@@ -32,8 +32,8 @@ p(x=>1, y=>0), p(x=>1//2, y=>1//2), p(x=>0, y=>1)
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import Ipopt
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model = Model (Ipopt. Optimizer)
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- @variable (model, 0 <= a )
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- @variable (model, 0 <= b )
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+ @variable (model, a >= 0 )
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+ @variable (model, b >= 0 )
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@constraint (model, a + b >= 1 )
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@objective (model, Min, a^ 3 - a^ 2 + 2 a* b - b^ 2 + b^ 3 )
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optimize! (model)
@@ -99,8 +99,6 @@ import Alpine, HiGHS, Ipopt, Pavito
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ipopt = optimizer_with_attributes (
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Ipopt. Optimizer,
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MOI. Silent () => true ,
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- # "sb" => "yes",
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- # "max_iter" => 9999,
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)
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highs = optimizer_with_attributes (
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HiGHS. Optimizer,
@@ -119,16 +117,12 @@ alpine = optimizer_with_attributes(
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" nlp_solver" => ipopt,
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" mip_solver" => pavito,
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)
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- set_optimizer (model, () -> PolyJuMP. QCQP. Optimizer (alpine))
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+ set_optimizer (model, () -> PolyJuMP. QCQP. Optimizer (MOI . instantiate ( alpine) ))
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optimize! (model)
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- # We can see the summary here:
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+ # We can see that it found the optimal solution
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- solution_summary (m)
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-
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- # It found the optimal solution
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-
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- value (a), value (b)
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+ termination_status (model), value (a), value (b)
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# ## Sum-of-Squares approach
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