Learn R Programming

deSolve (version 1.2-1)

lsodes: General solver for ordinary differential equations (ODE) with sparse Jacobian

Description

Solves the initial value problem for stiff systems 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. The Rfunction lsodes provides an interface to the Fortran ODE solver of the same name, written by Alan C. Hindmarsh and Andrew H. Sherman The system of ODE's is written as an Rfunction or be defined in compiled code that has been dynamically loaded.

Usage

lsodes(y, times, func, parms, rtol=1e-6, atol=1e-6, 
  jacvec=NULL, sparsetype = "sparseint", nnz=NULL, inz=NULL,     
  verbose=FALSE, tcrit=NULL, hmin=0, hmax=NULL, hini=0, ynames=TRUE, 
  maxord=NULL, maxsteps=5000, lrw=NULL, liw=NULL,  
 dllname=NULL, initfunc=dllname, initpar=parms,  
  rpar=NULL, ipar=NULL, nout=0, outnames=NULL, ...)

Arguments

y
the initial (state) values for the ODE system. If y has a name attribute, the names will be used to label the output matrix.
times
time sequence for which output is wanted; the first value of times must be the initial time; if only one step is to be taken; set times = NULL
func
either an R-function that computes the values of the derivatives in the ODE system (the model definition) at time t, or a character string giving the name of a compiled function in a dynamically loaded shared library.
parms
vector or list of parameters used in func or jacfunc.
rtol
relative error tolerance, either a scalar or an array as long as y. See details.
atol
absolute error tolerance, either a scalar or an array as long as y. See details.
jacvec
if not NULL, an Rfunction that computes a column of the jacobian of the system of differential equations dydot(i)/dy(j), or a string giving the name of a function or subroutine in dllname that computes the colum
sparsetype
the sparsity structure of the Jacobian, one of "sparseint" or "sparseusr", sparsity estimated internally by lsodes or given by user
nnz
the number of nonzero elements in the sparse Jacobian (if this is unknown, use an estimate)
inz
(row,column) indices to the nonzero elements in the sparse Jacobian. Necessary if sparsetype = "sparseusr"; else ignored
verbose
if TRUE: full output to the screen, e.g. will output the settings of vectors *istate* and *rstate* - see details
tcrit
if not NULL, then lsodes cannot integrate past tcrit. The Fortran routine lsodes overshoots its targets (times points in the vector times), and interpolates values for the desired time
hmin
an optional minimum value of the integration stepsize. In special situations this parameter may speed up computations with the cost of precision. Don't use hmin if you don't know why!
hmax
an optional maximum value of the integration stepsize. If not specified, hmax is set to the largest difference in times, to avoid that the simulation possibly ignores short-term events. If 0, no maximal size is specified
hini
initial step size to be attempted; if 0, the initial step size is determined by the solver
ynames
if FALSE: names of state variables are not passed to function func ; this may speed up the simulation especially for multi-D models
maxord
the maximum order to be allowed. NULL uses the default, i.e. order 12 if implicit Adams method (meth=1), order 5 if BDF method (meth=2). Reduce maxord to save storage space
maxsteps
maximal number of steps during one call to the solver
lrw
the length of the real work array rwork; due to the sparsicity, this cannot be readily predicted. If NULL, a guess will be made, and if not sufficient, lsodes will return with a message indicating the size of rwork actually required.
liw
the length of the integer work array iwork; due to the sparsicity, this cannot be readily predicted. If NULL, a guess will be made, and if not sufficient, lsodes will return with a message indicating the size of iwork actually required.
dllname
a string giving the name of the shared library (without extension) that contains all the compiled function or subroutine definitions refered to in func and jacfunc. See package vignette.
initfunc
if not NULL, the name of the initialisation function (which initialises values of parameters), as provided in dllname. See package vignette.
initpar
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++)
rpar
only when dllname is specified: a vector with double precision values passed to the dll-functions whose names are specified by func and jacfunc
ipar
only when dllname is specified: a vector with integer values passed to the dll-functions whose names are specified by func and jacfunc
nout
only used if dllname is specified and the model is defined in compiled code: the number of output variables calculated in the compiled function func, present in the shared library. Note: it is not automatically checked whet
outnames
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
...
additional arguments passed to func and jacfunc allowing this to be a generic function

Value

  • A matrix with up to as many rows as elements in times and as many columns as elements in y plus the number of "global" values returned in the next elements of the return from func, plus an additional column (the first) for the time value. There will be one row for each element in times unless the Fortran routine `lsodes' returns with an unrecoverable error. If y has a names attribute, it will be used to label the columns of the output value. The output will have the attributes istate, and rstate, two vectors with several useful elements. See details. The first element of istate returns the conditions under which the last call to lsoda returned. Normal is istate[1] = 2. If verbose = TRUE, the settings of istate and rstate will be written to the screen

Details

The work is done by the Fortran subroutine lsodes, whose documentation should be consulted for details (it is included as comments in the source file src/opkdmain.f). The implementation is based on the November, 2003 version of lsodes, from Netlib. lsodes is applied for stiff problems, where the Jacobian has a sparse structure. There are four choices depending on whether jacvec and inz is specified. If function jacvec is present, then it should return the j-th column of the Jacobian matrix, If matrix inz is present, then it should contain indices (row, column) to the nonzero elements in the Jacobian matrix. If jacvec and inz are present, then the jacobian is fully specified by the user If jacvec is present, but not nnz then the structure of the jacobian will be obtained from NEQ+1 calls to jacvec If nnz is present, but not jacvec then the jacobian will be estimated internally, by differences. If neither nnz nor jacvec is present, then the jacobian will be generated internally by differences, its structure (indices to nonzero elements) will be obtained from NEQ+1 initial calls to func If nnz is not specified, it is advisable to provide an estimate of the number of non-zero elements in the Jacobian (inz) The input parameters rtol, and atol determine the error control performed by the solver. See lsoda for details. Models may be defined in compiled C or Fortran code, as well as in an R-function. See package vignette for details. The output will have the attributes *istate*, and *rstate*, two vectors with several useful elements. if verbose = TRUE, the settings of istate and rstate will be written to the screen. the following elements of istate are meaningful: \itemel 1 : returns the conditions under which the last call to lsodes returned. 2 if lsodes was successful, -1 if excess work done, -2 means excess accuracy requested. (Tolerances too small), -3 means illegal input detected. (See printed message.), -4 means repeated error test failures. (Check all input), -5 means repeated convergence failures. (Perhaps bad Jacobian supplied or wrong choice of MF or tolerances.), -6 means error weight became zero during problem. (Solution component i vanished, and atol or atol(i) = 0.) \itemel 12 : The number of steps taken for the problem so far. \itemel 13 : The number of function evaluations for the problem so far, excluding those for structure determination.", \itemel 14 : The number of Jacobian evaluations and LU decompositions so far, excluding those for structure determination.", \itemel 15 : The method order last used (successfully)., \itemel 16 : The order to be attempted on the next step., \itemel 17 : if el 1 =-4,-5: the largest component in the error vector, \itemel 18 : The length of rwork actually required., \itemel 19 : The length of iwork actually required., \itemel 20 : The number of nonzero elements in the sparse jacobian, rstate contains the following: \item1: The step size in t last used (successfully). \item2: The step size to be attempted on the next step. \item3: The current value of the independent variable which the solver has actually reached, i.e. the current internal mesh point in t. \item4: A tolerance scale factor, greater than 1.0, computed when a request for too much accuracy was detected. For more information, see the comments in the original code lsodes.f

References

\itemAlan C. Hindmarsh, ODEPACK, A Systematized Collection of ODE Solvers, in Scientific Computing, R. S. Stepleman et al. (Eds.), North-Holland, Amsterdam, 1983, pp. 55-64. \itemS. C. Eisenstat, M. C. Gursky, M. H. Schultz, and A. H. Sherman, Yale Sparse Matrix Package: I. The Symmetric Codes, Int. J. Num. Meth. Eng., 18 (1982), pp. 1145-1151. \itemS. C. Eisenstat, M. C. Gursky, M. H. Schultz, and A. H. Sherman, Yale Sparse Matrix Package: II. The Nonsymmetric Codes, Research Report No. 114, Dept. of Computer Sciences, Yale University, 1977.

See Also

\itemode, lsoda, lsode, lsodar, vode, daspk, rk.

Examples

Run this code
# Various ways to solve the same model.
  
  ###############################
  ## The example from lsodes code
  ## A chemical model
  ###############################
  
  n  <- 12
  y  <- rep(1,n)
  dy <- rep(0,n)
  
  times <- c(0,0.1*(10^(0:4)))
  
  rtol = 1.0e-4
  atol = 1.0e-6
  
  parms <- c(rk1=0.1,   rk2=10.0, rk3=50.0,  rk4=2.5,  rk5=0.1,
             rk6=10.0,  rk7=50.0, rk8=2.5,   rk9=50.0, rk10=5.0,
             rk11=50.0, rk12=50.0,rk13=50.0, rk14=30.0,
             rk15=100.0,rk16=2.5, rk17=100.0,rk18=2.5,
             rk19=50.0, rk20=50.0)
  
  #
  chemistry <- function (time,Y,pars)
  {
  with (as.list(pars),{
  
   dy[1] <- -rk1 *Y[1]
   dy[2] <-  rk1 *Y[1]        + rk11*rk14*Y[4]  + rk19*rk14*Y[5]  - 
             rk3 *Y[2]*Y[3]   - rk15*Y[2]*Y[12] - rk2*Y[2]
   dy[3] <-  rk2 *Y[2]        - rk5 *Y[3]       - rk3*Y[2]*Y[3]   -
             rk7*Y[10]*Y[3]   + rk11*rk14*Y[4]   + rk12*rk14*Y[6]
   dy[4] <-  rk3 *Y[2]*Y[3]   - rk11*rk14*Y[4]  - rk4*Y[4]
   dy[5] <-  rk15*Y[2]*Y[12]  - rk19*rk14*Y[5]  - rk16*Y[5]
   dy[6] <-  rk7 *Y[10]*Y[3]  - rk12*rk14*Y[6]  - rk8*Y[6]
   dy[7] <-  rk17*Y[10]*Y[12] - rk20*rk14*Y[7]  - rk18*Y[7]
   dy[8] <-  rk9 *Y[10]       - rk13*rk14*Y[8]  - rk10*Y[8]
   dy[9] <-  rk4 *Y[4]        + rk16*Y[5]       + rk8*Y[6]         +
             rk18*Y[7]
   dy[10]<-  rk5 *Y[3]        + rk12*rk14*Y[6]  + rk20*rk14*Y[7]   +
             rk13*rk14*Y[8]   - rk7 *Y[10]*Y[3] - rk17*Y[10]*Y[12] -
             rk6 *Y[10]       - rk9*Y[10]
   dy[11]<-  rk10*Y[8]
   dy[12]<-  rk6 *Y[10]       + rk19*rk14*Y[5]  + rk20*rk14*Y[7]   -
             rk15*Y[2]*Y[12]  - rk17*Y[10]*Y[12]
   return(list(dy))
  })
  }
  
  #--------------
  # application 1. lsodes estimates the structure of the jacobian 
  #                and calculates the jacobian by differences    
  out <- lsodes(func= chemistry, y = y, parms=parms, times=times,
                atol=atol,rtol=rtol,verbose=TRUE)
  
  #--------------
  # application 2. the structure of the jacobian is input
  #                lsodes calculates the jacobian by differences    
  # this is not so efficient... 
  
  # elements of Jacobian that are not zero
  nonzero <-  matrix(nc=2,byrow=TRUE,data=c(
   1, 1,   2, 1,    #influence of sp1 on rate of change of others
   2, 2,   3, 2,   4, 2,   5, 2,  12, 2,
   2, 3,   3, 3,   4, 3,   6, 3,  10, 3,
   2, 4,   3, 4,   4, 4,   9, 4,  #d (dyi)/dy4
   2, 5,   5, 5,   9, 5,  12, 5,
   3, 6,   6, 6,   9, 6,  10, 6,
   7, 7,   9, 7,  10, 7,  12, 7,
   8, 8,  10, 8,  11, 8,
   3,10,   6,10,   7,10,  10,10,  12,10,
   2,12,   5,12,   7,12,  10,12,  12,12))
  
  # when run, the default length of rwork is too small
  # lsodes will tell the length actually needed
  #out2<- lsodes(func= chemistry, y = y, parms=parms, times=times,
  #             inz=nonzero, atol=atol,rtol=rtol)  #gives warning
  out2<- lsodes(func= chemistry, y = y, parms=parms, times=times, 
              sparsetype="sparseusr", inz=nonzero,   
               atol=atol,rtol=rtol,verbose=TRUE,lrw=351)
                              
  #--------------
  # application 3. lsodes estimates the structure of the jacobian 
  #                the jacobian (vector) function is input
  #
  chemjac <- function (time,Y,j,pars)
  {
   with (as.list(pars),{
   PDJ <- rep(0,n)
  
   if (j == 1){
      PDJ[1] <- -rk1
      PDJ[2] <- rk1
   } else if (j == 2) {
      PDJ[2] <- -rk3*Y[3] - rk15*Y[12] - rk2
      PDJ[3] <- rk2 - rk3*Y[3]
      PDJ[4] <- rk3*Y[3]
      PDJ[5] <- rk15*Y[12]
      PDJ[12] <- -rk15*Y[12]
   } else if (j == 3) {
      PDJ[2] <- -rk3*Y[2]
      PDJ[3] <- -rk5 - rk3*Y[2] - rk7*Y[10]
      PDJ[4] <- rk3*Y[2]
      PDJ[6] <- rk7*Y[10]
      PDJ[10] <- rk5 - rk7*Y[10]
   } else if (j == 4) {
      PDJ[2] <- rk11*rk14
      PDJ[3] <- rk11*rk14
      PDJ[4] <- -rk11*rk14 - rk4
      PDJ[9] <- rk4
   } else if (j == 5) {
      PDJ[2] <- rk19*rk14
      PDJ[5] <- -rk19*rk14 - rk16
      PDJ[9] <- rk16
      PDJ[12] <- rk19*rk14
   } else if (j == 6) {
      PDJ[3] <- rk12*rk14
      PDJ[6] <- -rk12*rk14 - rk8
      PDJ[9] <- rk8
      PDJ[10] <- rk12*rk14
   } else if (j == 7) {
      PDJ[7] <- -rk20*rk14 - rk18
      PDJ[9] <- rk18
      PDJ[10] <- rk20*rk14
      PDJ[12] <- rk20*rk14
   } else if (j == 8) {
      PDJ[8] <- -rk13*rk14 - rk10
      PDJ[10] <- rk13*rk14
      PDJ[11] <- rk10
   } else if (j == 10) {
      PDJ[3] <- -rk7*Y[3]
      PDJ[6] <- rk7*Y[3]
      PDJ[7] <- rk17*Y[12]
      PDJ[8] <- rk9
      PDJ[10] <- -rk7*Y[3] - rk17*Y[12] - rk6 - rk9
      PDJ[12] <- rk6 - rk17*Y[12]
   } else if (j == 12) {
      PDJ[2] <- -rk15*Y[2]
      PDJ[5] <- rk15*Y[2]
      PDJ[7] <- rk17*Y[10]
      PDJ[10] <- -rk17*Y[10]
      PDJ[12] <- -rk15*Y[2] - rk17*Y[10]
   }
  return(PDJ)
  })
  } 
  
  out3<- lsodes(func= chemistry, y = y, parms=parms, times=times,
                jacvec=chemjac, atol=atol,rtol=rtol)          
  
  #--------------
  # application 4. The structure of the jacobian (nonzero elements) AND
  #                the jacobian (vector) function is input
  # not very efficient...
  
  out4<- lsodes(func= chemistry, y = y, parms=parms, times=times,lrw=351,  
                sparsetype="sparseusr", inz=nonzero, jacvec=chemjac,
                atol=atol, rtol=rtol, verbose=TRUE)

Run the code above in your browser using DataLab