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2

The error is coming from an old version of CPLEX.jl. You should install CPLEX 20.1 and the latest version of CPLEX.jl, which doesn't have this issue.


2

You can try setting the MIP Emphasis parameter (Java name loCplex.Param.Emphasis.MIP) to 3, which emphasizes tightening the best bound. This may result in slower progress on the primal bound (best known solution), and it is not guaranteed to help. There are also a variety of parameters related to adding cuts (how frequently cuts are added, how much emphasis ...


2

You can use the automatic parameter tuning in cplex, or try parameters yourself and see which ones work best. There is a guide by IBM on what they suggest: https://www.ibm.com/support/pages/cplex-performance-tuning-mixed-integer-programs You can give the problem more runtime and it will most likely improve the gap. You can try to find a better inital ...


7

Aggressive cut generation will slow the processing of the root node (and other nodes, if cuts are generated beyond the root), so it's more likely to slow finding a feasible solution than to speed it up. Setting MIP_Strategy_Subalgorithm to 1 tells CPLEX to use primal simplex on node subproblems. To emphasize finding feasible solutions, you want to set ...


2

I want to expand on @Sunes answer, as I too wanted to solve the LP relaxation of my MIP and thought, there must be another solution than to convert each variable manually (at least this is how I interpreted @Sunes answer and it sounded needlessly complex). Therefore, I unnecessarily used up a lot more time searching through the internet for alternatives (...


3

It seems you should take a look at the solution pool feature in CPLEX. This allows you to collect multiple solutions during the branch and bound search and to examine these solutions afterwards. I don't know how to do this in pyomo but you can find more information here https://www.ibm.com/support/knowledgecenter/SSSA5P_12.5.1/ilog.odms.ide.help/OPL_Studio/...


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