odeint

Is it possible to solve a time delay differential equations using C++ Boost - odeint library ? For an instance below equation: x'(t) = r*x(t)*(1 - x(t-tau)), where tau is a constant value for time delay. Yes, you can. But odeint is not explicitly designed for DDEs. There are two possibilities to solve DDEs with odeint: You consider x and its discretized history as dependend variables and use directly the steppers. You consider only x as dependent variable and pass the history with the system function (your r.h.s.). But in this case you should only use steppers which evaluate the state at

I want to solve a stiff ode with odeint. I was following this (rosenbrock4_dense_output stepper) but, my function can grow pretty quickly so I want to stop the integration if x(t)>xMAX. In this question , they have a solution for it but since I'm new with c++ I don't know how to implement this when using a rosenbrock4_dense_output stepper. I would like to see how to write this specifically for rosenbrock4_dense_output stepper. Currently, this is not easily possible with odeint. If you can use the range library here you can combine a for_each and a find_if algorithm. Otherwise you need to write

I have two numpy arrays: 9x9 and 9x1. I'd like to solve the differential equation at discrete time points, but am having trouble getting ODEInt to work. I do am unsure if I'm even doing the right thing. With Mathematica, the equation is: Solution = {A[t]} /. NDSolve[{A'[t] == Ab.A[t] && A[0] == A0}, {A[t]}, {t, 0, .5}, MaxSteps -> \[Infinity]]; time = 0.25; increment = 0.05; MA = Table[Solution, {t, 0, time, increment}]; Where Ab is the 9x9 matrix, A0 is the 9x1 matrix (initial). Here, I solve for time and life is good. In Python implementation I have the following code which gives me the

During my work, it would be a requirement for me to use Eigen::VectorXcd as state type, to solve a huge linear ODE system. In that project, the matrix in the ODE system is sparse. Multiplying a it with a dense vector can be computed in parallel in a simple way using Eigen. However, I have faced some problems in that situation that I will tell in details below. Currently I am applying a not-so efficient solution, namely, I use arma::cx_vec as state type, and declare the matrix corresponding the ode arma::cx_mat, which is a dense matrix type. Unfortunately, sparse-matrix-dense vector

Can anyone provide an example of providing a jacobian to a integrate.odeint function in SciPy?. I try to run this code from SciPy tutorial odeint example but seems that Dfunc (gradient) is never called. from numpy import * # added from scipy.integrate import odeint from scipy.special import gamma, airy y1_0 = 1.0/3**(2.0/3.0)/gamma(2.0/3.0) y0_0 = -1.0/3**(1.0/3.0)/gamma(1.0/3.0) y0 = [y0_0, y1_0] def func(y, t): return [t*y[1],y[0]] def gradient(y,t): print 'jacobian' # added return [[0,t],[1,0]] x = arange(0,4.0, 0.01) t = x ychk = airy(x)[0] y = odeint(func, y0, t) y2 = odeint(func, y0, t,