Performance of algorithms
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研究方向
使用 Julia 程式碼、HiGHS、MOA 的 TambyVanderpooten 演算法和 MOKP_p-6_n-30_1.dat 重現回報的執行。檢查演算法的搜尋框排除行為和終止狀態；完成的標準是判定長時間執行和中斷結果是否符合預期，或找出需要修正的不正確行為。
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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.
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難度 5/5 一週以上 新手友好度 30/100
jump-dev/MultiObjectiveAlgorithms.jl#113 · 8 則留言 ·
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