MATH 210A, FALL 2017 Let V Be a Vector Space Over a Field F, and Let

Total Page:16

File Type:pdf, Size:1020Kb

MATH 210A, FALL 2017 Let V Be a Vector Space Over a Field F, and Let MATH 210A, FALL 2017 HW 9 SOLUTIONS WRITTEN BY DAN DORE (If you find any errors, please email [email protected]) Let V be a vector space over a field F, and let ! : V × V ! F be an alternating form. An !-symplectic basis is an ordered basis a1; b1; a2; b2; : : : ; an; bn for V with the property that !(ai; bi) = 1 for all i !(ai; aj) = !(ai; bj) = !(bi; aj) = !(bi; bj) = 0 if i 6= j Question 1. Suppose that ! is a nondegenerate alternating form over an arbitrary1 field F. Prove there exists an !-symplectic basis. Solution. Note that in particular, we are showing that any vector space which admits an alternating non- degenerate form has even dimension 2n. We will show by induction on n that a vector space of dimension 2n with a non-degenerate alternating form admits a symplectic basis and that a vector space of dimension 2n + 1 does not admit a non-degenerate symplectic form. The base case is n = 0. When V has dimension 0 the claim is vacuously true. When V = F · e has dimension 1, there cannot be a non-degenerate alternating form ! on V : !(xe; ye) = xy!(e; e) = 0 for any x; y 2 F. Now, both inductive steps will rest on the following lemma: Lemma 1. Let V be a vector space with a non-degenerate alternating form ! on V . If V1 ⊆ V is a two- ? dimensional subspace with basis vectors e; f with !(e; f) = 1, then if V2 := V1 = fv 2 V j !(v; V1) = 0g is the orthogonal complement of V1 in V with respect to !, we have V = V1 ⊕ V2 and furthermore !jV2 is alternating and non-degenerate. Before we prove the lemma, let’s see why it suffices for both inductive steps. For arbitrary a 2 V , there 0 0 1 0 exists b 2 V with !(a; b ) 6= 0 by nondegeneracy. Taking b = !(a;b) b we have !(a; b) = 1. In particular a and b are linearly independent (because otherwise !(a; b) = !(a; ca) = c!(a; a) = 0). So let V1 be the space of a; b. Thus, we may apply the lemma. In particular it implies dim V2 = dim V − 2. If the dimension of V is odd, so is the dimension of V2, but the lemma shows that !jV2 is an alternating non-degenerate form on V2. By induction, we know that this is impossible. If the dimension of V is even, so is the dimension of V2, so we may find a symplectic basis a2; b2; : : : ; an; bn for V2. Then letting a1 = a; b1 = b, we can see that a1; b1; a2; b2; : : : ; an; bn is a symplectic basis since !(a1; b1) = 1, a2; b2; : : : ; an; bn is a symplectic basis for V2, and a1; b1 live in the orthogonal complement to V2. Now, we must prove the lemma: Proof. First, note that V1 \ V2 = 0. To see this, let ce + df 2 V1 with c; d 2 F. If this is in V2, then we have 0 = !(ce + df; e) = d!(f; e) = −d and 0 = !(ce + df; f) = c!(e; f) = c, so c = 0 and d = 0. Now, we need to show that V = V1 + V2. To do this, since V1 \ V2 = 0, it suffices to show that _ dim V2 = dim V − 2. Now, the bilinear form ! induces a map !e : V ! V by sending v 2 V to the map 1Note we do not need to assume anything about which elements are squares, nor anything about char F. 1 w 7! !(v; w). Non-degeneracy of ! means exactly that !e is injective: if !(v; w) = 0 for all w 2 V , then _ v = 0. Since V; V are vector spaces of the same finite dimension, this implies that !e is an isomorphism. V v !(v)j = 0 V _ (V )_ ' 'j 2 exactly consists of the such that e V1 . But the map from to 1 sending to V1 is a _ _ surjection onto the two-dimensional vector space (V1) , so its kernel has codimension 2 in V . Therefore, V2 has codimension 2 in V , so we have shown V = V1 ⊕ V2. Now, we need to show that !jV2 is alternating and non-degenerate. The fact that it is alternating is obvious: for v 2 V2, !jV2 (v; v) = !(v; v) = 0. To see that it is non-degenerate, fix some v 2 V2. We need to find some w 2 V2 with !jV2 (v; w) = !(v; w) 6= 0. Since ! is non-degenerate, we may pick some w0 2 V with !(v; w0) 6= 0. Since V = V1 ⊕ V2, we may uniquely write w0 = w1 + w2 with wi 2 Vi. Then we have !(v; w0) = !(v; w1) + !(v; w2) = !(v; w2), since w1 2 V1 and V2 is !-orthogonal to V1. Thus, !(v; w2) 6= 0, so we are done. Question 2. Let V be a 2n-dimensional vector space over F. Recall that V _ denotes the dual vector space _ V = HomF(V; F). Let ! : V × V ! F be an alternating form. We can view ! as an element of V2(V _). (make sure you understand how this correspondence works) Is it true that ! is nondegenerate as a bilinear form if and only if ! ^ · · · ^ ! 2 V2n(V _) is nonzero? Solution. First, let’s make the correspondence between the space of alternating forms on V and V2(V _) precise. 2n V2 _ Proposition 2. For a vector space V ' F , there is a natural linear isomorphism ! 7! ev! between V and the vector space of alternating bilinear forms on V . Proof. Note that the space of skew-symmetric bilinear forms on V is canonically isomorphic to (V2V )_: the universal property of exterior powers says exactly that a linear map from V2V to R is the same thing as a skew-symmetric bilinear form on V . So we are defining a map from V2(V _) to (V2V )_. We define this by mapping ' ^ to the bilinear form ev'^ : (v; w) 7! '(v) (w) − (v)'(w). Since this map is clearly _ _ _ linear in each of v; w; '; , this at least defines a map from V ⊗ V to (V ⊗ V ) . Since ev'^ = −ev ^', it factors through the canonical projection V _ ⊗ V _ ! V2(V _), so it gives us a map V2(V _) to (V ⊗ V )_. Finally, since ev'^ (v; v) = '(v) (v) − '(v) (v) = 0, the image lands inside the subspace of alternating _ _ forms (V ^ V ) ⊆ (V ⊗ V ) . Note that this definition makes it clear that ev• is functorial in V , i.e. that if T : V ! W is a linear map, then evT ∗('^ )(v1; v2) = ev(T ∗'^T ∗ )(v1; v2) = '(T (v1)) (T (v2)) − (T (v2))'(T (v1)) = ev'; (T (v1);T (v2)) ∗ = (T ev'; )(v1; v2) We can compute what this is explicitly in a basis (note that the above construction was basis-independent!) 1 2n _ i i V2 _ v1; : : : ; v2n of V , with associated dual basis v ; : : : ; v of V (defined by v (vj) = δj). For ! 2 (V ), P i j P P we may write ! = i<j aijv ^ v , v = i bivi, and w = j cjvj. Then we have X ev!(v; w) = aij(bicj − bjci) (1) i<j 2 P We can check explicitly that this is alternating: if bi = ci, this is i<j aij(bibj − bjbi) = 0. To see that the map is an isomorphism, note that for i < j, ev!(vi; vj) = aij. Thus, if ev! = 0, we have aij = 0 for all V2 V2 _ _ 2n i < j, so ! = 0. This shows that ev• is injective, and since V and ( V ) both have dimension 2 , we conclude that ev• is an isomorphism. V2n _ We can show one direction right away: if ! ^ · · · ^ ! 6= 0 in (V ), then ev! is non-degenerate. Indeed, assume that ev! is degenerate so that there exists some v 2 V with ev!(v; w) = 0 for all w 2 V . Let V1 = F · v, and let W = V=V1. Then also ev!(w; v) = −ev!(v; w) = 0, so ev! descends to an alternating form on W . By functoriality of the map ! 7! ev!, this means that ! is in the image of the natural inclusion V2(W _) ,−! V2(V _). Thus, ! ^ · · · ^ ! 2 V2n(V _) is in the image of the natural inclusion of V2n(W _). But this space is 0 since W has dimension 2n − 1. We can also work explicitly in a basis: assume that ev!(v; w) = 0 for all w 2 V . Then we can choose 1 2n _ i i a basis v1; : : : ; v2n of V with v1 = v. Let ' ;:::;' be the dual basis of V , i.e. ' (vj) = δj. Write P i j 1 ! = i<j aij' ^ ' . Then for all j = 1;:::; 2n, we have 0 = ev!(v1; vj) = a1j. Thus, ' does not occur in the expression for !, so ! is in V2V 0, where V 0 is the span of '2;:::;'2n. Thus, ^n(!) is in V2nV 0 = 0, so ^n(!) = 0. Now, assume that ev! is non-degenerate as a bilinear form. By Question 1, we may pick a symplectic basis a1; b1; : : : ; an; bn for V , i.e. ev!(ai; bi) = 1 and ev!(ai; aj) = ev!(bi; bj) = ev!(ai; bj) = 0 for all i 6= j. If we let a1; b1; : : : ; an; bn be the dual basis for V _ and express ! in terms of this basis, Equation (1) shows that ! = a1 ^ b1 + a2 ^ b2 + ··· + an ^ bn.
Recommended publications
  • Baire Category Theorem and Uniform Boundedness Principle)
    Functional Analysis (WS 19/20), Problem Set 3 (Baire Category Theorem and Uniform Boundedness Principle) Baire Category Theorem B1. Let (X; k·kX ) be an innite dimensional Banach space. Prove that X has uncountable Hamel basis. Note: This is Problem A2 from Problem Set 1. B2. Consider subset of bounded sequences 1 A = fx 2 l : only nitely many xk are nonzerog : Can one dene a norm on A so that it becomes a Banach space? Consider the same question with the set of polynomials dened on interval [0; 1]. B3. Prove that the set L2(0; 1) has empty interior as the subset of Banach space L1(0; 1). B4. Let f : [0; 1) ! [0; 1) be a continuous function such that for every x 2 [0; 1), f(kx) ! 0 as k ! 1. Prove that f(x) ! 0 as x ! 1. B5.( Uniform Boundedness Principle) Let (X; k · kX ) be a Banach space and (Y; k · kY ) be a normed space. Let fTαgα2A be a family of bounded linear operators between X and Y . Suppose that for any x 2 X, sup kTαxkY < 1: α2A Prove that . supα2A kTαk < 1 Uniform Boundedness Principle U1. Let F be a normed space C[0; 1] with L2(0; 1) norm. Check that the formula 1 Z n 'n(f) = n f(t) dt 0 denes a bounded linear functional on . Verify that for every , F f 2 F supn2N j'n(f)j < 1 but . Why Uniform Boundedness Principle is not satised in this case? supn2N k'nk = 1 U2.( pointwise convergence of operators) Let (X; k · kX ) be a Banach space and (Y; k · kY ) be a normed space.
    [Show full text]
  • 3 Bilinear Forms & Euclidean/Hermitian Spaces
    3 Bilinear Forms & Euclidean/Hermitian Spaces Bilinear forms are a natural generalisation of linear forms and appear in many areas of mathematics. Just as linear algebra can be considered as the study of `degree one' mathematics, bilinear forms arise when we are considering `degree two' (or quadratic) mathematics. For example, an inner product is an example of a bilinear form and it is through inner products that we define the notion of length in n p 2 2 analytic geometry - recall that the length of a vector x 2 R is defined to be x1 + ... + xn and that this formula holds as a consequence of Pythagoras' Theorem. In addition, the `Hessian' matrix that is introduced in multivariable calculus can be considered as defining a bilinear form on tangent spaces and allows us to give well-defined notions of length and angle in tangent spaces to geometric objects. Through considering the properties of this bilinear form we are able to deduce geometric information - for example, the local nature of critical points of a geometric surface. In this final chapter we will give an introduction to arbitrary bilinear forms on K-vector spaces and then specialise to the case K 2 fR, Cg. By restricting our attention to thse number fields we can deduce some particularly nice classification theorems. We will also give an introduction to Euclidean spaces: these are R-vector spaces that are equipped with an inner product and for which we can `do Euclidean geometry', that is, all of the geometric Theorems of Euclid will hold true in any arbitrary Euclidean space.
    [Show full text]
  • Representation of Bilinear Forms in Non-Archimedean Hilbert Space by Linear Operators
    Comment.Math.Univ.Carolin. 47,4 (2006)695–705 695 Representation of bilinear forms in non-Archimedean Hilbert space by linear operators Toka Diagana This paper is dedicated to the memory of Tosio Kato. Abstract. The paper considers representing symmetric, non-degenerate, bilinear forms on some non-Archimedean Hilbert spaces by linear operators. Namely, upon making some assumptions it will be shown that if φ is a symmetric, non-degenerate bilinear form on a non-Archimedean Hilbert space, then φ is representable by a unique self-adjoint (possibly unbounded) operator A. Keywords: non-Archimedean Hilbert space, non-Archimedean bilinear form, unbounded operator, unbounded bilinear form, bounded bilinear form, self-adjoint operator Classification: 47S10, 46S10 1. Introduction Representing bounded or unbounded, symmetric, bilinear forms by linear op- erators is among the most attractive topics in representation theory due to its significance and its possible applications. Applications include those arising in quantum mechanics through the study of the form sum associated with the Hamil- tonians, mathematical physics, symplectic geometry, variational methods through the study of weak solutions to some partial differential equations, and many oth- ers, see, e.g., [3], [7], [10], [11]. This paper considers representing symmetric, non- degenerate, bilinear forms defined over the so-called non-Archimedean Hilbert spaces Eω by linear operators as it had been done for closed, positive, symmetric, bilinear forms in the classical setting, see, e.g., Kato [11, Chapter VI, Theo- rem 2.23, p. 331]. Namely, upon making some assumptions it will be shown that if φ : D(φ) × D(φ) ⊂ Eω × Eω 7→ K (K being the ground field) is a symmetric, non-degenerate, bilinear form, then there exists a unique self-adjoint (possibly unbounded) operator A such that (1.1) φ(u, v)= hAu, vi, ∀ u ∈ D(A), v ∈ D(φ) where D(A) and D(φ) denote the domains of A and φ, respectively.
    [Show full text]
  • A Symplectic Banach Space with No Lagrangian Subspaces
    transactions of the american mathematical society Volume 273, Number 1, September 1982 A SYMPLECTIC BANACHSPACE WITH NO LAGRANGIANSUBSPACES BY N. J. KALTON1 AND R. C. SWANSON Abstract. In this paper we construct a symplectic Banach space (X, Ü) which does not split as a direct sum of closed isotropic subspaces. Thus, the question of whether every symplectic Banach space is isomorphic to one of the canonical form Y X Y* is settled in the negative. The proof also shows that £(A") admits a nontrivial continuous homomorphism into £(//) where H is a Hilbert space. 1. Introduction. Given a Banach space E, a linear symplectic form on F is a continuous bilinear map ß: E X E -> R which is alternating and nondegenerate in the (strong) sense that the induced map ß: E — E* given by Û(e)(f) = ü(e, f) is an isomorphism of E onto E*. A Banach space with such a form is called a symplectic Banach space. It can be shown, by essentially the argument of Lemma 2 below, that any symplectic Banach space can be renormed so that ß is an isometry. Any symplectic Banach space is reflexive. Standard examples of symplectic Banach spaces all arise in the following way. Let F be a reflexive Banach space and set E — Y © Y*. Define the linear symplectic form fiyby Qy[(^. y% (z>z*)] = z*(y) ~y*(z)- We define two symplectic spaces (£,, ß,) and (E2, ß2) to be equivalent if there is an isomorphism A : Ex -» E2 such that Q2(Ax, Ay) = ß,(x, y). A. Weinstein [10] has asked the question whether every symplectic Banach space is equivalent to one of the form (Y © Y*, üy).
    [Show full text]
  • Properties of Bilinear Forms on Hilbert Spaces Related to Stability Properties of Certain Partial Differential Operators
    JOURNAL OF hlATHEhlATICAL ANALYSIS AND APPLICATIONS 20, 124-144 (1967) Properties of Bilinear Forms on Hilbert Spaces Related to Stability Properties of Certain Partial Differential Operators N. SAUER National Research Institute for Mathematical Sciences, Pretoria, South Africa Submitted by P. Lax INTRODUCTION The continuous dependence of solutions of positive definite, self-adjoint partial differential equations on the domain was investigated by Babugka [I], [2]. In a recent paper, [3], Babugka and Vjibornjr also studied the continuous dependence of the eigenvalues of strongly-elliptic, self-adjoint partial dif- ferential operators on the domain. It is the aim of this paper to study formalisms in abstract Hilbert space by means of which results on various types of continuous dependences can be obtained. In the case of the positive definite operator it is found that the condition of self-adjointness can be dropped. The properties of operators of this type are studied in part II. In part III the behavior of the eigenvalues of a self- adjoint elliptic operator under various “small changes” is studied. In part IV some applications to partial differential operators and variational theory are sketched. I. BASIC: CONCEPTS AND RESULTS Let H be a complex Hilbert space. We use the symbols X, y, z,... for vectors in H and the symbols a, b, c,... for scalars. The inner product in H is denoted by (~,y) and the norm by I( .v 11 = (x, x)llz. Weak and strong convergence of a sequence (x~} to an element x0 E H are denoted by x’, - x0 and x’, + x,, , respectively. A functional f on H is called: (i) continuous if it is continuous in the strong topology on H, (ii) weakly continuous (w.-continuous) if it is continuous in the weak topology on H.
    [Show full text]
  • Fact Sheet Functional Analysis
    Fact Sheet Functional Analysis Literature: Hackbusch, W.: Theorie und Numerik elliptischer Differentialgleichungen. Teubner, 1986. Knabner, P., Angermann, L.: Numerik partieller Differentialgleichungen. Springer, 2000. Triebel, H.: H¨ohere Analysis. Harri Deutsch, 1980. Dobrowolski, M.: Angewandte Funktionalanalysis, Springer, 2010. 1. Banach- and Hilbert spaces Let V be a real vector space. Normed space: A norm is a mapping k · k : V ! [0; 1), such that: kuk = 0 , u = 0; (definiteness) kαuk = jαj · kuk; α 2 R; u 2 V; (positive scalability) ku + vk ≤ kuk + kvk; u; v 2 V: (triangle inequality) The pairing (V; k · k) is called a normed space. Seminorm: In contrast to a norm there may be elements u 6= 0 such that kuk = 0. It still holds kuk = 0 if u = 0. Comparison of two norms: Two norms k · k1, k · k2 are called equivalent if there is a constant C such that: −1 C kuk1 ≤ kuk2 ≤ Ckuk1; u 2 V: If only one of these inequalities can be fulfilled, e.g. kuk2 ≤ Ckuk1; u 2 V; the norm k · k1 is called stronger than the norm k · k2. k · k2 is called weaker than k · k1. Topology: In every normed space a canonical topology can be defined. A subset U ⊂ V is called open if for every u 2 U there exists a " > 0 such that B"(u) = fv 2 V : ku − vk < "g ⊂ U: Convergence: A sequence vn converges to v w.r.t. the norm k · k if lim kvn − vk = 0: n!1 1 A sequence vn ⊂ V is called Cauchy sequence, if supfkvn − vmk : n; m ≥ kg ! 0 for k ! 1.
    [Show full text]
  • Math 611 Homework 7
    Math 611 Homework 7 Paul Hacking November 14, 2013 Reading: Dummit and Foote, 12.2{12.3 (rational canonical form and Jor- dan normal form), 11.1{11.3 (review of vector spaces including dual space). Unfortunately the material on bilinear forms is not covered in Dummit and Foote. I recommend Artin, Algebra, Chapter 7 (1st ed.) / Chapter 8 (2nd ed.). All vector spaces are assumed finite dimensional. (1) Classify matrices A 2 GL4(Q) of orders 4 and 5 up to conjugacy A P AP −1. (2) Let J(n; λ) denote the n × n matrix 0λ 0 0 ··· 0 01 B1 λ 0 ··· 0 0C B C B0 1 λ ··· 0 0C B C B . C B . C B C @0 0 0 ··· λ 0A 0 0 0 ··· 1 λ We call J(n; λ) the Jordan block of size n with eigenvalue λ. (Note: In DF they use the transpose of the above matrix. This corresponds to a change of basis given by reversing the order of the basis.) Show that λ is the unique eigenvalue of J(n; λ) and dim ker(J(n; λ) − λI)k = min(k; n): (3) Let V be a vector space and T : V ! V be a linear transformation from V to itself. What is the Jordan normal form of T ? Suppose that 1 the eigenvalues of T are λi, i = 1; : : : ; r, and the sizes of the Jordan blocks with eigenvalue λi are mi1 ≤ mi2 ≤ ::: ≤ misi . (a) What is the characteristic polynomial of T ? What is the minimal polynomial of T ? k (b) Let dik = dim ker(T − λi idV ) .
    [Show full text]
  • Contents 5 Bilinear and Quadratic Forms
    Linear Algebra (part 5): Bilinear and Quadratic Forms (by Evan Dummit, 2020, v. 1.00) Contents 5 Bilinear and Quadratic Forms 1 5.1 Bilinear Forms . 1 5.1.1 Denition, Associated Matrices, Basic Properties . 1 5.1.2 Symmetric Bilinear Forms and Diagonalization . 3 5.2 Quadratic Forms . 5 5.2.1 Denition and Basic Properties . 6 5.2.2 Quadratic Forms Over Rn: Diagonalization of Quadratic Varieties . 7 5.2.3 Quadratic Forms Over Rn: The Second Derivatives Test . 9 5.2.4 Quadratic Forms Over Rn: Sylvester's Law of Inertia . 11 5 Bilinear and Quadratic Forms In this chapter, we will discuss bilinear and quadratic forms. Bilinear forms are simply linear transformations that are linear in more than one variable, and they will allow us to extend our study of linear phenomena. They are closely related to quadratic forms, which are (classically speaking) homogeneous quadratic polynomials in multiple variables. Despite the fact that quadratic forms are not linear, we can (perhaps surprisingly) still use many of the tools of linear algebra to study them. 5.1 Bilinear Forms • We begin by discussing basic properties of bilinear forms on an arbitrary vector space. 5.1.1 Denition, Associated Matrices, Basic Properties • Let V be a vector space over the eld F . • Denition: A function Φ: V × V ! F is a bilinear form on V if it is linear in each variable when the other variable is xed. Explicitly, this means Φ(v1 + αv2; y) = Φ(v1; w) + αΦ(v2; w) and Φ(v; w1 + αw2) = Φ(v; w1) + αΦ(v; w2) for arbitrary vi; wi 2 V and α 2 F .
    [Show full text]
  • Metric Vector Spaces: the Theory of Bilinear Forms
    Chapter 11 Metric Vector Spaces: The Theory of Bilinear Forms In this chapter, we study vector spaces over arbitrary fields that have a bilinear form defined on them. Unless otherwise mentioned, all vector spaces are assumed to be finite- dimensional. The symbol -- denotes an arbitrary field and denotes a finite field of size . Symmetric, Skew-Symmetric and Alternate Forms We begin with the basic definition. Definition Let = be a vector space over - . A mapping ºÁ »¢ = d = ¦ - is called a bilinear form if it is linear in each coordinate, that is, if º %b &Á'»~ º%Á'»b º&Á'» and º'Á % b &» ~ º'Á %» b º'Á &» A bilinear form is 1) symmetric if º%Á &» ~ º&Á %» for all %Á & = . 2)() skew-symmetric or antisymmetric if º%Á &» ~ cº&Á %» for all %Á & = . 260 Advanced Linear Algebra 3)( alternate or alternating ) if º%Á %» ~ for all %=. A bilinear form that is either symmetric, skew-symmetric, or alternate is referred to as an inner product and a pair ²=ÁºÁ»³, where = is a vector space and ºÁ » is an inner product on = , is called a metric vector space or inner product space. As usual, we will refer to = as a metric vector space when the form is understood. 4) A metric vector space = with a symmetric form is called an orthogonal geometry over - . 5) A metric vector space = with an alternate form is called a symplectic geometry over - . The term symplectic, from the Greek for “intertwined,” was introduced in 1939 by the famous mathematician Hermann Weyl in his book The Classical Groups, as a substitute for the term complex.
    [Show full text]
  • Bilinear Forms and Their Matrices
    Bilinear forms and their matrices Joel Kamnitzer March 11, 2011 0.1 Definitions A bilinear form on a vector space V over a field F is a map H : V × V → F such that (i) H(v1 + v2, w) = H(v1, w) + H(v2, w), for all v1,v2, w ∈ V (ii) H(v, w1 + w2) = H(v, w1) + H(v, w2), for all v, w1, w2 ∈ V (iii) H(av, w) = aH(v, w), for all v, w ∈ V, a ∈ F (iv) H(v, aw) = aH(v, w), for all v, w ∈ V, a ∈ F A bilinear form H is called symmetric if H(v, w) = H(w,v) for all v, w ∈ V . A bilinear form H is called skew-symmetric if H(v, w) = −H(w,v) for all v, w ∈ V . A bilinear form H is called non-degenerate if for all v ∈ V , there exists w ∈ V , such that H(w,v) 6= 0. A bilinear form H defines a map H# : V → V ∗ which takes w to the linear map v 7→ H(v, w). In other words, H#(w)(v) = H(v, w). Note that H is non-degenerate if and only if the map H# : V → V ∗ is injective. Since V and V ∗ are finite-dimensional vector spaces of the same dimension, this map is injective if and only if it is invertible. 0.2 Matrices of bilinear forms If we take V = Fn, then every n × n matrix A gives rise to a bilinear form by the formula t HA(v, w) = v Aw Example 0.1.
    [Show full text]
  • Hilbert Space by Linear Operatorso
    REPRESENTATION OF BILINEAR FORMS IN HILBERT SPACE BY LINEAR OPERATORSO BY ALAN McINTOSH 1. Introduction. This paper is concerned with representing accretive bilinear forms in a Hubert space by maximal accretive operators in the same space. The operator A} associated with a bilinear form J is defined to be the operator with largest domain satisfying J[u, v] = (A}u, v) for all v e 3>(J), the domain of J. If J is accretive (ReJ[u, u]^0, ue!3(J)), then A} is accretive (Re (A3u, u)^0, u e 3i(Aj)). An accretive form J is defined to be representable if A, is maximal accretive (i.e. has no proper accretive extension). This implies that the spectrum of Aj is contained in the right half of the complex plane. Our aim is to give conditions on J under which J is representable. It is clear that a bounded accretive form on J? is representable (cf. Appendix). The first result of this kind for an unbounded form was derived by Friedrichs (1934) when he proved that the operator associated with a closed semibounded hermitian form is selfadjoint (cf. [2, Chapter 6]). This result was extended to a more general case by T. Kato who showed that a closed regularly accretive form is representable (see §4). (Recall that an accretive form is regularly accretive if \lm J[u, u]\ ^y Re J[u, u] for all u e S>(J) and some y ^0.) In this paper we consider accretive forms that are not necessarily regularly accretive. A representation theorem for such forms is presented in §3.
    [Show full text]
  • Lesson: Bilinear, Quadratic and Hermitian Forms Lesson Developer: Vivek N Sharma College / Department: Department of Mathematics, S.G.T.B
    Bilinear, Quadratic and Hermitian Forms Lesson: Bilinear, Quadratic and Hermitian Forms Lesson Developer: Vivek N Sharma College / Department: Department of Mathematics, S.G.T.B. Khalsa College, University of Delhi Institute of Lifelong Learning, University of Delhi pg. 1 Bilinear, Quadratic and Hermitian Forms Table of Contents 1. Learning Outcomes 2. Introduction 3. Definition of a Bilinear Form on a Vector Space 4. Various Types of Bilinear Forms 5. Matrix Representation of a Bilinear Form on a Vector Space 6. Quadratic Forms on ℝ푛 7. Hermitian Forms on a Vector Space 8. Summary 9. Exercises 10. Glossary and Further Reading 11. Solutions/Hints for Exercises 1. Learning Outcomes: After studying this unit, you will be able to define the concept of a bilinear form on a vector space. explain the equivalence of bilinear forms with matrices. represent a bilinear form on a vector space as a square matrix. define the concept of a quadratic form on ℝ푛 . explain the notion of a Hermitian form on a vector space. Institute of Lifelong Learning, University of Delhi pg. 2 Bilinear, Quadratic and Hermitian Forms 2. Introduction: Bilinear forms occupy a unique place in all of mathematics. The study of linear transformations alone is incapable of handling the notions of orthogonality in geometry, optimization in many variables, Fourier series and so on and so forth. In optimization theory, the relevance of quadratic forms is all the more. The concept of dot product is a particular instance of a bilinear form. Quadratic forms, in particular, play an all important role in deciding the maxima-minima of functions of several variables.
    [Show full text]