The Maslov index for Lagrangian pairs on $\mathbb {R}^{2n} $

THE MASLOV INDEX FOR LAGRANGIAN PAIRS ON R2n

arXiv:1608.00632v1 [math.DS] 1 Aug 2016

P. HOWARD, Y. LATUSHKIN, AND A. SUKHTAYEV Abstract. We discuss a definition of the Maslov index for Lagrangian pairs on R2n based on spectral flow, and develop many of its salient properties. We provide two applications to illustrate how our approach leads to a straightforward analysis of the relationship between the Maslov index and the Morse index for Sch¨ odinger operators on [0, 1] and R.

1. Introduction With origins in the work of V. P. Maslov [45] and subsequent development by V. I. Arnol’d [2], the Maslov index on R2n is a tool for determining the nature of intersections between two evolving Lagrangian subspaces (see Definition 1.1). As discussed in [17], several equivalent definitions are available, and we focus on a definition for Lagrangian pairs based on the development in [6] (using the definition of spectral flow introduced in [48]). We note at the outset that the theory associated with the Maslov index has now been extended well beyond the simple setting of our analysis (see, for example, [6, 23]); nonetheless, the Maslov index for Lagrangian pairs on R2n is a useful tool, and a systematic development of its properties is certainly warranted. As a starting point, we define what we will mean by a Lagrangian subspace of R2n . Definition 1.1. We say ℓ ⊂ R2n is a Lagrangian subspace if ℓ has dimension n and (Jx, y)R2n = 0, for all x, y ∈ ℓ. Here, (·, ·)R2n denotes Euclidean inner product on R2n , and 0 −In J= , In 0 with In the n × n identity matrix. We sometimes adopt standard notation for symplectic forms, ω(x, y) = (Jx, y)R2n . Finally, we denote by Λ(n) the collection of all Lagrangian subspaces of R2n , and we will refer to this as the Lagrangian Grassmannian. A simple example, important for intuition, is the case n = 1, for which (Jx, y)R2 = 0 if and only if x and y are linearly dependent. In this case, we see that any line through the origin is a Lagrangian subspace of R2 . As a foreshadowing of further discussion, we note that each such Lagrangian subspace can be identified with precisely two points on the unit circle S 1 . Date: January 17, 2018. Key words and phrases. Eigenvalues; Maslov index; Morse index; Schrödinger operators. 1

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P. HOWARD, Y. LATUSHKIN, AND A. SUKHTAYEV

More generally, any Lagrangian subspace of R2n can be spanned by a choice of n linearly independent vectors in R2n . We will generally find it convenient to collect these n vectors as the columns of a 2n × n matrix X, which we will refer to as a frame for ℓ. Moreover, we will often write X = X , where X and Y are n × n matrices. Y Given any two Lagrangian subspaces ℓ1 and ℓ2 , with associated frames X1 = XY11 and X2 = XY22 , we can define the complex n × n matrix ˜ = −(X1 + iY1 )(X1 − iY1 )−1 (X2 − iY2 )(X2 + iY2 )−1 , W

(1.1)

which we will see in Section 3 is unitary. (We will also verify in Section 3 that (X1 − iY1 ) ˜ is independent of the choice of frames we take and X2 + iY2 are both invertible, and that W ˜ will be replaced by for ℓ1 and ℓ2 .) Notice that if we switch the roles of ℓ1 and ℓ2 then W −1 ∗ ˜ , and since W ˜ is unitary this is W ˜ . We conclude that the eigenvalues in the switched W case will be complex conjugates of those in the original case. ˜ from Remark 1.2. We use the tilde to distinguish the n × n complex-valued matrix W the Souriau map (see equation (3.8) below), which is a related 2n × 2n matrix often—as ˜ appears in a less general context in [21, 31]. For here—denoted W . The general form of W 0 the special case X2 = I (associated, for example, with Dirichlet boundary conditions for a Sturm-Liouville eigenvalue problem) we see that ˜ = (X1 + iY1 )(X1 − iY1 )−1 , W (1.2) which has been extensively studied, perhaps most systematically in [4] (particularly Chapter 0 ˜ 10). If we let WD denote (1.2) for X1 = I and for j = 1, 2 set ˜ j = (Xj + iYj )(Xj − iYj )−1 , W

˜ can be viewed as the composition map then our form for W ˜ 1W ˜ D (W ˜ 2W ˜ D )−1 = −W ˜ 1 (W ˜ 2 )−1 . −W

(1.3)

For a related observation regarding the Souirau map see Remark 3.3. Combining observations from Sections 2 and 3, we will establish the following theorem (cf. Lemma 1.3 in [6]). Theorem 1.3. Suppose ℓ1 , ℓ2 ⊂ R2n are Lagrangian subspaces, with respective frames X1 = X2 X1 ˜ be as defined in (1.1). Then and X2 = Y2 , and let W Y1

˜ + I) = dim(ℓ1 ∩ ℓ2 ). dim ker(W ˜ associated with the eigenvalue −1 is precisely That is, the dimension of the eigenspace of W the dimension of the intersection of the Lagrangian subspaces ℓ1 and ℓ2 . Given a parameter interval I = [a, b], which can be normalized to [0, 1], we consider maps ℓ : I → Λ(n), which will be expressed as ℓ(t). In order to specify a notion of continuity, we need to define a metric on Λ(n), and following [23] (p. 274), we do this in terms of orthogonal projections onto elements ℓ ∈ Λ(n). Precisely, let Pi denote the orthogonal projection matrix


3

onto ℓi ∈ Λ(n) for i = 1, 2. I.e., if Xi denotes a frame for ℓi , then Pi = Xi (Xti Xi )−1 Xti . We take our metric d on Λ(n) to be defined by d(ℓ1 , ℓ2 ) := kP1 − P2 k, where k · k can denote any matrix norm. We will say that ℓ : I → Λ(n) is continuous provided it is continuous under the metric d. Likewise, for L = (ℓ1 , ℓ2 ) ∈ Λ(n) × Λ(n) and M = (m1 , m2 ) ∈ Λ(n) × Λ(n), we take ρ(L, M) = (d(ℓ1 , m1 )2 + d(ℓ2 , m2 )2 )1/2 .

(1.4)

Given two continuous maps ℓ1 (t), ℓ2 (t) on a parameter interval I, we denote by L(t) the path L(t) = (ℓ1 (t), ℓ2 (t)). In what follows, we will define the Maslov index for the path L(t), which will be a count, including both multiplicity and direction, of the number of times the Lagrangian paths ℓ1 and ℓ2 intersect. In order to be clear about what we mean by multiplicty and direction, we observe that associated with any path L(t) we will have a path of unitary complex matrices as described in (1.1). We have already noted that the Lagrangian subspaces ℓ1 and ℓ2 ˜ (t0 ) has -1 as an eigenvalue. In the event of such intersect at a value t0 ∈ I if and only if W an intersection, we define the multiplicity of the intersection to be the multiplicity of -1 as ˜ is unitary the algebraic and geometric multiplicites are the same). an eigenvalue (since W When we talk about the direction of an intersection, we mean the direction the eigenvalues ˜ are moving (as t varies) along the unit circle S 1 as they pass through −1 (we take of W counterclockwise as the positive direction). We note that the eigenvalues certainly do not all need to be moving in the same direction, and that we will need to take care with what we mean by a crossing in the following sense: we must decide whether to increment the Maslov index upon arrival or upon departure. Following [6, 23, 48], we proceed by choosing a partition a = t0 < t1 < · · · < tn = b of ˜ (t)−ei(π±ǫj ) I = {0} for tj−1 < t < tj ; I = [a, b], along with numbers ǫj ∈ (0, π) so that ker W ˜ (t)), for tj−1 < t < tj and j = 1, . . . , n. Moreover, for each that is, ei(π±ǫj ) ∈ C \ σ(W j = 1, . . . , n and any t ∈ [tj−1 , tj ] there are only finitely many values θ ∈ [0, ǫj ] for which ˜ (t)). ei(π+θ) ∈ σ(W Fix some j ∈ {1, 2, . . . , n} and consider the value X ˜ (t) − ei(π+θ) I . dim ker W (1.5) k(t, ǫj ) := 0≤θ 0 (i.e., an eigenvalue rotated slightly clockwise from −1). If λ is the ǫ ǫ associated eigenvalue of W then we will have −λ2 = ei(π−ǫ) , and so λ = ei(π− 2 ) , ei(2π− 2 ) . ǫ If the eigenvalue of W rotates through −1 then its counterpart ei(π− 2 ) will rotate through −1 in the same direction. Other cases are similar, and we see that indeed the directions associated with the crossings agree. 4. Monotoncity For many applications, such as the ones discussed in Section 5, we have monotonicity in the following sense: as the parameter t ∈ I varies in a fixed direction, the eigenvalues of


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˜ (t) move monotonically around S 1 . In this section, we develop a general framework for W checking monotonicity in specific cases. As a starting point, we take the following lemma from [31] (see also Theorem V.6.1 in [4]): ˜ (t) be a smooth family of unitary n × n matrices Lemma 4.1 ([31], Lemma 3.11.). Let W ˜ (t) = iW ˜ (t)Ω(t), ˜ ˜ where Ω(t) is on some interval I, satisfying the differential equation dtd W ˜ a continuous, self-adjoint and negative-definite n × n matrix. Then the eigenvalues of W (t) move (strictly) monotonically clockwise on the unit circle as τ increases. In order to employ Lemma 4.1 we need to obtain a convenient form for ˜ (t) = −W ˜ 1 (t)W ˜ 2 (t), where begin by writing W

˜ dW dt

. For this, we

˜ 1 (t) = (X1 (t) + iY1 (t))(X1 (t) − iY1 (t))−1 W ˜ 2 (t) = (X2 (t) − iY2 (t))(X2 (t) + iY2 (t))−1 . W ˜ 1 (t) we have For W ˜1 dW = (X1′ (t) + iY1′ (t))(X1 (t) − iY1 (t))−1 dt − (X1 (t) + iY1 (t))(X1 (t) − iY1 (t))−1 (X1′ (t) − iY1′ (t))(X1 (t) − iY1 (t))−1 = (X1′ (t) + iY1′ (t))(X1 (t) − iY1 (t))−1 ˜ 1 (X ′ (t) − iY ′ (t))(X1 (t) − iY1 (t))−1 −W 1

1

˜ 1W ˜ 1∗ (X1′ (t) + iY1′ (t))(X1 (t) − iY1 (t))−1 =W ˜ 1 (X ′ (t) − iY ′ (t))(X1 (t) − iY1 (t))−1 −W 1 1 n o ˜1 W ˜ ∗ (X ′ (t) + iY ′ (t)) − (X ′ (t) − iY ′ (t)) (X1 (t) − iY1 (t))−1 , =W 1 1 1 1 1 ˜ is unitary. Here, where we have liberally taken advantage of the fact that W {· · · } = (X1 (t)t + iY1 (t)t )−1 (X1 (t)t − iY1 (t)t )(X1′ (t) + iY1′ (t)) − (X1′ (t) − iY1′ (t)) h t t −1 = (X1 (t) + iY1 (t) ) (X1 (t)t − iY1 (t)t )(X1′ (t) + iY1′ (t)) i − (X1 (t)t + iY1 (t)t )(X1′ (t) − iY1′ (t)) , and [· · · ] = 2i(X1 (t)t Y1′ (t) − Y1 (t)t X1′ (t)). We conclude that

˜1 dW ˜ 1 (t)Ω ˜ 1 (t), = iW dt

where ∗ ˜ 1 (t) = 2 (X1 (t) − iY1 (t))−1 Ω X1 (t)t Y1′ (t) − Y1 (t)t X1′ (t) (X1 (t) − iY1 (t))−1 .

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˜ 2 (t) we find Proceeding similarly for W ˜2 dW dt

˜ 2 (t)Ω ˜ 2 (t), = iW

where ∗ ˜ 2 (t) = −2 (X2 (t) + iY2 (t))−1 X2 (t)t Y ′ (t) − Y2 (t)t X ′ (t) (X2 (t) + iY2 (t))−1 . Ω 2

2

Combining these observations, we compute ˜ ˜ ˜1 dW dW ˜2 − W ˜ 1 dW2 W =− dt dt dt ˜ ˜ ˜ ˜ 1 (t)W ˜ 2 (t)Ω ˜ 2 (t) = −iW1 (t)Ω1 (t)W2 (t) − iW ˜ 1 (t)W ˜ 2 (t))W ˜ 2 (t)∗ Ω ˜ 1 (t)W ˜ 2 (t) + i(−W ˜ 1 (t)W ˜ 2 (t))Ω ˜ 2 (t) = i(−W n o ˜ (t) W ˜ 2 (t)∗ Ω ˜ 1 (t)W ˜ 2 (t) + Ω ˜ 2 (t) . = iW

That is, we have

˜ dW ˜ (t)Ω(t), ˜ = iW dt

where

˜ ˜ 2 (t)∗ Ω ˜ 1 (t)W ˜ 2 (t) + Ω ˜ 2 (t). Ω(t) =W We notice particularly that we can write ∗ ∗˜ ˜ −1 ˜ t ′ t ′ −1 ˜ ˜ W2 Ω1 W2 = 2 (X1 − iY1 ) W2 (X1 Y1 − Y1 X1 ) (X1 − iY1 ) W2 .

˜ will be determined by the matrices (X1 (t)t Y ′ (t)−Y1 (t)t X ′ (t)) We see that the nature of Ω(t) 1 1 t ′ t ′ and (X2 (t) Y2 (t) − Y2 (t) X2 (t)). In order to check that these matrices are symmetric, we differentiate the Lagrangian property X1 (t)t Y1 (t) − Y1 (t)t X1 (t) = 0 to see that X1 (t)t Y1′ (t) − Y1 (t)t X1′ (t) = Y1′ (t)t X1 (t) − X1′ (t)t Y1 (t). Symmetry of (X1 (t)t Y1′ (t)−Y1 (t)t X1′ (t)) is immediate, and we proceed similarly for (X2 (t)t Y2′ (t)− ˜ Y2 (t)t X2′ (t)). We conclude that Ω(t) is self-adjoint. ˜ Finally, for monontonicity, we need to check that Ω(t) is definite. We show how to do this in certain cases in Section 5. For convenient reference, we summarize these observations into a lemma. Lemma 4.2. Suppose ℓ1 ,ℓ2 : I → Λ(n) denote paths of Lagrangian subspaces with C 1 frames X1 = XY11 and X2 = XY22 (respectively). If the matrices −Xt1 JX1 = X1 (t)t Y1′ (t) − Y1 (t)t X1′ (t)

and (noting the sign change) Xt2 JX2 = −(X2 (t)t Y2′ (t) − Y2 (t)t X2′ (t))


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˜ (t) rotate are both non-negative and at least one is positive definite then the eigenvalues of W in the counterclockwise direction as t increases. Likewise, if both of these matrices are non˜ (t) rotate in the positive, and at least one is negative definite then the eigenvalues of W clockwise direction as t increases. 4.1. Monotonicity at Crossings. We are often interested in the rotation of eigenvalues of ˜ through −1; i.e., the rotation associated with an intersection of our Lagrangian subspaces. W Let t∗ denote the time of intersection. As discussed in [31], if we let P˜ denote projection onto ˜ + I), then the rotation of eigenvalues through −1 is determined by the eigenvalues of ker(W ˜ ∗ )P. ˜ Notice that if v˜ ∈ ker(W ˜ + I) we will have the matrix P˜ Ω(t −(X1 (t∗ ) + iY1 (t∗ ))(X1 (t∗ ) − iY1 (t∗ ))−1 (X2 (t∗ ) − iY2 (t∗ ))(X2 (t∗ ) + iY2 (t∗ ))−1 v˜ = −˜ v, and correspondingly ˜ 2 (t∗ )˜ (X1 (t∗ ) − iY1 (t∗ ))−1 W v = (X1 (t∗ ) + iY1 (t∗ ))−1 v˜. Recalling relations (3.6), we find that (X1 (t∗ ) + iY1 (t∗ ))−1 v˜ = M1 (t∗ )2 (X1 (t∗ )t − iY1 (t∗ )t )˜ v. ˜ ∗ ) acts on ker(W ˜ + I) we can replace it with We see that if Ω(t ∗ 2 t t t ′ t ′ ˜ ΩP (t∗ ) := 2 M1 (t∗ ) (X1 (t∗ ) − Y1 (t∗ ) ) X1 (t∗ ) Y1 (t∗ ) − Y1 (t∗ )X1 (t∗ ) × M1 (t∗ )2 (X1 (t∗ )t − Y1 (t∗ )t ) ∗ − 2 M2 (t∗ )2 (X2 (t∗ )t − Y2 (t∗ )t ) X2 (t∗ )t Y2′ (t∗ ) − Y2t (t∗ )X2′ (t∗ ) × M2 (t∗ )2 (X2 (t∗ )t − Y2 (t∗ )t ).

If we express v˜ = v1 + iv2 , we can write ˜ 2 (t∗ )˜ (X1 (t∗ ) − iY1 (t∗ ))−1 W v = M1 (t∗ )2 (X1 (t∗ )t − iY1 (t∗ )t )(v1 + iv2 ) n o = M1 (t∗ )2 X1 (t∗ )t v1 + Y1 (t∗ )t v2 + i(X1 (t∗ )t v2 − Y1 (t∗ )t v1 ) n o t t 2 = M1 (t∗ ) X1 (t∗ ) v1 + Y1 (t∗ ) v2 .

Here, we have observed that it follows from the Lagrangian property that X1 (t∗ )t v2 − Y1 (t∗ )t v1 = 0. Likewise, n o 2 t t 2 t t M2 (t∗ ) (X2 (t∗ ) − Y2 (t∗ ) )(˜ v ) = M2 (t∗ ) X2 (t∗ ) v1 + Y2 (t∗ ) v2 . If we now write

˜ P (t∗ ) = Ω ˜ (1) (t∗ ) + Ω ˜ (2) (t∗ ), Ω P P (1) ˜ (t∗ ) will take the form then the quadratic form associated with Ω P n o t ′ t ′ 2 t t ˜ (1) (t∗ )˜ Ω v , v ˜ = 2 (X (t ) Y (t ) − Y (t )X (t ))M (t ) X (t ) v + Y (t ) v 1 ∗ 1 ∗ 1 ∗ 1 1 ∗ 2 , 1 ∗ 1 ∗ 1 ∗ P Cn n o (4.1) M1 (t∗ )2 X1 (t∗ )t v1 + Y1 (t∗ )t v2 , Cn

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˜ (2) (t∗ ) will take the form and likewise the quadratic form associated with Ω P n o (2) t t t ′ t ′ 2 ˜ ΩP (t∗ )˜ v , v˜ = 2 (X2 (t∗ ) Y2 (t∗ ) − Y2 (t∗ )X2 (t∗ ))M2 (t∗ ) X2 (t∗ ) v1 + Y2 (t∗ ) v2 , Cn n o (4.2) M2 (t∗ )2 X2 (t∗ )t v1 + Y2 (t∗ )t v2 . Cn

We will use (4.1) and (4.2) in our next section in which we relate our approach to the development of [50], based on crossing forms. 4.2. Relation to Crossing Forms. In this section, we discuss the relation between our development and the crossing forms of [50]. As a starting point, let ℓ1 (t) denote a path of Lagrangian subspaces, and let ℓ2 denote a fixed target Lagrangian subspace. Let the respective frames be X2 X1 (t) , ; X2 = X1 (t) = Y2 Y1 (t) and let t∗ denote the time of a crossing; i.e., ℓ1 (t∗ ) ∩ ℓ2 6= {0}. ˜ (t) will be The corresponding matrix W ˜ (t) = −(X1 (t) + iY1 (t))(X1 (t) − iY1 (t))−1 (X2 − iY2 )(X2 + iY2 )−1 . W ˜ ′ (t∗ ) with the information Our goal is to compare the information obtained by computing W we get from the crossing form at t∗ . Following [50], we construct the crossing form at t∗ as a map Γ(ℓ1 , ℓ2 ; t∗ ) : ℓ1 (t∗ ) ∩ ℓ2 → R defined as follows: given v ∈ ℓ1 (t∗ ) ∩ ℓ2 , we find u ∈ Rn so that v = X1 (t∗ )u, and compute Γ(ℓ1 , ℓ2 ; t∗ )(v) = (X1 (t∗ )u, Y1′ (t∗ )u)Rn − (X1 (t∗ )u, Y1′ (t∗ )u)Rn = (X1 (t∗ )t Y1′ (t∗ ) − Y1 (t∗ )t X1′ (t∗ ))u, u . Since v ∈ ℓ1 (t∗ ) ∩ ℓ2 ⊂ ℓ1 (t∗ ) the vector u is uniquely defined and we can compute it in terms of the Moore-Penrose pseudo-inverse of X1 , u = (Xt1 X1 )−1 Xt1 v = M12 (X1t v1 + Y1t v2 ), where v = vv12 . Comparing with (4.1), and taking X2 in this setting to be X2 (t∗ ) in the setting of (4.1), we see that 1 ˜ (1) ΩP (t∗ )˜ v , v˜ . (4.3) Γ(ℓ1 , ℓ2 ; t∗ )(v) = 2 Cn ˜ When computing the Maslov index with crossing forms, the rotation of eigenvalues of W through −1 is determined by the signature of the crossing form. We see from (4.3) that this ˜ (1) (t∗ ). information is encoded in the eigenvalues of Ω P


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Turning now to path pairs, we recall that in [50] the crossing form for a pair of Lagrangian paths ℓ1 (t) and ℓ2 (t) is defined as Γ(ℓ1 , ℓ2 ; t∗ ) = Γ(ℓ1 , ℓ2 (t∗ ); t∗ ) − Γ(ℓ2 , ℓ1 (t∗ ); t∗ ). Here, ℓ2 (t∗ ) is viewed as a constant Lagrangian subspace, so that our previous development can be applied to Γ(ℓ1 , ℓ2 (t∗ ); t∗ ), and similary for Γ(ℓ2 , ℓ1 (t∗ ); t∗ ), in which case ℓ1 (t∗ ) is viewed as a constant Lagrangian subspace. In the previous calculations, we have already checked that 1 ˜ (1) Γ(ℓ1 , ℓ2 (t∗ ); t∗ )(v) = ΩP (t∗ )˜ v, v˜ , 2 Cn and we similarly find that 1 ˜ (2) Γ(ℓ2 , ℓ1 (t∗ ); t∗ )(v) = ΩP (t∗ )˜ v, v˜ . 2 Cn Combining these expressions, we see that the crossing form for the Lagrangian pair (ℓ1 (t), ℓ2 (t)) at a crossing point t∗ is 1˜ ΩP (t∗ )˜ v , v˜ . Γ(ℓ1 , ℓ2 ; t∗ ) = 2 Cn 5. Applications Although full applications will be carried out in separate papers, we indicate two motivating applications for completeness. Application 1. In [31], the authors consider Schrödinger equations −y ′′ + V (x)y = λy α1 y(0) + α2 y ′(0) = 0

(5.1)

′

β1 y(1) + β2 y (1) = 0, where V ∈ C([0, 1]) is a real-valued symmetric matrix, rank α1 α2 = n; rank β1 β2 = n,

(5.2)

and we assume separated, self-adjoint boundary conditions, for which we have α1 α2t − α2 α1t = 0;

(5.3)

β1 β2t − β2 β1t = 0. By a choice of scaling we can take, without loss of generality, α1 α1t + α2 α2t = I; β1 β1t + β2 β2t = I. In order to place this system in the current framework, we set so that it can be expressed as a first-order system dp 0 = A(x; λ)p; A(x; λ) = V (x) − λI dx

p = y, q = y ′, and p = I . 0

p q

,

(5.4)

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Since rank α1 α2 = n, there exists an n-dimensional space of solutions to the left boundary condition α1 α2 p(0) = 0 (i.e., the kernel of α1 α2 ). In particular, we see from (5.3) that we can take t α2 X1 (0, λ) = . −α1t By virtue of the Lagrangian property, we see that X1 (0; λ) is the frame for a Lagrangian subspace. Let X1 (x, λ) be a path of frames created by starting with X1 (0, λ) and evolving according to (5.4). In order to see that X1 (x, λ) continues to be a frame for a Lagrangian subspace for all x ∈ [0, 1], we begin by setting Z(x, λ) = X1 (x, λ)t Y1 (x, λ) − Y1 (x, λ)t X1 (x, λ), and noting that Z(0, λ) = 0. Also (using prime to denote differentiation with respect to x), Z ′ = (X1′ )t Y1 + X1t Y1′ − (Y1′ )t X1 − Y1t X1′ = Y1t Y1 + X1t (V (x)X1 − λX1 ) − (V (x)X1 − λX1 )t X1 − Y1t Y1 = 0, where we have observed X1′ = Y1 , Y1′ = V (x)X1 − λX1 , and have used our assumption that V is symmetric. We see that Z(x, λ) is constant in x, and since Z(0, λ) = 0 this means Z(x, λ) = 0 for all x ∈ [0, 1]. We conclude from Lemma 2.1 that X1 (x, λ) is the frame for a Lagrangian subspace for all x ∈ [0, 1]. As usual, we denote the Lagrangian subspace associated with X1 by ℓ1 . In this case, the second (“target”) Lagrangian subspace is the one associated with the boundary conditions at x = 1. I.e., t β2 X2 , = X2 = −β1t Y2 which is Lagrangian due to our boundary condition and the Lagrangian property. We denote the Lagrangian subspace associated with X2 by ℓ2 . We find that ˜ (x, λ) = −(X1 (x, λ) + iY1 (x, λ))(X1 (x, λ) − iY1 (x, λ))−1 (β t + iβ t )(β t − iβ t )−1 . W 2

1

2

1

For comparison with [31], we observe that (β2t + iβ1t )(β2t − iβ1t )−1 = β2t β2 − β1t β1 + 2i(β2t β1 ),

(5.5)

and this right-hand side, along with the negative sign, is the form that appears in [31] (see p. 4517). In order to verify (5.5), we directly compute (β2 + iβ1 )(β2t − iβ1t ) = β2 β2t + β1 β1t + i(β1 β2t − β2 β1t ) = I, showing that (β2t − iβ1t )−1 = (β2 + iβ1 ).


But then

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(β2t + iβ1t )(β2t − iβ1t )−1 = (β2t + iβ1t )(β2 + iβ1 ) = β2t β2 − β1t β1 + i(β2t β1 + β1t β2 )

= β2t β2 − β1t β1 + 2i(β2t β1 ). (These are the same considerations that led to (3.6).) Turning to the important property of monotoncity, we see that we can consider monotonicity as x varies or as λ varies (or, in principle, we could consider any other path in the x-λ plane). We find that while monotoncity doesn’t generally hold as x varies (except in special cases, such as Dirichlet boundary conditions), it does hold generally as λ varies. In order to see this, we observe that in light of Section 4 we can write ˜ ∂W ˜ Ω, ˜ = iW ∂λ where ∗ −1 ˜ t t −1 ˜ ˜ Ω = 2 (X1 − iY1 ) W2 X1 ∂λ Y1 − Y1 ∂λ X1 (X1 − iY1 ) W2 , and

˜ 2 = (β t + iβ t )(β t − iβ t )−1 . W 2 1 2 1 We see that monotonicity is determined by the matrix A(x, λ) = X1 (x, λ)t ∂λ Y1 (x, λ) − Y1 (x, λ)t ∂λ X1 (x, λ), where our introduction of the notation A(x, λ) is simply for the convenience of the next calculation. Differentiating with respect to x, we find A′ = (X1′ )t ∂λ Y1 + X1t ∂λ Y1′ − (Y1′ )t ∂λ X1 − Y1t ∂λ X1′ = Y1t ∂λ Y1 + X1t ∂λ (V (x)X1 − λX1 ) − (V (x)X1 − λX1 )t ∂λ X1 − Y1t ∂λ Y1 = −X1t X1 . Integrating on [0, x], we find t

t

A(x, λ) = X1 (0, λ) ∂λ Y1 (0, λ) − Y1 (0, λ) ∂λ X1 (0, λ) −

Z

x

X1 (y, λ)t X1 (y, λ)dy.

0

We observe that since X1 (0, λ) = ∂λ Y1 (0, λ) = 0, and so

α2t

and Y1 (0, λ) =

A(x, λ) = −

Z

−α1t ,

we have ∂λ X1 (0, λ) = 0 and

x

X1 (y, λ)t X1 (y, λ)dy,

0

˜ is negative definite, and so for any x ∈ [0, 1], which is negative definite. We conclude that Ω ˜ rotate monotonically in the clockwise direction. as λ increases the eigenvalues of W In order to summarize the result that these observations lead to, we will find it productive to fix s0 > 0 (taken sufficiently small during the analysis) and λ∞ > 0 (taken sufficiently large during the analysis), and to consider the rectangular path Γ = Γ1 ∪ Γ2 ∪ Γ3 ∪ Γ4 , where the paths {Γi }4i=1 are depicted in Figure 1 (taken from [31]).

22


s

✻

H-eigenvalues Γ3

1

s

Γ4

s s

❄

Γ2 ✻

conjugate points

no conjugate points

s✛ s

V (0) − (PR0 Λ0 PR0 )2 , B-eigenvalues ✲ s Γ1

−λ∞

s0

λ✲

0

Figure 1. Schematic of the path Γ = Γ1 ∪ Γ2 ∪ Γ3 ∪ Γ4 . Due to path additivity, Mas(ℓ1 , ℓ2 ; Γ) = Mas(ℓ1 , ℓ2 ; Γ1 ) + Mas(ℓ1 , ℓ2 ; Γ2 ) + Mas(ℓ1 , ℓ2 ; Γ3 ) + Mas(ℓ1 , ℓ2 ; Γ4 ), and by homotopy invariance the Maslov index around any closed path will be 0, so that Mas(ℓ1 , ℓ2 ; Γ) = 0. In order to deal efficiently with our self-adjoint boundary conditions, we adapt an elegant theorem from [8] (see also an earlier version in [42]). Theorem 5.1 (Adapted from [8]). Let α1 and α2 be as described in (5.2)-(5.3). Then there exist three orthogonal (and mutually orthogonal) projection matrices PD (the Dirichlet projection), PN (the Neumann projection), and PR = I − PD − PN (the Robin projection), and an invertible self-adjoint operator Λ acting on the space PR Rn such that the boundary condition α1 y(0) + α2 y ′(0) = 0 can be expressed as PD y(0) = 0 PN y ′(0) = 0 PR y ′(0) = ΛPR y(0). Moreover, PD can be constructed as the projection onto the kernel of α2 and PN can be constructed as the projection onto the kernel of α1 . Construction of the operator Λ is discussed in more detail in [8], and also in [31]. Precisely the same statement holds for β1 and β2 for the boundary condition at x = 1. We also take the following from [31]. Definition 5.2. Let (PD0 , PN0 , PR0 , Λ0 ) denote the projection quadruplet associated with our boundary conditions at x = 0, and let (PD1 , PN1 , PR1 , Λ1 ) denote the projection quadruplet associated with our boundary conditions at x = 1. We denote by B the self-adjoint operator obtained by restricting (PR0 Λ0 PR0 − PR1 Λ1 PR1 ) to the space (ker PD0 ) ∩ (ker PD1 ). The main result of [31] is the following theorem.


23

Theorem 5.3. For system (5.1), let V ∈ C([0, 1]) be a symmetric matrix in Rn×n , and let α1 , α2 , β1 , and β2 be as in (5.2)-(5.3). In addition, let Q denote projection onto the kernel of B, and make the non-degeneracy assumption 0 ∈ / σ(Q(V (0) − (PR0 Λ0 PR0 )2 )Q). Then we have Mor(H) = − Mas(ℓ, ℓ1 ; Γ2 ) + Mor(B) + Mor(Q(V (0) − (PR0 Λ0 PR0 )2 )Q). In order to clarify the nature of the terms Mor(B) + Mor(Q(V (0) − (PR0 Λ0 PR0 )2 )Q), we show here how they easily arise from a naive perturbation argument; for a rigorous treatment, the reader is referred to [31]. First, we observe that a crossing at a point (s, λ) corresponds with a solution to the system −y ′′ + V (x)y = λy α1 y(0) + α2 y ′(0) = 0

(5.6)

β1 y(s) + β2 y ′ (s) = 0. Setting ξ = x/s and u(ξ) = y(x), we obtain the system H(s)u := −u′′ + s2 V (sξ)y = s2 λu 1 α1 u(0) + α2 u′ (0) = 0 (5.7) s 1 β1 u(1) + β2 u′ (1) = 0. s Employing a straightforward energy estimate similar to the proof of Lemma 3.12 in [31], we find that there exists a constant c so that any eigenvalue of (5.6) satisfies c λ(s) ≥ − − kV kL∞ (0,1) . s This means that by taking λ∞ sufficiently large we can ensure that there are no crossings ˜ = s2 λ(s) along the left shelf. In order to understand crossings along the bottom shelf we set λ and take the naive expansions ˜ ˜0 + λ ˜1s + λ ˜ 2 s2 + · · · λ(s) =λ φ(ξ; s) = φ0 (ξ) + φ1 (ξ)s + φ2 (ξ)s2 + · · · ,

(5.8)

˜ where φ(ξ; s) is an eigenfunction corresponding with eigenvalue λ(s). We emphasize that the spectral curves we are looking for will have the corresponding form ˜0 λ ˜1 λ ˜2 + . . . . + +λ s2 s Using Theorem 5.1, we can express the boundary conditions for (5.7) as λ(s) =

PD0 u(0) = 0;

PD1 u(1) = 0;

PN0 u′(0) = 0;

PN1 u′ (1) = 0;

PR0 u′(0) = sΛ0 PR0 u(0);

PR1 u′ (1) = sΛ1 PR1 u(1).

(5.9)

24


Upon substitution of (5.8) into (5.7) with projection boundary conditions, we find that the ˜ 0 φ0 with boundary conditions zeroth order equation is −φ′′0 = λ PD0 φ0 (0) = 0;

PD1 φ0 (1) = 0;

PN0 φ′0 (0) = 0;

PN1 φ′0 (1) = 0;

PR0 φ′0 (0) = 0;

PR1 φ′0 (1) = 0.

Taking an L2 (0, 1) inner product of this equation with φ0 we obtain ′′ ˜ 0 kφ0 k2 2 λ L (0,1) = hφ0 , φ0 i = kφ′0 k2L2 (0,1) − (φ′0 (1), φ0 (1))Rn + (φ′0 (0), φ0 (0))Rn . Observing that (φ′0 (1), φ0 (1))Rn = (φ′0 (1), PD1 φ0 (1) + PN1 φ0 (1) + PR1 φ0 (1))Rn = (PN1 φ′0 (1) + PR1 φ′0 (1), φ0 (1))Rn = 0,

(5.10)

and noting that similarly (φ′0 (0), φ0(0))Rn = 0, we see that ′ 2 ˜ 0 kφ0 k2 2 λ L (0,1) = kφ0 kL2 (0,1) . ˜ 0 ≥ 0, and if λ ˜ 0 > 0 the associated spectral curve will lie in the right Clearly, we must have λ ˜ 0 = 0 then quarter-plane and will not cross into the Maslov Box. On the other hand, if λ ′ kφ0 kL2 (0,1) = 0 and φ0 will be a constant function. In this case, the only requirement on the constant vector φ0 is (from the projection boundary conditions) φ0 ∈ (ker PD0 ) ∩ (ker PD1 ). Let P denote the orthogonal projection onto the space (ker PD0 ) ∩ (ker PD1 ) and set B = P (PR0 Λ0 PR0 − PR1 Λ1 PR1 )P (i.e., B is the matrix defined in (5.2)). Since B is symmetric and maps (ker PD0 ) ∩ (ker PD1 ) to itself, we can create an orthonormal basis for (ker PD0 ) ∩ (ker PD1 ) from the eigenvectors of B. Moreover, let Q denote the orthogonal projection onto ker B (as in the statement of Theorem 5.3) and create an orthonormal basis for ker B from the eigenvectors of Q(V (0) − (PR0 Λ0 PR0 )2 )Q. ˜ 0 = 0. For any φ0 Now, we are ready for the order 1 equation, assuming already that λ ˜ 1 φ0 , selected from our chosen basis for (ker PD0 ) ∩ (ker PD1 ), we obtain the equation −φ′′1 = λ with projection boundary conditions PD0 φ1 (0) = 0;

PD1 φ1 (1) = 0;

PN0 φ′1 (0) PR0 φ′1 (0)

= 0;

PN1 φ′1 (1) = 0;

(5.11)

= Λ0 PR0 φ0 ;

PR1 φ′1 (1) = Λ1 PR1 φ0 .

(5.12)

Upon taking an L2 (0, 1) inner product with φ0 , we find ˜ 1 |φ0 |2 n = −hφ′′ , φ0 i λ 1 R = (PR0 Λ0 PR0 − PR1 Λ1 PR1 )φ0 , φ0

Rn

= Bφ0 , φ0 , Rn


25

˜ 1 will be an eigenvalue using a calculation similar to (5.10). Since φ0 is an eigenvector for B, λ ˜ 1 > 0 this eigenvalue will be in the right half-plane for s small and so won’t cross of B. If λ ˜ 1 < 0 we will obtain a spectral curve with the into the Maslov Box. On the other hand, if λ ˜1 λ asymptotic form λ(s) ∼ s , and (for λ∞ chosen sufficiently large) this will enter the Maslov Box through the bottom shelf. These crossings are precisely counted by the term Mor(B) in Theorem 5.3. ˜ 1 = 0 we need to proceed with the next order of our perturbation argument. Finally, if λ ˜ 0 = 0 and λ ˜ 1 = 0, and that we now restrict to φ0 ∈ ker B. For this step, we note that we have λ ˜ 2 φ0 subject to the conditions Our second order perturbation equation is −φ′′2 + V (0)φ0 = λ PD0 φ2 (0) = 0;

PD1 φ2 (1) = 0;

PN0 φ′2 (0) = 0;

PN1 φ′2 (1) = 0;

PR0 φ′2 (0) = Λ0 PR0 φ1 (0);

PR1 φ′2 (1) = Λ1 PR1 φ1 (1).

We take an L2 (0, 1) inner product of this equation with φ0 and compute ˜ 2 |φ0 |2 n − (V (0)φ0 , φ0 )Rn = −hφ′′ , φ0 i = −(φ′ (1), φ0)Rn + (φ′ (0), φ0)Rn λ R 2 2 2 = (PR0 Λ0 PR0 φ1 (0) − PR1 Λ1 PR1 φ1 (1), φ0)Rn . ˜ 1 = 0 we have φ′′ = 0 with In order to understand this last inner product, we note that for λ 1 boundary conditions (5.11). We can write φ1 (x) = ax + b for constant vectors a, b ∈ Rn , and the conditions PR0 φ′1 (0) = Λ0 PR0 φ0 and PR1 φ′1 (1) = Λ1 PR1 φ0 imply PR0 a = PR0 Λ0 PR0 φ0 and likewise PR1 a = PR1 Λ1 PR1 φ0 . Noting also that φ1 (1) − φ1 (0) = a, we compute (PR0 Λ0 PR0 φ1 (0) − PR1 Λ1 PR1 φ1 (1), φ0)Rn = (φ1 (0), PR0 Λ0 PR0 φ0 )Rn − (φ1 (1), PR1 Λ1 PR1 φ0 )Rn = (φ1 (0) − φ1 (1), PR0 Λ0 PR0 φ0 )Rn = −(a, PR0 Λ0 PR0 φ0 )Rn = −(PR0 a, PR0 Λ0 PR0 φ0 )Rn = −(PR0 Λ0 PR0 φ0 , PR0 Λ0 PR0 φ0 )Rn = −((PR0 Λ0 PR0 )2 φ0 , φ0 )Rn . We see that ˜ 2 |φ0 |2 n = (V (0) − (PR Λ0 PR )2 )φ0 , φ0 λ . 0 0 R Rn

Recalling that we have selected the vectors φ0 to be orthonormal eigenvectors for the matrix Q(V (0) − (PR0 Λ0 PR0 )2 )Q, we see that we have a spectral curve entering the Maslov Box if ˜ 2 is a negative eigenvalue of this matrix. and only if λ ˜ 2 = 0 we can proceed to the next step in the perturbation argument, but In principle, if λ this is the case that we have eliminated by our non-degeneracy assumption. Application 2. In [32], the authors consider Schrödinger equations on R, Hy := −y ′′ + V (x)y = λy, dom(H) = H 1 (R),

(5.13)

where y ∈ Rn and V ∈ C(R) is a symmetric matrix satisfying the following asymptotic conditions:

26


(A1) The limits limx→±∞ V (x) = V± exist, and for all M ∈ R, Z ∞ Z M (1 + |x|)|V (x) − V+ |dx < ∞; (1 + |x|)|V (x) − V− |dx < ∞. −M

−∞

(A2) The eigenvalues of V± are all non-negative. As verified in [32], if λ < 0 then (5.13) will have n linearly independent solutions that decay as x → −∞ and n linearly independent solutions that decay as x → +∞. We express these respectively as µ− n+j (λ)x (r − + E − (x; λ)) φ− n+j (x; λ) = e j j +

µj (λ)x + (rn+1−j + Ej+ (x; λ)), φ+ j (x; λ) = e

with also

µ− − ˜− n+j (λ)x (µ− ∂x φ− n+j (x; λ) = e n+j rj + Ej (x; λ)) +

µj (λ)x + ˜+ ∂x φ+ (µ+ j rn+1−j + Ej (x; λ)), j (x; λ) = e ± ± ˜± for j = 1, 2, . . . , n, where the nature of the µ± j , rj , and Ej (x; λ), Ej (x; λ) are developed in [32], but won’t be necessary for this brief discussion, except for the observation that under assumptions (A1) and (A2)

lim Ej± (x; λ) = 0;

x→±∞

If we create a frame X− (x; λ) = ′

X − (x;λ) Y − (x;λ) −

lim E˜j± (x; λ) = 0.

x→±∞

(5.14)

n − and by taking {φ− n+j }j=1 as the columns of X

n then it is straightforward to verify that X− is a {φ− n+j }j=1 as the respective columns of Y frame for a Lagrangian subspace, which we will denote ℓ− (see [32]). Likewise, we can create + (x;λ) ′ n n + a frame X+ (x; λ) = X by taking {φ+ and {φ+ j }j=1 as the columns of X j }j=1 as the Y + (x;λ) respective columns of Y + . Then X+ is a frame for a Lagrangian subspace, which we will denote ℓ+ . ± In either case, we can view the exponential multipliers eµj x as expansion coefficients, and if we drop these off we retain frames for the same spaces. That is, we can create an alternative frame for ℓ− by taking the expressions rj− + Ej− (x; λ) as the columns of X − and − − ˜− the expressions µ− n+j rj + Ej (x; λ) as the corresponding columns for Y . Using (5.14) we see − that in the limit as x tends to −∞ we obtain the frame R− (λ) = SR − (λ) , where R− = r1− r2− . . . rn− − − − µ− . . . µ− S − (λ) = µ− n+1 r1 n+2 r2 2n rn .

As discussed in [32], R− is the frame for a Lagrangian subspace, which we will denote ℓ− ∞. with frame Proceeding similarly with ℓ+ , we obtain the asymptotic Lagrangian subspace ℓ+ ∞ + , where R+ (λ) = SR + (λ) + . . . r1+ R+ = rn+ rn−1 (5.15) + + + + µ+ S + (λ) = µ+ 1 rn 2 rn−1 . . . µn r1 .


27

˜ (x, λ) in this case as We can now construct W ˜ (x; λ) = −(X − (x; λ) + iY − (x; λ))(X − (x; λ) − iY − (x; λ))−1 (R+ − iS + (λ))(R+ + iS + (λ))−1 . W (5.16) We will be interested in a closed path in the x-λ plane, determined by a sufficiently large value λ∞ . First, if we fix λ = 0 and let x run from −∞ to +∞, we denote the resulting path Γ0 (the right shelf). Next, we let Γ+ denote a path in which λ decreases from 0 to −λ∞ . (We can view this as a path corresponding with the limit x → +∞, but the limiting behavior will be captured by the nature of the Lagrangian subspaces; we refer to this path as the top shelf.) Continuing counterclockwise along our path, we denote by Γ∞ the path obtained by fixing λ = −λ∞ and letting x run from +∞ to −∞ (the left shelf). Finally, we close the path in an asysmptotic sense by taking a final path, Γ− , with λ running from −λ∞ to 0 (viewed as the asymptotic limit as x → +∞; we refer to this as the bottom shelf). The principal result of [32] is as follows. Theorem 5.4. Let V ∈ C(R) be a symmetric real-valued matrix, and suppose (A1) and (A2) hold. Then Mor(H) = − Mas(ℓ− , ℓ+ ∞ ; Γ0 ). Remark 5.5. As discussed in Section 5 of [32], Theorem 5.4 can be extended to the case Hs y := −y ′′ + sy ′ + V (x)y = λy,

(5.17)

for any s ∈ R. This observation—for which the authors are indebted to [7]—allows the application of these methods in the study of spectral stability for traveling wave solutions in Allen-Cahn equations. Appendix In this brief appendix, we verify (P2) (homotopy invariance) for our definition of the Maslov index. We assume L(s, t) = (ℓ1 (s, t), ℓ2(s, t)) is continuous on a cartesian product of closed, bounded intervals I × J = [0, 1] × [a, b], and that L(s, a) = La for all s ∈ I and ˜ (s, t) likewise L(s, b) = Lb for all s ∈ I, for some fixed La , Lb ∈ Λ(n) ×Λ(n). We denote by W the matrix (1.1) associated with L(s, t). It’s straightforward to see from our metric (1.4) that continuity of L implies continuity of the associated frame X(s, t), which in turn (and ˜ (s, t). We know from Theorem II.5.1 along with non-degeneracy) implies continuity of W ˜ (s, t) must vary continuously with s and t. Moreover, we in [39] that the eigenvalues of W see from Theorem II.5.2 in the same reference that these eigenvalues can be tracked as n continuous paths {µk (s, t)}nk=1, which in our case will be restricted to S 1 . For notational convenience, let’s fix s1 , s2 ∈ I suitably close together (in a manner that ˜ 1 (t) := W ˜ (s1 , t) and W ˜ 2 (t) := W ˜ (s2 , t). we make precise below) and set W ˜ 1 (t) and Claim 5.6. Suppose µ(t) and ν(t) are any two continuous eigenvalue paths of W ˜ 2 (t) respectively, with µ(a) = ν(a) and µ(b) = ν(b). Then there exists ǫ > 0 sufficiently W small so that if max |µ(t) − ν(t)| < ǫ t∈J

28


then the spectral flow of µ(t) is the same as the spectral flow of ν(t). Proof. First, suppose neither µ(a) nor µ(b) is -1 (and so the same is true for ν(a) and ν(b)). Take ǫ small enough so that Bǫ (µ(a)) (the ball in C centered at µ(a) with radius ǫ) does not contain -1, and similarly for µ(b). According to our hypothesis, we will have µ(t), ν(t) ∈ Bǫ (µ(t)) for all t ∈ J, and so the spectral flows for µ(t) and ν(t) will both match the flow for Bǫ (µ(t)). Suppose next that µ(a) = −1, but µ(b) does not. In this case, there must be a first time, t∗ , at which Bǫ (µ(t∗ )) does not contain -1. By assumption, we must have ν(t∗ ) ∈ Bǫ (µ(t∗ )), and this allows us to apply an argument on [t∗ , b] similar to our argument on [a, b] in the previous paragraph. A similar argument holds if µ(b) = −1, but µ(a) does not. Last, suppose µ(a) = −1 and µ(b) = −1. If µ(t) and ν(t) are both -1 for all t ∈ J then we’re fininshed. If not, i.e., if there exists a time t∗ at which one or both µ(t∗ ) and ν(t∗ ) is not −1, then we can apply one of the first two cases to complete the proof. ˜ (s, t) are uniformly continuous on I × J. Since I × J is closed and bounded, the matrices W This means that given any ǫ˜ > 0 we can find δ > 0 sufficiently small so that ˜ 1 (t) − W ˜ 2 (t)k < ˜ǫ. |s1 − s2 | < δ =⇒ max kW t∈J

Fix any k ∈ {1, 2, . . . , n}, and set µk1 (t) = µk (s1 , t) and µk2 (t) = µk (s2 , t). By eigenvalue continuity, this means we can take δ small enough to ensure that max |µk1 (t) − µk2 (t)| < ǫ t∈J

for all k ∈ {1, 2, . . . , n}. But since ǫ is arbitrary, we see from our claim that the flow associated with each of these eigenvalue pairs must be the same, and so the spectral flow for ˜ 1 (t) must agree with that of W ˜ 2 (t). W Finally, then, by starting with s1 = 0, and proceeding to s2 = 2δ , s3 = δ etc., we see that the Maslov index will be the same at each step, and that since the steps have fixed length we eventually arrive at s = 1. This concludes the proof of property (P2). Acknowledgements. Y. Latushkin was supported by NSF grant DMS-1067929, by the Research Board and Research Council of the University of Missouri, and by the Simons Foundation. References [1] A. Abbondandolo, Morse Theory for Hamiltonian Systems. Chapman & Hall/CRC Res. Notes Math. 425, Chapman & Hall/CRC, Boca Raton, FL, 2001. [2] V. I. Arnold, Characteristic class entering in quantization conditions, Func. Anal. Appl. 1 (1967) 1 – 14. [3] V. I. Arnold, The Sturm theorems and symplectic geometry, Func. Anal. Appl. 19 (1985) 1–10. [4] F. V. Atkinson, Discrete and Continuous Boundary Problems, in the series Mathematics in Science and Engineering (vol. 8), Academic Press 1964. [5] R. Bott, On the iteration of closed geodesics and the Sturm intersection theory, Comm. Pure Appl. Math. 9 (1956) 171 – 206.


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Mathematics Department, University of Missouri, Columbia, MO 65211, USA Mathematics Department, Indiana University, Bloomington, IN 47405, USA E-mail address: [email protected] E-mail address: [email protected] E-mail address: [email protected]

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