optimization - Pyomo: Access Solution From Python Code -

i have linear integer programme want solve. installed solver glpk (thanks this answer) , pyomo. wrote code this:

from pyomo.environ import * pyomo.opt import solverfactory  = 370 b = 420 c = 2  model             = concretemodel() model.x           = var([1,2], domain=nonnegativeintegers) model.objective   = objective(expr = * model.x[1] + b * model.x[2], sense=minimize) model.constraint1 = constraint(expr = model.x[1] + model.x[2] == c) # ... more constraints  opt = solverfactory('glpk')  results = opt.solve(model) 

this produces solution file results.yaml.

i have many problems want solve using same model different a, b, , c values. want assign different values a, b, , c, solve model, obtain solution of model.x[1] , model.x[2], , have listing of a, b, c, model.x[1] , model.x[2]. read documentation examples write solutions file such results.yaml.

is there way can access solution values code?

thanks,

i'm not sure if looking for, way have variables being printed in 1 of scripts.

from pyomo.environ import * pyomo.opt import solverfactory pyomo.core import var  m = abstractmodel() opt = solverfactory('glpk')  # vars, params, objective, constraints....  instance = m.create_instance('input.dat') # reading in datafile results = opt.solve(instance, tee=true) results.write() instance.solutions.load_from(results)  v in instance.component_objects(var, active=true):     print ("variable",v)     varobject = getattr(instance, str(v))     index in varobject:         print ("   ",index, varobject[index].value)