# package odepack

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Binding to ODEPACK. This is a collection of solvers for the initial value problem for ordinary differential equation systems. See the ODEPACK page and Netlib.

An `example_of_use` of this library is presented at the end.

```type vec = (float, Bigarray.float64_elt, Bigarray.fortran_layout) Bigarray.Array1.t```

Representation of vectors (parametrized by their layout).

```type mat = (float, Bigarray.float64_elt, Bigarray.fortran_layout) Bigarray.Array2.t```

Representation of matrices (parametrized by their layout).

`type t`

A mutable value holding the current state of solving the ODE.

`type jacobian = `
1. `| Auto_full`
(*

Internally generated (difference quotient) full Jacobian

*)
2. `| Auto_band of int * int`
(*

Internally generated (difference quotient) band Jacobian. It takes `(l,u)` where `l` (resp. `u`) is the number of lines below (resp. above) the diagonal (excluded).

*)
3. `| Full of float -> vec -> mat -> unit`
(*

`Full df` means that a function `df` is provided to compute the full Jacobian matrix (∂f_i/∂y_j) of the vector field f(t,y). `df t y jac` must store ∂f_i/∂y_j(`t`,`y`) into `jac.{i,j}`.

*)
4. `| Band of int * int * float -> vec -> int -> mat -> unit`
(*

`Band(l, u, df)` means that a function `df` is provided to compute the banded Jacobian matrix with `l` (resp. `u`) diagonals below (resp. above) the main one (not counted). `df t y d jac` must store ∂f_i/∂y_j(`t`,`y`) into `jac.{i-j+d, j}`. `d` is the row of `jac` corresponding to the main diagonal of the Jacobian matrix.

*)

Types of Jacobian matrices.

```val lsoda : ?rtol:float -> ?rtol_vec:vec -> ?atol:float -> ?atol_vec:vec -> ?jac:jacobian -> ?mxstep:int -> ?copy_y0:bool -> ?debug:bool -> ?debug_switches:bool -> (float -> vec -> vec -> unit) -> vec -> float -> float -> t```

`lsoda f y0 t0 t` solves the ODE dy/dt = F(t,y) with initial condition y(`t0`) = `y0`. The execution of `f t y y'` must compute the value of the F(`t`, `y`) and store it in `y'`. It uses a dense or banded Jacobian when the problem is stiff, but it automatically selects between nonstiff (Adams) and stiff (BDF) methods. It uses the nonstiff method initially, and dynamically monitors data in order to decide which method to use.

• parameter rtol

relative error tolerance parameter. Default `1e-6`.

• parameter rtol_vec

relative error tolerance vector.

• parameter atol

absolute error tolerance parameter. Default `1e-6`.

• parameter atol_vec

absolute error tolerance vector.

If `rtol_vec` (resp. `atol_vec`) is specified, it is used in place of `rtol` (resp. `atol`). Specifying only `rtol` (resp. `atol`) is equivalent to pass a constant `rtol_vec` (resp. `atol_vec`). The solver will control the vector E = (E(i)) of estimated local errors in `y`, according to an inequality of the form max-norm(E(i)/EWT(i)) <= 1, where ```EWT(i) = rtol_vec.{i} * abs_float(y.{i}) +. atol_vec.{i}```.

• parameter jac

is an optional Jabobian matrix. If the problem is expected to be stiff much of the time, you are encouraged to supply `jac`, for the sake of efficiency. Default: `Auto_full`.

• parameter mxstep

maximum number of (internally defined) steps allowed during one call to the solver. The default value is 500.

• parameter copy_y0

if `false`, the vector `y0` is MODIFIED to contain the value of the solution at time `t`. Otherwise `y0` is unchanged. Default: `true`.

• parameter debug

allows `lsoda` to print messages. Default `true`. The messages contain valuable information, it is not recommended to turn them off.

• parameter debug_switches

prints a message to stdout on each (automatic) method switch (between nonstiff and stiff). Default: `false`.

```val lsodar : ?rtol:float -> ?rtol_vec:vec -> ?atol:float -> ?atol_vec:vec -> ?jac:jacobian -> ?mxstep:int -> ?copy_y0:bool -> ?debug:bool -> ?debug_switches:bool -> g:(float -> vec -> vec -> unit) -> ng:int -> (float -> vec -> vec -> unit) -> vec -> float -> float -> t```

`lsodar f y0 t0 t ~g ~ng` is like `lsoda` but has root searching capabilities. The algorithm will stop before reacing time `t` if a root of one of the `ng` constraints is found. You can determine whether the `lsodar` stopped at a root using `has_root`. It only finds those roots for which some component of `g`, as a function of t, changes sign in the interval of integration. The function `g` is evaluated like `f`, that is: `g t y gout` must write to `gout.{1},..., gout.{ng}` the value of the `ng` constraints.

`val vec : t -> vec`

`vec ode` returns the current value of the solution vector.

`val time : t -> float`

`t ode` returns the current time at which the solution vector was computed.

`val advance : ?time:float -> t -> unit`

`advance ode ~time:t` modifies `ode` so that an approximation of the value of the solution at times `t` is computed. Note that, if the solver has root searching capabilities and a time is provided, the solver may stop before that time if a root is found. The time is recorded for future calls to `advance ode`. If the solver has no root finding capabilities and no time is provided, this function does nothing.

`val has_root : t -> bool`

`has_root ode` says wheter the solver stopped (i.e. the current state of `ode` is) because a root was found. If the solver has no root searching capabilities, this returns `false`.

`val root : t -> int -> bool`

`root t i` returns true iff the `i`th constraint in `lsodar` has a root. It raises `Invalid_argument` if `i` is not between 1 and `ng`, the number of constraints (included). This only makes sense if `has_root t` holds.

`val roots : t -> bool array`

`roots t` returns an array `r` such that `r.(i)` holds if and only if the `i`th constraint has a root.

`val sol : t -> float -> vec`

`sol ode t` modifies `ode` so that it holds an approximation of the solution at `t` and returns this approximation. Any root that might be found is ignored.

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