update some recommendations

SciML · Jul 7, 2017 · b33155d · b33155d
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diff --git a/docs/src/solvers/ode_solve.md b/docs/src/solvers/ode_solve.md
@@ -37,6 +37,7 @@ very large (`>10,000` ODEs?) or the function calculation is very expensive.
 
 For stiff problems at high tolerances (`>1e-2`?) it is recommended that you use
 `Rosenbrock23`. At medium tolerances (`>1e-8`?) it is recommended you use `Rodas4`
+or `Rodas4P` (the former is slightly more efficient but the later is much more reliable).
 As native DifferentialEquations.jl solvers, many Julia numeric types
 (such as BigFloats, [ArbFloats](https://github.com/JuliaArbTypes/ArbFloats.jl), or
 [DecFP](https://github.com/stevengj/DecFP.jl)) will work. When the equation is

diff --git a/docs/src/tutorials/ode_example.md b/docs/src/tutorials/ode_example.md
@@ -142,6 +142,7 @@ In DifferentialEquations.jl, some good "go-to" choices for ODEs are:
   most cases.
 - `Vern7()` for high accuracy non-stiff.
 - `Rodas4()` for stiff equations with Julia-defined types, events, etc.
+- `radau()` for really high accuracy stiff equations (requires installing ODEInterfaceDiffEq.jl)
 
 For a comprehensive list of the available algorithms and detailed recommendations,
 [Please see the solver documentation](../../solvers/ode_solve.html). Every problem