How to settle down the "auto-reset" of programming-code using Gurobi.jl #477
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To clarify, does
mean that Julia quit? Is there anything else that gets printed? If so, that looks like a bug in Gurobi! We can diagnose things going wrong if you print out the MPS file. Change these two lines: # scuc = JuMP.Model(Gurobi.Optimizer)
scuc = JuMP.direct_model(Gurobi.Optimizer())# JuMP.optimize!(scuc)
Gurobi.GRBwrite(scuc, "issue_477.mps")And then upload the MPS file. (You can upload files to Github through the "Attach files by dragging & dropping" dialog at the bottom of the comment field.) If you get an error about constraints not supported, only change one line: # JuMP.optimize!(scuc)
JuMP.write_to_file(scuc, "issue477.mps") |
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Thank you for your response. I run my source code in a Atom/JunPro environment. And Julia will be auto-reset and quite after printing this message. Now, I have being testing these source codes after modifying several input parameters. So, I may submit the MPS file several minus later. Please wait for me. Big thanks. Another thing I thought might be interesting. The below lists another successful information for my new case after corrected input boundary. |
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Hi, GitHub seems to have a file size limitation of uploaded files. So, I have to export and sent the "issue 447.mps" file to you through Google Drive. Please check that. Thank you very much.
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Oh wow pretty big! Can you try calling Gurobi from the cmd prompt? Can you post the link to the Google Drive file? |
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No Problem. The below shows the prompt. |
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Is that for the big original problem that crashes Julia? Hmm. It's going to be hard to find the problem if we can't reproduce via |
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Yes. I am trying to test my code through actual cases. Therefore, the size of model structure is a bit large.
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Great! "gurobi_cl" work well. I run "gurobi_cl issue477.mps" on my person PC desktop, and successfully calculate the optimal result. |
I don't know. That's the problem! Can you try with all of your options that you had set? |
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Of course. The below shows the calculating process. |
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Hmm. Did you use
Is there any randomness in how you create the problem? |
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Oh.... During my case, there are some randomness input data representing the stochastic realization of uncertain resources. Last night till now, I used this source code to continue running other cases. But I forget to comment this line “Gurobi.GRBwrite(scuc, "issue_447.mps")”, which generating issue447.mps files. As for this, the source code may overwrite the previous .mps file and regenerate a new issue477.mps file. I have downloaded the issue447,mps file that I shared yesterday, and ran this command. But I found the model fingerprint is still 0xfe150f41. Please check it below.
Maybe, I should remove any randomness of my input data, and rerun these processes. Is it right ? |
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To be able to debug this, we need an MPS file that can trigger the segfault when run via Of course, if you can reliably trigger that via Gurobi.jl but not via using JuMP, Gurobi
env = Gurobi.Env(; start = false)
GRBsetintparam(env.ptr_env, GRB_INT_PAR_RECORD, 1)
GRBstartenv(env.ptr_env)
model = Model(() -> Gurobi.Optimizer(env))You'll have to be on the latest release of Gurobi.jl for this to work: https://github.com/jump-dev/Gurobi.jl/releases/tag/v0.11.2 "GRB_INT_PAR_RECORD" is https://www.gurobi.com/documentation/9.5/refman/record.html#parameter:Record |
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OK, I will try that, and feedback to you ASAP. Thanks for your help. ..., I will upload related code and continue finding this prob. in the next days. Thanks for waiting. |
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Closing as stale and non-reproducible, and because this doesn't appear to be a bug in Gurobi.jl. If you can reproduce this from the |
Hello,
I am attempting to calculate the optimal result of a mixed integer linearized programming (MILP) using JuMP/Gurobi packages.
My source code is a classical unit commitment, which is also a mathematical MILP problem.
Some code snippets list below:
When I run this code, I get the following error:
I am not sure if my code is incorrect or if there is a potential bug here. Anyone helps me would be much appreciated.
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