Add missing COPS instances in JuMP + refactor test II

docs/src/contributing.md

@@ -26,13 +26,11 @@ The documentation should be added to the file in the `PureJuMP` folder.
 * A first version of the `meta` can be generated using `generate_meta`. A `String` is returned that can be copy-pasted into the `Meta` folder, and then edited.
 
 ```julia
-    using ADNLPModels, NLPModels, NLPModelsJuMP, OptimizationProblems
+    using ADNLPModels, Distributed, NLPModels, NLPModelsJuMP, OptimizationProblems, Test
     include("test/utils.jl")
-    # there must exists a function `problem_name` which loads the model in the environment
-    name = "problem_name"
-    open("$name.jl", "w") do io
-      print(io, generate_meta(name))
-    end
+    # there must exists a function `problem_name` which loads the model in the environment,
+    # it must be exported.
+    create_meta_files(String["catmix", "gasoil", "glider", "methanol", "pinene", "rocket", "steering"])
 ```
 
 * Problems modeled with `ADNLPModels` should be type-stable, i.e. they should all have keyword argument `type::Type{T} = Float64` where `T` is the type of the initial guess and the type used by the `NLPModel` API.

src/Meta/catmix.jl

@@ -0,0 +1,25 @@
+catmix_meta = Dict(
+  :nvar => 2302,
+  :variable_nvar => true,
+  :ncon => 2002,
+  :variable_ncon => true,
+  :minimize => true,
+  :name => "catmix",
+  :has_equalities_only => true,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :general,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => 0.0,
+  :is_feasible => true,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_catmix_nvar(; n::Integer = default_nvar, kwargs...) = 23 * n + 2
+get_catmix_ncon(; n::Integer = default_nvar, kwargs...) = 20 * n + 2
+get_catmix_nlin(; n::Integer = default_nvar, kwargs...) = 14 * n + 2
+get_catmix_nnln(; n::Integer = default_nvar, kwargs...) = 6 * n + 0
+get_catmix_nequ(; n::Integer = default_nvar, kwargs...) = 20 * n + 2
+get_catmix_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/gasoil.jl

@@ -0,0 +1,25 @@
+gasoil_meta = Dict(
+  :nvar => 2603,
+  :variable_nvar => true,
+  :ncon => 2600,
+  :variable_ncon => true,
+  :minimize => true,
+  :name => "gasoil",
+  :has_equalities_only => true,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :general,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => Inf,
+  :is_feasible => missing,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_gasoil_nvar(; n::Integer = default_nvar, kwargs...) = 26 * n + 3
+get_gasoil_ncon(; n::Integer = default_nvar, kwargs...) = 26 * n + 0
+get_gasoil_nlin(; n::Integer = default_nvar, kwargs...) = 18 * n + 0
+get_gasoil_nnln(; n::Integer = default_nvar, kwargs...) = 8 * n + 0
+get_gasoil_nequ(; n::Integer = default_nvar, kwargs...) = 26 * n + 0
+get_gasoil_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/glider.jl

@@ -0,0 +1,25 @@
+glider_meta = Dict(
+  :nvar => 506,
+  :variable_nvar => true,
+  :ncon => 407,
+  :variable_ncon => true,
+  :minimize => false,
+  :name => "glider",
+  :has_equalities_only => true,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :general,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => Inf,
+  :is_feasible => missing,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_glider_nvar(; n::Integer = default_nvar, kwargs...) = 5 * n + 6
+get_glider_ncon(; n::Integer = default_nvar, kwargs...) = 4 * n + 7
+get_glider_nlin(; n::Integer = default_nvar, kwargs...) = 7
+get_glider_nnln(; n::Integer = default_nvar, kwargs...) = 4 * n + 0
+get_glider_nequ(; n::Integer = default_nvar, kwargs...) = 4 * n + 7
+get_glider_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/methanol.jl

@@ -0,0 +1,25 @@
+methanol_meta = Dict(
+  :nvar => 3005,
+  :variable_nvar => true,
+  :ncon => 3000,
+  :variable_ncon => true,
+  :minimize => true,
+  :name => "methanol",
+  :has_equalities_only => true,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :general,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => Inf,
+  :is_feasible => missing,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_methanol_nvar(; n::Integer = default_nvar, kwargs...) = 30 * n + 5
+get_methanol_ncon(; n::Integer = default_nvar, kwargs...) = 30 * n + 0
+get_methanol_nlin(; n::Integer = default_nvar, kwargs...) = 21 * n + 0
+get_methanol_nnln(; n::Integer = default_nvar, kwargs...) = 9 * n + 0
+get_methanol_nequ(; n::Integer = default_nvar, kwargs...) = 30 * n + 0
+get_methanol_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/minsurf.jl

@@ -0,0 +1,25 @@
+minsurf_meta = Dict(
+  :nvar => 144,
+  :variable_nvar => true,
+  :ncon => 256,
+  :variable_ncon => true,
+  :minimize => true,
+  :name => "minsurf",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => false,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :linear,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => Inf,
+  :is_feasible => missing,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_minsurf_nvar(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2)*(ny + 2)
+get_minsurf_ncon(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2)*(ny + 2) + 2*(nx + ny + 4) + ((2*divrem(nx + 1, 4)[1] + divrem(nx + 1, 4)[2] + 1) * (2*divrem(ny + 1, 4)[1] + divrem(ny + 1, 4)[2] + 1))
+get_minsurf_nlin(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2)*(ny + 2) + 2*(nx + ny + 4) + ((2*divrem(nx + 1, 4)[1] + divrem(nx + 1, 4)[2] + 1) * (2*divrem(ny + 1, 4)[1] + divrem(ny + 1, 4)[2] + 1))
+get_minsurf_nnln(; n::Integer = default_nvar, kwargs...) = 0
+get_minsurf_nequ(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = 2*(nx + ny + 4)
+get_minsurf_nineq(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2) * (ny + 2) + ((2*divrem(nx + 1, 4)[1] + divrem(nx + 1, 4)[2] + 1) * (2*divrem(ny + 1, 4)[1] + divrem(ny + 1, 4)[2] + 1))

src/Meta/pinene.jl

@@ -0,0 +1,25 @@
+pinene_meta = Dict(
+  :nvar => 5005,
+  :variable_nvar => true,
+  :ncon => 5000,
+  :variable_ncon => true,
+  :minimize => true,
+  :name => "pinene",
+  :has_equalities_only => true,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :general,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => Inf,
+  :is_feasible => missing,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_pinene_nvar(; n::Integer = default_nvar, kwargs...) = 50 * n + 5
+get_pinene_ncon(; n::Integer = default_nvar, kwargs...) = 50 * n + 0
+get_pinene_nlin(; n::Integer = default_nvar, kwargs...) = 35 * n + 0
+get_pinene_nnln(; n::Integer = default_nvar, kwargs...) = 15 * n + 0
+get_pinene_nequ(; n::Integer = default_nvar, kwargs...) = 50 * n + 0
+get_pinene_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/rocket.jl

@@ -0,0 +1,25 @@
+rocket_meta = Dict(
+  :nvar => 405,
+  :variable_nvar => true,
+  :ncon => 304,
+  :variable_ncon => true,
+  :minimize => false,
+  :name => "rocket",
+  :has_equalities_only => true,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :general,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => Inf,
+  :is_feasible => missing,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_rocket_nvar(; n::Integer = default_nvar, kwargs...) = 4 * n + 5
+get_rocket_ncon(; n::Integer = default_nvar, kwargs...) = 3 * n + 4
+get_rocket_nlin(; n::Integer = default_nvar, kwargs...) = 4
+get_rocket_nnln(; n::Integer = default_nvar, kwargs...) = 3 * n + 0
+get_rocket_nequ(; n::Integer = default_nvar, kwargs...) = 3 * n + 4
+get_rocket_nineq(; n::Integer = default_nvar, kwargs...) = 0

src/Meta/steering.jl

@@ -0,0 +1,25 @@
+steering_meta = Dict(
+  :nvar => 506,
+  :variable_nvar => true,
+  :ncon => 408,
+  :variable_ncon => true,
+  :minimize => true,
+  :name => "steering",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => true,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :general,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => Inf,
+  :is_feasible => missing,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_steering_nvar(; n::Integer = default_nvar, kwargs...) = 5 * n + 6
+get_steering_ncon(; n::Integer = default_nvar, kwargs...) = 4 * n + 8
+get_steering_nlin(; n::Integer = default_nvar, kwargs...) = 8
+get_steering_nnln(; n::Integer = default_nvar, kwargs...) = 4 * n + 0
+get_steering_nequ(; n::Integer = default_nvar, kwargs...) = 4 * n + 7
+get_steering_nineq(; n::Integer = default_nvar, kwargs...) = 1

src/Meta/torsion.jl

@@ -0,0 +1,25 @@
+torsion_meta = Dict(
+  :nvar => 144,
+  :variable_nvar => true,
+  :ncon => 144,
+  :variable_ncon => true,
+  :minimize => true,
+  :name => "torsion",
+  :has_equalities_only => false,
+  :has_inequalities_only => false,
+  :has_bounds => false,
+  :has_fixed_variables => false,
+  :objtype => :other,
+  :contype => :linear,
+  :best_known_lower_bound => -Inf,
+  :best_known_upper_bound => -0.3305785123966928,
+  :is_feasible => true,
+  :defined_everywhere => missing,
+  :origin => :unknown,
+)
+get_torsion_nvar(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2) * (ny + 2)
+get_torsion_ncon(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2) * (ny + 2)
+get_torsion_nlin(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2) * (ny + 2)
+get_torsion_nnln(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = 0
+get_torsion_nequ(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = 0
+get_torsion_nineq(; n::Integer = default_nvar, nx = Int(round(sqrt(max(1, n - 2)))), ny = Int(round(sqrt(max(1, n - 2)))), kwargs...) = (nx + 2) * (ny + 2)

src/OptimizationProblems.jl

@@ -10,9 +10,9 @@ const default_nvar = ADNLPProblems.default_nvar
 export PureJuMP, ADNLPProblems
 
 path = dirname(@__FILE__)
-files = filter(x -> x[(end - 2):end] == ".jl", readdir(path * "/Meta"))
+files = filter(x -> x[(end - 2):end] == ".jl", readdir(joinpath(path, "Meta")))
 for file in files
-  include("Meta/" * file)
+  include(joinpath(path, "Meta", file))
 end
 number_of_problems = length(files)
 

src/PureJuMP/catmix.jl

@@ -0,0 +1,78 @@
+# Catalyst Mixing Problem
+# Collocation formulation
+# COPS 3.0 - November 2002
+# COPS 3.1 - March 2004
+
+export catmix
+
+function catmix(args...; n::Int = default_nvar, kwargs...)
+  ne = 2
+  nc = 3
+
+  tf = 1
+  h = tf / n   # Final time
+  fact = [factorial(k) for k in 0:nc]
+
+  rho = [
+    0.11270166537926,
+    0.50000000000000,
+    0.88729833462074,
+  ]
+  bc = [1.0, 0.0]  # Boundary conditions for x
+  alpha = 0.0      # Smoothing parameter
+
+  model = Model()
+
+  @variable(model, 0.0 <= u[i=1:n, j=1:nc] <= 1.0, start=0.0)
+  @variable(model, v[i=1:n, s=1:ne], start=mod(s, ne))
+  @variable(model, w[i=1:n, j=1:nc, s=1:ne], start=0.0)
+  @variable(model, pp[i=1:n, j=1:nc, s=1:ne], start=mod(s, ne))
+  @variable(model, Dpp[i=1:n, j=1:nc, s=1:ne], start=0.0)
+  @variable(model, ppf[s=1:ne], start=mod(s, ne))
+
+  @objective(
+    model,
+    Min,
+    -1.0 + ppf[1] + ppf[2] +
+    alpha/h*sum((u[i+1, j] - u[i, j])^2 for i in 1:n-1, j in 1:nc)
+  )
+
+  # Collocation model
+  @constraint(
+    model,
+    [i=1:n, k=1:nc, s=1:ne],
+    pp[i, k, s] == v[i, s] + h*sum(w[i, j, s]*(rho[k]^j/fact[j+1]) for j in 1:nc)
+  )
+  @constraint(
+    model,
+    [i=1:n, k=1:nc, s=1:ne],
+    Dpp[i, k, s] == sum(w[i, j, s]*(rho[k]^(j-1)/fact[j]) for j in 1:nc)
+  )
+  @constraint(
+    model,
+    [s=1:ne],
+    ppf[s] == v[n, s] + h * sum(w[n, j, s] / fact[j+1] for j in 1:nc)
+  )
+  # Continuity
+  @constraint(
+    model,
+    continuity[i=1:n-1, s=1:ne],
+    v[i, s] + sum(w[i, j, s] * h / fact[j+1] for j in 1:nc) == v[i+1, s]
+  )
+  # Dynamics
+  @constraint(
+    model,
+    de1[i=1:n, j=1:nc],
+    Dpp[i,j,1] == u[i,j] * (10.0*pp[i,j,2] - pp[i,j,1]),
+  )
+  @constraint(
+    model,
+    de2[i=1:n, j=1:nc],
+    Dpp[i,j,2] == u[i,j] * (pp[i,j,1] - 10.0*pp[i,j,2]) - (1 - u[i,j])*pp[i,j,2]
+  )
+  @constraint(model, b_eqn[s=1:ne], v[1, s] == bc[s])
+
+  return model
+end
+
+