pracma (version 1.7.9)

ode23: Non-stiff (and stiff) ODE solvers

Description

Runge-Kutta (2, 3)-method with variable step size, resp. (4,5)-method with Dormand-Price coefficients, or (7,8)-pairs with Fehlberg coefficients. The function f(t, y) has to return the derivative as a column vector.

Usage

ode23(f, t0, tfinal, y0, ..., rtol = 1e-3, atol = 1e-6)
ode23s(f, t0, tfinal, y0, jac = NULL, ...,
            rtol = 1e-03, atol = 1e-06, hmax = 0.0)
ode45(f, t0, tfinal, y0, ..., atol = 1e-6, hmax = 0.0)
ode78(f, t0, tfinal, y0, ..., atol = 1e-6, hmax = 0.0)

Arguments

f

function in the differential equation $y' = f(x, y)$; defined as a function $R \times R^m \rightarrow R^m$, where $m$ is the number of equations.

t0, tfinal

start and end points of the interval.

y0

starting values as column vector; for $m$ equations u0 needs to be a vector of length m.

jac

jacobian of f as a function of x alone; if not specified, a finite difference approximation will be used.

rtol, atol

relative and absolute tolerance.

hmax

maximal step size, default is (tfinal - t0)/10.

...

Additional parameters to be passed to the function.

Value

• List with components t for grid (or `time') points between t0 and tfinal, and y an n-by-m matrix with solution variables in columns, i.e. each row contains one time stamp.

Details

ode23 is an integration method for systems of ordinary differential equations using second and third order Runge-Kutta-Fehlberg formulas with automatic step-size.

ode23s can be used to solve a stiff system of ordinary differential equations, based on a modified Rosenbrock triple method of order (2,3); See section 4.1 in [Shampine and Reichelt].

ode45 implements Dormand-Prince (4,5) pair that minimizes the local truncation error in the 5th-order estimate which is what is used to step forward (local extrapolation). Generally it produces more accurate results and costs roughly the same computationally.

ode78 implements Fehlberg's (7,8) pair and is a 7th-order accurate integrator therefore the local error normally expected is O(h^8). However, because this particular implementation uses the 8th-order estimate for xout (i.e. local extrapolation) moving forward with the 8th-order estimate will yield errors on the order of O(h^9). It requires 13 function evaluations per integration step.

References

Ascher, U. M., and L. R. Petzold (1998). Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations. SIAM.

L.F. Shampine and M.W. Reichelt (1997). The MATLAB ODE Suite. SIAM Journal on Scientific Computing, Vol. 18, pp. 1-22.

Moler, C. (2004). Numerical Computing with Matlab. Revised Reprint, SIAM. http://www.mathworks.com/moler/chapters.html.

https://sites.google.com/site/comperem/home/ode_solvers

See Also

rk4sys, deval

Examples

##  Example1: Three-body problem
f <- function(t, y)
		as.matrix(c(y[2]*y[3], -y[1]*y[3], 0.51*y[1]*y[2]))
y0 <- as.matrix(c(0, 1, 1))
t0 <- 0; tf <- 20
sol <- ode23(f, t0, tf, y0, rtol=1e-5, atol=1e-10)
matplot(sol$t, sol$y, type = "l", lty = 1, lwd = c(2, 1, 1),
        col = c("darkred", "darkblue", "darkgreen"),
        xlab = "Time [min]", ylab= "",
        main = "Three-body Problem")
grid()

##  Example2: Van der Pol Equation
#   x'' + (x^2 - 1) x' + x = 0
f <- function(t, x)
        as.matrix(c(x[1] * (1 - x[2]^2) -x[2], x[1]))
t0 <- 0; tf <- 20
x0 <- as.matrix(c(0, 0.25))
sol <- ode23(f, t0, tf, x0)
plot(c(0, 20), c(-3, 3), type = "n",
     xlab = "Time", ylab = "", main = "Van der Pol Equation")
lines(sol$t, sol$y[, 1], col = "blue")
lines(sol$t, sol$y[, 2], col = "darkgreen")
grid()

##  Example3: Van der Pol as stiff equation
vdP  <- function(t,y) as.matrix(c(y[2], 10*(1-y[1]^2)*y[2]-y[1]))
ajax <- function(t, y)
            matrix(c(0, 1, -20*y[1]*y[2]-1, 10*(1-y[1]^2)), 2,2, byrow = TRUE)
sol <- ode23s(vdP, t0, tf, c(2, 0), jac = ajax, hmax = 1.0)
plot(sol$t, sol$y[, 1], col = "blue")
lines(sol$t, sol$y[, 1], col = "blue")
lines(sol$t, sol$y[, 2]/8, col = "red", lwd = 2)
grid()

##  Example4: pendulum
m = 1.0;  l = 1.0   # [kg] resp. [m]
g = 9.81; b = 0.7   # [m/s^2] resp. [N s/m]
fp = function(t, x)
        c( x[2] , 1/(1/3*m*l^2)*(-b*x[2]-m*g*l/2*sin(x[1])) )
t0 <- 0.0; tf <- 5.0; hmax = 0.1
y0 = c(30*pi/180, 0.0)
sol = ode45(fp, t0, tf, y0, hmax = 0.1)
matplot(sol$t, sol$y, type = "l", lty = 1)
grid()

##  Example: enforced pendulum
g <- 9.81
L <- 1.0; Y <- 0.25; w <- 2.5
f <- function(t, y) {
    as.matrix(c(y[2], -g/L * sin(y[1]) + w^2/L * Y * cos(y[1]) * sin(w*t)))
}
y0 <- as.matrix(c(0, 0))
sol <- ode78(f, 0.0, 60.0, y0, hmax = 0.05)
plot(sol$t, sol$y[, 1], type="l", col="blue")
grid()