Make the placement master deterministic across runs - #76
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The score-maximising placement MILP iterated genes_in_scope, a set, so string hash randomisation reordered its variables and constraints between processes and a degenerate objective returned a different co-optimal placement on each run. Iterate the genes sorted, and pin the Gurobi solve (single thread, fixed seed, presolve level and zero MIP gap) so the same placement is returned every time. A parameter sweep on the yeast master showed thread count, seed and presolve level are the settings that change which co-optimal placement is chosen.
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The score-maximising placement MILP iterated
genes_in_scope, a set, so string hash randomisation reordered its variables and constraints between processes and a degenerate objective returned a different co-optimal placement on each run.Fix: iterate the genes sorted and pin the Gurobi solve (single thread, fixed seed, presolve level 2, zero MIP gap). A parameter sweep on the yeast master showed thread count, seed and presolve level are the settings that change which co-optimal placement is chosen. Verified deterministic across
PYTHONHASHSEEDvalues, and byte-identical to the MATLAB port's placement.