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Remove d_prototype & create type tests for p_prototype (#104)
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* Removed d_prototype

* bug fix: removed d_prototype keywords in tests

* Update test/runtests.jl

Co-authored-by: Hendrik Ranocha <[email protected]>

* Set version to v0.2.0

* Updated News

---------

Co-authored-by: Hendrik Ranocha <[email protected]>
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@SKopecz @ranocha
SKopecz and ranocha authored Jul 18, 2024
1 parent 69db9ab commit f86c263
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Showing 6 changed files with 97 additions and 31 deletions.
7 changes: 7 additions & 0 deletions NEWS.md
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Expand Up @@ -5,6 +5,11 @@ PositiveIntegrators.jl.jl follows the interpretation of
used in the Julia ecosystem. Notable changes will be documented in this file
for human readability.

## Changes when updating to v0.2 from v0.1.x

#### Removed

- The optional keyword argument `d_prototype` has been removed from `PDSProblem`

## Changes in the v0.1 lifecycle

Expand All @@ -21,3 +26,5 @@ for human readability.
`prob_pds_linmod`, `prob_pds_nonlinmod`, `prob_pds_npzd`, `prob_pds_robertson`,
`prob_pds_sir`, `prob_pds_stratreac`
- Modified Patankar methods `MPE` and `MPRK22`


2 changes: 1 addition & 1 deletion Project.toml
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@@ -1,7 +1,7 @@
name = "PositiveIntegrators"
uuid = "d1b20bf0-b083-4985-a874-dc5121669aa5"
authors = ["Stefan Kopecz, Hendrik Ranocha, and contributors"]
version = "0.1.16"
version = "0.2.0"

[deps]
FastBroadcast = "7034ab61-46d4-4ed7-9d0f-46aef9175898"
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12 changes: 6 additions & 6 deletions src/mprk.jl
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Expand Up @@ -363,7 +363,7 @@ function alg_cache(alg::MPE, u, rate_prototype, ::Type{uEltypeNoUnits},
linsolve = init(linprob, alg.linsolve, alias_A = true, alias_b = true,
assumptions = LinearSolve.OperatorAssumptions(true))

MPECache(P, zero(u), σ, tab, linsolve_rhs, linsolve)
MPECache(P, similar(u), σ, tab, linsolve_rhs, linsolve)
else
throw(ArgumentError("MPE can only be applied to production-destruction systems"))
end
Expand Down Expand Up @@ -670,8 +670,8 @@ function alg_cache(alg::MPRK22, u, rate_prototype, ::Type{uEltypeNoUnits},
assumptions = LinearSolve.OperatorAssumptions(true))

MPRK22Cache(tmp, P, P2,
zero(u), # D
zero(u), # D2
similar(u), # D
similar(u), # D2
σ,
tab, #MPRK22ConstantCache
linsolve)
Expand Down Expand Up @@ -1246,9 +1246,9 @@ function alg_cache(alg::Union{MPRK43I, MPRK43II}, u, rate_prototype, ::Type{uElt
assumptions = LinearSolve.OperatorAssumptions(true))
MPRK43ConservativeCache(tmp, tmp2, P, P2, P3, σ, tab, linsolve)
elseif f isa PDSFunction
D = zero(u)
D2 = zero(u)
D3 = zero(u)
D = similar(u)
D2 = similar(u)
D3 = similar(u)

linprob = LinearProblem(P3, _vec(tmp))
linsolve = init(linprob, alg.linsolve, alias_A = true, alias_b = true,
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21 changes: 7 additions & 14 deletions src/proddest.jl
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Expand Up @@ -3,9 +3,8 @@ abstract type AbstractPDSProblem end

"""
PDSProblem(P, D, u0, tspan, p = NullParameters();
p_prototype = nothing,
d_prototype = nothing,
analytic = nothing)
p_prototype = nothing,
analytic = nothing)
A structure describing a system of ordinary differential equations in form of a production-destruction system (PDS).
`P` denotes the production matrix.
Expand All @@ -20,13 +19,9 @@ The functions `P` and `D` can be used either in the out-of-place form with signa
### Keyword arguments: ###
- `p_prototype`: If `P` is given in in-place form, `p_prototype` is used to store evaluations of `P`.
- `p_prototype`: If `P` is given in in-place form, `p_prototype` or copies thereof are used to store evaluations of `P`.
If `p_prototype` is not specified explicitly and `P` is in-place, then `p_prototype` will be internally
set to `zeros(eltype(u0), (length(u0), length(u0)))`.
- `d_prototype`: If `D` is given in in-place form, `d_prototype` is used to store evaluations of `D`.
If `d_prototype` is not specified explicitly and `D` is in-place, then `d_prototype` will be internally
set to `zeros(eltype(u0), (length(u0),))`.
- `analytic`: The analytic solution of a PDS must be given in the form `f(u0,p,t)`.
Specifying the analytic solution can be useful for plotting and convergence tests.
Expand Down Expand Up @@ -86,18 +81,16 @@ end
# (arbitrary functions)
function PDSProblem{iip}(P, D, u0, tspan, p = NullParameters();
p_prototype = nothing,
d_prototype = nothing,
analytic = nothing,
kwargs...) where {iip}

# p_prototype is used to store evaluations of P, if P is in-place.
if isnothing(p_prototype) && iip
p_prototype = zeros(eltype(u0), (length(u0), length(u0)))
end
# d_prototype is used to store evaluations of D, if D is in-place.
if isnothing(d_prototype) && iip
d_prototype = zeros(eltype(u0), (length(u0),))
end
# If a PDSFunction is to be evaluated and D is in-place, then d_prototype is used to store
# evaluations of D.
d_prototype = similar(u0)

PD = PDSFunction{iip}(P, D; p_prototype = p_prototype, d_prototype = d_prototype,
analytic = analytic)
Expand Down Expand Up @@ -177,7 +170,7 @@ The function `P` can be given either in the out-of-place form with signature
### Keyword arguments: ###
- `p_prototype`: If `P` is given in in-place form, `p_prototype` is used to store evaluations of `P`.
- `p_prototype`: If `P` is given in in-place form, `p_prototype` or copies thereof are used to store evaluations of `P`.
If `p_prototype` is not specified explicitly and `P` is in-place, then `p_prototype` will be internally
set to `zeros(eltype(u0), (length(u0), length(u0)))`.
- `analytic`: The analytic solution of a PDS must be given in the form `f(u0,p,t)`.
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10 changes: 5 additions & 5 deletions src/sspmprk.jl
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Expand Up @@ -252,8 +252,8 @@ function alg_cache(alg::SSPMPRK22, u, rate_prototype, ::Type{uEltypeNoUnits},
assumptions = LinearSolve.OperatorAssumptions(true))

SSPMPRK22Cache(tmp, P, P2,
zero(u), # D
zero(u), # D2
similar(u), # D
similar(u), # D2
σ,
tab, #MPRK22ConstantCache
linsolve)
Expand Down Expand Up @@ -760,9 +760,9 @@ function alg_cache(alg::SSPMPRK43, u, rate_prototype, ::Type{uEltypeNoUnits},
assumptions = LinearSolve.OperatorAssumptions(true))
SSPMPRK43ConservativeCache(tmp, tmp2, P, P2, P3, σ, ρ, tab, linsolve)
elseif f isa PDSFunction
D = zero(u)
D2 = zero(u)
D3 = zero(u)
D = similar(u)
D2 = similar(u)
D3 = similar(u)

linprob = LinearProblem(P3, _vec(tmp))
linsolve = init(linprob, alg.linsolve, alias_A = true, alias_b = true,
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76 changes: 71 additions & 5 deletions test/runtests.jl
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Expand Up @@ -495,17 +495,14 @@ end
# problem with sparse matrices
p_prototype = spdiagm(-1 => ones(eltype(u0), N - 1),
N - 1 => ones(eltype(u0), 1))
d_prototype = zero(u0)
linear_advection_fd_upwind_PDS_sparse = PDSProblem(linear_advection_fd_upwind_P!,
linear_advection_fd_upwind_D!,
u0, tspan;
p_prototype = p_prototype,
d_prototype = d_prototype)
p_prototype = p_prototype)
linear_advection_fd_upwind_PDS_sparse_2 = PDSProblem{true}(linear_advection_fd_upwind_P!,
linear_advection_fd_upwind_D!,
u0, tspan;
p_prototype = p_prototype,
d_prototype = d_prototype)
p_prototype = p_prototype)
linear_advection_fd_upwind_ConsPDS_sparse = ConservativePDSProblem(linear_advection_fd_upwind_P!,
u0, tspan;
p_prototype = p_prototype)
Expand Down Expand Up @@ -1201,6 +1198,75 @@ end
end
end

# Here we check that the type of p_prototype actually
# defines the types of the Ps inside the algorithm caches.
# We test sparse, tridiagonal, and dense matrices.
@testset "Prototype type check" begin
#prod and dest functions
prod_inner! = (P, u, p, t) -> begin
fill!(P, zero(eltype(P)))
for i in 1:(length(u) - 1)
P[i, i + 1] = i * u[i]
end
return nothing
end
prod_sparse! = (P, u, p, t) -> begin
@test P isa SparseMatrixCSC
prod_inner!(P, u, p, t)
return nothing
end
prod_tridiagonal! = (P, u, p, t) -> begin
@test P isa Tridiagonal
prod_inner!(P, u, p, t)
return nothing
end
prod_dense! = (P, u, p, t) -> begin
@test P isa Matrix
prod_inner!(P, u, p, t)
return nothing
end
dest! = (D, u, p, t) -> begin
fill!(D, zero(eltype(D)))
end
#prototypes
P_tridiagonal = Tridiagonal([0.1, 0.2, 0.3],
[0.0, 0.0, 0.0, 0.0],
[0.4, 0.5, 0.6])
P_dense = Matrix(P_tridiagonal)
P_sparse = sparse(P_tridiagonal)
# problem definition
u0 = [1.0, 1.5, 2.0, 2.5]
tspan = (0.0, 1.0)
dt = 0.5
## conservative PDS
prob_default = ConservativePDSProblem(prod_dense!, u0, tspan)
prob_tridiagonal = ConservativePDSProblem(prod_tridiagonal!, u0, tspan;
p_prototype = P_tridiagonal)
prob_dense = ConservativePDSProblem(prod_dense!, u0, tspan;
p_prototype = P_dense)
prob_sparse = ConservativePDSProblem(prod_sparse!, u0, tspan;
p_prototype = P_sparse)
## nonconservative PDS
prob_default2 = PDSProblem(prod_dense!, dest!, u0, tspan)
prob_tridiagonal2 = PDSProblem(prod_tridiagonal!, dest!, u0, tspan;
p_prototype = P_tridiagonal)
prob_dense2 = PDSProblem(prod_dense!, dest!, u0, tspan;
p_prototype = P_dense)
prob_sparse2 = PDSProblem(prod_sparse!, dest!, u0, tspan;
p_prototype = P_sparse)
#solve and test
for alg in (MPE(), MPRK22(0.5), MPRK22(1.0), MPRK43I(1.0, 0.5),
MPRK43I(0.5, 0.75),
MPRK43II(2.0 / 3.0), MPRK43II(0.5), SSPMPRK22(0.5, 1.0),
SSPMPRK43())
for prob in (prob_default, prob_tridiagonal, prob_dense, prob_sparse,
prob_default2,
prob_tridiagonal2, prob_dense2, prob_sparse2)
solve(prob, alg; dt, adaptive = false)
end
end
end

# Here we check the convergence order of pth-order schemes for which
# also an interpolation of order p is available
@testset "Convergence tests (conservative)" begin
Expand Down

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Registration pull request created: JuliaRegistries/General/111313

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## Breaking changes

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Tagging

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