module Dls:`sig`

..`end`

Direct Linear Solvers operating on dense, banded and sparse matrices.

`include Sundials_LinearSolver.Direct`

type`'m`

jac_fn =`(Sundials.RealArray.t Ida.triple, Sundials.RealArray.t) Ida.jacobian_arg ->`

'm -> unit

Callback functions that compute dense approximations to a Jacobian
matrix. In the call `jac arg jm`

, `arg`

is a `Ida.jacobian_arg`

with three work vectors and the computed Jacobian must be stored
in `jm`

.

The callback should load the `(i,j)`

th entry of `jm`

with
$\frac{\partial F_i}{\partial y_j} + c_j\frac{\partial F_i}{\partial\dot{y}_j}$,
i.e., the partial derivative of the `i`

th equation with respect to
the `j`

th variable, evaluated at the values of `t`

, `y`

, and `y'`

obtained from `arg`

. Only nonzero elements need be loaded into `jm`

.

Raising `Sundials.RecoverableFailure`

indicates a recoverable error.
Any other exception is treated as an unrecoverable error.

Neither the elements of

`arg`

nor the matrix `jm`

should
be accessed after the function has returned.- See Sundials: IDALsJacFn

`val solver : ``?jac:'m jac_fn ->`

('m, Sundials.RealArray.t, [> Nvector_serial.kind ] as 'a, [> `Dls ])

Sundials.LinearSolver.t -> 'a Ida.serial_linear_solver

Create an Ida-specific linear solver from a Jacobian approximation
function and a generic direct linear solver.
The Jacobian approximation function is optional for dense and banded
solvers (if not given an internal difference quotient approximation is
used), but must be provided for other solvers (or `Invalid_argument`

is
raised).

- See Sundials: IDASetLinearSolver
- See Sundials: IDASetJacFn

`val get_work_space : ``[> Nvector_serial.kind ] Ida.serial_session -> int * int`

Returns the sizes of the real and integer workspaces used by a direct linear solver.

**Returns**(`real_size`

,`integer_size`

)- See Sundials: IDAGetLinWorkSpace

`val get_num_jac_evals : ``[> Nvector_serial.kind ] Ida.serial_session -> int`

Returns the number of calls made by a direct linear solver to the Jacobian approximation function.

- See Sundials: IDAGetNumJacEvals

`val get_num_lin_res_evals : ``[> Nvector_serial.kind ] Ida.serial_session -> int`

Returns the number of calls to the residual callback due to the finite difference Jacobian approximation.

- See Sundials: IDAGetNumLinResEvals