First of all, congratulate you on the excellent work in python MIP.
In search of knowledge about linear and integer programming, we found Professor Túlio Toffolo's classes, where we were led to this open source project, having Professor Túlio and Professor Haroldo as authors.
I represent a team of 3 researchers (2 engineering, electrical and mechanical students, and a master in electrical engineering), and we raised some questions about the module:
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How is the “best possible” value estimated? What does it mean if this value is a non integer and it keeps changing along the iterations of the optimization?
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Is there a class of complex problems that can’t be solved using the Python MIP? Like the RCPSP/max-cal or MRCPSP (Variants of the Resource-constrained Project Scheduling Problem)
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What is the stop criteria of the optimizer? In some cases, it converges to a value that doesn’t correspond to the “best possible” estimated, and in Other cases it Only converges When the “best possible” value changes and matches with the solution found.
-
What are the main hyperparameters of the optimizer that we should change to help in the solution of complex and large scaled problems?
-
Is there a different method to estimate the initial solution? Even in simple RCPSP cases and with the maximum iteration of the feasible pump method (100), it was not possible to found this solution in the initial stage of the optimizer.
-
In many cases, the solver seems to get stucked in a “local minumum”. Is there any strategy to avoid this situation and speed up the optimization process?
First of all, congratulate you on the excellent work in python MIP.
In search of knowledge about linear and integer programming, we found Professor Túlio Toffolo's classes, where we were led to this open source project, having Professor Túlio and Professor Haroldo as authors.
I represent a team of 3 researchers (2 engineering, electrical and mechanical students, and a master in electrical engineering), and we raised some questions about the module:
How is the “best possible” value estimated? What does it mean if this value is a non integer and it keeps changing along the iterations of the optimization?
Is there a class of complex problems that can’t be solved using the Python MIP? Like the RCPSP/max-cal or MRCPSP (Variants of the Resource-constrained Project Scheduling Problem)
What is the stop criteria of the optimizer? In some cases, it converges to a value that doesn’t correspond to the “best possible” estimated, and in Other cases it Only converges When the “best possible” value changes and matches with the solution found.
What are the main hyperparameters of the optimizer that we should change to help in the solution of complex and large scaled problems?
Is there a different method to estimate the initial solution? Even in simple RCPSP cases and with the maximum iteration of the feasible pump method (100), it was not possible to found this solution in the initial stage of the optimizer.
In many cases, the solver seems to get stucked in a “local minumum”. Is there any strategy to avoid this situation and speed up the optimization process?