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Output of `bash reproduce.sh &> example-output.txt`
---
### Creating MWE_17967
  Generating  project MWE_17967:
    MWE_17967/Project.toml
    MWE_17967/src/MWE_17967.jl
   Resolving package versions...
      Compat entries added for JET
    Updating `/tmp/tmp.rybqwgsbQ5/MWE_17967/Project.toml`
⌅ [c3a54625] + JET v0.9.19

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### A Pluto.jl notebook ###
# v0.19.32

using Markdown
using InteractiveUtils

# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
    quote
        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end

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beta
1000
1000
400
40
0.7
0.3
0.03

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using PyCall

pd = pyimport("pandas")
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
data = pd.read_csv(download(url), header=nothing, names=["x1", "x2", "x3", "x4", "target"])

sns = pyimport("seaborn")
plt = pyimport("matplotlib.pyplot")

plt.figure()

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[deps]
JSOSolvers = "10dff2fc-5484-5881-a0e0-c90441020f8a"
JuMP = "4076af6c-e467-56ae-b986-b466b2749572"
NLPModelsJuMP = "792afdf1-32c1-5681-94e0-d7bf7a5df49e"

[compat]
JSOSolvers = "0.2.0"
JuMP = "0.18"
NLPModelsJuMP = "0.5.0"

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using CUTEst, JSOFilter, NLPModelsIpopt, Plots, SolverBenchmark, SolverTools
pyplot()

function runcutest()
  #ps = CUTEst.select(max_var=300, min_con=1, max_con=300, only_free_var=true, only_equ_con=true, objtype=2:6)
  #ps = CUTEst.select(max_var=300, min_con=1, max_con=300, only_free_var=true, objtype=2:6)
  ps = CUTEst.select(max_var=300, min_con=1, max_con=300, objtype=2:6)
  #ps = CUTEst.select(max_var=2, min_con=1, max_con=2, objtype=2:6)
  problems = (CUTEstModel(p) for p in ps)

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using QuiverMutation

function exemplo1()
  lt = Lattice(2, 4)
  P = [(3,2), (2,2), (1,2), (1,1), (2,1)]

  plot_lattice(lt, title="Inicial", filename="quiver-0")

  for (i,p) in enumerate(P)
    lt = activate(lt, p[1], p[2])

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name	nvar	ncon	status	objective	elapsed_time	neval_obj	dual_feas	primal_feas
HS7	2	1	first_order	-1.7e+00	1.2e-01	33	1.3e-06	7.5e-24
MARATOS	2	1	first_order	-1.0e+00	2.4e+00	651	1.1e-06	1.2e-32
HS6	2	1	first_order	8.0e-19	2.3e-02	6	1.8e-09	1.2e-30
HS10	2	1	first_order	-1.0e+00	6.5e-01	88	1.3e-06	2.5e-09
HS8	2	2	first_order	-1.0e+00	5.1e-03	3	0.0e+00	4.7e-24

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# put in file lops.jl
# LinearOperator and hcat precompilation problem
using LinearOperators
using Profile

function foo()
  N = 15; n = 5
  lops = [ LinearOperator(randn(n,n)) for i=1:N ]

  #C = hvcat(ntuple(x -> N, N), lops...)

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# put in file lops.jl
# LinearOperator and hcat precompilation problem
using LinearOperators
using Profile

function foo()
  N = 15; n = 5
  lops = [ LinearOperator(randn(n,n)) for i=1:N ]

  #C = hvcat(ntuple(x -> N, N), lops...)