the vector space V V, set B((v, ϕ), (w, ψ)) = ψ(v) ϕ(w). This is alternating. (Symbolically, B((v, ϕ), (w, ψ)) = w ψ ϕ v.) Can you interpret Example 1.5 in this context? Example Let s look at an infinite-dimensional example. On C[0, 1], the space of real-valued continuous functions [0, 1] R, the function B(f, g) = 1 0 f(x)g(x) dx is a symmetric bilinear form. To get other examples of bilinear forms, choose any continuous function k : [0, 1] 2 R and set B k (f, g) = f(x)g(y)k(x, y) dx dy. [0,1] 2 Can you find a k that makes B k = B? (The function k = 1 does not work.) Example On C n, let H((z 1,..., z n ), (w 1,..., w n )) = n i=1 z iw i. Regarding C n as a real vector space, H is bilinear. But viewing C n as a complex vector space, H is linear in its first component but it is not linear in its second component: H(v, cw) equals ch(v, w) instead of ch(v, w). Therefore H is not bilinear. Moreover, H(v, w) = H(w, v). Pairings such as H on a complex vector space, which are linear in one component, conjugate-linear in the other component, and get conjugated when the arguments are exchanged, are called Hermitian. Our focus is on bilinear forms. A bilinear form is a generalization of the dot product, so the condition B(v, w) = 0 is considered to be a generalization of perpendicularity. With this in mind, write v w when B(v, w) = 0 and call v and w perpendicular or orthogonal. (We could write v B w to stress the dependence of this notion of orthogonality on the choice of B, but this will not be done.) Since B is bilinear, perpendicularity behaves linearly: v w 1, v w 2 = v (c 1 w 1 + c 2 w 2 ); v 1 w, v 2 w = (c 1 v 1 + c 2 v 2 ) w where c 1, c 2 F. For a subspace W V and a vector v V we write v W when v w for all w W and write W v similarly. In a general bilinear space the relation might not be symmetric: we can have v w and w v. That is, we could have B(v, w) = 0 and B(w, v) 0. Example On V = R 2, let B((x, y), (x, y )) = xx + xy x y yy. (1, 0) (1, 1) but (1, 1) (1, 0). We have Knowing the bilinear forms where v w w v (that is, the relation is symmetric) is a key foundational result. Here it is. Theorem The perpendicularity relation on a bilinear space (V, B) is symmetric if and only if B is either symmetric or alternating. The proof is a series of elementary but somewhat tedious calculations. Nothing will be lost by skipping the proof on a first reading and coming back to it after the significance of the two types of bilinear forms becomes clearer. Proof. If B is symmetric or alternating then we have B(v, w) = ±B(w, v), so B(v, w) vanishes if and only if B(w, v) vanishes.

5 BILINEAR FORMS 5 To prove the converse direction, assume is a symmetric relation. Pick any vectors u, v, w V. We first will find a linear combination av + bw such that (av + bw) u. This is the same as (1.4) ab(v, u) + bb(w, u) = 0 since B is linear in its first component. We can achieve (1.4) using a = B(w, u) and b = B(v, u). Therefore set x = B(w, u)v B(v, u)w. Then B(x, u) = 0, so B(u, x) = 0 by symmetry of the relation. Computing B(u, x) by linearity of B in its second component and setting it equal to zero, we obtain (1.5) B(w, u)b(u, v) = B(v, u)b(u, w). This holds for all u, v, w V. We will show a bilinear form satisfying (1.5) is symmetric or alternating. Use w = u in (1.5): (1.6) B(u, u)b(u, v) = B(v, u)b(u, u). Notice B(u, u) appears on both sides of (1.6). Thus, for all u and v in V, (1.7) B(u, v) B(v, u) = B(u, u) = 0 (and similarly B(v, v) = 0). Now assume that the relation for B is symmetric and B is not a symmetric bilinear form. We will prove B is alternating. By assumption, there are u 0, v 0 V such that (1.8) B(u 0, v 0 ) B(v 0, u 0 ). From this we will show B(w, w) = 0 for all w V, relying ultimately on (1.7). Note by (1.7) and (1.8) that (1.9) B(u 0, u 0 ) = 0, B(v 0, v 0 ) = 0. Pick any w V. If B(u 0, w) B(w, u 0 ) or B(v 0, w) B(w, v 0 ) then (1.7) shows B(w, w) = 0. Therefore to prove B(w, w) = 0 we may assume (1.10) B(u 0, w) = B(w, u 0 ), B(v 0, w) = B(w, v 0 ). In (1.5), set u = u 0 and v = v 0. Then By (1.10), This implies, by (1.8) and (1.10), that B(w, u 0 )B(u 0, v 0 ) = B(v 0, u 0 )B(u 0, w). B(u 0, w)(b(u 0, v 0 ) B(v 0, u 0 )) = 0. (1.11) B(u 0, w) = B(w, u 0 ) = 0. Similarly, setting u = v 0 and v = u 0 in (1.5) tells us by (1.8) and (1.10) that (1.12) B(v 0, w) = B(w, v 0 ) = 0. By (1.11), B(u 0, v 0 + w) = B(u 0, v 0 ) and B(v 0 + w, u 0 ) = B(v 0, u 0 ). These are distinct by (1.8), so (1.7) with u = v 0 + w and v = u 0 implies Then by (1.9) and (1.12), B(w, w) = 0. B(v 0 + w, v 0 + w) = 0.

6 6 KEITH CONRAD The proof of Theorem 1.17 did not assume finite-dimensionality and it used additivity rather than linearity. When is a symmetric relation on V, for any subspace W of V we set (1.13) W = {v V : v w for all w W } = {v V : w v for all w W }. and call this the orthogonal space W. (This is often called the orthogonal complement of W in the literature, although it may not really look like a complement: it can happen that W + W V.) For nonzero v V, let v = {v V : v v}. In the notation of (1.13), v = (F v). The notation W for a subspace W of a bilinear space V makes sense only when V is symmetric or alternating. (A third class of vector spaces where a perpendicularity relation is symmetric is the Hermitian spaces, as in Example 1.15, but they are not bilinear spaces so Theorem 1.17 doesn t include them.) When perpendicularity for a general bilinear form is a symmetric relation, it can still have nonintuitive features compared with the dot product on R n. The main such feature is v v with v 0. In the symmetric bilinear space of Example 1.9 we have (1, 1) (1, 1). It takes time to become accustomed to the idea that the condition v v need not force v = 0. Using the dot product on R n, for any subspace W we have R n = W W, but such a direct sum decomposition is not generally valid for subspaces of other bilinear spaces. Returning to Example 1.9, the subspace W = R(1, 1) has W = W, so W + W R 2. Here are two constructions of new bilinear spaces from old ones. Subspace: If (V, B) is a bilinear space and W is a subspace of V, then B restricts to a bilinear form on W, so we get a bilinear subspace denoted (W, B W ) or simply (W, B). (Strictly speaking, we should write B W W since B is a function of two variables, but the more concise B W shouldn t cause confusion.) It is obvious that if B is either symmetric, alternating, or skew-symmetric on V then that property is inherited by any subspace. Direct Sum: If (V 1, B 1 ) and (V 2, B 2 ) are bilinear spaces over the same field then V 1 V 2 becomes a bilinear space using the bilinear form (B 1 B 2 )((v 1, v 2 ), (v 1, v 2 )) := B 1 (v 1, v 1 ) + B 2(v 2, v 2 ). This formula is not mysterious; the idea is to treat V 1 and V 2 separately, just as the direct sum treats V 1 and V 2 separately. In B 1 B 2 we pair up the first components, then the second components, and add. If B 1 and B 2 are both symmetric, both alternating, or both skew-symmetric then B 1 B 2 inherits this property. Definition The bilinear space (V 1 V 2, B 1 B 2 ) constructed above is called the orthogonal direct sum of V 1 and V 2 and is denoted V 1 V 2. Example Thinking about R as a bilinear space under multiplication (Example 1.4), R R is R 2 with the dot product and the n-fold orthogonal direct sum R n = R R is R n with the dot product. We embed V 1 into the orthogonal direct sum V 1 V 2 in a natural way: v 1 (v 1, 0). Similarly we embed V 2 into V 1 V 2 by v 2 (0, v 2 ). If V 1 and V 2 are subspaces of a bilinear space V then we write V 1 V 2 as a relation if v 1 v 2 for all v 1 V 1 and v 2 V 2. This use of as a relation on subspaces should not be confused with the use of in the construction of the orthogonal direct sum of two bilinear spaces. Theorem Let (V 1, B 1 ) and (V 2, B 2 ) be bilinear spaces. Viewing V 1 and V 2 as subspaces of V 1 V 2 in the natural way, B 1 B 2 restricts to B i on V i and we have both V 1 V 2

7 BILINEAR FORMS 7 and V 2 V 1 relative to B 1 B 2. These conditions determine B 1 B 2 as a bilinear form on V 1 V 2. Proof. Since (B 1 B 2 )((v 1, 0), (v 1, 0)) = B 1(v 1, v 1 ), (B 1 B 2 ) V1 = B 1. In a similar way, (B 1 B 2 ) V2 = B 2. For v 1 V 1 and v 2 V 2, (B 1 B 2 )((v 1, 0), (0, v 2 )) = B 1 (v 1, 0) + B 2 (0, v 2 ) = 0 and (B 1 B 2 )((0, v 2 ), (v 1, 0)) = B 1 (0, v 1 ) + B 2 (v 2, 0) = 0. Therefore v 1 v 2 and v 2 v 1 in (V 1 V 2, B 1 B 2 ). Let B 0 be any bilinear form on V 1 V 2 such that B 0 Vi = B i and V 1 V 2 and V 2 V 1 relative to B 0. Then so B 0 = B 1 B 2. B 0 ((v 1, v 2 ), (v 1, v 2)) = B 0 ((v 1, 0), (v 1, 0)) + B 0 ((v 1, 0), (0, v 2)) + B 0 ((0, v 2 ), (v 1, 0)) + B 0 ((0, v 2 ), (0, v 2)) = B 0 ((v 1, 0), (v 1, 0)) + B 0 ((0, v 2 ), (0, v 2)) = B 1 (v 1, v 1) + B 2 (v 2, v 2), If a bilinear space V can be expressed as a direct sum of two subspaces W and W such that W W and W W then Theorem 1.20 shows V behaves just like the orthogonal direct sum of W and W. Most decompositions of a bilinear space into a direct sum of subspaces are not orthogonal direct sums since the subspaces may not be mutually perpendicular. This is already familiar from R n, which admits many decompositions into a direct sum of linear subspaces that are not mutually perpendicular. We end this section with a very important link between bilinear forms and the dual space. For a bilinear form B on V, we can think about B(v, w) as a function of w with v fixed or as a function of v with w fixed. Taking the first point of view, we think about the function B(v, ) : V F that sends each w to B(v, w). Since B is linear in its second component when the first is fixed, w B(v, w) is a linear map from V to F, so B(v, ) V for each v. Set L B : V V by L B : v B(v, ), so L B (v) = B(v, ). The values of L B are in V, so they are functions on V (with the unknown substituted into the empty slot of B(v, )). Since B B(v + v, w) = B(v, w) + B(v, w) for all w, B(v + v, ) = B(v, ) + B(v, ) in V, which means L B (v + v ) = L B (v) + L B (v ). Similarly, since B(cv, w) = cb(v, w), L B (cv) = cl B (v). Thus L B is linear, so any bilinear form B on V gives a linear map L B from V to its dual space V. Because L B (v)(w) = (B(v, ))(w) = B(v, w), we can recover B from L B by evaluating L B at any element v V and then evaluating L B (v) V at any w V to get B(v, w). Conversely, if we have a linear map L: V V then to each v V we have L(v) V, so we get a bilinear form B(v, w) := L(v)(w) such that B(v, ) = L(v). These correspondences from bilinear forms on V to linear maps V V and back are inverses of one another. In a similar way, from a bilinear form B we get functions B(, w) V (sending v to B(v, w)). Let R B : V V by R B : w B(, w), so R B (w) = B(, w). The map R B is linear from V to V, and passing from B to R B is a second one-to-one correspondence between bilinear forms on V and linear maps V V (Exercise 1.5). These two ways of viewing a bilinear form B as a linear map V V (using L B or R B ) are related through double duality:

8 8 KEITH CONRAD Theorem When V is finite-dimensional and B is a bilinear form on V, the linear maps L B and R B are dual to each other. Specifically, if we dualize L B : V V to L B : V V and identify V with V in the natural way then L B = R B. Similarly, R B = L B. Proof. For a linear map L: V W, the dual L : W V is defined by L (ϕ)(v) = ϕ(l(v)) for ϕ W and v V. Taking W = V, L = L B, and writing the elements of W = V as evaluation maps at elements in V, L B(ev v )(v) = ev v (L B (v)) = ev v (B(v, )) = B(v, v ) = R B (v )(v). Thus L B = R B when we identify V with V in the usual way. The proof that RB = L B is similar, or dualize the equation L B = R B. There are two ways of identifying bilinear forms on V with linear maps V V because a bilinear form is a function of two variables in V and we can take preference for one variable over the other to get a linear map out of V. In Section 8, tensor products will be used to interpret a bilinear form on V as a linear map without biasing L B over R B. Exercises. 1. Let B be a bilinear form on V. Prove B is skew-symmetric if and only if the diagonal function V F given by v B(v, v) is additive. 2. Show any alternating bilinear form on R 3 is some B u as in Example In Example 1.14, show B k is symmetric if and only if k(x, y) = k(y, x) for all x and y. What condition on k makes B k alternating? 4. Define a bilinear form on a module over a commutative ring and check any alternating bilinear form is skew-symmetric. Show the converse is true if there is no 2-torsion in the ring (2x = 0 x = 0 for x in the ring). 5. Let Bil(V ) be the set of all bilinear forms on V. It is a vector space under addition and scaling. For a bilinear form B on V, show the correspondence B R B is a vector space isomorphism from Bil(V ) to Hom F (V, V ) (V need not be finitedimensional). 6. Let B be a bilinear form on V. Set V L = {v V : v V } and V R = {v V : V v}. Since B(v, w + w ) = B(v, w) when w V R, L B induces a linear map V (V/V R). Show this linear map has kernel V L, so we get a linear embedding V/V L (V/V R). Use this and the analogous argument with R B in place of L B to show dim V L = dim V R when V is finite-dimensional. 2. Bilinear forms and matrices From now on, all vector spaces are understood to be finite-dimensional. A linear transformation L: V W between two finite-dimensional vector spaces over F can be written as a matrix once we pick (ordered) bases for V and W. When V = W and we use the same basis for the inputs and outputs of L then changing the basis leads to a new matrix representation that is conjugate to the old matrix. In particular, the trace, determinant, and (more generally) characteristic polynomial of a linear operator L: V V are well-defined, independent of the choice of basis. In this section we will see how bilinear forms and related constructions can be described using matrices.

9 BILINEAR FORMS 9 We start with a concrete example. In addition to the dot product on R n, additional bilinear forms on R n are obtained by throwing a matrix into one side of the dot product: for an n n real matrix M, the formula B(v, w) = v Mw is a bilinear form on R n. It turns out this kind of construction describes all bilinear forms on any finite-dimensional vector space, once we fix a basis. Let V have dimension n 1 with basis {e 1,..., e n }. Pick v and w in V and express them in this basis: v = n i=1 x ie i and w = n j=1 y je j. For any bilinear form B on V, its bilinearity gives n n B(v, w) = B x i e i, y j e j = = i=1 n x i B e i, i=1 n i=1 j=1 j=1 n y j e j j=1 n x i y j B(e i, e j ) Set M := (B(e i, e j )), which is an n n matrix. By a calculation the reader can carry out, (2.1) B(v, w) = [v] M[w] for all v and w in V, where on the right is the usual dot product on F n and [v] = x 1. x n, [w] = are the coordinate vectors of v and w for our choice of basis {e 1,..., e n }. The coordinate isomorphism [ ]: V F n will be understood to refer to a fixed choice of basis throughout a given discussion. We call the matrix M = (B(e i, e j )) appearing in (2.1) the matrix associated to B in the basis {e 1,..., e n }. Example 2.1. The matrix associated to the dot product on F n in the standard basis of F n is the identity matrix. Example 2.2. In Example 1.5, xy x y = ( ) ( x 0 1 y 1 0 y 1. y n ) ( x It is easy to read off the matrix from the formula on the left: there are no xx or yy terms, so the diagonal entries of the matrix are 0. Since xy has coefficient 1, the (1, 2) entry of the matrix is 1. The term x y = yx corresponds to the (2, 1) entry (because it involves the second of x and y and the first of x and y, in that order), which must be the coefficient 1. Theorem 2.3. Let V be a vector space over F of dimension n 1. For a fixed choice of basis {e 1,..., e n } of V, which gives an isomorphism v [v] from V to F n by coordinatization, each bilinear form on V has the expression (2.1) for a unique n n matrix M over F. y ).

10 10 KEITH CONRAD Proof. We have shown every bilinear form looks like (2.1) once we choose a basis. It remains to verify uniqueness. Suppose B(v, w) = [v] N[w] for some matrix N. Then B(e i, e j ) = [e i ] N[e j ], which is the (i, j) entry of N, so N = (B(e i, e j )). Note the zero vector space has 1 bilinear form but no matrix. We will not be pedantic about including the zero vector space in our discussion. Example 2.4. Let V = R n. Pick nonnegative integers p and q such that p + q = n. For v = (x 1,..., x n ) and v = (x 1,..., x n) in R n, set v, v p,q := x 1 x x p x p x p+1 x p+1 x n x n = v ( Ip O O I q )v. This symmetric bilinear form is like the dot product, except the coefficients involve p plus signs and n p = q minus signs. The dot product on R n is the special case (p, q) = (n, 0). Example 1.9 is the special case (p, q) = (1, 1). The space R n with the bilinear form, p,q is denoted R p,q. We call R p,q a pseudo- Euclidean space when p and q are both positive. Example 1.9 is R 1,1. The example R 1,3 or R 3,1 is called Minkowski space and arises in relativity theory. A pseudo-euclidean space is the same vector space as R n, but its geometric structure (e.g., the notion of perpendicularity) is different. The label Euclidean space is actually not just another name for R n as a vector space, but it is the name for R n equipped with a specific bilinear form: the dot product. Bilinear forms are not linear maps, but we saw at the end of Section 1 that each bilinear form B on V can be interpreted as a linear map V V, in fact in two ways, as L B and R B. The matrix of B turns out to be the same as the matrix of one of these linear maps! Which one? Theorem 2.5. If B is a bilinear form on V, the matrix for B in the basis {e 1,..., e n } of V equals the matrix of the linear map R B : V V with respect to the given basis of V and its dual basis in V. Proof. Let [ ]: V F n be the coordinate isomorphism coming from the basis in the theorem and let [ ] : V F n be the coordinate isomorphism using the dual basis. The matrix for R B has columns [R B (e 1 )],..., [R B (e n )]. To compute the entries of the jth column, we simply have to figure out how to write R B (e j ) as a linear combination of the dual basis {e 1,..., e n} of V and use the coefficients that occur. There is one expression for R B (e j ) in the dual basis: R B (e j ) = c 1 e c n e n in V, with unknown c i s. To find c i we just evaluate both sides at e i : the left side is (R B (e j ))(e i ) = (B(, e j ))(e i ) = B(e i, e j ) and the right side is c i 1 = c i. Therefore the ith entry of the column vector [R B (e j )] is B(e i, e j ), which means the matrix for R B is the matrix (B(e i, e j )); they agree column-by-column. In terms of a commutative diagram, Theorem 2.5 says (2.2) V R B [ ] F n (B(e i,e j )) V [ ] F n

11 commutes: [R B (v)] = (B(e i, e j ))[v] for all v in V. BILINEAR FORMS 11 Remark 2.6. That the matrix associated to B is the matrix of R B rather than L B is related to our convention that we view bilinear forms concretely using [v] A[w] instead of A[v] [w]. If we adopted the latter convention then the matrix associated to B would equal the matrix for L B. Theorem 2.7. Let (V, B) be a bilinear space and let B have associated matrix M in some basis. Then (1) B is symmetric if and only if M = M, (2) B is skew-symmetric if and only if M = M, (3) B is alternating if and only if M = M and the diagonal entries of M are zero. Matrices satisfying the conditions in (1), (2), and (3) are called symmmetric, skewsymmetric, and alternating matrices respectively. Proof. The matrix M represents the linear map R B : V V using the given basis of V and its dual basis. Since L B and R B are dual maps in the sense of Theorem 1.21, the matrix representing L B in these same bases is M. Since B is symmetric precisely when R B = L B, the matrix condition for B to be symmetric is M = M. Similarly, skew-symmetry of B means R B = L B, which becomes M = M in matrix language. The matrix condition on an alternating form is left as an exercise. The correspondence in (2.1) between bilinear forms and square matrices (once a basis is chosen) behaves well for some natural operations with bilinear forms. For instance, given bilinear forms B and B on V, we can talk about their sum B + B, a scalar multiple cb, and the function with reversed arguments B r : (B + B)(v, w) = B(v, w) + B(v, w), (cb)(v, w) = cb(v, w), B r (v, w) = B(w, v). These are all bilinear forms on V. If we fix a basis of V, so V is identified with F n and each bilinear form on V is identified with an n n matrix by (2.1), the sum and scalar multiple of bilinear forms corresponds to the sum and scalar multiple of the corresponding matrices. Conceptually, this means R B is linear in B. Since L Br = R B and R Br = L B, the matrix associated to reversing the arguments is the transposed matrix. Once we pick a basis of V, linear transformations V V and bilinear forms on V both get described by square matrices. Addition and scaling of either linear transformations or bilinear forms pass to addition and scaling of the corresponding matrices, and composition of linear transformations passes to multiplication of the corresponding matrices. There is no natural operation for bilinear forms on V that corresponds to multiplication of the corresponding matrices. This makes sense from the viewpoint of Exercise 1.5: bilinear forms on V can be viewed as linear maps V V, and these can t naturally be composed. When a linear transformation L: V V has matrix M in some basis, and C is the change-of-basis matrix expressing a new basis in terms of the old basis, then the matrix for L in the new basis is C 1 MC. Let s recall two proofs of this and then adapt them to compute the way a change of basis changes the matrix for a bilinear form. The change-of-basis matrix C, whose columns express the coordinates of the second basis in terms of the first basis, satisfies (2.3) [v] 1 = C[v] 2

12 12 KEITH CONRAD for all v V, where [ ] i is the coordinate isomorphism of V with F n using the ith basis. Indeed, both sides are linear in v, so it suffices to check this identity when v runs through the second basis, which recovers the definition of C by its columns. Since [Lv] 1 = M[v] 1 for all v V, [Lv] 2 = C 1 [Lv] 1 = C 1 M[v] 1 = C 1 MC[v] 2, so we ve proved the matrix for L in the second basis is C 1 MC. For a second proof, the identity (2.3) can be expressed as the commutative diagram (2.4) V id V V [ ] 2 [ ] 1 F n C F n and the fact that M is the matrix for L in the first basis means (2.5) V L V [ ] 1 [ ] 1 F n M F n commutes. To find the matrix for L in the second basis amounts to finding the linear map for the bottom row that makes L V V [ ] 2 [ ] 2 F n? F n commute. Only one map fits since the vertical maps in this diagram are isomorphisms, so? = [ ] 2 L [ ] 1 2. But what is? concretely? We can obtain such a commutative diagram as the boundary of the commutative diagram with (2.5) in the middle and (2.4) on the two ends V id V V L V id V V [ ] 2 [ ] 1 [ ] 1 [ ] 2 F n C F n M F n C 1 F n where the composite across the top is L, so? = C 1 MC (since composition is written right to left). Theorem 2.8. Let C be a change-of-basis matrix on V. A bilinear form on V with matrix M in the first basis has matrix C MC in the second basis. Proof. Let B be the bilinear form in the theorem. For a short matrix-based proof of this theorem, start with (2.3). It tells us 1 B(v, w) = [v] 1 M[w] 1 = C[v] 2 MC[w] 2 = [v] 2 C MC[w] 2, 1 In F n, Ax y = x A y for any A M n(f ). Apply this with A = C.

13 BILINEAR FORMS 13 so the matrix for B in the second basis is C MC. Now we give a proof using commutative diagrams. By (2.2), the matrix M for B occurs in the commutative diagram (2.6) V R B V [ ] 1 [ ] 1 F n M F n where [ ] 1 is the coordinate isomorphism using the dual basis to the first basis of V. Finding the matrix for B in the second basis amounts to finding the matrix for the bottom row of a commutative diagram (2.7) V R B V [ ] 2 [ ] 2 F n? F n where [ ] 2 is the coordinate isomorphism for the dual basis of the second basis of V. Dualizing the maps and spaces in (2.4) gives the commutative diagram 2 (2.8) F n C F n [ ] 1 [ ] 2 V id V V and now we use Exercise 2.7: for any coordinate isomorphism [ ]: V F n for a basis of V, the coordinate isomorphism [ ] : V F n for the dual basis of V is the inverse of the dual map [ ] : F n V (where F n is identified with its dual space using the dot product). Therefore reversing the direction of the vertical maps in (2.8) by using their inverses lets us rewrite (2.8) as (2.9) V id V V [ ] 1 [ ] 2 F n C F n so our desired diagram (2.7) can be found by sticking (2.4) and (2.9) on either side of (2.6) and looking at the boundary: V id V V R B V id V V [ ] 2 [ ] 1 [ ] 1 [ ] 2 F n C F n M F n C F n The composite across the top is R B and the composite along the bottom is C MC, so C MC is our desired matrix. 2 When C : F n F n is dualized and we think about (F n ) as F n using the dot product, the dual map to C is C.

14 14 KEITH CONRAD Definition 2.9. Two bilinear forms B 1 and B 2 on the respective vector spaces V 1 and V 2 are called equivalent if there is a vector space isomorphism A: V 1 V 2 such that for all v and w in V 1. B 2 (Av, Aw) = B 1 (v, w) Equivalence of bilinear forms is an equivalence relation. Concretely, if we write everything in coordinates so V 1 and V 2 are replaced by F n (same n; otherwise there couldn t possibly be an equivalence of bilinear forms on the spaces), then Definition 2.9 says: two bilinear forms on F n are equivalent when there is a linear change of variables turning one into the other. In particular, when B 1 and B 2 are symmetric bilinear forms on F n, so they are determined by their diagonal values B 1 (v, v) and B 2 (v, v) (Theorem 1.8), B 1 and B 2 are equivalent when there is a linear change of variables turning B 2 (v, v) into B 1 (v, v). Example On R 2, let B(v, w) = v ( )w, B(v, w) = v ( 0 1/2 1/2 0 )w. Both of these are symmetric. For v = w = (x, y), we have B(v, v) = x 2 y 2 and B(v, v) = xy. Since x 2 y 2 = (x + y)(x y), we can pass from B to B by the linear change of variables x = x + y and y = x y. Then B((x, y), (x, y)) = B((x, y ), (x, y )). Since ( x ) y = ( )( ) x y, B(v, v) = B(( )v, ( )v). Therefore B(v, w) = B(( )v, ( )w), so B and B are equivalent by the matrix ( ). In terms of commutative diagrams, B 1 and B 2 are equivalent when there is a vector space isomorphism A: V 1 V 2 such that the diagram (2.10) A V 1 R B1 V1 V 2 R B2 V 2 commutes. (Verify!) We saw in Theorem 2.8 that matrix representations M 1 and M 2 of a single bilinear form in two different bases are related by the rule M 2 = C M 1 C for an invertible matrix C. Let s show this rule more generally links matrix representations of equivalent bilinear forms on possibly different vector spaces. Theorem Let bilinear forms B 1 and B 2 on V 1 and V 2 have respective matrix representations M 1 and M 2 in two bases. Then B 1 is equivalent to B 2 if and only if M 1 = C M 2 C for some invertible matrix C. Proof. The equivalence of B 1 and B 2 means, by (2.10), there is an isomorphism A: V 1 V 2 such that A R B2 A = R B1. Using the bases on V i (i = 1, 2) in which B i is represented by M i and the dual bases on Vi, this equation is equivalent to C M 2 C = M 1, where C represents A. (Invertibility of C is equivalent to A being an isomorphism.) Example Returning to Example 2.10, ( ) = C ( 0 1/2 )C for C = ( ). A 1/2 0

15 BILINEAR FORMS 15 Although all matrix representations of a linear transformation V V have the same determinant (det(c 1 MC) = det M), the matrix representations of a bilinear form on V have the same determinant only up to a nonzero square factor: det(c MC) = (det C) 2 det M. Since equivalent bilinear forms can be represented by the same matrix using suitable bases, the determinants of any matrix representations for two equivalent bilinear forms must differ by a nonzero square factor. This provides a sufficient (although far from necessary) condition to show two bilinear forms are inequivalent. Example Let d be a squarefree positive integer. On Q 2, the bilinear form B d (v, w) = v ( 1 0 d 0 )w has a matrix with determinant d, so different (squarefree) d s give inequivalent bilinear forms on Q 2. As bilinear forms on R 2, however, these B d s are equivalent: ( 1 0 d 0 ) = C I 2 C for C = ( d ). Another way of putting this is that, relative to coordinates in the basis {(1, 0), (0, 1/ d)} of R 2, B d looks like the dot product B 1. Example When q is positive and even,, p,q and the dot product on R p+q both are represented by matrices with determinant 1, but they are not equivalent: the dot product takes only nonnegative values at diagonal pairs (v, v) while, p,q assumes some negative values on the diagonal when q > 0. We will see in Section 6 that all the bilinear forms, p,q (with p + q fixed) are inequivalent for different pairs (p, q). Exercises. 1. Compute the matrix associated to the bilinear forms in Examples 1.9, 1.10, 1.11 and 1.16 relative to the standard basis of column vectors. 2. For v R 3, let L v : R 3 R 3 by L v (w) = v w. Set B(v, w) = Tr(L v L w ). Show B is a symmetric bilinear form on R 3 and compute its matrix relative to the standard basis of R For x, y F, ( x y ) ( )( ) x y = x 2 and ( x y ) ( )( x y) = x 2. Why doesn t this contradict Theorem 2.3? 4. Complete the proof of Theorem Show a matrix representation for the trace form on M 2 (F ) (Example 1.12) has determinant When V has dimension n, the vector space Bil(V ) of all bilinear forms on V also has dimension n 2 (Exercise 1.5). What are the dimensions of the two subspaces of symmetric and alternating bilinear forms? 7. Let V be n-dimensional over F. Given a basis of V, let [ ]: V F n be the corresponding coordinate isomorphism and let [ ] : V F n be the coordinate isomorphism coming from the dual basis on V. When we identify F n with its dual space using the dot product (that is, view elements of (F n ) as the maps dot with a fixed vector ), show the dual map [ ] : F n V is the inverse of [ ]. 8. Let m 4 be even and B(v, w) = v ( m/2 1 1 m/2 )w for v, w (Z/(m))2. Viewing (Z/(m)) 2 as a Z/(m)-module, show B is a bilinear form that is skew-symmetric but not symmetric or alternating. Where does the argument break down if m = 2? 3. Nondegenerate bilinear forms Bilinear forms are represented by matrices, but there isn t a natural operation on bilinear forms that corresponds to multiplication of those matrices. However, there is a condition on a bilinear form that corresponds to invertibility of its matrix representations.

16 16 KEITH CONRAD Theorem 3.1. Let (V, B) be a bilinear space. The following conditions are equivalent: (1) for some basis {e 1,..., e n } of V, the matrix (B(e i, e j )) is invertible, (2) if B(v, v ) = 0 for all v V then v = 0, or equivalently if v 0 then B(v, v ) 0 for some v V, (3) every element of V has the form B(v, ) for some v V, (4) every element of V has the form B(v, ) for a unique v V. When this occurs, every matrix representation for B is invertible. Proof. The matrix (B(e i, e j )) is a matrix representation of the linear map R B : V V by Theorem 2.5. So condition (1) says R B is an isomorphism. The functions B(v, ) in V are the values of L B : V V, so condition (2) says L B : V V is injective. Condition (3) says L B is surjective and (4) says L B is an isomorphism. Since L B is a linear map between vector spaces of the same dimension, injectivity, surjectivity, and isomorphy are equivalent properties. So (2), (3), and (4) are equivalent. Since L B and R B are dual to each other (Theorem 1.21), (1) and (4) are equivalent. Different matrix representations M and M of a bilinear form are related by M = C MC for some invertible matrix C, so if one matrix representation is invertible then so are the others. The key point of Theorem 3.1 is that V parametrizes its own dual space by the functions B(v, ) exactly when a matrix for B is invertible. When this happens, each element of the dual space is also described as B(, v) for a some v, necessarily unique, by interchanging the roles of L B and R B in the proof of Theorem 3.1. Definition 3.2. Let (V, B) be a nonzero bilinear space. We call V or B nondegenerate if the equivalent conditions in Theorem 3.1 hold. A bilinear space or bilinear form that is not nondegenerate is called degenerate. A bilinear form on V is essentially the same thing as a linear map V V (Exercise 1.5), so a choice of a nondegenerate bilinear form on V is really the same thing as a choice of an isomorphism V V. Since V = V when V = {0}, for completeness the zero vector space with its only (zero) bilinear form is considered to be nondegenerate although there is no matrix. Example 3.3. The dot product on R n is nondegenerate: if v w = 0 for all w R n, then in particular v v = 0, so v = 0. (Alternatively, the matrix representation for the dot product in the standard basis is I n, which is invertible.) Thus each element of (R n ) has the form ϕ(w) = v w for a unique v R n ; the elements of (R n ) are just dotting with a fixed vector. Example 3.4. The symmetric bilinear form, p,q that defines R p,q (Example 2.4) is nondegenerate: for nonzero v = (c 1,..., c n ) in R p,q, it may happen that v, v p,q = 0, but certainly some coordinate c i is nonzero, so v, e i p,q = ±c i 0 for that i. Alternatively, the matrix for, p,q in the standard basis is M = ( Ip O O I q ), which is invertible. Thus each ϕ (R p,q ) looks like ϕ(w) = v, w p,q for a unique v R p,q. Letting n = p + q, R p,q equals R n as vector spaces, so their dual spaces are the same. How can we reconcile the description of the dual space of R p,q using, p,q above and the dual space of R n using the dot product from Example 3.3? Well, v, w p,q = v Mw, with

17 BILINEAR FORMS 17 M symmetric and M = M 1. Therefore the dual space of R p,q = R n using, p,q and the dot product match up as follows: v, p,q = Mv ( ), v ( ) = Mv, p,q. Example 3.5. Example 1.5 is nondegenerate: pairing a nonzero vector with at least one of (1, 0) or (0, 1) will give a nonzero result. Alternatively, this bilinear form is represented by an invertible matrix. Example 3.6. The alternating bilinear form on V V in Example 1.13 is nondegenerate. Assume (v, ϕ) (V V ), so ψ(v) = ϕ(w) for all w V and ψ V. Taking for ψ the zero dual vector, ϕ(w) = 0 for all w, so ϕ = 0. Therefore ψ(v) = 0 for all ψ V, so v = 0. Example 3.7. Let s see a degenerate bilinear form. On R 2 set B(v, w) = v ( )w. In coordinates, B((x, y), (x, y )) = xx. This is degenerate since the matrix ( ) is not invertible. We have (0, 1) w for all w. The matrix representing B is not invertible. Remark 3.8. On a real vector space, a bilinear form B is called positive definite if B(v, v) > 0 for every v 0. The dot product on R n is positive definite. A positive-definite bilinear form B is nondegenerate since a vector v is zero if B(v, v) = 0. The idea behind nondegeneracy, as a generalization of positive definiteness, is that v = 0 if B(v, w) = 0 for all w, not just if B(v, v) = 0. Equivalently, to show v 0 check B(v, w) 0 for some w that need not be v itself (perhaps B(v, v) = 0 but B(v, w) 0 for some w). Positive-definite symmetric bilinear forms play an important role in analysis (real Hilbert spaces) and geometry (Riemannian manifolds). For geometers, the impetus to explore the consequences of weakening positive definitness to nondegeneracy came from physics, where the local model spaces in relativity theory are pseudo-euclidean (Example 2.4); they carry a symmetric bilinear form that is not positive definite but is nondegenerate. Real vector spaces equipped with a nondegenerate alternating bilinear form are the local models for phase spaces in Hamiltonian mechanics. Example 3.9. Let g be a finite-dimensional Lie algebra over F and for each x g set ad(x) = [x, ] on g, i.e., ad(x)(y) = [x, y]. The symmetric bilinear form on g defined by B(x, x ) = Tr(ad(x)ad(x )) is called the Killing form of g. If F has characteristic 0, g is semisimple if and only if its Killing form is nondegenerate. Example Although R 2,1 is nondegenerate, the plane spanned by v 1 = (1, 0, 1) and v 2 = (0, 1, 0) inside R 2,1 is degenerate (that is, the restriction of, 2,1 to this plane is degenerate) since v 1 v 1 and v 1 v 2. There are vectors in R 2,1 that are not perpendicular to v 1, such as (1, 0, 0), but such vectors don t lie in the plane of v 1 and v 2. Example 3.10 is good to remember: a nondegenerate bilinear form on a vector space might restrict to a degenerate bilinear form on a subspace! Such behavior is impossible if F = R and B is positive definite (Remark 3.8): when B(v, v) > 0 for all nonzero v V, this property remains true on any nonzero subspace W V, so the restriction B W is also positive definite and thus is also nondegenerate. Theorem Let V be a bilinear space that is either symmetric or alternating and let W be a subspace of V. (1) The following are equivalent: W is nondegenerate, W W = {0},

18 18 KEITH CONRAD V = W W. (2) For nondegenerate V, dim W + dim W = dim V and (W ) = W. In particular, if V is nondegenerate then a subspace W is nondegenerate if and only if W is nondegenerate. Part (1) characterizes the subspaces of a symmetric or alternating bilinear space that are nondegenerate: they are exactly the subspaces that are complementary to their orthogonal space in the sense of linear algebra. The validity of this theorem for W = {0} is a reason for declaring the zero space to be nondegenerate. Proof. Let B be the given bilinear form on V. (1) Suppose B is nondegenerate on W. This is equivalent to saying no nonzero element of W lies in W, or equivalently W W = {0}. Trivially if V = W W then W W = {0}. Now assume W W = {0}. We will show V = W + W ; the directness of the sum is immediate since the subspaces intersect in {0}. Since W W is the kernel of the linear map W W given by w B(w, ) W, this map is an isomorphism: every element of W has the form B(w, ) W for some w W. To show each v V is a sum of elements of w W and w W, think about B(v, ) W. It has the form B(w, ) W for a w W. Then B(v, w ) = B(w, w ) for all w W, so B(v w, w ) = 0 for all w. Thus v w W, so v W + W and we re done. (2) Consider how elements of v pair with elements in W. This amounts to looking at the map v B(v, ) W, which is the composite of L B : V V with the restriction map V W. The first is an isomorphism (since V is nondegenerate) and the second is onto (why?), so the composite is onto. The kernel of the composite is W, so V/W = W. Taking the dimension of both sides, dim V dim W = dim W = dim W. Easily W (W ) ; since their dimensions are equal, the spaces coincide. Since (W ) = W for nondegenerate V, the condition on W being nondegenerate is symmetric in the roles of W and W, so W is nondegenerate if and only if W is. Example We continue with Example Let W be the plane in R 2,1 spanned by (1, 0, 1) and (0, 1, 0). Since, 2,1 is nondegenerate on R 2,1, dim W + dim W = 3, so W is one-dimensional. A direct calculation shows W = R(1, 0, 1). Since W W, R 2,1 is not the (direct) sum of W and W, which is consistent with W being a degenerate subspace of R 2,1. Example We look at the symmetric bilinear space (R 2, B) in Example 3.7, which is degenerate. Let W = R(1, 0). This subspace is nondegenerate, so R 2 = W W. Indeed, W = R(0, 1). However, since the whole space is degenerate we need not have (W ) = W, and in fact (W ) = R 2. Thus W is nondegenerate but W is degenerate. Remark Do not confuse the conditions V = W W and dim W = dim V dim W. The first implies the second, but the converse is false: W and W can overlap nontrivially while their dimensions are still complementary. See Example 3.12! By Theorem 3.11, when V is symmetric or alternating we have dim W = dim V dim W if V is nondegenerate and W is an arbitrary subspace or if V is arbitrary and W is a nondegenerate subspace. Theorem Let (V, B) be nondegenerate.

19 BILINEAR FORMS 19 (1) Every hyperplane 3 in V has the form {w : v w} for some v 0 and {w : w v } for some v 0. (2) If B(v, w) = B(v, w ) for all v V then w = w. (3) If A and A are linear maps V V and B(v, Aw) = B(v, A w) for all v and w in V then A = A. (4) Every bilinear form on V looks like B(v, Aw) for some linear map A: V V. Proof. (1) Let H V be a hyperplane. The quotient space V/H has dimension 1, so it is (noncanonically) isomorphic to F. Pick an isomorphism V/H = F. The composite V V/H = F is a nonzero linear map to F, with kernel H, so H = ker ϕ for some nonzero ϕ V. (This has nothing to do with bilinear forms: hyperplanes in V always are kernels of nonzero elements of the dual space of V ; the converse is true as well.) Since (V, B) is nondegenerate, ϕ = B(v, ) for some nonzero v and ϕ = B(, v ) for some nonzero v, so H = {w : B(v, w) = 0} = {w : B(w, v ) = 0}. (2) The hypothesis of (2) says R B (w) = R B (w ), so w = w since R B is an isomorphism. (3) By (2), Aw = A w for all w, so A = A. (4) When A: V V is linear, let ϕ A : V V F by ϕ A (v, w) = B(v, Aw). Then ϕ A is a bilinear form on V. The correspondence A ϕ A is a map from End F (V, V ) to the space Bil(V ) of all bilinear forms on V (Exercise 1.5), and it is linear. (That is, ϕ A+A = ϕ A +ϕ A and ϕ ca = cϕ A.) Part (2) says A ϕ A is injective. Since End F (V, V ) and Bil(V ) have the same dimension, this correspondence is an isomorphism. Concerning the second property in Theorem 3.15, if B(v, w) = B(v, w ) for just one v we can t conclude w = w, even in R n with the dot product. Although all bilinear forms have the form B(v, Aw), we can certainly write down bilinear forms in other ways, such as B(Av, w). Theorem 3.15 says this bilinear form can be written as B(v, A w) for some linear map A : V V. Definition When (V, B) is nondegenerate and A: V V is linear, the unique linear map A : V V satisfying (3.1) B(Av, w) = B(v, A w) for all v and w in V is called the adjoint of A relative to B. Example On F n, let B(v, w) = v w. For A M n (F ), Av w = v A w for all v and w in F n, so the adjoint of A relative to the dot product is the transpose of A. This close relation between the dot product and transpose is one of the reasons that the transpose is important, especially when F = R. Example On R 2, let B(v, w) = v ( )w. Let A = ( a b c d ), viewed as a linear map ) ( and w = x ) y in R 2, B(Av, w) = 3(ax + by)x 2(cx + dy)y R 2 R 2. We want to work out the map A : R 2 R 2. For v = ( x y Writing A = ( a b c d ), = 3axx + 3bx y 2cxy 2dyy. B(v, A w) = 3x(a x + b y ) 2y(c x + d y ) = 3a xx 2c x y + 3b xy 2d yy. 3 A hyperplane is a subspace with dimension n 1, where n = dim V ; they are the natural complements to linear subspaces.

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