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Use PolyAlgorithms for Nonlinear Solves #137

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Nov 7, 2023
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Use PolyAlgorithms for Nonlinear Solves #137

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0699e4e
Use PolyAlgorithms for Nonlinear Solves
avik-pal Nov 6, 2023
b904229
Test default choices from NonlinearSolve.jl
avik-pal Nov 6, 2023
d666585
Merge branch 'master' into ap/stable_default
avik-pal Nov 7, 2023
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2 changes: 1 addition & 1 deletion Project.toml
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Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
name = "BoundaryValueDiffEq"
uuid = "764a87c0-6b3e-53db-9096-fe964310641d"
version = "5.4.0"
version = "5.4.1"

[deps]
ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
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26 changes: 12 additions & 14 deletions src/algorithms.jl
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Expand Up @@ -3,14 +3,15 @@ abstract type BoundaryValueDiffEqAlgorithm <: SciMLBase.AbstractBVPAlgorithm end
abstract type AbstractMIRK <: BoundaryValueDiffEqAlgorithm end

"""
Shooting(ode_alg; nlsolve = NewtonRaphson(), jac_alg = BVPJacobianAlgorithm())
Shooting(ode_alg = nothing; nlsolve = nothing, jac_alg = BVPJacobianAlgorithm())

Single shooting method, reduces BVP to an initial value problem and solves the IVP.

## Arguments

- `ode_alg`: ODE algorithm to use for solving the IVP. Any solver which conforms to the
SciML `ODEProblem` interface can be used!
SciML `ODEProblem` interface can be used! (Defaults to `nothing` which will use
poly-algorithm if `DifferentialEquations.jl` is loaded else this must be supplied)

## Keyword Arguments

Expand Down Expand Up @@ -39,7 +40,7 @@ function concretize_jacobian_algorithm(alg::Shooting, prob)
return Shooting(alg.ode_alg, alg.nlsolve, BVPJacobianAlgorithm(diffmode))
end

function Shooting(ode_alg; nlsolve = NewtonRaphson(), jac_alg = nothing)
function Shooting(ode_alg = nothing; nlsolve = nothing, jac_alg = nothing)
jac_alg === nothing && (jac_alg = __propagate_nlsolve_ad_to_jac_alg(nlsolve))
return Shooting(ode_alg, nlsolve, jac_alg)
end
Expand All @@ -61,7 +62,7 @@ function __propagate_nlsolve_ad_to_jac_alg(nlsolve::N) where {N}
end

"""
MultipleShooting(nshoots::Int, ode_alg; nlsolve = NewtonRaphson(),
MultipleShooting(nshoots::Int, ode_alg = nothing; nlsolve = nothing,
grid_coarsening = true, jac_alg = BVPJacobianAlgorithm())

Multiple Shooting method, reduces BVP to an initial value problem and solves the IVP.
Expand All @@ -71,7 +72,8 @@ Significantly more stable than Single Shooting.

- `nshoots`: Number of shooting points.
- `ode_alg`: ODE algorithm to use for solving the IVP. Any solver which conforms to the
SciML `ODEProblem` interface can be used!
SciML `ODEProblem` interface can be used! (Defaults to `nothing` which will use
poly-algorithm if `DifferentialEquations.jl` is loaded else this must be supplied)

## Keyword Arguments

Expand Down Expand Up @@ -121,7 +123,7 @@ function update_nshoots(alg::MultipleShooting, nshoots::Int)
alg.grid_coarsening)
end

function MultipleShooting(nshoots::Int, ode_alg; nlsolve = NewtonRaphson(),
function MultipleShooting(nshoots::Int, ode_alg = nothing; nlsolve = nothing,
grid_coarsening = true, jac_alg = BVPJacobianAlgorithm())
@assert grid_coarsening isa Bool || grid_coarsening isa Function ||
grid_coarsening isa AbstractVector{<:Integer} ||
Expand All @@ -141,7 +143,7 @@ for order in (2, 3, 4, 5, 6)
"""
$($alg)(; nlsolve = NewtonRaphson(), jac_alg = BVPJacobianAlgorithm())

$($order)th order Monotonic Implicit Runge Kutta method, with Newton Raphson nonlinear solver as default.
$($order)th order Monotonic Implicit Runge Kutta method.

## Keyword Arguments

Expand Down Expand Up @@ -173,13 +175,9 @@ for order in (2, 3, 4, 5, 6)
pages={479-497}
}
"""
struct $(alg){N, J <: BVPJacobianAlgorithm} <: AbstractMIRK
nlsolve::N
jac_alg::J
end

function $(alg)(; nlsolve = NewtonRaphson(), jac_alg = BVPJacobianAlgorithm())
return $(alg)(nlsolve, jac_alg)
Base.@kwdef struct $(alg){N, J <: BVPJacobianAlgorithm} <: AbstractMIRK
nlsolve::N = nothing
jac_alg::J = BVPJacobianAlgorithm()
end
end
end
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2 changes: 1 addition & 1 deletion test/mirk/nonlinear_least_squares.jl
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Expand Up @@ -2,7 +2,7 @@ using BoundaryValueDiffEq, LinearAlgebra, Test

@testset "Overconstrained BVP" begin
SOLVERS = [mirk(; nlsolve) for mirk in (MIRK4, MIRK5, MIRK6),
nlsolve in (LevenbergMarquardt(), GaussNewton())]
nlsolve in (LevenbergMarquardt(), GaussNewton(), nothing)]

# OOP MP-BVP
f1(u, p, t) = [u[2], -u[1]]
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2 changes: 2 additions & 0 deletions test/shooting/nonlinear_least_squares.jl
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Expand Up @@ -2,8 +2,10 @@ using BoundaryValueDiffEq, LinearAlgebra, LinearSolve, OrdinaryDiffEq, Test

@testset "Overconstrained BVP" begin
SOLVERS = [
Shooting(Tsit5()),
Shooting(Tsit5(); nlsolve = LevenbergMarquardt()),
Shooting(Tsit5(); nlsolve = GaussNewton()),
MultipleShooting(10, Tsit5()),
MultipleShooting(10, Tsit5(); nlsolve = LevenbergMarquardt()),
MultipleShooting(10, Tsit5(); nlsolve = GaussNewton())]

Expand Down
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