Symplectic Algorithm for Non-Separable Hamiltonian

In summary, the conversation discusses the need for a symplectic algorithm to solve a Hamiltonian system with non-separable variables. The user is having difficulty finding such an algorithm and is seeking more information on how symplectic methods differ from regular numerical methods. They also mention a previous experience working with an integral invariant of Poincare and how it relates to Hamiltonian systems. Finally, they provide an example of a symplectic numerical method for an undamped pendulum and how it preserves the constancy of the Hamiltonian.
  • #1
Heimdall
42
0
Hi,

I have this hamiltonian
[tex] K = \frac{1}{2}(P_1^2 + P_2^2) + P_1 Q_2 - P_2 Q_1 - (\frac{1-\mu}{R_1}+\frac{\mu}{R_2})[/tex]
(see this link if there is any latex problem)

with non separable variables.

I am looking for a symplectic algorithm (runge kutta would be good) to solve the correspondant first order equations, but I can't find it on the internet.

moreover it seems difficult and I don't understand enough what is a symplectic algorithm to code do it by myself..

thx a lot.
 
Last edited:
Physics news on Phys.org
  • #2
Well, for starters are these those:

[tex]\frac{dP_1}{dt}=\frac{\partial K}{\partial P_2},\quad \frac{dP_2}{dt}=-\frac{\partial K}{\partial P_1}[/tex]

[tex]\frac{dQ_1}{dt}=\frac{\partial K}{\partial Q_2},\quad \frac{dQ_2}{dt}=-\frac{\partial K}{\partial Q_1}[/tex]

[tex]\frac{dR_1}{dt}=\frac{\partial K}{\partial R_2},\quad \frac{dR_2}{dt}=-\frac{\partial K}{\partial R_1}[/tex]

What then are the initial conditions?
 
  • #3
Know what, I'm just gonna' run with this. Someone in the group just recently said, "you don't do mathematics by just staring at the problem and hope it comes to you".

Anyway, Mathematica has a method called "SymplecticPartitionedRungeKutta". I shall wish to try this method once I figure out how symplectic methods differ from just regular numerical methods as I advocate learning the technique (in general) before using it in some math package. Perhaps I should just start with a pendulum first. Someone else said, "sometimes you have to take two steps backwards in order to move forwards". Might take a while.:smile:
 
  • #4
For the record my equations posted above are totally incorrect and stems from my lack of familiarity with Hamilitoian systems in general. But that's ok cus' I ain't staring and I'm not standing still. So it's 3:00a and I need to get up in a few hours to go to work. Well, that would mean I'm already sleeping but I'm fiddling with this instead. Whatever. Anyway I've learned that the Hamiltonian is conserved in Hamiltonian systems and that is the origin of the problem to seek symplectic methods in their numerical solution: most numerical methods do not preserve the constancy of the hamiltonian. Methods which do are called symplectic. Anyway, I have some references to work with and will pick it up later. Think I'll try and get in an hour or 2 of sleep. :zzz:
 
  • #5
Some time ago I worked on what I considered a very beautiful problem (well ugly if you're a . . . nevermind). It's called an integral invariant of Poincare'. At the time, I didn't understand the connection to Hamiltonian systems. If you allow a set of initial points to evolve according to a Hamiltonian flow (run all the Hamiltonian differential equations for some time on a set of initial points and then compare the initial set of points to the set of final points), then a certain "measure" of the points is preserved in the flow. This is compactly expressed by the following integral invariant:

[tex]\sum_{i=n}\oint_{\omega_i} p_idq_i=C[/tex]

That is, the sum of areas projected onto the set of [itex](p_i,q_i)[/itex] planes is constant. This is shown graphically in the attached plot. I wish to verify this with a real set of Hamiltonian equations but I digress.

Well, symplectic numerical methods are designed to preserve this measure. Consider first the undamped pendulum:

[tex]\frac{dq}{dt}=p[/tex]

[tex]\frac{dp}{dt}=-Sin(q)[/tex]

The Hamiltonian function is:

[tex]H(q,p)=1/2 p^2-Cos(q)[/tex]

The standard (non-symplectic) Euler method for this system would be:

[tex]q_{k+1}=q_k+hp_k[/tex]

[tex]p_{k+1}=p_k-hSin(q_k)[/tex]

A phase portrait (q vs. p) for 100 seconds is shown in the second plot. It's dissapating and not reflective of the actual dynamics of an undamped pendulum. A plot of the hamiltonian function would reveal a curve with a non-constant slope.

We can slightly modify this method and convert it to a symplectic form as follows:

[tex]q_{k+1}=q_k+hp_{k+1}[/tex]

[tex]p_{k+1}=p_k-hSin(q_k)[/tex]

The phase-portrait of this numerical simulation for 100 seconds is shown in the 3rd plot and reflects the actual dynamics of an undamped pendulum (back and forth and never loosing energy, that is the energy remains constant and reflects the invariant measure of Hamiltonian systems. A plot of the hamiltonian function for this simulation would be a straight line with zero slope. (can we get two more spaces for plots?)
 

Attachments

  • Invariant.gif
    Invariant.gif
    2 KB · Views: 602
  • non-symplectic Euler.JPG
    non-symplectic Euler.JPG
    21.8 KB · Views: 519
  • symplectic euler.JPG
    symplectic euler.JPG
    7.2 KB · Views: 541
Last edited:

Related to Symplectic Algorithm for Non-Separable Hamiltonian

1. What is the Symplectic Algorithm for Non-Separable Hamiltonian?

The Symplectic Algorithm for Non-Separable Hamiltonian is a numerical method used to solve the equations of motion in classical mechanics for systems with non-separable Hamiltonians. It is based on the symplectic geometry and preserves the symplectic structure of the system, resulting in accurate and stable solutions.

2. How does the Symplectic Algorithm work?

The Symplectic Algorithm works by breaking down the system into smaller, independent sub-problems that can be solved using conventional techniques. It then combines these solutions in a way that preserves the symplectic structure of the system. This allows for efficient and accurate numerical computations of the equations of motion.

3. What are the advantages of using the Symplectic Algorithm?

One of the main advantages of using the Symplectic Algorithm is its ability to accurately preserve the symplectic structure of the system, resulting in long-term stable solutions. It also has a high computational efficiency, making it suitable for large-scale simulations. Additionally, it is relatively easy to implement and can handle a wide range of non-separable Hamiltonian systems.

4. Are there any limitations to the Symplectic Algorithm?

While the Symplectic Algorithm has many benefits, it also has some limitations. It is primarily designed for conservative systems and may not work well for systems with dissipation or external forces. It also requires knowledge of the system's Hamiltonian, which may not always be available in practical applications.

5. What are some applications of the Symplectic Algorithm?

The Symplectic Algorithm has various applications in physics, engineering, and other fields where classical mechanics is applicable. It is commonly used in molecular dynamics simulations, celestial mechanics, and particle accelerators, among others. It is also used in the design and analysis of spacecraft trajectories and in the study of chaotic systems.

Similar threads

  • Differential Equations
Replies
2
Views
3K
  • Advanced Physics Homework Help
Replies
2
Views
584
  • Quantum Physics
Replies
2
Views
755
  • Calculus and Beyond Homework Help
Replies
3
Views
612
  • Advanced Physics Homework Help
Replies
3
Views
647
  • Advanced Physics Homework Help
Replies
3
Views
3K
  • Classical Physics
Replies
1
Views
682
Replies
3
Views
2K
  • High Energy, Nuclear, Particle Physics
Replies
1
Views
1K
  • Advanced Physics Homework Help
Replies
1
Views
1K
Back
Top