python - Pyomo scaling with GLPK -
i have (fairly) simple pyomo model 5 parameters , set of size 48 (which represent time intervals). glpk works absolutely fine if use particular data file:
# data file param : n := 48; param : e_demand := 1 231.674545 2 223.328638 3 218.047274 4 212.285910 5 214.539544 6 213.940455 7 216.871637 8 205.824183 9 208.905001 (this continues in similar vein index 48 , 4 more parameters).
but if use (only different) data file, problem takes longer solve (from less second more 20 minutes, wasn't bothered find out how longer). if change 2 of parameters 1/3 of value (like below), problem takes longer solve.
param : e_demand := 1 76.464996 2 69.815002 3 71.355003 4 75.004997 5 72.360001 6 71.065002 7 70.669998 8 71.809998 9 72.309998 i think problem must scaling, since if gradually replace smaller values 1 data file other problem takes more time until becomes cumbersomely slow. there way of changing glpk scaling using pyomo? using different solver potentially solve problem?
for concretemodel, implement form of scaling during model construction checking parameter values , applying relevant scaling factors in model formulation.
a similar discussion on variable/constraint scaling can found on pyomo issues page: https://github.com/pyomo/pyomo/issues/219
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