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Æçæá Ä Ìê Æë Çêå Ìáçæë ¾ CAÆÇÆÁCAÄ ÌÊAÆËFÇÊÅAÌÁÇÆË As for a generic system of differential equations, coordinate transformations can be used in order to bring the system to a simpler form. If the system is Hamiltonian it is desirable to keep the Hamiltonian form of the equations when the system is transformed. The search for a class of transformations satisfying the latter property leads to introducing the group of the so called canonical transformations. The condition of canonicity can be expressed in terms of Poisson brackets, La- grange brackets and differential forms. In this chapter I will discuss five different criteria of canonicity. Precisely, a coordinate transformation is canonical in case: (i) the Jacobian matrix of the transformation is a symplectic matrix; (ii) the transformation preserves the Poisson brackets; (iii) the transformation preserves the Lagrange brackets; (iv) the transformation preserves the differential 2–form dpj dqj; j ∧ (v) the transformation preserves the integral over a closed curve of the 1–form P j pjdqj. An useful method for constructing canonicals transformations is furnished by the the- ory of generatingP functions. This is the basis for further development of the integration methods, eventually leading to the Hamilton–Jacobi’s equation. 2.1 Elements of symplectic geometry Before entering the discussion of canonical transformations we recall a few aspects of symplectic geometry that will be useful. A relevant role is played by the bilinear antisymmetric form induced by the matrix J introduced in sect. 1.1.3, formula (1.16). Symplectic geometry is characterized by the skew symmetric matrix J in the same sense as Euclidean geometry is characterized by the identity matrix I. Indeed, Euclidean geometry is characterized by transformations preserving the bilinear sym- metric form (2.1) x, y = xjyj . h i j X 26 Chapter2 This is called the inner product, and is preserved by the group of orthogonal transfor- mations, namely, matrices U satisfying (2.2) U⊤U = I . Similarly, symplectic geometry is characterized by transformations preserving the bi- linear form (2.3) [x, y] = x, Jy , h i where J is the matrix defined by (1.16). This is called the symplectic product. It is antisymmetric, i.e., [x, y] = [y, x], and non degenerate, i.e., [x, y] = 0 for all y R2n implies x = 0. − ∈ 2.1.1 The symplectic group Let us consider a linear mapping in R2n (2.4) x = Uy , where U is a 2n 2n nonsingular matrix. The matrix U is said to be symplectic if × (2.5) U⊤JU = J . This condition is actually equivalent to (2.6) UJU⊤ = J . For, by (2.5) we have U⊤ = JU−1J−1, and so also UJU⊤ = UJ2U−1J−1 = J−1 = J in view of J2 = I. By the way, this shows that if U is symplectic then U⊤ is− symplectic, too. − The set of symplectic matrices forms a group with respect to matrix multiplica- tion. For, the identity matrix I is clearly symplectic; if U and V are symplectic then (UV)⊤J(UV) = V⊤U⊤JUV = V⊤JV = J, so that (UV) is symplectic; if U is symplectic then J = IJI =(U−1)⊤U⊤JUU−1 =(U−1)⊤JU−1, so that U−1 is symplectic. Finally, if U is symplectic so is U⊤. Going back to the canonically conjugated coordinates (q, p) it is immediately seen that the symplectic product of two 2n vectors z = (q, p) and z′ = (q′, p′) is given n ′ ′ by the expression j=1(qjpj qj pj). That is, the symplectic product is obtained by − ′ projecting the parallelogram with sides z, z onto each of the planes qj, pj, and then adding up algebraicallyP the oriented areas of all these projections. The set of linear transformations which preserve the symplectic product is charac- terized as the group of symplectic matrices. For, [Ux, Uy] = Ux, JUy = x, U⊤JUy , and this coincides with [x, y] if and only if U satisfies (2.5). h i h i 2.1.2 Symplectic spaces and symplectic–orthogonality A symplectic space is a real linear vector space V equipped with a bilinear antisym- metric nondegenerate form [ , ]. We shall consider here only vector spaces of finite · · Canonicaltransformations 27 dimension. Two vectors x, y are said to be symplectic–orthogonal in case [x, y] = 0. We shall write x6 y. To any subspace W of V we associate the set 6 (2.7) W = x V x6 y for all y W ; { ∈ | ∈ } by the bilinearity of the symplectic form W 6 is a subspace, which is said to be symplectic–orthogonal1 to W . We emphasize that the concept of symplectic– orthogonality presents sharp differences with respect to the concept of orthogonality in Euclidean geometry. Here are three main differences. (i) Every vector is self symplectic–orthogonal, since the symplectic product is antisymmetric; therefore, any one–dimensional subspace is self symplectic– orthogonal, too. (ii) The restriction of the symplectic product to a subspace is still a bilinear form, but in general it fails to be nondegenerate. For instance, the restriction to a one–dimensional subspace is clearly degenerate. (iii) The subspaces W and W 6 need not be complementary. Two basic properties that are common to both geometries are given by the fol- lowing Lemma 2.1: Let W be a subspace of a symplectic space V . Then 6 (2.8) dim W + dim W = dim V . and 6 6 (2.9) W = W Proof. If dim W = 0 or dim W = dimV , then the statement is trivial. So, let us suppose that dim W = m with 0 < m < dim V . Denoting n = dim V , let u ,..., un { 1 } be a basis of V , with u1,..., um a basis of W . Writing a generic vector v V as n { } ∈ v = j=1 vj uj, the symplectic product takes the form [v, w] = j,k ajkvjwk , where ajk = [uj, uk] is an element of an antisymmetric nondegenerate matrix. If w W , P P ∈ then wm = ... = wn = 0, because by hypothesis u ,..., um is a basis of W . If +1 { 1 } moreover v W 6 , then the relation of symplectic orthogonality is ∈ m n βjwj =0 , βj = ajkvk . j k X=1 X=1 Since w W is arbitrary, the first equation implies β = ... = βm = 0 and ∈ 1 βm ,...,βn arbitrary; so, there are n m independent solutions. Since the ma- +1 − trix ajk is nondegenerate, the second relation above guarantees the existence of exactly{ n} m independent vectors symplectic–orthogonal to the subspace W , so that 6 − dim W = n m , as claimed. − 6 Coming to (2.9), if w W , then v6 w for all v W , because by definition every ∈ ∈ element of W 6 is symplectic–orthogonal to every element of W , and so also to w, so 1 Some authors use the name left–orthogonality. See for instance [7]. 28 Chapter2 that W (W 6 )6 . On the other hand, by (2.8), dim(W 6 )6 = dim W , so that (2.9) follows. ⊂ Q.E.D. We now go deeper into the concept of symplectic–orthogonality, pointing out some properties that do not appear in Euclidean geometry. To this end, we first need some definitions. A subspace W of a symplectic space V is said to be: (i) isotropic in case W W 6 ; ⊂ (ii) coisotropic in case W W 6 , namely, if its symplectic–orthogonal subspace is isotropic; ⊃ (iii) Lagrangian in case W = W 6 , namely, if it is both isotropic and coisotropic; (iv) symplectic in case the symplectic product restricted to W is still nondegenerate. These definitions are easily illustrated by considering the space R2n . We denote by 2n e1,..., en, d1,..., dn the canonical basis of R and by (q1,...,qn, p1,...,pn) the { } 2n coordinates, so that a vector x R is represented as q e + ...+ qnen + p d + ... + ∈ 1 1 1 1 pndn. The symplectic bilinear form is defined by the relations [ej, ek]=[dj, dk]=0 , [ej, dk] = δj,k , j,k =1,...,n . We shall refer to the basis e1,..., en, d1,..., dn satisfying the latter relations as the canonical symplectic basis{. } Example 2.1: Arithmetic planes. Let us call arithmetic plane the subspace spanned by any subset of the vectors e ,..., en, d ,..., dn . Formally, given any subsets J, K { 1 1 } of 1,...,n , we consider the plane spanned by the vectors ej j∈J dk k∈K . The following{ examples} are easily understood: { } ∪{ } (i) the arithmetic plane spanned by any subset of the vectors e1,..., en is an isotropic subspace; { } (ii) the direct sum of any of the arithmetic planes of the point (i) with the arithmetic plane span(d1,..., dn) is a coisotropic subspace; (iii) the arithmetic plane span(e1,..., en) is a Lagrangian subspace. (iv) for every j 1,...,n , the arithmetic 2–dimensional plane span(ej, dj) is symplectic; further∈ { symplectic} subspaces are generated by direct sum of such arithmetic planes. In the examples (i)–(iii) the role of the vectors ej and dj can be exchanged, of course. A more interesting example is the following: Example 2.2: Lagrangian arithmetic planes. Consider any partition of the indexes 1,...,n into two disjoint subsets J and K (i.e., J K = and J K = 1,...,n ). { } ∩ ∅ ∪ { } Then the arithmetic plane spanned by the n vectors ej dk is a Lagrangian { }j∈J ∪{ }k∈K plane. There are 2n different Lagrangian planes that are generated that way.2 2 There are 2n different partitions of n objects into two disjoint subsets. Canonicaltransformations 29 2.1.3 Canonical basis of a symplectic space The examples above are in fact quite general. For, any symplectic space can be equipped with a canonical symplectic basis.
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