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The function `lsode`

can be used to solve ODEs of the form

dx -- = f (x, t) dt

using Hindmarsh’s ODE solver LSODE.

- :
*[*`x`,`istate`,`msg`] =**lsode***(*¶`fcn`,`x_0`,`t`) - :
*[*`x`,`istate`,`msg`] =**lsode***(*¶`fcn`,`x_0`,`t`,`t_crit`) Ordinary Differential Equation (ODE) solver.

The set of differential equations to solve is

dx -- = f (x, t) dt

with

x(t_0) = x_0

The solution is returned in the matrix

`x`, with each row corresponding to an element of the vector`t`. The first element of`t`should be*t_0*and should correspond to the initial state of the system`x_0`, so that the first row of the output is`x_0`.The first argument,

`fcn`, is a string, inline, or function handle that names the function*f*to call to compute the vector of right hand sides for the set of equations. The function must have the form`xdot`= f (`x`,`t`)in which

`xdot`and`x`are vectors and`t`is a scalar.If

`fcn`is a two-element string array or a two-element cell array of strings, inline functions, or function handles, the first element names the function*f*described above, and the second element names a function to compute the Jacobian of*f*. The Jacobian function must have the form`jac`= j (`x`,`t`)in which

`jac`is the matrix of partial derivatives| df_1 df_1 df_1 | | ---- ---- ... ---- | | dx_1 dx_2 dx_N | | | | df_2 df_2 df_2 | | ---- ---- ... ---- | df_i | dx_1 dx_2 dx_N | jac = ---- = | | dx_j | . . . . | | . . . . | | . . . . | | | | df_M df_M df_M | | ---- ---- ... ---- | | dx_1 dx_2 dx_N |

The second argument specifies the initial state of the system

*x_0*. The third argument is a vector,`t`, specifying the time values for which a solution is sought.The fourth argument is optional, and may be used to specify a set of times that the ODE solver should not integrate past. It is useful for avoiding difficulties with singularities and points where there is a discontinuity in the derivative.

After a successful computation, the value of

`istate`will be 2 (consistent with the Fortran version of LSODE).If the computation is not successful,

`istate`will be something other than 2 and`msg`will contain additional information.You can use the function

`lsode_options`

to set optional parameters for`lsode`

.See Alan C. Hindmarsh, ODEPACK, A Systematized Collection of ODE Solvers, in Scientific Computing, R. S. Stepleman, editor, (1983) or https://computing.llnl.gov/projects/odepack for more information about the inner workings of

`lsode`

.Example: Solve the Van der Pol equation

fvdp = @(

`y`,`t`) [`y`(2); (1 -`y`(1)^2) *`y`(2) -`y`(1)];`t`= linspace (0, 20, 100);`y`= lsode (fvdp, [2; 0],`t`);

- :
**lsode_options***()*¶ - :
*val =***lsode_options***(*¶`opt`) - :
**lsode_options***(*¶`opt`,`val`) Query or set options for the function

`lsode`

.When called with no arguments, the names of all available options and their current values are displayed.

Given one argument, return the value of the option

`opt`.When called with two arguments,

`lsode_options`

sets the option`opt`to value`val`.Options include

`"absolute tolerance"`

Absolute tolerance. May be either vector or scalar. If a vector, it must match the dimension of the state vector.

`"relative tolerance"`

Relative tolerance parameter. Unlike the absolute tolerance, this parameter may only be a scalar.

The local error test applied at each integration step is

abs (local error in x(i)) <= ... rtol * abs (y(i)) + atol(i)

`"integration method"`

A string specifying the method of integration to use to solve the ODE system. Valid values are

`"adams"`

`"non-stiff"`

No Jacobian used (even if it is available).

`"bdf"`

`"stiff"`

Use stiff backward differentiation formula (BDF) method. If a function to compute the Jacobian is not supplied,

`lsode`

will compute a finite difference approximation of the Jacobian matrix.

`"initial step size"`

The step size to be attempted on the first step (default is determined automatically).

`"maximum order"`

Restrict the maximum order of the solution method. If using the Adams method, this option must be between 1 and 12. Otherwise, it must be between 1 and 5, inclusive.

`"maximum step size"`

Setting the maximum stepsize will avoid passing over very large regions (default is not specified).

`"minimum step size"`

The minimum absolute step size allowed (default is 0).

`"step limit"`

Maximum number of steps allowed (default is 100000).

Here is an example of solving a set of three differential equations using
`lsode`

. Given the function

## oregonator differential equation function xdot = f (x, t) xdot = zeros (3,1); xdot(1) = 77.27 * (x(2) - x(1)*x(2) + x(1) ... - 8.375e-06*x(1)^2); xdot(2) = (x(3) - x(1)*x(2) - x(2)) / 77.27; xdot(3) = 0.161*(x(1) - x(3)); endfunction

and the initial condition `x0 = [ 4; 1.1; 4 ]`

, the set of
equations can be integrated using the command

t = linspace (0, 500, 1000); y = lsode ("f", x0, t);

If you try this, you will see that the value of the result changes
dramatically between `t` = 0 and 5, and again around `t` = 305.
A more efficient set of output points might be

t = [0, logspace(-1, log10(303), 150), ... logspace(log10(304), log10(500), 150)];

An m-file for the differential equation used above is included with the
Octave distribution in the examples directory under the name
`oregonator.m`.

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