Double tangent bundle: Difference between revisions

From formulasearchengine
Jump to navigation Jump to search
en>Josetomas2008
m Bibliography
 
en>Dthomsen8
m clean up, typos fixed: , → , using AWB
Line 1: Line 1:
by Nas, is very fitting and the film agrees with it. It is very easy to customize plugins according to the needs of a particular business. The Word - Press Dashboard : an administrative management tool that supports FTP content upload  2. Word - Press also provides protection against spamming, as security is a measure issue. It's as simple as hiring a Wordpress plugin developer or learning how to create what is needed. <br><br>As you know today Word - Press has turn out to be a tremendously popular open source publishing and blogging display place. The higher your blog ranks on search engines, the more likely people will find your online marketing site. You are able to set them within your theme options and so they aid the search engine to get a suitable title and description for the pages that get indexed by Google. It primarily lays emphasis on improving the search engine results of your website whenever a related query is typed in the search box. This can be done by using a popular layout format and your unique Word - Press design can be achieved in other elements of the blog. <br><br>If you adored this article therefore you would like to receive more info pertaining to [http://www.shortlinki.com/wordpress_dropbox_backup_9267297 backup plugin] kindly visit our own page. Usually, Wordpress owners selling the ad space on monthly basis and this means a residual income source. After sending these details, your Word - Press blog will be setup within a few days. Use this section to change many formatting elements. These frequent updates have created menace in the task of optimization. After that the developer adds the unordered list for navigations. <br><br>If all else fails, please leave a comment on this post with the issue(s) you're having and help will be on the way. The SEOPressor Word - Press SEO Plugin works by analysing each page and post against your chosen keyword (or keyword phrase) and giving a score, with instructions on how to improve it. Exacting subjects in reality must be accumulated in head ahead of planning on your high quality theme. Can you imagine where you would be now if someone in your family bought an original painting from van Gogh during his lifetime. This includes enriching the content with proper key words, tactfully defining the tags and URL. <br><br>A sitemap is useful for enabling web spiders and also on rare occasions clients, too, to more easily and navigate your website. I don't want that for my visitors and I'm quite sure they don't either. The days of spending a lot of time and money to have a website built are long gone. Working with a Word - Press blog and the appropriate cost-free Word - Press theme, you can get a professional internet site up and published in no time at all. You can check out the statistics of page of views for your web pages using free tools that are available on the internet.
'''Variational integrators''' are [[Numerical ordinary differential equations|numerical integrators]] for [[Hamiltonian system]]s derived from the [[Euler-Lagrange equations]] of a discretized [[Hamilton's principle]]. Variational integrators are momentum-preserving and [[Symplectic integrator|symplectic]].
 
==Derivation of a Simple Variational Integrator==
 
Consider a mechanical system with a single particle degree of freedom described by the Lagrangian
 
: <math>L(t,q,v) = \frac{1}{2} m v^2 - V(q)</math>,
 
where <math>m</math> is the mass of the particle, and <math>V</math> is a potential. To construct a variational integrator for this system, we begin by forming the '''discrete Lagrangian'''.  The discrete Lagrangian approximates the action for the system over a short time interval:
 
: <math>L_d\left(t_0, t_1, q_0, q_1\right) = \frac{t_1 - t_0}{2} \left[ L\left(t_0, q_0, \frac{q_1-q_0}{t_1-t_0}\right) + L\left(t_1, q_1, \frac{q_1-q_0}{t_1-t_0}\right) \right] \approx \int_{t_0}^{t_1} dt\, L(t, q(t), v(t)) </math>.
 
Here we have chosen to approximate the time integral using the trapezoid method, and we use a linear approximation to the trajectory,
 
: <math>q(t) \approx \frac{q_1 - q_0}{t_1-t_0} \left( t - t_0 \right) + q_0</math>
 
between <math>t_0</math> and <math>t_1</math>, resulting in a constant velocity <math>v \approx \left(q_1 - q_0 \right)/\left(t_1 - t_0 \right)</math>. Different choices for the approximation to the trajectory and the time integral give different variational integrators.  The order of accuracy of the integrator is controlled by the accuracy of our approximation to the action; since
 
: <math>L_d\left( t_0, t_1, q_0, q_1 \right) = \int_{t_0}^{t_1} dt\, L(t,q(t),v(t)) + \mathcal{O}\left(t_1 - t_0\right)^3</math>,
 
our integrator will be second-order accurate.
 
Evolution equations for the discrete system can be derived from a stationary-action principle.  The discrete action over an extended time interval is a sum of discrete Lagrangians over many sub-intervals:
 
: <math>S_d = L_d\left(t_0, t_1, q_0, q_1 \right) + L_d\left( t_1, t_2, q_1, q_2 \right) + \ldots</math>.
 
The principle of stationary action states that the action is stationary with respect to variations of coordinates that leave the endpoints of the trajectory fixed. So, varying the coordinate <math>q_1</math>, we have
 
: <math>\frac{\partial S_d}{\partial q_1} = 0 = \frac{\partial}{\partial q_1} L_d\left(t_0, t_1, q_0, q_1 \right) + \frac{\partial}{\partial q_1} L_d\left( t_1, t_2, q_1, q_2 \right)</math>.
 
Given an initial condition <math>(q_0, q_1)</math>, and a sequence of times <math>(t_0,t_1,t_2)</math> this provides a relation that can be solved for <math>q_2</math>. The solution is
 
: <math>q_2 = q_1 + \frac{t_2 - t_1}{t_1 - t_0} \left( q_1 - q_0 \right) - \frac{\left( t_2 - t_0 \right) \left( t_2 - t_1 \right)}{2m} \frac{d}{dq_1}V\left(q_1\right)</math>.
 
We can write this in a simpler form if we define the discrete momenta,
 
: <math>p_0 \equiv -\frac{\partial}{\partial q_0} L_d\left( t_0, t_1, q_0, q_1 \right)</math>
 
and
 
: <math>p_1 \equiv \frac{\partial}{\partial q_1} L_d\left( t_0, t_1, q_0, q_1 \right)</math>.
 
 
Given an initial condition <math>(q_0,p_0)</math>, the stationary action condition is equivalent to solving the first of these equations for <math>q_1</math>, and then determining <math>p_1</math> using the second equation.  This evolution scheme gives
 
: <math>q_1 = q_0 + \frac{t_1 - t_0}{m} p_0 - \frac{\left(t_1 - t_0\right)^2}{2m} \frac{d}{dq_0} V\left( q_0 \right)</math>
 
and  
 
: <math>p_1 = m \frac{q_1 - q_0}{t_1 - t_0} - \frac{t_1 - t_0}{2} \frac{d}{dq_1} V\left(q_1\right)</math>.
 
This is a [[leapfrog integration]] scheme for the system; two steps of this evolution are equivalent to the formula above for <math>q_2</math>
 
==References==
* E. Hairer, C. Lubich, and G. Wanner. ''Geometric Numerical Integration''. Springer, 2002.
* J. Marsden and M. West. ''Discrete mechanics and variational integrators''. Acta Numerica, 2001, pp. 357–514.
 
 
 
 
[[Category:Numerical differential equations]]

Revision as of 17:13, 1 June 2013

Variational integrators are numerical integrators for Hamiltonian systems derived from the Euler-Lagrange equations of a discretized Hamilton's principle. Variational integrators are momentum-preserving and symplectic.

Derivation of a Simple Variational Integrator

Consider a mechanical system with a single particle degree of freedom described by the Lagrangian

,

where is the mass of the particle, and is a potential. To construct a variational integrator for this system, we begin by forming the discrete Lagrangian. The discrete Lagrangian approximates the action for the system over a short time interval:

.

Here we have chosen to approximate the time integral using the trapezoid method, and we use a linear approximation to the trajectory,

between and , resulting in a constant velocity . Different choices for the approximation to the trajectory and the time integral give different variational integrators. The order of accuracy of the integrator is controlled by the accuracy of our approximation to the action; since

,

our integrator will be second-order accurate.

Evolution equations for the discrete system can be derived from a stationary-action principle. The discrete action over an extended time interval is a sum of discrete Lagrangians over many sub-intervals:

.

The principle of stationary action states that the action is stationary with respect to variations of coordinates that leave the endpoints of the trajectory fixed. So, varying the coordinate , we have

.

Given an initial condition , and a sequence of times this provides a relation that can be solved for . The solution is

.

We can write this in a simpler form if we define the discrete momenta,

and

.


Given an initial condition , the stationary action condition is equivalent to solving the first of these equations for , and then determining using the second equation. This evolution scheme gives

and

.

This is a leapfrog integration scheme for the system; two steps of this evolution are equivalent to the formula above for

References

  • E. Hairer, C. Lubich, and G. Wanner. Geometric Numerical Integration. Springer, 2002.
  • J. Marsden and M. West. Discrete mechanics and variational integrators. Acta Numerica, 2001, pp. 357–514.