DDE Solvers

The standard algorithm choice is MethodOfSteps(Tsit5()). This is a highly efficient FSAL 5th order algorithm with free interpolants which should handle most problems. For fast solving where non-strict error control is needed, choosing MethodOfSteps(BS3()) can do well. Using BS3 is similar to the MATLAB dde23. For algorithms where strict error control is needed, it is recommended that one uses MethodOfSteps(Vern6()). Benchmarks show that going to higher order methods like MethodOfSteps(DP8()) may not be beneficial.

For stiff DDEs, the SDIRK and Rosenbrock methods are very efficient, as they will reuse the Jacobian in the unconstrained stepping iterations. One should choose from the methods which have stiff-aware interpolants for better stability. MethodOfSteps(Rosenbrock23()) is a good low order method choice. Additionally, the Rodas methods like MethodOfSteps(Rodas4()) are good choices because of their higher order stiff-aware interpolant.

Additionally, DADEs can be solved by specifying the problem in mass matrix form. The Rosenbrock methods are good choices in these situations.

Lags are declared separately from their use. One can use any lag by simply using the interpolant of h at that point. However, one should use caution in order to achieve the best accuracy. When lags are declared, the solvers can be more efficient and accurate. Constant delays are propagated until the order is higher than the order of the integrator. If state-dependent delays are declared, the algorithm will detect discontinuities arising from these delays and adjust the step size such that these discontinuities are included in the mesh, if steps are rejected. This way, all discontinuities are treated exactly.

If there are undeclared lags, the discontinuities due to delays are not tracked. In this case, one should only use residual control methods like MethodOfSteps(RK4()), which is the current best choice, as these will step more accurately. Still, residual control is an error-prone method. We recommend setting the tolerances lower in order to get accurate results, though this may be costly since it will use a rejection-based approach to adapt to the delay discontinuities.

If the method is having trouble, one may want to adjust the fixed-point iteration. Decreasing the absolute tolerance and the relative tolerance by specifying the keyword arguments abstol and reltol when solving the DDE problem, and increasing the maximal number of iterations by specifying the keyword argument max_iter in the MethodOfSteps algorithm, can help ensure that the steps are correct. If the problem still is not correctly converging, one should lower dtmax. For problems with only constant delays, in the worst-case scenario, one may need to set constrained = true which will constrain timesteps to at most the size of the minimal lag and hence forces more stability at the cost of smaller timesteps.