i have written following python code using pulp library solving knapsack problem using integer programming formulation. using strings generate lpvariable commands , add constraints , executing them eval. there way without using eval?
from pulp import * #knapsack problem items = input ('enter number of items :') items = int(items) #print('enter %d items 1 one') print ('enter {0} items profit 1 one'.format(items)) obj = [] weight = [] knapweight = 0 in range(0,items): print('enter {0} item profit : '.format(i+1)) obj.append(input()) in range(0, items): print('the profit @ {0} {1}'.format(i, obj[i])) print ('\nenter {0} items weights 1 one'.format(items)) in range(0, items): print('enter {0} item weight'.format(i+1)) weight.append(input()) in range(0, items): print('the profit @ {0} {1}'.format(i, weight[i])) print ('\nenter weight of knapsack :') knapweight = input() print ('the weight of knapsack : {0}'.format(knapweight)) #generating variables in range(0, items): str = 'x{0} = lpvariable("x{1}", cat=\'binary\')'.format(i+1,i+1) print (str) exec(str) prob = lpproblem('knapsack', lpmaximize) print ('\ndynamic generaion\n') #weight constraint generation str = "prob += " in range(0, items): if == (items-1): str = str + weight[i] + '*x{0}'.format(i+1) else: str = str + weight[i] + '*x{0}'.format(i+1) + '+' str = str + '<=' + knapweight exec(str) print(str) #objective function generation str = "prob += " in range(0, items): if == (items-1): str = str + obj[i] + '*x{0}'.format(i+1) else: str = str + obj[i] + '*x{0}'.format(i+1) + '+' exec(str) print(str) status = prob.solve() print(lpstatus[status]) print ('\nthe values of variables : \n') in range(0, items): print('x{0} = {1}'.format(i+1, value(eval('x{0}'.format(i+1)))))
the key recognizing it's okay have object -- 1 in list or dictionary -- don't have explicitly bound name. can make object, append list, , ever refer some_list[2]. once allow freedom, code can become simpler.
here i've hardcoded inputs, because doesn't matter:
from pulp import * objs = [2,3,2,5,3] weights = [1,2,2,1,3] knapweight = 5 prob = lpproblem('knapsack', lpmaximize) xs = [lpvariable("x{}".format(i+1), cat="binary") in range(len(objs))] # add objective total_prof = sum(x * obj x,obj in zip(xs, objs)) prob += total_prof # add constraint total_weight = sum(x * w x,w in zip(xs, weights)) prob += total_weight <= knapweight status = prob.solve() print(lpstatus[status]) print("objective value:", value(prob.objective)) print ('\nthe values of variables : \n') v in prob.variables(): print(v.name, "=", v.varvalue) which gives me
optimal objective value: 10.0 values of variables : x1 = 1.0 x2 = 1.0 x3 = 0.0 x4 = 1.0 x5 = 0.0 here i'm building lpvariables in list comprehension
xs = [lpvariable("x{}".format(i+1), cat="binary") in range(len(objs))] where objects live in list xs.
>>> xs [x1, x2, x3, x4, x5] >>> xs[3] x4 >>> type(xs[3]) <class 'pulp.pulp.lpvariable'> 
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