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unit test for colgen loop when infeasible master (Farkas pricing) #1029

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unit test for colgen loop when infeasible master (Farkas pricing) #1029

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90 changes: 87 additions & 3 deletions test/unit/ColGen/colgen_default.jl
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Original file line number Diff line number Diff line change
Expand Up @@ -1566,8 +1566,6 @@ function test_red_cost_calc_with_non_robust_cuts()
end
register!(unit_tests, "colgen", test_red_cost_calc_with_non_robust_cuts)



function jet_report_colgen_loop()
env, master, sps, reform = min_toy_gap_for_colgen_loop()
# We need subsolvers to optimize the master and subproblems.
Expand All @@ -1592,4 +1590,90 @@ function jet_report_colgen_loop()
return
end
# Excluded at the moment because too many errors come from DynamicSparseArrays.
register!(unit_tests, "colgen_default", jet_report_colgen_loop; x = true)
register!(unit_tests, "colgen_default", jet_report_colgen_loop; x = true)


function min_toy_gap_without_art_var_for_colgen()
# We use very large costs to go through phase 1.
form = """
master
min
800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_5 + 0.0 PricingSetupVar_sp_4
s.t.
1.0 x_11 + 1.0 x_21 >= 1.0
1.0 x_12 + 1.0 x_22 >= 1.0
1.0 x_13 + 1.0 x_23 >= 1.0
1.0 x_14 + 1.0 x_24 >= 1.0
1.0 x_15 + 1.0 x_25 >= 1.0
1.0 x_16 + 1.0 x_26 >= 1.0
1.0 x_17 + 1.0 x_27 >= 1.0
1.0 PricingSetupVar_sp_5 >= 0.0 {MasterConvexityConstr}
1.0 PricingSetupVar_sp_5 <= 1.0 {MasterConvexityConstr}
1.0 PricingSetupVar_sp_4 >= 0.0 {MasterConvexityConstr}
1.0 PricingSetupVar_sp_4 <= 1.0 {MasterConvexityConstr}

dw_sp
min
800.0 x_11 + 500.0 x_12 + 1100.0 x_13 + 2100.0 x_14 + 600.0 x_15 + 500.0 x_16 + 1900.0 x_17 + 0.0 PricingSetupVar_sp_5
s.t.
2.0 x_11 + 3.0 x_12 + 3.0 x_13 + 1.0 x_14 + 2.0 x_15 + 1.0 x_16 + 1.0 x_17 <= 5.0

dw_sp
min
100.0 x_21 + 1200.0 x_22 + 1100.0 x_23 + 1200.0 x_24 + 1400.0 x_25 + 800.0 x_26 + 500.0 x_27 + 0.0 PricingSetupVar_sp_4
s.t.
5.0 x_21 + 1.0 x_22 + 1.0 x_23 + 3.0 x_24 + 1.0 x_25 + 5.0 x_26 + 4.0 x_27 <= 8.0

integer
pricing_setup
PricingSetupVar_sp_4, PricingSetupVar_sp_5

binary
representatives
x_11, x_21, x_12, x_22, x_13, x_23, x_14, x_24, x_15, x_25, x_16, x_26, x_17, x_27

bounds
0.0 <= x_11 <= 1.0
0.0 <= x_21 <= 1.0
0.0 <= x_12 <= 1.0
0.0 <= x_22 <= 1.0
0.0 <= x_13 <= 1.0
0.0 <= x_23 <= 1.0
0.0 <= x_14 <= 1.0
0.0 <= x_24 <= 1.0
0.0 <= x_15 <= 1.0
0.0 <= x_25 <= 1.0
0.0 <= x_16 <= 1.0
0.0 <= x_26 <= 1.0
0.0 <= x_17 <= 1.0
0.0 <= x_27 <= 1.0
1.0 <= PricingSetupVar_sp_4 <= 1.0
1.0 <= PricingSetupVar_sp_5 <= 1.0
"""
env, master, sps, _, reform = reformfromstring(form)
return env, master, sps, reform
end

function test_farkas_pricing()
env, master, sps, reform = min_toy_gap_without_art_var_for_colgen()
# We need subsolvers to optimize the master and subproblems.
# We relax the master formulation.
ClMP.push_optimizer!(master, () -> ClA.MoiOptimizer(GLPK.Optimizer())) # we need warm start
ClMP.relax_integrality!(master)
for sp in sps
ClMP.push_optimizer!(sp, () -> ClA.MoiOptimizer(GLPK.Optimizer()))
end

phase = ClA.ColGenPhase0()
ctx = ClA.ColGenContext(reform, ClA.ColumnGeneration())
ColGen.setup_reformulation!(reform, phase)
Coluna.set_optim_start_time!(env)
input = Coluna.Algorithm.GlobalPrimalBoundHandler(reform)
stage = ColGenIterationTestStage()
stab = Coluna.Algorithm.NoColGenStab()
output = ColGen.run_colgen_phase!(ctx, phase, stage, env, input, stab)

#@test output.mlp ≈ 70.33333333
#@test output.db ≈ 70.33333333
end
register!(unit_tests, "colgen", test_farkas_pricing; x = true)
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