Estimates the steady-state condition for a system of ordinary differential equations (ODE) in the form: $$dy/dt = f(t,y)$$
and where the jacobian matrix df/dy has an arbitrary sparse structure.
Uses a newton-raphson method, implemented in Fortran.
The system of ODE's is written as an R function or defined in compiled code that has been dynamically loaded.
stodes(y, time = 0, func, parms = NULL, rtol = 1e-6, atol = 1e-8,
ctol = 1e-8, sparsetype = "sparseint", verbose = FALSE,
nnz = NULL, inz = NULL, lrw = NULL, ngp = NULL,
positive = FALSE, maxiter = 100, ynames = TRUE,
dllname = NULL, initfunc = dllname, initpar = parms,
rpar = NULL, ipar = NULL, nout = 0, outnames = NULL,
forcings = NULL, initforc = NULL, fcontrol = NULL,
spmethod = "yale", control = NULL, times = time, ...)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 for which steady-state is wanted; the default is
times=0.
(note- since version 1.7, 'times' has been added as an alias to 'time').
either a user-supplied function that computes the values of the
derivatives in the ode system (the model definition) at time
time, or a character string giving the name of a
compiled function in a dynamically loaded shared library.
If func is a user-supplied function, it must be called as:
yprime = func(t, y, parms). t is the time point
at which the steady-state is wanted, 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 of parameters (which may have a names attribute).
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 (possibly with a
names attribute) are global values that are required as
output.
The derivatives
should be specified in the same order as the state variables y.
If func is a string, then dllname must give the name
of the shared library (without extension) which must be loaded
before stodes() is called. see Details for more information.
other parameters passed to func.
relative error tolerance, either a scalar or a vector, one value for each y.
absolute error tolerance, either a scalar or a vector, one value for each y.
if between two iterations, the maximal change in y is less than this amount, steady-state is assumed to be reached.
the sparsity structure of the Jacobian, one of "sparseint" or "sparseusr", "sparsejan", ..., The sparsity can be estimated internally by stodes (first option) or given by the user (last two). See details.
if TRUE: full output to the screen, e.g. will output the steady-state settings.
the number of nonzero elements in the sparse Jacobian (if
this is unknown, use an estimate); If NULL, a guess will be made, and
if not sufficient, stodes will return with a message indicating
the size actually required.
If a solution is found, the minimal value of nnz actually required
is returned by the solver (1st element of attribute dims).
if sparsetype equal to "sparseusr", a two-columned matrix
with the (row, column) indices to the nonzero elements in the sparse
Jacobian. If sparsetype = "sparsejan", a vecotr with the elements
ian followed by he elements jan as used in the stodes code. See details.
In all other cases, ignored.
If inz is NULL, the sparsity will be determined by stodes.
the length of the work array of the solver; due to the sparsicity,
this cannot be readily predicted. If NULL, a guess will be made, and
if not sufficient, stodes will return with a message indicating
that lrw should be increased. Therefore, some experimentation may be
necessary to estimate the value of lrw.
If a solution is found, the minimal value of lrw actually required
is returned by the solver (3rd element of attribute dims).
In case of an error induced by a too small value of lrw, its value
can be assessed by the attributes()$dims value.
number of groups of independent state variables. Due to the
sparsicity, this cannot be readily predicted. If NULL, a guess will be
made, and if not sufficient, stodes will return with a message
indicating the size actually required. Therefore, some experimentation
may be necessary to estimate the value of ngp
If a solution is found, the minimal value of ngp actually required
is returned by the solver (2nd element of attribute dims.
either a logical or a vector with indices of the state variables that have to be non-negative; if TRUE, the state variables are forced to be non-negative numbers.
maximal number of iterations during one call to the solver.
if FALSE: names of state variables are not passed to
function func ; this may speed up the simulation especially
for multi-D models.
a string giving the name of the shared library (without
extension) that contains all the compiled function or subroutine
definitions referred to in func.
if not NULL, the name of the initialisation function
(which initialises values of parameters), as provided in dllname.
See details.
only when dllname is specified and an initialisation
function initfunc is in the dll: the parameters passed to the
initialiser, to initialise the common blocks (FORTRAN) or global variables
(C, C++).
only when dllname is specified: a vector with double
precision values passed to the dll-functions whose names are specified
by func.
only when dllname is specified: a vector with integer
values passed to the dll-functions whose names are specified by func.
only used if dllname is specified: the number of output
variables calculated in the compiled function func, present in the
shared library.
only used if dllname is specified and
nout > 0: the names of output variables calculated in the
compiled function func, present in the shared library.
the sparse method to be used, one of "yale", "ilut",
"ilutp". The default uses the yale sparse matrix solver; the other
use preconditioned GMRES (generalised minimum residual method)
solvers from FORTRAN package sparsekit. ilut stands for incomplete LU
factorisation with trheshold (or tolerances, droptol); the "p" iin ilutp stands for pivoting.
only used if spmethod not equal to "yale",
a list with the control options of the preconditioned solvers. The
default is list( droptol = 1e-3, permtol = 1e-3, fillin = 10,
lenplufac = 2).
droptol is the tolerance in ilut, ilutp to decide when to drop a value.
permtol is used in ilutp, to decide whether or not to permute variables.
See Saad 1994, the manual of sparskit and Saad 2003, chapter 10 for details.
only used if dllname is specified: a vector with the
forcing function values, or a list with the forcing function data sets,
each present as a two-columned matrix, with (time,value); interpolation
outside the interval [min(times), max(times)] is done by
taking the value at the closest data extreme.
This feature is here for compatibility with models defined in compiled code
from package deSolve; see deSolve's package vignette "compiledCode".
if not NULL, the name of the forcing function
initialisation function, as provided in
dllname. It MUST be present if forcings has been given a
value.
See deSolve's package vignette "compiledCode".
A list of control parameters for the forcing functions.
See deSolve's package vignette "compiledCode".
additional arguments passed to func allowing this to be
a generic function.
A list containing
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 an estimate of the precision attained during each iteration, the mean absolute rate of change (sum(abs(dy))/n).
The work is done by a Fortran 77 routine that implements the Newton-Raphson method.
stodes is to be used for problems, where the Jacobian has a sparse
structure.
There are several choices for the sparsity specification, selected by
argument sparsetype.
sparsetype = "sparseint". The sparsity is estimated
by the solver, based on numerical differences.
In this case, it is advisable to provide an estimate of the number
of non-zero elements in the Jacobian (nnz).
This value can be approximate; upon return the number of nonzero
elements actually required will be known (1st element of attribute
dims).
In this case, inz need not be specified.
sparsetype = "sparseusr". The sparsity is determined by
the user.
In this case, inz should be a matrix, containing indices
(row, column) to the nonzero elements in the Jacobian matrix.
The number of nonzeros nnz will be set equal to the number of rows
in inz.
sparsetype = "sparsejan". The sparsity is also determined by
the user.
In this case, inz should be a vector, containting the ian and
jan elements of the sparse storage format, as used in the sparse solver.
Elements of ian should be the first n+1 elements of this vector, and
contain the starting locations in jan of columns 1.. n.
jan contains the row indices of the nonzero locations of
the jacobian, reading in columnwise order.
The number of nonzeros nnz will be set equal to the length of inz - (n+1).
sparsetype = "1D", "2D", "3D".
The sparsity is estimated by the solver, based on numerical differences.
Assumes finite differences in a 1D, 2D or 3D regular grid - used by
functions ode.1D, ode.2D, ode.3D.
Similar are "2Dmap", and "3Dmap", which also include a
mapping variable (passed in nnz).
The Jacobian itself is always generated by the solver (i.e. there is no provision to provide an analytic Jacobian).
This is done by perturbing simulataneously a combination of state variables that do not affect each other.
This significantly reduces computing time. The number of groups with
independent state variables can be given by ngp.
The input parameters rtol, atol and ctol determine
the error control performed by the solver. See help for stode
for details.
Models may be defined in compiled C or Fortran code, as well as in R. See package vignette for details on how to write models in compiled code.
When the spmethod equals ilut or ilutp, a number of parameters
can be specified in argument control. They are:
fillin, the fill-in parameter. Each row of L and each row of U will have a maximum of lfil elements (excluding the diagonal element). lfil must be >= 0.
droptol, sets the threshold for dropping small terms in the factorization.
When ilutp is chosen the following arguments can also be specified:
permtol = tolerance ratio used to determne whether or not to permute two columns. At step i columns i and j are permuted when abs(a(i,j))*permtol .gt. abs(a(i,i)) [0 --> never permute; good values 0.1 to 0.01]
lenplufac = sets the working array - increase its value if a warning.
For a description of the Newton-Raphson method, e.g.
Press, WH, Teukolsky, SA, Vetterling, WT, Flannery, BP, 1996. Numerical Recipes in FORTRAN. The Art of Scientific computing. 2nd edition. Cambridge University Press.
When spmethod = "yale" then the algorithm uses linear algebra routines from the Yale sparse matrix package:
Eisenstat, S.C., Gursky, M.C., Schultz, M.H., Sherman, A.H., 1982. Yale Sparse Matrix Package. i. The symmetric codes. Int. J. Num. meth. Eng. 18, 1145-1151.
else the functions ilut and ilutp from sparsekit package are used:
Yousef Saad, 1994. SPARSKIT: a basic tool kit for sparse matrix computations. VERSION 2
Yousef Saad, 2003. Iterative methods for Sparse Linear Systems. Society for Industrial and Applied Mathematics.
steady, for a general interface to most of the steady-state
solvers
steady.band, to find the steady-state of ODE models with a
banded Jacobian
steady.1D, steady.2D,
steady.3D, steady-state solvers for 1-D, 2-D and 3-D
partial differential equations.
stode, iterative steady-state solver for ODEs with full
or banded Jacobian.
runsteady, steady-state solver by dynamically running to
steady-state
# NOT RUN {
## =======================================================================
## 1000 simultaneous equations
## =======================================================================
model <- function (time, OC, parms, decay, ing)
{
# model describing C in a sediment,
# Upper boundary = imposed flux, lower boundary = zero-gradient
Flux <- v * c(OC[1] ,OC) + # advection
-Kz*diff(c(OC[1],OC,OC[N]))/dx # diffusion;
Flux[1]<- flux # imposed flux
# Rate of change= Flux gradient and first-order consumption
dOC <- -diff(Flux)/dx - decay*OC
# Fraction of OC in first 5 layers is translocated to mean depth
# (layer N/2)
dOC[1:5] <- dOC[1:5] - ing*OC[1:5]
dOC[N/2] <- dOC[N/2] + ing*sum(OC[1:5])
list(dOC)
}
v <- 0.1 # cm/yr
flux <- 10
dx <- 0.01
N <- 1000
dist <- seq(dx/2, by = dx, len = N)
Kz <- 1 #bioturbation (diffusion), cm2/yr
ss <- stodes(runif(N), func = model, parms = NULL,
positive = TRUE, decay = 5, ing = 20, verbose = TRUE)
plot(ss$y[1:N], dist, ylim = rev(range(dist)), type = "l", lwd = 2,
xlab = "Nonlocal exchange", ylab = "sediment depth",
main = "stodes, sparse jacobian")
# the size of lrw is in the attributes()$dims vector.
attributes(ss)
# }
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