Marine problem issue when trying to set size

[farhadrclass]

I think we have a bug in ADNLPModels line 473:

When I run this code with n_max_size =10 or smaller than 100 :

all_problem_names = OptimizationProblems.meta[!, :name]
# Initialize problems with type T and size n=n_max_size
all_problems = [eval(Meta.parse(name))(type = T, n = n_max_size) for name in all_problem_names]
# Filter for unconstrained problems
probs_list = [prob for prob in all_problems if (unconstrained(prob) && get_nvar(prob) >= 5)]
# probs_list = [prob for prob in all_problems if (unconstrained(prob))]
# Run the benchmark with the selected solvers and problems
stats = bmark_solvers(solvers, probs_list)

I get this error:

1-element ExceptionStack:
LoadError: MethodError: reducing over an empty collection is not allowed; consider supplying `init` to the reducer
Stacktrace:
  [1] mapreduce_empty(::typeof(identity), op::Function, T::Type)
    @ Base .\reduce.jl:372
  [2] reduce_empty(op::Base.MappingRF{typeof(identity), typeof(max)}, ::Type{Int64})
    @ Base .\reduce.jl:361
  [3] reduce_empty_iter
    @ .\reduce.jl:384 [inlined]
  [4] mapreduce_empty_iter(f::Function, op::Function, itr::Vector{Int64}, ItrEltype::Base.HasEltype)
    @ Base .\reduce.jl:380
  [5] _mapreduce
    @ .\reduce.jl:432 [inlined]
  [6] _mapreduce_dim
    @ .\reducedim.jl:365 [inlined]
  [7] mapreduce
    @ .\reducedim.jl:357 [inlined]
  [8] _maximum
    @ .\reducedim.jl:1015 [inlined]
  [9] _maximum
    @ .\reducedim.jl:1014 [inlined]
 [10] maximum
    @ .\reducedim.jl:1010 [inlined]
 [11] ADNLPModel!(f::Function, x0::Vector{Float64}, lvar::Vector{Float64}, uvar::Vector{Float64}, clinrows::Vector{Int64}, clincols::Vector{Int64}, clinvals::Vector{Float64}, c!::OptimizationProblems.ADNLPProblems.var"#c!#1750"{Int64, OptimizationProblems.ADNLPProblems.var"#c!#1740#1751", Vector{Int64}, Rational{Int64}, Int64, Int64}, lcon::Vector{Float64}, ucon::Vector{Float64}; y0::Vector{Float64}, name::String, minimize::Bool, kwargs::@Kwargs{})
    @ ADNLPModels C:\Users\Farhad\.julia\packages\ADNLPModels\hHKR7\src\nlp.jl:473
 [12] ADNLPModel!
    @ C:\Users\Farhad\.julia\packages\ADNLPModels\hHKR7\src\nlp.jl:451 [inlined]
 [13] marine(; n::Int64, nc::Int64, type::Type{Float64}, kwargs::@Kwargs{})
    @ OptimizationProblems.ADNLPProblems C:\Users\Farhad\.julia\packages\OptimizationProblems\G0vFO\src\ADNLPProblems\marine.jl:195
 [14] (::var"#715#717"{DataType, Int64})(name::String)
    @ Main .\none:0
 [15] iterate
    @ .\generator.jl:47 [inlined]
 [16] collect_to!(dest::Vector{ADNLPModel{Float64, Vector{Float64}, Vector{Int64}}}, itr::Base.Generator{Vector{String}, var"#715#717"{DataType, Int64}}, offs::Int64, st::Int64)
    @ Base .\array.jl:892
 [17] collect_to_with_first!(dest::Vector{ADNLPModel{Float64, Vector{Float64}, Vector{Int64}}}, v1::ADNLPModel{Float64, Vector{Float64}, Vector{Int64}}, itr::Base.Generator{Vector{String}, var"#715#717"{DataType, Int64}}, st::Int64)
    @ Base .\array.jl:870
 [18] collect(itr::Base.Generator{Vector{String}, var"#715#717"{DataType, Int64}})
    @ Base .\array.jl:844
 [19] run_benchmark(solvers::Dict{Symbol, Function}; T::Type, problems::Vector{Any}, n_max_size::Int64)
    @ Main C:\Users\Farhad\Documents\Github\Farhad-Phd\iR2_experiments\Experiments_code\current\experiment_update.jl:56
 [20] run_exp(solvers::Dict{Symbol, Function}; problems::Vector{Any}, T::Type, wr_path::String, name::String, n_max_size::Int64, ATOL::Float64, RTOL::Float64)
    @ Main C:\Users\Farhad\Documents\Github\Farhad-Phd\iR2_experiments\Experiments_code\current\experiment_update.jl:91
 [21] my_main(; RUN_FLAG::Int64, problems::Vector{Any}, n_max_size::Int64, T::Type, RTOL::Float64, ATOL::Float64)
    @ Main C:\Users\Farhad\Documents\Github\Farhad-Phd\iR2_experiments\Experiments_code\current\experiment_update.jl:267
 [22] top-level scope
    @ C:\Users\Farhad\Documents\Github\Farhad-Phd\iR2_experiments\Experiments_code\current\experiment_update.jl:307
 [23] include(fname::String)
    @ Base.MainInclude .\client.jl:489
 [24] top-level scope
    @ REPL[20]:1
 [25] eval
    @ .\boot.jl:385 [inlined]
 [26] eval
    @ .\Base.jl:88 [inlined]
 [27] repleval(m::Module, code::Expr, ::String)
    @ VSCodeServer c:\Users\Farhad\.vscode\extensions\julialang.language-julia-1.127.2\scripts\packages\VSCodeServer\src\repl.jl:229
 [28] (::VSCodeServer.var"#112#114"{Module, Expr, REPL.LineEditREPL, REPL.LineEdit.Prompt})()
    @ VSCodeServer c:\Users\Farhad\.vscode\extensions\julialang.language-julia-1.127.2\scripts\packages\VSCodeServer\src\repl.jl:192
 [29] with_logstate(f::Function, logstate::Any)
    @ Base.CoreLogging .\logging.jl:515
 [30] with_logger
    @ .\logging.jl:627 [inlined]
 [31] (::VSCodeServer.var"#111#113"{Module, Expr, REPL.LineEditREPL, REPL.LineEdit.Prompt})()
    @ VSCodeServer c:\Users\Farhad\.vscode\extensions\julialang.language-julia-1.127.2\scripts\packages\VSCodeServer\src\repl.jl:193
 [32] #invokelatest#2
    @ .\essentials.jl:892 [inlined]
 [33] invokelatest(::Any)
    @ Base .\essentials.jl:889
 [34] (::VSCodeServer.var"#64#65")()
    @ VSCodeServer c:\Users\Farhad\.vscode\extensions\julialang.language-julia-1.127.2\scripts\packages\VSCodeServer\src\eval.jl:34
in expression starting at C:\Users\Farhad\Documents\Github\Farhad-Phd\iR2_experiments\Experiments_code\current\experiment_update.jl:307

I tried to loop through the problem and the issue happens:

n_max_size=10 
T= Float64
solvers = Dict(
            :R2N => nlp -> R2N(nlp; rtol = RTOL, atol = ATOL),
            :lbfgs => nlp -> lbfgs(nlp; rtol = RTOL, atol = ATOL)
        )
all_problem_names = OptimizationProblems.meta[!, :name]
# Initialize problems with type T and size n=n_max_size

for name in all_problem_names
    if name == "marine"
        continue
    end
    println(name)
    println("-------------------------------------------------------------------")
    eval(Meta.parse(name))(type = T, n = n_max_size)
end

The issue is Marine problem: https://github.com/JuliaSmoothOptimizers/OptimizationProblems.jl/blob/main/src/ADNLPProblems/marine.jl

[dpo]

What error does

marine(type = Float64, n = 10)

raise?

[farhadrclass]

What error does

marine(type = Float64, n = 10)

raise?
julia> marine(type = Float64, n = 10) ERROR: MethodError: reducing over an empty collection is not allowed; consider supplyinginitto the reducer Stacktrace: [1] mapreduce_empty(::typeof(identity), op::Function, T::Type) @ Base .\reduce.jl:372 [2] reduce_empty(op::Base.MappingRF{typeof(identity), typeof(max)}, ::Type{Int64}) @ Base .\reduce.jl:361 [3] reduce_empty_iter @ .\reduce.jl:384 [inlined] [4] mapreduce_empty_iter(f::Function, op::Function, itr::Vector{Int64}, ItrEltype::Base.HasEltype) @ Base .\reduce.jl:380 [5] _mapreduce @ .\reduce.jl:432 [inlined] [6] _mapreduce_dim @ .\reducedim.jl:365 [inlined] [7] mapreduce @ .\reducedim.jl:357 [inlined] [8] _maximum @ .\reducedim.jl:1015 [inlined] [9] _maximum @ .\reducedim.jl:1014 [inlined] [10] maximum @ .\reducedim.jl:1010 [inlined] [11] ADNLPModel!(f::Function, x0::Vector{…}, lvar::Vector{…}, uvar::Vector{…}, clinrows::Vector{…}, clincols::Vector{…}, clinvals::Vector{…}, c!::OptimizationProblems.ADNLPProblems.var"#c!#1750"{…}, lcon::Vector{…}, ucon::Vector{…}; y0::Vector{…}, name::String, minimize::Bool, kwargs::@Kwargs{}) @ ADNLPModels C:\Users\Farhad\.julia\packages\ADNLPModels\hHKR7\src\nlp.jl:473 [12] ADNLPModel! @ C:\Users\Farhad\.julia\packages\ADNLPModels\hHKR7\src\nlp.jl:451 [inlined] [13] marine(; n::Int64, nc::Int64, type::Type{Float64}, kwargs::@Kwargs{}) @ OptimizationProblems.ADNLPProblems C:\Users\Farhad\.julia\packages\OptimizationProblems\G0vFO\src\ADNLPProblems\marine.jl:195 [14] top-level scope @ REPL[35]:1 Some type information was truncated. Useshow(err)to see complete types.

[amontoison]

@farhadrclass
Do you need a new release?

[tmigot]

@farhadrclass
Do you need a new release?

Yes, a minor release please.

[amontoison]

Tangi, the question was for Farhad :)
I plan to do a new release next week if we finalize the support of Enzyme with Michel.
It's why I was asking Farhad if it's urgent or not.

But do you need a new release quickly Tangi?
I can do two releases too.

[tmigot]

Okay, but it makes sense to have a release that fix OptimizationProblems first, and then do another later.

[amontoison]

Done, I tagged the release 0.8.9.

[farhadrclass]

@tmigot @amontoison thank you for the support and sorry for delays, I need to check my emails more often,.

yes it would be great to have it (I think it is already done anyway :) )