# SDDE Solvers

solve(prob::AbstractSDDEProblem, alg; kwargs)

Solves the SDDE defined by prob using the algorithm alg. If no algorithm is given, a default algorithm will be chosen.

The recommended method for SDDE problems are the SDE algorithms. On SDEs you simply reuse the same algorithm as the SDE solver, and StochasticDelayDiffEq.jl will convert it to an SDDE solver. The recommendations for SDDE solvers match those of SDEs, except that only up to strong order 1 is recommended. Note too that order 1 is currently only attainable if there is no delay term in the diffusion function $g$: delays in the drift function $f$ are compatible with first order convergence. Theoretical issues with higher order methods (1.5+) on SDDEs is currently unknown.

Note that adaptive time stepping utilizes the same rejection sampling with memory technique as SDEs, but no proof of convergence is known for SDDEs.

## Example

function hayes_modelf(du,u,h,p,t)
τ,a,b,c,α,β,γ = p
du .= a.*u .+ b .* h(p,t-τ) .+ c
end
function hayes_modelg(du,u,h,p,t)
τ,a,b,c,α,β,γ = p
du .= α.*u .+ γ
end
h(p,t) = (ones(1) .+ t);
tspan = (0.,10.)

pmul = [1.0,-4.,-2.,10.,-1.3,-1.2, 1.1]
padd = [1.0,-4.,-2.,10.,-0.0,-0.0, 0.1]

prob = SDDEProblem(hayes_modelf, hayes_modelg, [1.], h, tspan, pmul; constant_lags = (pmul[1],));
sol = solve(prob,RKMil())