module Dls:sig..end
Direct Linear Solvers operating on dense, banded, and sparse matrices.
include Sundials_LinearSolver.Direct
type'mjac_fn_no_sens =(Sundials.RealArray.t Idas.Adjoint.triple, Sundials.RealArray.t)
Idas.Adjoint.jacobian_arg -> 'm -> unit
Callback functions that compute dense approximations to a Jacobian
matrix without forward sensitivities. In the call
jac arg jm, arg is a Idas.Adjoint.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 ith equation with respect to
the jth 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.
arg nor the matrix jm should
be accessed after the function has returned.type'mjac_fn_with_sens =(Sundials.RealArray.t Idas.Adjoint.triple, Sundials.RealArray.t)
Idas.Adjoint.jacobian_arg ->
Sundials.RealArray.t array -> Sundials.RealArray.t array -> 'm -> unit
Callback functions that compute dense approximations to a Jacobian
matrix with forward sensitivities. In the call
jac arg ys yps jm, arg is a Idas.Adjoint.jacobian_arg with
three work vectors, ys contains the sensitivities of the forward
solution, yps contains the derivatives of the forward solution
sensitivities, 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 ith equation with respect to
the jth 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.
arg nor the matrix jm should
be accessed after the function has returned.type 'm jac_fn =
| |
NoSens of |
(* | Does not depend on forward sensitivities. | *) |
| |
WithSens of |
(* | Depends on forward sensitivities. | *) |
Callback functions that compute dense approximations to a Jacobian matrix.
val solver : ?jac:'m jac_fn ->
('m, Sundials.RealArray.t, [> Nvector_serial.kind ] as 'a, 't)
Sundials.LinearSolver.t -> 'a Idas.Adjoint.serial_linear_solverCreate an Idas-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).
val get_work_space : [> Nvector_serial.kind ] Idas.Adjoint.serial_bsession -> int * intReturns the sizes of the real and integer workspaces used by a direct linear solver.
real_size, integer_size)val get_num_jac_evals : [> Nvector_serial.kind ] Idas.Adjoint.serial_bsession -> intReturns the number of calls made by a direct linear solver to the Jacobian approximation function.
val get_num_lin_res_evals : [> Nvector_serial.kind ] Idas.Adjoint.serial_bsession -> intReturns the number of calls to the residual callback due to the finite difference Jacobian approximation.