# DAE Solvers

## Recomended Methods

For medium to low accuracy DAEs in mass matrix form, the `Rodas4`

and `Rodas42`

methods are good choices which will get good efficiency. The OrdinaryDiffEq.jl methods are also the only methods which allow for Julia-defined number types. For high accuracy (error `<1e-7`

) on problems of `Vector{Float64}`

defined in mass matrix form, `radau`

is an efficient method.

If the problem cannot be defined in mass matrix form, the recommended method for performance is `IDA`

from the Sundials.jl package if you are solving problems with `Float64`

. It's a very well-optimized method, and allows you to have a little bit of control over the linear solver to better tailor it to your problem. A similar algorithm is `daskr`

. Which one is more efficient is problem-dependent.

# Full List of Methods

## OrdinaryDiffEq.jl

These methods require the DAE to be an `ODEProblem`

in mass matrix form. For extra options for the solvers, see the ODE solver page.

### Rosenbrock Methods

`ROS3P`

- 3rd order A-stable and stiffly stable Rosenbrock method. Keeps high accuracy on discretizations of nonlinear parabolic PDEs.`Rodas3`

- 3rd order A-stable and stiffly stable Rosenbrock method.`Rodas4`

- A 4th order A-stable stiffly stable Rosenbrock method with a stiff-aware 3rd order interpolant`Rodas42`

- A 4th order A-stable stiffly stable Rosenbrock method with a stiff-aware 3rd order interpolant`Rodas4P`

- A 4th order A-stable stiffly stable Rosenbrock method with a stiff-aware 3rd order interpolant. 4th order on linear parabolic problems and 3rd order accurate on nonlinear parabolic problems (as opposed to lower if not corrected).`Rodas5`

- A 5th order A-stable stiffly stable Rosenbrock method. Currently has a Hermite interpolant because its stiff-aware 3rd order interpolant is not yet implemented.

### SDIRK Methods

SDIRK Methods

`ImplicitEuler`

- Stage order 1. A-B-L-stable. Adaptive timestepping through a divided differences estimate via memory. Strong-stability presurving (SSP).`ImplicitMidpoint`

- Stage order 1. Symplectic. Good for when symplectic integration is required.

## Sundials.jl

`IDA`

- This is the IDA method from the Sundials.jl package.

Note that the constructors for the Sundials algorithms take a main argument:

`linearsolver`

- This is the linear solver which is used in the Newton iterations. The choices are:`:Dense`

- A dense linear solver`:Band`

- A solver specialized for banded Jacobians. If used, you must set the position of the upper and lower non-zero diagonals via`jac_upper`

and`jac_lower`

.`:Diagonal`

- This method is specialized for diagonal Jacobians.`BCG`

- A Biconjugate gradient method.`TFQMR`

- A TFQMR method.

Example:

```
IDA() # Newton + Dense solver
IDA(linear_solver=:Band,jac_upper=3,jac_lower=3) # Banded solver with nonzero diagonals 3 up and 3 down
IDA(linear_solver=:BCG) # Biconjugate gradient method
```

All of the additional options are available. The constructor is:

```
IDA(;linear_solver=:Dense,jac_upper=0,jac_lower=0,krylov_dim=0,
max_order = 5,
max_error_test_failures = 7,
max_nonlinear_iters = 3,
nonlinear_convergence_coefficient = 0.33,
nonlinear_convergence_coefficient_ic = 0.0033,
max_num_steps_ic = 5,
max_num_jacs_ic = 4,
max_num_iters_ic = 10,
max_num_backs_ic = 100,
use_linesearch_ic = true,
max_convergence_failures = 10)
```

See the Sundials manual for details on the additional options.

## DASKR.jl

DASKR.jl is not automatically included by DifferentialEquations.jl. To use this algorithm, you will need to install and use the package:

```
Pkg.add("DASKR")
using DASKR
```

`daskr`

- This is a wrapper for the well-known DASKR algorithm.

## DASSL.jl

`dassl`

- A native Julia implementation of the DASSL algorithm.

## ODEInterfaceDiffEq.jl

`seulex`

- Extrapolation-algorithm based on the linear implicit Euler method.`radau`

- Implicit Runge-Kutta (Radau IIA) of variable order between 5 and 13.`radau5`

- Implicit Runge-Kutta method (Radau IIA) of order 5.`rodas`

- Rosenbrock 4(3) method.