Add QP-rand problem

Project.toml

@@ -10,6 +10,7 @@ NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 Noise = "81d43f40-5267-43b7-ae1c-8b967f377efa"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
+SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
 
 [compat]
 ADNLPModels = "^0.3, 0.4, 0.7"
@@ -24,7 +25,8 @@ julia = "^1.3.0"
 ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+QuadraticModels = "f468eda6-eac5-11e8-05a5-ff9e497bcd19"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 
 [targets]
-test = ["ADNLPModels", "DifferentialEquations", "MLDatasets", "Test"]
+test = ["ADNLPModels", "DifferentialEquations", "MLDatasets", "QuadraticModels", "Test"]

docs/Project.toml

@@ -3,6 +3,7 @@ ADNLPModels = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 DifferentialEquations = "0c46a032-eb83-5123-abaf-570d42b7fbaa"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
+QuadraticModels = "f468eda6-eac5-11e8-05a5-ff9e497bcd19"
 RegularizedProblems = "ea076b23-609f-44d2-bb12-a4ae45328278"
 
 [compat]

docs/make.jl

@@ -1,5 +1,5 @@
 using Documenter
-using ADNLPModels, DifferentialEquations, MLDatasets
+using ADNLPModels, DifferentialEquations, QuadraticModels
 using RegularizedProblems
 
 makedocs(

src/RegularizedProblems.jl

@@ -1,6 +1,6 @@
 module RegularizedProblems
 
-using LinearAlgebra
+using LinearAlgebra, SparseArrays
 using Random, Requires
 using NLPModels
 using Distributions, Noise
@@ -22,6 +22,9 @@ function __init__()
   @require MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458" begin
     include("nonlin_svm_model.jl")
   end
+  @require QuadraticModels = "f468eda6-eac5-11e8-05a5-ff9e497bcd19" begin
+    include("qp_rand_model.jl")
+  end
 end
 
 end

src/qp_rand_model.jl

@@ -0,0 +1,34 @@
+export qp_rand_model
+using .QuadraticModels
+
+"""
+    model = qp_rand_model(n; dens = 1.0e-4, convex = false)
+
+Return an instance of a `QuadraticModel` representing
+
+   min cᵀx + ½ xᵀHx   s.t.  l ≤ x ≤ u,
+
+with H = A + A' or H = A * A' (see the `convex` keyword argument) where A is a random square matrix with density `dens`, `l = -e - tₗ` and `u = e + tᵤ` where `e` is the vector of ones, and `tₗ` and `tᵤ` are sampled from a uniform distribution between 0 and 1.    
+
+## Arguments
+
+* `n :: Int`: size of the problem,
+
+## Keyword arguments
+
+* `dens :: Real`: density of `A` with `0 < dens ≤ 1` used to generate the quadratic model (default: `1.0e-4`).
+* `convex :: Bool`: true to generate positive definite `H` (default: `false`).
+
+## Return Value
+
+An instance of a `QuadraticModel`.
+"""
+function qp_rand_model(n::Int; dens::R = 1.0e-4, convex::Bool = false) where {R <: Real}
+  @assert 0 < dens ≤ 1
+  A = sprandn(R, n, n, dens)
+  H = convex ? (A * A') : (A + A')
+  c = randn(R, n)
+  l = -one(R) .- rand(R, n)
+  u = one(R) .+ rand(R, n)
+  QuadraticModel(c, H; lvar = l, uvar = u, x0 = zeros(R, n))
+end

test/runtests.jl

@@ -1,5 +1,5 @@
 using LinearAlgebra, Test
-using ADNLPModels, DifferentialEquations, NLPModels, MLDatasets
+using ADNLPModels, DifferentialEquations, NLPModels, MLDatasets, QuadraticModels
 using RegularizedProblems
 
 function test_well_defined(model, nls_model, sol)
@@ -151,3 +151,16 @@ end
   @test all(nls_model.meta.uvar .== Inf)
   test_objectives(model, nls_model)
 end
+
+@testset "QP-rand" begin
+  n, dens = 100, 0.1
+  model = qp_rand_model(n; dens = dens, convex = false)
+  @test all(-2.0 .≤  model.meta.lvar .≤ 0.0)
+  @test all(0.0 .≤  model.meta.uvar .≤ 2.0)
+  @test all(model.meta.x0 .== 0)
+
+  model = qp_rand_model(n; dens = dens, convex = true)
+  @test all(-2.0 .≤  model.meta.lvar .≤ 0.0)
+  @test all(0.0 .≤  model.meta.uvar .≤ 2.0)
+  @test all(model.meta.x0 .== 0)
+end