Incremental PW linear to MIP transformation#3287
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…ify variables mode does not work, gives infeasible models
…hoice, but not J1 implementation yet.
… transformation. However, gurobi is not magical enough to see the structure here so this basically amounts to asking for a Hamiltonian path in a big graph, and then some. I think I will have to do the 70s stuff after all. Some optimizations would reduce this to "only" asking for a Hamiltonian path on a sparser graph but that is still terrible.
… mild assumptions
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…n its file, and wrapping it in a function so that it doesn't get built when pyomo.environ is imported
emma58
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@sadavis1, I put in a test of the 3D Hamiltonian paths, but I'm not quite sure how to check that they are the right paths given the keys, so would appreciate input if you get a chance. Otherwise this looks good to go. (We've agreed to refactor the simplices and triangulation arguments for PiecewiseLinearFunctions in a follow-on PR (not targeting the upcoming release)).
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@jsiirola this is ready for you to take a look! :) |
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Some minor questions (that can be dealt with in the future), but overall looks good.
| # starting permutation, such that a fixed target symbol is either the image | ||
| # rho(1), or it is rho(n), depending on whether first or last is requested, | ||
| # where rho is the final permutation. | ||
| def _get_Gn_hamiltonian(n, start_permutation, target_symbol, last, _cache={}): |
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This is dangerous (although maybe you intend it?): all calls to _get_Gn_hamiltonian that do not provide _cache share the same dict object.
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I'm pretty sure this is intended. But maybe @sadavis1 can confirm at some point... (And perhaps we should add a comment).
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Summary/Motivation:
Add another transformation, the incremental MIP formulation, to our slowly growing library of things to do with piecewise-linear functions. This one requires a specially ordered triangulation, so some infrastructure around triangulations is also needed.
Changes proposed in this PR:
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