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 |
View thurber-x0.csv
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beta | |
1000 | |
1000 | |
400 | |
40 | |
0.7 | |
0.3 | |
0.03 |
View classification-01.jl
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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() |
View Project.toml
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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" |
View runcutest.jl
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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) |
View exemplo1.jl
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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]) |
View filter.md
View lops.jl
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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...) |
View lops.jl
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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...) |
View profile
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12.589108 seconds (956.13 k allocations: 46.069 MiB, 0.08% gc time) | |
0.000016 seconds (58 allocations: 11.453 KiB) | |
9669 ./client.jl:464; _start() | |
9669 ./client.jl:295; exec_options(::Base.JLOptions) | |
9669 ./Base.jl:31; include(::Module, ::String) | |
9669 ./loading.jl:1094; include_relative(::Module, ::St... | |
9669 ./boot.jl:328; include | |
113 ...mpiler/typeinfer.jl:599; typeinf_ext(::Core.MethodInstan... | |
113 ...mpiler/typeinfer.jl:568; typeinf_ext(::Core.MethodInstan... | |
107 ...mpiler/typeinfer.jl:12; typeinf(::Core.Compiler.Inferen... |
View gist:faababf1a5f5d06bf0142552246ce54a
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assim |
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