Test new signature

src/apinene.jl

@@ -2,7 +2,7 @@ export apinene
 
 # Isometrization of α-pinene COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
-function apinene(args...; n = 400, kwargs...)
+function apinene(n :: Int = 400, args...; kwargs...)
   T = 36420.0
   model = CartesianDiscreteModel((0, T), n)
 

src/bearing.jl

@@ -3,7 +3,8 @@ export bearing
 # Journal Bearing COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n= 50, 75, 100
-function bearing(args...; n = 50, kwargs...)
+function bearing(n :: Int = 50, args...; kwargs...)
+
   ϵ = 0.1
   b = 10
 

src/burger1d.jl

@@ -23,7 +23,7 @@ Suggestions:
 - FEOperatorFromTerms has only one term. We might consider splitting linear and
 nonlinear terms.
 """
-function burger1d(args...; n::Int = 512, kwargs...)
+function burger1d(n :: Int = 512, args...; kwargs...)
 
   #Domain
   domain = (0, 1)

src/burger1d_param.jl

@@ -1,6 +1,6 @@
 export burger1d_param
 
-function burger1d_param(args...; n = 512, kwargs...)
+function burger1d_param(n :: Int = 512, args...; kwargs...)
   domain = (0, 1)
   partition = n
   model = CartesianDiscreteModel(domain, partition)

src/catmix.jl

@@ -3,7 +3,7 @@ export catmix
 # Catalyst Mixing COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=100, 200, 400
-function catmix(args...; n::Int = 100, kwargs...)
+function catmix(n :: Int = 100, args...; kwargs...)
 
   #Domain
   domain = (0, 1)

src/cellincrease.jl

@@ -5,7 +5,7 @@ Mairet, F., & Bayen, T. (2021). The promise of dawn: microalgae photoacclimation
 
 Using a photosynthetic rate proportional to the photosynthetic apparatus mass fraction.
 """
-function cellincrease(args...; x0 = [0.6, 0.1], n = 10, T = 7, kwargs...)
+function cellincrease(n :: Int = 10, args...; x0 = [0.6, 0.1], T = 7, kwargs...)
   kp(x) = 1.01
   kr(x) = 2.03
 

src/cellincrease_MichaelisMenten.jl

@@ -5,7 +5,7 @@ Mairet, F., & Bayen, T. (2021). The promise of dawn: microalgae photoacclimation
 
 Using Michaelis-Menten's function for the photosynthetic rate.
 """
-function cellincrease_MichaelisMenten(args...; x0 = [0.6, 0.1], n = 10, T = 7, kwargs...)
+function cellincrease_MichaelisMenten(n :: Int = 10, args...; x0 = [0.6, 0.1], T = 7, kwargs...)
   kp(x) = 1.6
   kr(x) = 2.1
   K(x) = 140.

src/channel.jl

@@ -2,7 +2,7 @@ export channel
 
 # Isometrization of Flow in a Channel COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
-function channel(args...; n = 400, kwargs...)
+function channel(n :: Int = 400, args...; kwargs...)
   model = CartesianDiscreteModel((0, 1), n)
 
   labels = get_face_labeling(model)

src/controlelasticmembrane1.jl

@@ -16,7 +16,7 @@ The force term is h(x_1,x_2) = - sin( ω x_1)sin( ω x_2) with  ω = π - 1/8.
 In this first case, the bound constraints are constants with
 umin(x) = 0.0 and umax(x) = 1.0.
 """
-function controlelasticmembrane1(args...; n::Int = 10, kargs...)
+function controlelasticmembrane1(n :: Int = 10, args...; kargs...)
 
   #Domain
   domain = (-1, 1, -1, 1)

src/controlelasticmembrane2.jl

@@ -15,7 +15,7 @@ The force term is h(x_1,x_2) = - sin( ω x_1)sin( ω x_2) with  ω = π - 1/8.
 In this second case, the bound constraints are
 umin(x) = x_1+x_2 and umax(x) = x_1^2+x_2^2 applied at the midpoint of the cells.
 """
-function controlelasticmembrane2(; n::Int = 10, args...)
+function controlelasticmembrane2(n :: Int = 10, args...; kargs...)
 
   # Domain
   domain = (-1, 1, -1, 1)

src/controlsir.jl

@@ -1,6 +1,6 @@
 export controlsir
 
-function controlsir(args...; x0 = [1, 2], n = 10, a = 0.2, b = 0.1, μ = 0.1, T = 1, kwargs...)
+function controlsir(n :: Int = 10, args...; x0 = [1, 2], a = 0.2, b = 0.1, μ = 0.1, T = 1, kwargs...)
   model = CartesianDiscreteModel((0, T), n)
 
   labels = get_face_labeling(model)

src/dirichlet.jl

@@ -3,7 +3,8 @@ export dirichlet
 # Transition States for the Dirichlet Problem COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=10, 20, 40
-function dirichlet(args...; n = 10, kwargs...)
+function dirichlet(n :: Int = 10, args...; kwargs...)
+
   domain = (-1, 1, -1, 1)
   model = CartesianDiscreteModel(domain, n)
 

src/dynamicsir.jl

@@ -1,6 +1,6 @@
 export dynamicsir
 
-function dynamicsir(args...; x0 = [1, 2], n = 10, T = 1, kwargs...)
+function dynamicsir(n :: Int = 10, args...; x0 = [1, 2], T = 1, kwargs...)
   model = CartesianDiscreteModel((0, T), n)
   labels = get_face_labeling(model)
   add_tag_from_tags!(labels, "diri0", [1]) #initial time condition

src/gasoil.jl

@@ -3,7 +3,8 @@ export gasoil
 # Catalytic Cracking of Gas Oil COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=100, 200, 400
-function gasoil(args...; n = 100, kwargs...)
+function gasoil(n :: Int = 100, args...; kwargs...)
+
   model = CartesianDiscreteModel((0, 1), n)
 
   valuetype = Float64

src/glider.jl

@@ -3,7 +3,8 @@ export glider
 # Hang Glider COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=100, 200, 400
-function glider(args...; n = 100, kwargs...)
+function glider(n :: Int = 100, args...; kwargs...)
+
   model = CartesianDiscreteModel((0, 1), n)
   labels = get_face_labeling(model)
   add_tag_from_tags!(labels, "diri0", [1]) #initial time condition

src/henon.jl

@@ -3,7 +3,8 @@ export henon
 # Transition States for the Henon Problem COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=10, 20, 40
-function henon(args...; n = 10, kwargs...)
+function henon(n :: Int = 10, args...; kwargs...)
+
   domain = (-1, 1, -1, 1)
   model = CartesianDiscreteModel(domain, n)
 

src/ignition.jl

@@ -4,7 +4,7 @@ export ignition
 # Example 2. IGNITION
 # Multilevel Active-Set Trust-Region (MASTR) Method for Bound Constrained Minimization
 # Alena Kopaničáková and Rolf Krause
-function ignition(args...; n = 3, kwargs...)
+function ignition(n :: Int = 3, args...; kwargs...)
   # n est la taille de la discrétisation (entier)
   domain = (0, 1, 0, 1)
   model = CartesianDiscreteModel(domain, (n, n))

src/incompressibleNavierStokes.jl

@@ -10,7 +10,7 @@ https://gridap.github.io/Tutorials/stable/pages/t008_inc_navier_stokes/
 
 It has no objective function and no control, just the PDE.
 """
-function incompressiblenavierstokes(args...; n = 3, kwargs...)
+function incompressiblenavierstokes(n :: Int = 3, args...; kwargs...)
   domain = (0, 1, 0, 1)
   partition = (n, n)
   model = CartesianDiscreteModel(domain, partition)

src/inversepoissonproblem2d.jl

@@ -28,7 +28,7 @@ This example has been used in [Section 9.2](Estrin, R., Friedlander, M. P., Orba
 Implementing a smooth exact penalty function for equality-constrained nonlinear optimization.
 SIAM Journal on Scientific Computing, 42(3), A1809-A1835.)
 """
-function inversepoissonproblem2d(; n::Int = 100)
+function inversepoissonproblem2d(n :: Int = 100, args...; kargs...)
 
   #Domain
   domain = (-1, 1, -1, 1)

src/lane_emden.jl

@@ -3,7 +3,8 @@ export lane_emden
 # Transition States for the Lane-Emden Problem COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=10, 20, 40
-function lane_emden(args...; n = 10, kwargs...)
+function lane_emden(n :: Int = 10, args...; kwargs...)
+
   domain = (-1, 1, -1, 1)
   model = CartesianDiscreteModel(domain, n)
 

src/membrane.jl

@@ -9,7 +9,7 @@ Alena Kopaničáková and Rolf Krause
 The solution and original problem is given in
 Domorádová, M., & Dostál, Z. (2007). Projector preconditioning for partially bound‐constrained quadratic optimization. Numerical Linear Algebra with Applications, 14(10), 791-806.
 """
-function membrane(args...; n = 3, kwargs...)
+function membrane(n :: Int = 3, args...; kwargs...)
   # n est la taille de la discrétisation (entier)
   domain = (0, 1, 0, 1)
   model = CartesianDiscreteModel(domain, (n, n))

src/methanol.jl

@@ -3,7 +3,8 @@ export methanol
 # Methanol to Hydrocarbons COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=100, 200, 400
-function methanol(args...; n = 100, kwargs...)
+function methanol(n :: Int = 100, args...; kwargs...)
+
   model = CartesianDiscreteModel((0, 1), n)
 
   valuetype = Float64

src/minsurf.jl

@@ -3,7 +3,8 @@ export minsurf
 # Minimal Surface with Obstacle COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n= 50, 75, 100
-function minsurf(args...; n = 50, kwargs...)
+function minsurf(n :: Int = 50, args...; kwargs...)
+
   domain = (0, 1, 0, 1)
   model = CartesianDiscreteModel(domain, (n, n))
   trian = Triangulation(model)

src/morebv.jl

@@ -4,7 +4,7 @@ export morebv
 # Example 3. MOREBV
 # Multilevel Active-Set Trust-Region (MASTR) Method for Bound Constrained Minimization
 # Alena Kopaničáková and Rolf Krause
-function morebv(args...; n = 3, kwargs...)
+function morebv(n :: Int = 3, args...; kwargs...)
   # n est la taille de la discrétisation (entier)
   domain = (0, 1, 0, 1)
   model = CartesianDiscreteModel(domain, (n, n))

src/penalizedpoisson.jl

@@ -12,7 +12,7 @@ The minimizer of this problem is the solution of the Poisson equation:
 
 This example has been used in [Exercice 10.2.4 (p. 308)](G. Allaire, Analyse numérique et optimisation, Les éditions de Polytechnique)
 """
-function penalizedpoisson(args...; n = 2^4, kwargs...)
+function penalizedpoisson(n :: Int = 2^4, args...; kwargs...)
   domain = (0, 1, 0, 1)
   partition = (n, n)
   model = CartesianDiscreteModel(domain, partition)

src/poisson3d.jl

@@ -10,7 +10,7 @@ So, the minimal value is expected to be 0.
 It is inspired from the 2nd tutorial in Gridap.jl:
 https://gridap.github.io/Tutorials/stable/pages/t002_validation/
 """
-function poisson3d(; n::Int = 10)
+function poisson3d(n :: Int = 10, args...; kwargs...)
 
   #For instance, the following lines build a CartesianDiscreteModel for the unit cube
   # (0,1)^3 with n cells per direction

src/poisson_with_Neumann_and_Dirichlet.jl

@@ -1,6 +1,6 @@
 export poisson_with_Neumann_and_Dirichlet
 
-function poisson_with_Neumann_and_Dirichlet(args...; n::Int = 10, kwargs...)
+function poisson_with_Neumann_and_Dirichlet(n :: Int = 10, args...; kwargs...)
   #model = DiscreteModelFromFile("https://github.com/gridap/Tutorials/tree/master/models/model.json")
   # model = DiscreteModelFromFile("models/model.json")
   #writevtk(model,"model")

src/poissonboltzman2d.jl

@@ -24,7 +24,7 @@ The specificity of the problem:
 - quadratic objective function;
 - nonlinear constraints with AD jacobian;
 """
-function poissonboltzman2d(; n::Int = 100)
+function poissonboltzman2d(n :: Int = 100, args...; kwargs...)
 
   #Domain
   domain = (-1, 1, -1, 1)

src/poissonmixed.jl

@@ -11,7 +11,7 @@ export poissonmixed
 # * |k| = 2
 #
 ###############################################################################
-function poissonmixed(args...; n = 3, kwargs...)
+function poissonmixed(n :: Int = 3, args...; kwargs...)
   domain = (0, 1, 0, 1)
   partition = (n, n)
   model = CartesianDiscreteModel(domain, partition)

src/poissonmixed2.jl

@@ -11,7 +11,7 @@ export poissonmixed2
 # * |k| = 2
 #
 ###############################################################################
-function poissonmixed2(args...; n = 3, kwargs...)
+function poissonmixed2(n :: Int = 3, args...; kwargs...)
   domain = (0, 1, 0, 1)
   partition = (n, n)
   model = CartesianDiscreteModel(domain, partition)

src/poissonparam.jl

@@ -11,7 +11,7 @@ export poissonparam
 # * |k| = 1
 #
 ###############################################################################
-function poissonparam(args...; n = 3, kwargs...)
+function poissonparam(n :: Int = 3, args...; kwargs...)
   domain = (0, 1, 0, 1)
   partition = (n, n)
   model = CartesianDiscreteModel(domain, partition)

src/robot.jl

@@ -3,7 +3,7 @@ export robot
 # Isometrization of Robot Arm COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=200, 400, 800
-function robot(args...; n = 400, kwargs...)
+function robot(n :: Int = 400, args...; kwargs...)
   T = 1.0
   model = CartesianDiscreteModel((0, T), n)
   labels = get_face_labeling(model)

src/rocket.jl

@@ -3,7 +3,7 @@ export rocket
 # Goddard Rocket COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=400, 800, 1600
-function rocket(args...; n = 400, kwargs...)
+function rocket(n :: Int = 400, args...; kwargs...)
 
   model = CartesianDiscreteModel((0, 1), n)
   labels = get_face_labeling(model)

src/sis.jl

@@ -1,6 +1,6 @@
 export sis
 
-function sis(args...; x0 = [1, 2], n = 10, a = 0.2, b = 0.7, T = 1, kwargs...)
+function sis(n :: Int = 10, args...; x0 = [1, 2], a = 0.2, b = 0.7, T = 1, kwargs...)
   model = CartesianDiscreteModel((0, T), n)
   labels = get_face_labeling(model)
   add_tag_from_tags!(labels, "diri0", [1]) #initial time condition

src/smallestlaplacianeigenvalue.jl

@@ -23,7 +23,7 @@ TODO:
 - does the 1 work as it is? or should it be put in lcon, ucon?
 - it is 1D for now.
 """
-function smallestlaplacianeigenvalue(; n::Int = 10, args...)
+function smallestlaplacianeigenvalue(n :: Int = 10, args...; kwargs...)
 
   #Domain
   domain = (0, 1)

src/steering.jl

@@ -3,7 +3,7 @@ export steering
 # Isometrization of Particle Steering COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
 # n=200, 400, 800
-function steering(args...; n = 400, kwargs...)
+function steering(n :: Int = 400, args...; kwargs...)
   T = 1.0
   model = CartesianDiscreteModel((0, T), n)
   labels = get_face_labeling(model)

src/torebrachistochrone.jl

@@ -1,6 +1,6 @@
 export torebrachistochrone
 
-function torebrachistochrone(args...; n = 3, kwargs...)
+function torebrachistochrone(n :: Int = 3, args...; kwargs...)
   # n est la taille de la discrétisation (entier)
   # le domain au format (t₀, T)
   domain = (0, 1)

src/torsion.jl

@@ -2,7 +2,7 @@ export torsion
 
 # Elastic-Plastic Torsion COPS Problem v.0.3.1
 # https://www.mcs.anl.gov/~more//cops/cops3.pdf
-function torsion(args...; n = 3, kwargs...)
+function torsion(n :: Int = 3, args...; kwargs...)
   # n est la taille de la discrétisation (entier)
   # le domain au format (t₀, T)
   domain = (0, 1, 0, 1)

test/runtests.jl

@@ -6,15 +6,13 @@ include("utils.jl")
 
 # Test that every problem can be instantiated.
 for prob in setdiff(names(PDEOptimizationProblems), [:PDEOptimizationProblems])
-  @time begin
-    print(prob)
-    prob_fn = PDEOptimizationProblems.eval(prob)
-    nlp = prob_fn(n = 10)
-    println(" nvar=", nlp.meta.nvar, " ncon=", nlp.meta.ncon)
-    obj(nlp, nlp.meta.x0)
-    nlp.meta.ncon != 0 && cons(nlp, nlp.meta.x0)
-    hess(nlp, nlp.meta.x0, nlp.meta.y0)
-    # test meta information
-    meta_sanity(prob)
-  end
+  print(prob)
+  prob_fn = PDEOptimizationProblems.eval(prob)
+  nlp = prob_fn(10)
+  println(" nvar=",nlp.meta.nvar," ncon=",nlp.meta.ncon)
+  obj(nlp, nlp.meta.x0)
+  nlp.meta.ncon != 0 && cons(nlp, nlp.meta.x0)
+  hess(nlp, nlp.meta.x0, nlp.meta.y0)
+  # test meta information
+  meta_sanity(prob)
 end

test/utils.jl

@@ -3,7 +3,7 @@ function meta_sanity(name)
   meta = eval(Meta.parse("PDEOptimizationProblems.$(name)_meta"))
   n, m = eval(Meta.parse("PDEOptimizationProblems.get_$(name)_meta($ndef)"))
 
-  nlp = eval(Meta.parse("PDEOptimizationProblems.$(name)(n = $ndef)"))
+  nlp = eval(Meta.parse("PDEOptimizationProblems.$(name)($ndef)"))
 
   @show nlp.meta.name, meta[:name]
   if !(name in [:incompressiblenavierstokes])