Understanding the Product Property of Maslov Index: A Proof Using Homotopy Axiom

In summary: Hence \mu(\Phi(t)\Lambda(t))=2\mu(\Phi(t))+\mu(\Lambda(t))=2\mu(\Phi(t))+\mu(\Phi(t))=2\mu(\Phi(t))+\mu(\Lambda(t)). In summary, the Maslov index satisfies the product property.
  • #1
MathematicalPhysicist
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How do I show that Maslov index satisfies the next property (product property).

Let [itex]\Lambda : \mathbb{R} / \mathbb{Z} \rightarrow \mathcal{L}(n)[/itex], and [itex]\Psi : \mathbb{R}/\mathbb{Z} \rightarrow Sp(2n)[/itex] be two loops, then if [itex]\mu[/itex] is defined as the Maslov index then [itex] \mu(\Psi \Lambda )= \mu(\Lambda)+2\mu(\Psi)[/itex].

I have seen the proof in Mcduff of the Homotopy axiom of this index thought I am not sure how to use it to show the above.

Any hints?

Thanks, sorry if it's indeed that much easy.
 
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  • #2
Anyone?
 
  • #3
Let [itex]\Lambda\in\mathcal{L}(n)[/itex] be a lagrangian subspace of R2n. Then there exists [itex]\Psi\in U(n)= Sp(2n)\cap O(n)[/itex] such that [itex]\Lambda=\Psi\Lambda_{hor}[/itex], where [itex]\Lambda_{hor} = \mathbb{R}^n\oplus 0 \subset \mathbb{R}^{2n}[/itex]. This is the content of Lemma 2.31. If [itex]\Psi = X+iY[/itex] when considered a complex matrix, then the map [itex]\rho[/itex] in the proof of Theorem 2.35 is defined as [itex]\rho(\Lambda)=det(\Psi)^2[/itex].

Now, if [itex]\Lambda(t)[/itex] is a loop of lagrangians, then there is a corresponding loop [itex]\Psi(t)[/itex] of unitary matrices such that [itex]\Lambda(t)=\Psi(t)\Lambda_{hor}[/itex] and the Maslov index [itex]\mu(\Lambda(t))[/itex] is defined as [itex]deg(\rho(\Lambda(t)))=deg(det(\Psi(t))^2)=2deg(det(\Psi(t)))=2\mu(\Psi(t))[/itex] by definition of the Maslov index of loops of unitary matrices (see the proof of Theorem 2.29).

Now, getting closer to what we want, let [itex]\Lambda(t)=\Psi(t)\Lambda_{hor}[/itex] be a loop of lagrangians and let [itex]\Phi(t)[/itex] be a loop of unitary matrices. Exploiting the computation in the above paragraph + the product property for the Maslov index of matrices, we deduce that [itex]\mu(\Phi(t)\Lambda(t))=\mu(\Phi(t)\Psi(t)\Lambda_{hor})=2\mu(\Phi(t)\Psi(t))=2\mu(\Phi(t))+2\mu(\Psi(t))=2\mu(\Phi(t))+\mu(\Lambda(t))[/itex].

This is the result we want, except we want it for symplectic [itex]\Phi(t)[/itex] and not just unitary. But then the homotopy axiom takes over since any loop of symplectic matrices is homotopic to a loop of unitary ones (this we know since U(n) is the maximal compact subgroup of Sp(2n) (Proposition 2.22) and hence Sp(2n) deformation retracts onto U(n)).
 

1. What is the product property of Maslov index?

The product property of Maslov index is a mathematical concept that relates to the theory of symplectic geometry. It states that the Maslov index of a composition of two paths is equal to the sum of the Maslov indices of the individual paths.

2. How is the product property of Maslov index used in understanding the homotopy axiom?

The product property of Maslov index is an important tool in proving the homotopy axiom, which states that two paths with the same endpoints are homotopic if and only if their Maslov indices are equal. This relationship helps to understand the behavior of the Maslov index under homotopy and how it can be used to classify paths.

3. What is the role of the homotopy axiom in symplectic geometry?

The homotopy axiom plays a crucial role in symplectic geometry as it helps to define the Maslov index, which is a fundamental invariant used in the study of symplectic manifolds. It also allows for the classification of paths and the construction of symplectic structures on manifolds.

4. How is the product property of Maslov index proven using the homotopy axiom?

The proof of the product property of Maslov index using the homotopy axiom involves showing that the Maslov index is well-defined and satisfies the necessary properties, such as the product property. This is done by using the homotopy axiom and the definition of the Maslov index to establish a relationship between the indices of composed paths.

5. Are there any real-world applications of the product property of Maslov index?

The product property of Maslov index has applications in various fields, including physics, engineering, and computer science. It is used in the study of Hamiltonian systems and their stability, as well as in the development of algorithms for solving optimization problems. It also has applications in topological data analysis and the analysis of complex systems.

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