Numeric multivariable ode solver in Sage?

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Hi,

Another option is to use the function desolve_odeint (from version 4.6 on, see the docs on desolve_odeint?), which uses the odeint solver internally, but takes as an input symbolic funcions . For instance, to integrate the Lorenz attractor, you would do

 sage: x,y,z=var('x,y,z') # Declare the variables 
 sage: lorenz=[10.0*(y-x),x*(28.0-z)-y,x*y-(8.0/3.0)*z]
 sage: times=srange(0,50.05,0.05) # Integration Time 
 sage: ics=[0,1,1] # and initial conditions
 sage: sol=desolve_odeint(lorenz,ics,times,[x,y,z]) #integrate

to plot the attractor, simply:

sage: line3d(sol)

I do not know what you mean exactly by the double pendulum, but here it is something similar:

sage: theta1,theta2,x1,x2=var('theta1,theta2,x1,x2')
sage: dpendulum=[x1,x2,-sin(theta1)-0.1*sin(theta2),-sin(theta2)+0.1*sin(theta1)]
sage: # Time and initial conditions
sage: times=srange(0,50.05,0.05) 
sage: ics=[1,1,0,0] 
sage: sol=desolve_odeint(dpendulum,ics,times,[theta1,theta2,x1,x2])

and a nice plot is here

sage: graphics_array([line(zip(sol[:,0],sol[:,2])),line(zip(sol[:,1],sol[:,3]))])

Joaquim Puig

Most of Sage's numerics is done via Numpy/Scipy which is included in Sage and usable from the Sage Notebook. To solve an ODE using Scipy first reduce your problem to a system of 1st degree ODEs: $u'(x,y,t) = f(t,x,y,u)$. Then, follow the instructions on the relevant Scipy documentation: Ordinary differential equations (odeint). The key components to solving the numerical ODE is (1) writing a Python function for the right-hand side, $f$, (2) writing a function for its Jacobian, $J_f$, (3) and calling the scipy.integrate.odeint function.

Consult the scipy.integrate.odeint documentation for more information on its use. You can read this documentation by entering

sage: from scipy.integrate import odeint
sage: odeint?

Finally, I providing the Jacobian for your problem is an optional argument but it helps with convergence.