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使用 Julia 代码、HiGHS、MOA 的 TambyVanderpooten 算法和 MOKP_p-6_n-30_1.dat 重现报告中的运行。检查算法的搜索框排除行为和终止状态；完成的标准是确定长时间运行和中断结果是否符合预期，或识别出需要修复的不正确行为。
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描述
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.
- 主要语言
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难度 5/5 一周以上 新手友好度 30/100
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