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Benchmarks on KaHyPar selector and greedy merge #45
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I find the number of branches has a large variance. Can you confirm that? @nzy1997 |
I also got a larger branch count. That's a little weird, since there's no change on the default solvers. g = random_regular_graph(200, 3; seed = 2134)
mis1, count1 = mis_branch_count(g)
(88, 10882) When I check out to the pervious version, I can still get the original branch count 6496. |
Is that due to the change of KaHyPar config file? |
Gamma informed branching does not perform welljulia> bs = BranchingStrategy(table_solver = TensorNetworkSolver(), selector = MinBoundarySelector(2), measure = D3Measure(), set_cover_solver=IPSolver(max_itr=20, γ0=2.0))
julia> misk, countk = mis_branch_count(g; branching_strategy = bs, show_progress=true) Default: (88, 10290) Gamma informed: julia> bs = BranchingStrategy(table_solver = TensorNetworkSolver(), selector = MinBoundarySelector(2), measure = D3Measure(), set_cover_solver=IPSolver(max_itr=1, γ0=1.044))
julia> misk, countk = mis_branch_count(g; branching_strategy = bs, show_progress=true) (88, 26945) |
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CPU Info
M4
Setup:
1. MinBoundarySelector(2)
(mis1, count1) = (88, 6496)
2. KaHyParSelector(20)
(misk, countk) = (88, 3595)
3. KaHyParSelector(15)
(misk, countk) = (88, 4501)
4. KaHyPar(15) + GreedyMerge()
(miskg, countkg) = (88, 6248)
3. KaHyPar(15) + NaiveBranch()
The naive branch maximizes the length of each clause.
miskn, countkn = (88, 107727)
Profile on greedy merge
btime on greedy merge
9s for optimal branching, γ = 1.0814
246.635ms for greedy merge, γ = 1.0842073740067577
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