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remove precs #584

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2 changes: 1 addition & 1 deletion src/alg_utils.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -141,7 +141,7 @@ SciMLBase.alg_interpretation(alg::RKMil{interpretation}) where {interpretation}
SciMLBase.alg_interpretation(alg::SROCK1{interpretation,E}) where {interpretation,E} = interpretation
SciMLBase.alg_interpretation(alg::RKMilCommute) = alg.interpretation
SciMLBase.alg_interpretation(alg::RKMilGeneral) = alg.interpretation
SciMLBase.alg_interpretation(alg::ImplicitRKMil{CS,AD,F,P,FDT,ST,CJ,N,T2,Controller,interpretation}) where {CS,AD,F,P,FDT,ST,CJ,N,T2,Controller,interpretation} = interpretation
SciMLBase.alg_interpretation(alg::ImplicitRKMil{CS,AD,F,FDT,ST,CJ,N,T2,Controller,interpretation}) where {CS,AD,F,FDT,ST,CJ,N,T2,Controller,interpretation} = interpretation

SciMLBase.alg_interpretation(alg::RS1) = :Stratonovich
SciMLBase.alg_interpretation(alg::RS2) = :Stratonovich
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48 changes: 18 additions & 30 deletions src/algorithms.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -634,29 +634,27 @@ This is a theta method which defaults to theta=1 or the Trapezoid method on the
This method defaults to symplectic=false, but when true and theta=1/2 this is the implicit Midpoint method on the drift term and is symplectic in distribution.
Can handle all forms of noise, including non-diagonal, scalar, and colored noise. Uses a 1.0/1.5 heuristic for adaptive time stepping.
"""
struct ImplicitEM{CS,AD,F,F2,P,FDT,ST,CJ,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
struct ImplicitEM{CS,AD,F,F2,FDT,ST,CJ,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
linsolve::F
nlsolve::F2
precs::P
theta::T2
extrapolant::Symbol
new_jac_conv_bound::T2
symplectic::Bool
end
ImplicitEM(;chunk_size=0,autodiff=true,diff_type=Val{:central},
standardtag = Val{true}(),concrete_jac = nothing,
precs = OrdinaryDiffEq.DEFAULT_PRECS,
linsolve=nothing,nlsolve=NLNewton(),
extrapolant=:constant,
theta = 1,symplectic=false,
new_jac_conv_bound = 1e-3,
controller = :Predictive) =
ImplicitEM{chunk_size,autodiff,
typeof(linsolve),typeof(nlsolve),typeof(precs),diff_type,
typeof(linsolve),typeof(nlsolve),diff_type,
OrdinaryDiffEq._unwrap_val(standardtag),
OrdinaryDiffEq._unwrap_val(concrete_jac),
typeof(new_jac_conv_bound),controller}(
linsolve,nlsolve,precs,
linsolve,nlsolve,
symplectic ? 1/2 : theta,
extrapolant,new_jac_conv_bound,symplectic)

Expand All @@ -669,30 +667,28 @@ This is a theta method which defaults to theta=1/2 or the Trapezoid method on th
This method defaults to symplectic=false, but when true and theta=1 this is the implicit Midpoint method on the drift term and is symplectic in distribution.
Can handle all forms of noise, including non-diagonal, scalar, and colored noise. Uses a 1.0/1.5 heuristic for adaptive time stepping.
"""
struct ImplicitEulerHeun{CS,AD,F,P,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
struct ImplicitEulerHeun{CS,AD,F,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
linsolve::F
nlsolve::N
precs::P
theta::T2
extrapolant::Symbol
new_jac_conv_bound::T2
symplectic::Bool
end
ImplicitEulerHeun(;chunk_size=0,autodiff=true,diff_type=Val{:central},
standardtag = Val{true}(),concrete_jac = nothing,
precs = OrdinaryDiffEq.DEFAULT_PRECS,
linsolve=nothing,nlsolve=NLNewton(),
extrapolant=:constant,
theta = 1,symplectic = false,
new_jac_conv_bound = 1e-3,
controller = :Predictive) =
ImplicitEulerHeun{chunk_size,autodiff,
typeof(linsolve),typeof(precs),diff_type,
typeof(linsolve),diff_type,
OrdinaryDiffEq._unwrap_val(standardtag),
OrdinaryDiffEq._unwrap_val(concrete_jac),
typeof(nlsolve),
typeof(new_jac_conv_bound),controller}(
linsolve,nlsolve,precs,
linsolve,nlsolve,
symplectic ? 1/2 : theta,
extrapolant,
new_jac_conv_bound,symplectic)
Expand All @@ -705,30 +701,28 @@ Defaults to solving the Ito problem, but ImplicitRKMil(interpretation=:Stratonov
This method defaults to symplectic=false, but when true and theta=1/2 this is the implicit Midpoint method on the drift term and is symplectic in distribution.
Handles diagonal and scalar noise. Uses a 1.5/2.0 heuristic for adaptive time stepping.
"""
struct ImplicitRKMil{CS,AD,F,P,FDT,ST,CJ,N,T2,Controller,interpretation} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
struct ImplicitRKMil{CS,AD,F,FDT,ST,CJ,N,T2,Controller,interpretation} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
linsolve::F
nlsolve::N
precs::P
theta::T2
extrapolant::Symbol
new_jac_conv_bound::T2
symplectic::Bool
end
ImplicitRKMil(;chunk_size=0,autodiff=true,diff_type=Val{:central},
standardtag = Val{true}(),concrete_jac = nothing,
precs = OrdinaryDiffEq.DEFAULT_PRECS,
linsolve=nothing,nlsolve=NLNewton(),
extrapolant=:constant,
theta = 1,symplectic = false,
new_jac_conv_bound = 1e-3,
controller = :Predictive,interpretation=:Ito) =
ImplicitRKMil{chunk_size,autodiff,
typeof(linsolve),typeof(precs),diff_type,
typeof(linsolve),diff_type,
OrdinaryDiffEq._unwrap_val(standardtag),
OrdinaryDiffEq._unwrap_val(concrete_jac),
typeof(nlsolve),typeof(new_jac_conv_bound),
controller,interpretation}(
linsolve,nlsolve,precs,
linsolve,nlsolve,
symplectic ? 1/2 : theta,
extrapolant,
new_jac_conv_bound,symplectic)
Expand All @@ -740,30 +734,28 @@ This is a theta method which defaults to theta=1 or the Trapezoid method on the
This method defaults to symplectic=false, but when true and theta=1/2 this is the implicit Midpoint method on the drift term and is symplectic in distribution.
Can handle all forms of noise, including non-diagonal, scalar, and colored noise. Uses a 1.0/1.5 heuristic for adaptive time stepping.
"""
struct ISSEM{CS,AD,F,P,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
struct ISSEM{CS,AD,F,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
linsolve::F
nlsolve::N
precs::P
theta::T2
extrapolant::Symbol
new_jac_conv_bound::T2
symplectic::Bool
end
ISSEM(;chunk_size=0,autodiff=true,diff_type=Val{:central},
standardtag = Val{true}(),concrete_jac = nothing,
precs = OrdinaryDiffEq.DEFAULT_PRECS,
linsolve=nothing,nlsolve=NLNewton(),
extrapolant=:constant,
theta = 1,symplectic=false,
new_jac_conv_bound = 1e-3,
controller = :Predictive) =
ISSEM{chunk_size,autodiff,
typeof(linsolve),typeof(precs),diff_type,
typeof(linsolve),diff_type,
OrdinaryDiffEq._unwrap_val(standardtag),
OrdinaryDiffEq._unwrap_val(concrete_jac),
typeof(nlsolve),
typeof(new_jac_conv_bound),controller}(
linsolve,nlsolve,precs,
linsolve,nlsolve,
symplectic ? 1/2 : theta,
extrapolant,
new_jac_conv_bound,symplectic)
Expand All @@ -775,29 +767,27 @@ This is a theta method which defaults to theta=1 or the Trapezoid method on the
This method defaults to symplectic=false, but when true and theta=1/2 this is the implicit Midpoint method on the drift term and is symplectic in distribution.
Can handle all forms of noise, including non-diagonal,Q scalar, and colored noise. Uses a 1.0/1.5 heuristic for adaptive time stepping.
"""
struct ISSEulerHeun{CS,AD,F,P,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
struct ISSEulerHeun{CS,AD,F,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
linsolve::F
nlsolve::N
precs::P
theta::T2
extrapolant::Symbol
new_jac_conv_bound::T2
symplectic::Bool
end
ISSEulerHeun(;chunk_size=0,autodiff=true,diff_type=Val{:central},
standardtag = Val{true}(),concrete_jac = nothing,
precs = OrdinaryDiffEq.DEFAULT_PRECS,
linsolve=nothing,nlsolve=NLNewton(),
extrapolant=:constant,
theta = 1,symplectic=false,
new_jac_conv_bound = 1e-3,
controller = :Predictive) =
ISSEulerHeun{chunk_size,autodiff,
typeof(linsolve),typeof(precs),diff_type,
typeof(linsolve),diff_type,
OrdinaryDiffEq._unwrap_val(standardtag),
OrdinaryDiffEq._unwrap_val(concrete_jac),
typeof(nlsolve),typeof(new_jac_conv_bound),controller}(
linsolve,nlsolve,precs,
linsolve,nlsolve,
symplectic ? 1/2 : theta,
extrapolant,
new_jac_conv_bound,symplectic)
Expand All @@ -806,10 +796,9 @@ SKenCarp: Stiff Method
Adaptive L-stable drift-implicit strong order 1.5 for additive Ito and Stratonovich SDEs with weak order 2.
Can handle diagonal, non-diagonal and scalar additive noise.
"""
struct SKenCarp{CS,AD,F,P,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
struct SKenCarp{CS,AD,F,FDT,ST,CJ,N,T2,Controller} <: StochasticDiffEqNewtonAdaptiveAlgorithm{CS,AD,FDT,ST,CJ,Controller}
linsolve::F
nlsolve::N
precs::P
smooth_est::Bool
extrapolant::Symbol
new_jac_conv_bound::T2
Expand All @@ -818,15 +807,14 @@ end

SKenCarp(;chunk_size=0,autodiff=true,diff_type=Val{:central},
standardtag = Val{true}(),concrete_jac = nothing,
precs = OrdinaryDiffEq.DEFAULT_PRECS,
linsolve=nothing,nlsolve=NLNewton(),
smooth_est=true,extrapolant=:min_correct,
new_jac_conv_bound = 1e-3,controller = :Predictive,
ode_error_est = true) =
SKenCarp{chunk_size,autodiff,typeof(linsolve),typeof(precs),diff_type,
SKenCarp{chunk_size,autodiff,typeof(linsolve),diff_type,
OrdinaryDiffEq._unwrap_val(standardtag),OrdinaryDiffEq._unwrap_val(concrete_jac),
typeof(nlsolve),typeof(new_jac_conv_bound),controller}(
linsolve,nlsolve,precs,smooth_est,extrapolant,new_jac_conv_bound,
linsolve,nlsolve,smooth_est,extrapolant,new_jac_conv_bound,
ode_error_est)


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