steady.band {rootSolve} | R Documentation |
Estimates the steady-state condition for a system of ordinary differential equations.
Assumes a banded jacobian matrix, but does not rearrange the state variables. This is in contrast to steady.1D
.
Suitable for 1-D models that include transport only between adjacent layers and that model only one species
steady.band(y, time=0, func, parms=NULL, nspec=NULL, bandup=nspec, banddown=nspec, ...)
y |
the initial guess of (state) values for the ODE system, a vector. If y has a name attribute, the names will be used to label the output matrix. |
time |
time for which steady-state is wanted; the default is time =0 |
func |
either an R-function that computes the values of the derivatives in the ode system (the model defininition) at time time ,
or a character string giving the name of a compiled function in a dynamically loaded shared library.
If func is an R-function, it must be defined as:
yprime = func(t, y, parms,...) . t is the current time point
in the integration, y is the current estimate of the variables
in the ODE system. If the initial values y has a names
attribute, the names will be available inside func . parms is
a vector or list of parameters; ... (optional) are any other arguments passed to the function.
The return value of func should be a list, whose first element is a
vector containing the derivatives of y with respect to
time , and whose next elements are global values whose steady-state value is also required. |
parms |
parameters passed to func |
nspec |
the number of *species* (components) in the model. |
bandup |
the number of nonzero bands above the Jacobian diagonal |
banddown |
the number of nonzero bands below the Jacobian diagonal |
... |
additional arguments passed to function stode |
This is the method of choice for single-species 1-D models.
For multi-species 1-D models, this method can only be used if the state variables are arranged per box, per species (e.g. A[1],B[1],A[2],B[2],A[3],B[3],.... for species A, B).
Usually a 1-D *model* function will have the species arranged as A[1],A[2],A[3],....B[1],B[2],B[3],....
in this case, use steady.1D
A list containing
y |
A vector with the state variable values from the last iteration during estimation of steady-state condition of the system of equations.
If y has a names attribute, it will be used to label the output values. |
... |
the number of "global" values returned |
The output will have the attribute steady
, which returns TRUE, if steady-state has been reached and the attribute
precis
with the precision attained during each iteration.
Karline Soetaert <k.soetaert@nioo.knaw.nl>
stode
for the additional options
steady
, for solving steady-state when the jacobian matrix is full
steady.1D
, for solving steady-state of 1-D models
steady.2D
, for solving steady-state of 2-D models
############################################################# # 1000 simultaneous equations, solved 6 times for different # values of parameter "decay" ############################################################# model <- function (time,OC,parms,decay) { # model of particles (OC) that sink out of the water and decay # exponentially declining sinking rate, maximal 100 m/day sink <- 100*exp(-0.01*dist) # boundary flux; upper boundary=imposed concentration (100) Flux <- sink * c(100 ,OC) # Rate of change= Flux gradient and first-order consumption dOC <- -diff(Flux)/dx - decay*OC list(dOC,maxC=max(OC)) } dx <- 1 # thickness of boxes dist <- seq(0,1000,by=dx) # water depth at each modeled box interface ss <- NULL for (decay in seq(from=0.1,to=1.1,by=0.2)) ss <- cbind(ss,steady.band(runif(1000),func=model, parms=NULL,nspec=1,decay=decay)$y) matplot(ss,1:1000,type="l",lwd=2,main="steady.band", ylim=c(1000,0), ylab="water depth, m", xlab="concentration of sinking particles") legend("bottomright",legend=seq(from=0.1,to=1.1,by=0.2),lty=1:10, title="decay rate",col=1:10,lwd=2) ############################################################# # 5001 simultaneous equations: solve # dy/dt = 0 = d2y/dx2 + 1/x*dy/dx + (1-1/(4x^2)y - sqrx(x)*cos(x), # over the interval [1,6] # with boundary conditions: y(1)=1, y(6)=-0.5 ############################################################# derivs <- function(t,y,parms, x,dx,N,y1,y6) { # Numerical approximation of derivates: # d2y/dx2 = (yi+1-2yi+yi-1)/dx^2 d2y <- (c(y[-1],y6) -2*y + c(y1,y[-N])) /dx/dx # dy/dx = (yi+1-yi-1)/(2dx) dy <- (c(y[-1],y6) - c(y1,y[-N])) /2/dx res <- d2y+dy/x+(1-1/(4*x*x))*y-sqrt(x)*cos(x) return(list(res)) } dx <- 0.001 x <- seq(1,6,by=dx) N <- length(x) y <- steady.band(y=rep(1,N),time=0,func=derivs,x=x,dx=dx, N=N,y1=1,y6=-0.5,nspec=1)$y plot(x,y,type="l",main="5001 nonlinear equations - banded Jacobian") # add the analytic solution for comparison: xx <- seq(1,6,by=0.1) ana <- 0.0588713*cos(xx)/sqrt(xx)+1/4*sqrt(xx)*cos(xx)+ 0.740071*sin(xx)/sqrt(xx)+1/4*xx^(3/2)*sin(xx) points(xx,ana) legend("topright",pch=c(NA,1),lty=c(1,NA),c("numeric","analytic"))