Performance of algorithms
オープン
まだ誰も着手していません。
評価
調査の方向性
Juliaコード、HiGHS、MOAのTambyVanderpootenアルゴリズム、およびMOKP_p-6_n-30_1.datを使って、報告された実行を再現する。アルゴリズムの探索ボックスの除外動作と終了ステータスを調査する。長時間の実行と中断された結果が想定どおりかどうかを判断するか、修正すべき誤った動作を特定できれば完了とする。
索引モデルが issue の本文から書いたものです。
説明
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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