Originally posted by: davidrey123
Hi, I would like to implement a "manual" Benders' decomposition in DOcplex Python modeling API for a MILP. By "manual" I mean not using Benders' annotations or strategies (e.g. automatic Benders). One of the reason for this (besides curiosity) is that I would like to customize the way Benders' subproblem is solved, notably by solving possibly more than one optimization problem to generate hopefully stronger feasibility and optimality cuts.
My goal is to solve a MILP and decompose it by keeping the integer variables in the master and the continuous ones in the subproblem (so the subproblem is an LP). If I solve the primal LP and it's feasible, I believe I can get dual prices of my constraints by the command "docplex.mp.model.dual_values(cts)" and use that to generate my optimality cut. However if the LP is infeasible, how can I get the dual rays to generate my feasibility cut? Browsing the forum, it seems that there may be a way to use Farkas' Lemma to achieve this but it's not clear to me how to do this in docplex. Any suggestions would be highly appreciated.
Thanks,
-David
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