Update to support NLPModels 0.13 and update version to 0.2.0

.cirrus.yml

@@ -4,7 +4,7 @@ task:
   name: FreeBSD
   env:
     matrix:
-      - JULIA_VERSION: 1.0
+      - JULIA_VERSION: 1.3
       - JULIA_VERSION: 1
       - JULIA_VERSION: nightly
   allow_failures: $JULIA_VERSION == 'nightly'

.travis.yml

@@ -7,7 +7,7 @@ os:
   - freebsd
 
 julia:
-  - 1.0
+  - 1.3
   - 1
   - nightly
 

Project.toml

@@ -1,6 +1,6 @@
 name = "SolverTools"
 uuid = "b5612192-2639-5dc1-abfe-fbedd65fab29"
-version = "0.1.12"
+version = "0.2.0"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
@@ -15,8 +15,8 @@ Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 DataFrames = "^0.20, 0.21"
 JLD2 = "0.1.12"
 LinearOperators = "0.4.0, 0.5.0, 0.6.0, 0.7.0, 1.0"
-NLPModels = "0.7.0, 0.8.0, 0.9, 0.10.0, 0.11.0, 0.12"
-julia = "^1.0.0"
+NLPModels = "0.13"
+julia = "^1.3.0"
 
 [extras]
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

appveyor.yml

@@ -1,6 +1,6 @@
 environment:
   matrix:
-  - julia_version: 1.0
+  - julia_version: 1.3
   - julia_version: 1
   - julia_version: nightly
 

test/test_bmark.jl

@@ -10,8 +10,8 @@ end
 function test_bmark()
   @testset "Testing bmark" begin
     problems = [ADNLPModel(x -> sum(x.^2), ones(2), name="Quadratic"),
-                ADNLPModel(x -> sum(x.^2), ones(2), c=x->[sum(x)-1], lcon=[0.0], ucon=[0.0], name="Cons quadratic"),
-                ADNLPModel(x -> (x[1] - 1)^2 + 4 * (x[2] - x[1]^2)^2, ones(2), c=x->[x[1]^2 + x[2]^2 - 1], lcon=[0.0], ucon=[0.0], name="Cons Rosen")]
+                ADNLPModel(x -> sum(x.^2), ones(2), x->[sum(x)-1], [0.0], [0.0], name="Cons quadratic"),
+                ADNLPModel(x -> (x[1] - 1)^2 + 4 * (x[2] - x[1]^2)^2, ones(2), x->[x[1]^2 + x[2]^2 - 1], [0.0], [0.0], name="Cons Rosen")]
     callable = CallableSolver()
     stats = solve_problems(dummy_solver, problems)
     @test stats isa DataFrame

test/test_logging.jl

@@ -1,6 +1,6 @@
 function test_logging()
   nlps = [ADNLPModel(x -> sum(x.^k), ones(2k), name="Sum of power $k") for k = 2:4]
-  push!(nlps, ADNLPModel(x -> dot(x, x), ones(2), c=x->[sum(x)-1], lcon=[0.0], ucon=[0.0], name="linquad"))
+  push!(nlps, ADNLPModel(x -> dot(x, x), ones(2), x->[sum(x)-1], [0.0], [0.0], name="linquad"))
 
   @info "Testing logger"
   log_header([:col_float, :col_int, :col_symbol, :col_string], [Float64, Int, Symbol, String])

test/test_stats.jl

@@ -38,7 +38,7 @@ function test_stats()
       @test typeof(stats.dual_feas) == T
       @test typeof(stats.primal_feas) == T
 
-      nlp = ADNLPModel(x->dot(x, x), ones(T, 2), c=x->[sum(x)-1], lcon=[0.0], ucon=[0.0])
+      nlp = ADNLPModel(x->dot(x, x), ones(T, 2), x->[sum(x)-1], [0.0], [0.0])
 
       stats = GenericExecutionStats(:first_order, nlp)
       @test stats.status == :first_order
@@ -68,7 +68,7 @@ function test_stats()
       @test eltype(stats.multipliers_L) == T
       @test eltype(stats.multipliers_U) == T
 
-      nlp = ADNLPModel(x->dot(x, x), ones(T, 2), c=x->[sum(x)-1], lcon=[0.0], ucon=[0.0])
+      nlp = ADNLPModel(x->dot(x, x), ones(T, 2), x->[sum(x)-1], [0.0], [0.0])
 
       with_logger(NullLogger()) do
         stats = dummy_solver(nlp)