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Performance of algorithms

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Valutazione

Difficoltà
4/5
Tempo stimato
3-5 giorni
Idoneità per principianti
35/100
Tipo di issue
Bug
Chiarezza
Abbastanza chiara
Stato di attività
Ferma
Stack tecnologico
julia
Ambito
tooling

Direzione di ricerca

Riprodurre l'esecuzione segnalata con il codice Julia, HiGHS, l'algoritmo TambyVanderpooten di MOA e MOKP_p-6_n-30_1.dat. Esaminare il comportamento di esclusione della casella di ricerca dell'algoritmo e lo stato di terminazione; il lavoro è completato quando si determina se l'esecuzione lunga e il risultato interrotto sono previsti, oppure si identifica il comportamento errato da correggere.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Xavier says

I recently tried to solve an instance with TambyVanderpooten, MOKP_p-6_n-30_1.dat, which is on Tamby's repo and presented as solved by their algorithm in the paper. It does not run on the MOA implementation (nothing after 24 hours)

Here's my Julia code:

using JuMP, HiGHS
import MultiObjectiveAlgorithms as MOA

function solve_tamby_mokp(filename)
    lines = readlines(filename)
    p = parse(Int, lines[1])
    n = parse(Int, lines[2])
    b = parse(Float64, lines[3])
    C = collect(reduce(hcat, Float64.(x.args) for x in Meta.parse(lines[4]).args)')
    w = Meta.parse(lines[5]).args
    model = Model(() -> MOA.Optimizer(HiGHS.Optimizer))
    set_attribute(model, MOA.Algorithm(), MOA.TambyVanderpooten())
    @variable(model, x[1:n], Bin)
    @constraint(model, w' * x <= b)
    @objective(model, Max, C * x)
    optimize!(model)
    assert_is_solved_and_feasible(model)
    return [value.(x; result) for result in 1:result_count(model)]
end


solve_tamby_mokp(
    "/Users/odow/git/tambysatya/TamVan19/Instances/MOKP/MOKP_p-6_n-30_1.dat"
)

Output is

julia> using JuMP, HiGHS

julia> import MultiObjectiveAlgorithms as MOA

julia> function solve_tamby_mokp(filename)
           lines = readlines(filename)
           p = parse(Int, lines[1])
           n = parse(Int, lines[2])
           b = parse(Float64, lines[3])
           C = collect(reduce(hcat, Float64.(x.args) for x in Meta.parse(lines[4]).args)')
           w = Meta.parse(lines[5]).args
           model = Model(() -> MOA.Optimizer(HiGHS.Optimizer))
           set_attribute(model, MOA.Algorithm(), MOA.TambyVanderpooten())
           @variable(model, x[1:n], Bin)
           @constraint(model, w' * x <= b)
           @objective(model, Max, C * x)
           optimize!(model)
           assert_is_solved_and_feasible(model)
           return [value.(x; result) for result in 1:result_count(model)]
       end
solve_tamby_mokp (generic function with 1 method)

julia> solve_tamby_mokp(
           "/Users/odow/git/tambysatya/TamVan19/Instances/MOKP/MOKP_p-6_n-30_1.dat"
       )
--------------------------------------------------------------------------------------------------
        MultiObjectiveAlgorithms.jl
--------------------------------------------------------------------------------------------------
Algorithm: TambyVanderpooten
--------------------------------------------------------------------------------------------------
solve #     Obj. 1       Obj. 2       Obj. 3       Obj. 4       Obj. 5       Obj. 6       Time    
--------------------------------------------------------------------------------------------------
    1   -1.17700e+03 -1.03200e+03 -1.07100e+03 -8.85000e+02 -8.46000e+02 -1.14100e+03  1.42563e+00
    2   -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00  1.42763e+00
    3   -1.07200e+03 -1.22100e+03 -1.18100e+03 -9.83000e+02 -8.90000e+02 -1.13000e+03  1.43889e+00
    4   -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00  1.43945e+00
    5   -8.84000e+02 -1.05600e+03 -1.37000e+03 -9.59000e+02 -8.78000e+02 -9.51000e+02  1.44168e+00
    6   -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00  1.44223e+00
    7   -8.95000e+02 -9.81000e+02 -1.09800e+03 -1.14300e+03 -6.92000e+02 -1.04800e+03  1.44480e+00
    8   -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00  1.44535e+00
    9   -9.14000e+02 -1.00700e+03 -1.11600e+03 -8.59000e+02 -1.11800e+03 -1.15500e+03  1.44794e+00
   10   -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00  1.44849e+00
   11   -9.23000e+02 -1.01000e+03 -9.91000e+02 -8.57000e+02 -7.84000e+02 -1.41500e+03  1.45187e+00
   12   -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00 -0.00000e+00  1.45241e+00
   13  auxillary subproblem                                                            1.49690e+00
   14   -1.17700e+03 -1.03200e+03 -1.07100e+03 -8.85000e+02 -8.46000e+02 -1.14100e+03  1.69627e+00
   15  auxillary subproblem                                                            1.91702e+00
   16   -9.14000e+02 -1.00700e+03 -1.11600e+03 -8.59000e+02 -1.11800e+03 -1.15500e+03  1.91964e+00
   17  auxillary subproblem                                                            1.92204e+00
   18   -8.95000e+02 -9.81000e+02 -1.09800e+03 -1.14300e+03 -6.92000e+02 -1.04800e+03  1.92448e+00
   19  auxillary subproblem                                                            1.93331e+00
   20   -1.07200e+03 -1.22100e+03 -1.18100e+03 -9.83000e+02 -8.90000e+02 -1.13000e+03  1.93606e+00
   21  auxillary subproblem                                                            1.93798e+00
   22   -8.84000e+02 -1.05600e+03 -1.37000e+03 -9.59000e+02 -8.78000e+02 -9.51000e+02  1.94063e+00
   23  auxillary subproblem                                                            1.94374e+00
   24   -9.23000e+02 -1.01000e+03 -9.91000e+02 -8.57000e+02 -7.84000e+02 -1.41500e+03  1.95118e+00
   25  auxillary subproblem                                                            1.96168e+00
   26   -9.16000e+02 -1.00000e+03 -1.15100e+03 -1.13100e+03 -6.99000e+02 -1.02500e+03  1.97200e+00
   27  auxillary subproblem                                                            1.99172e+00
   28   -8.72000e+02 -1.04100e+03 -1.13500e+03 -1.12900e+03 -7.53000e+02 -9.86000e+02  1.99935e+00
   29  auxillary subproblem                                                            2.01168e+00
   30   -9.66000e+02 -9.78000e+02 -1.14400e+03 -1.12600e+03 -7.60000e+02 -1.01700e+03  2.02175e+00
   31  auxillary subproblem                                                            2.04473e+00
   32   -8.70000e+02 -1.06700e+03 -1.20700e+03 -1.12200e+03 -7.63000e+02 -1.04500e+03  2.06123e+00
   33  auxillary subproblem                                                            2.08916e+00
   34   -9.15000e+02 -1.06600e+03 -1.15200e+03 -1.12100e+03 -8.03000e+02 -1.14600e+03  2.09270e+00
   35  auxillary subproblem                                                            2.09913e+00
   36   -8.71000e+02 -9.77000e+02 -1.21300e+03 -1.12100e+03 -8.38000e+02 -1.02200e+03  2.10892e+00
   37  auxillary subproblem                                                            2.11703e+00
   38   -9.04000e+02 -1.09700e+03 -1.36600e+03 -9.91000e+02 -8.93000e+02 -1.09800e+03  2.11984e+00
   39  auxillary subproblem                                                            2.17713e+00
   40   -9.15000e+02 -1.06600e+03 -1.15200e+03 -1.12100e+03 -8.03000e+02 -1.14600e+03  2.18210e+00
   41  auxillary subproblem                                                            2.18672e+00
   42   -9.04000e+02 -1.00700e+03 -1.15700e+03 -8.87000e+02 -1.11300e+03 -1.08400e+03  2.18866e+00
   43  auxillary subproblem                                                            2.20184e+00
   44   -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03  2.20434e+00
   45  auxillary subproblem                                                            2.21396e+00
   46   -9.93000e+02 -1.03700e+03 -1.06000e+03 -8.98000e+02 -8.36000e+02 -1.41200e+03  2.22656e+00
   47  auxillary subproblem                                                            2.25049e+00
   48   -9.38000e+02 -1.05300e+03 -1.18700e+03 -1.11400e+03 -8.50000e+02 -1.08400e+03  2.25784e+00
   49  auxillary subproblem                                                            2.30797e+00
   50   -1.00700e+03 -1.11300e+03 -1.22100e+03 -1.10800e+03 -8.58000e+02 -1.15600e+03  2.31168e+00
  ... lines omitted ...
  977  auxillary subproblem                                                            1.39737e+02
  978   -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03  1.39740e+02
  979  auxillary subproblem                                                            1.40425e+02
  980   -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03  1.40427e+02
  981  auxillary subproblem                                                            1.41089e+02
  982   -9.96000e+02 -1.16100e+03 -1.29700e+03 -1.11800e+03 -8.49000e+02 -1.10800e+03  1.41092e+02
^C--------------------------------------------------------------------------------------------------
TerminationStatus: INTERRUPTED
ResultCount: 276
--------------------------------------------------------------------------------------------------
ERROR: The model was not solved correctly. Here is the output of `solution_summary` to help debug why this happened:

solution_summary(; result = 1, verbose = false)
├ solver_name          : MOA[algorithm=MultiObjectiveAlgorithms.TambyVanderpooten, optimizer=HiGHS]
├ Termination
│ ├ termination_status : INTERRUPTED
│ ├ result_count       : 276
│ ├ raw_status         : Solve complete. Found 276 solution(s)
│ └ objective_bound    : [1.17700e+03,1.22100e+03,1.37000e+03,1.14300e+03,1.11800e+03,1.41500e+03]
├ Solution (result = 1)
│ ├ primal_status        : FEASIBLE_POINT
│ ├ dual_status          : NO_SOLUTION
│ └ objective_value      : [1.17700e+03,1.03200e+03,1.07100e+03,8.85000e+02,8.46000e+02,1.14100e+03]
└ Work counters
  └ solve_time (sec)   : 1.41759e+02

Stacktrace:
 [1] error(s::String)
   @ Base ./error.jl:44
 [2] #assert_is_solved_and_feasible#103
   @ ~/.julia/packages/JuMP/e83v9/src/optimizer_interface.jl:1008 [inlined]
 [3] assert_is_solved_and_feasible
   @ ~/.julia/packages/JuMP/e83v9/src/optimizer_interface.jl:1002 [inlined]
 [4] solve_tamby_mokp(filename::String)
   @ Main ./REPL[4]:14
 [5] top-level scope
   @ REPL[5]:1

I don't know if this is expected behaviour of if we're not excluding some of the search boxes correctly.

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