Pulp obtains results as problem is infeasible, while problem is not feasible - python

I'm trying to solve an assignment problem with pulp. The basic part of the code is as follows:
set_I = range(1, numberOfPoints)
set_J = range(1, numberOfCentroids)
tau = 0.15
Q = 15
# decision variable
x_vars = LpVariable.dicts(name="x_vars", indexs=(set_I, set_J), lowBound=0, upBound=1, cat=LpInteger)
# model name
prob = LpProblem("MIP_Model", LpMinimize)
# constraints
for i in set_I:
prob += lpSum(x_vars[i][j] for j in set_J) == 1, ""
for j in set_J:
prob += lpSum(x_vars[i][j] for i in set_I) >= 1, ""
for j in set_J:
prob += lpSum(x_vars[i][j] for i in set_I) <= Q*(1-tau), ""
for j in set_J:
prob += lpSum(x_vars[i][j] for i in set_I) >= Q*(1+tau), ""
# objective
prob += lpSum(d[i, j]*x_vars[i][j] for i in set_I for j in set_J)
prob.solve()
The result is like this:
Problem MODEL has 31 rows, 76 columns and 304 elements
Coin0008I MODEL read with 0 errors
Problem is infeasible - 0.01 seconds
Option for printingOptions changed from normal to all
However, the problem is not infeasible and results are obtained with other solvers.
I wonder if there is a syntax error and is the problem caused by this?
I have asked a similar question in the next link:
Infeasible solution by pulp


When I run the problem locally, with d a matrix of ones, 20 points, and 3 centroids. It also becomes infeasible for me. Look at the constraints:
_C22: x_vars_10_1 + x_vars_11_1 + x_vars_12_1 + x_vars_13_1 + x_vars_14_1
+ x_vars_15_1 + x_vars_16_1 + x_vars_17_1 + x_vars_18_1 + x_vars_19_1
+ x_vars_1_1 + x_vars_2_1 + x_vars_3_1 + x_vars_4_1 + x_vars_5_1 + x_vars_6_1
+ x_vars_7_1 + x_vars_8_1 + x_vars_9_1 <= 12.75
_C23: x_vars_10_2 + x_vars_11_2 + x_vars_12_2 + x_vars_13_2 + x_vars_14_2
+ x_vars_15_2 + x_vars_16_2 + x_vars_17_2 + x_vars_18_2 + x_vars_19_2
+ x_vars_1_2 + x_vars_2_2 + x_vars_3_2 + x_vars_4_2 + x_vars_5_2 + x_vars_6_2
+ x_vars_7_2 + x_vars_8_2 + x_vars_9_2 <= 12.75
_C24: x_vars_10_1 + x_vars_11_1 + x_vars_12_1 + x_vars_13_1 + x_vars_14_1
+ x_vars_15_1 + x_vars_16_1 + x_vars_17_1 + x_vars_18_1 + x_vars_19_1
+ x_vars_1_1 + x_vars_2_1 + x_vars_3_1 + x_vars_4_1 + x_vars_5_1 + x_vars_6_1
+ x_vars_7_1 + x_vars_8_1 + x_vars_9_1 >= 17.25
_C25: x_vars_10_2 + x_vars_11_2 + x_vars_12_2 + x_vars_13_2 + x_vars_14_2
+ x_vars_15_2 + x_vars_16_2 + x_vars_17_2 + x_vars_18_2 + x_vars_19_2
+ x_vars_1_2 + x_vars_2_2 + x_vars_3_2 + x_vars_4_2 + x_vars_5_2 + x_vars_6_2
+ x_vars_7_2 + x_vars_8_2 + x_vars_9_2 >= 17.25
You require
x_vars_10_2 + x_vars_11_2 + x_vars_12_2 + x_vars_13_2 + x_vars_14_2
+ x_vars_15_2 + x_vars_16_2 + x_vars_17_2 + x_vars_18_2 + x_vars_19_2
+ x_vars_1_2 + x_vars_2_2 + x_vars_3_2 + x_vars_4_2 + x_vars_5_2 + x_vars_6_2
+ x_vars_7_2 + x_vars_8_2 + x_vars_9_2
to be greater than 17.25 and smaller than 12.75 at the same time. That's not possible, of course.

Related

argument must be a string or a number, not 'LpAffineExpression'

I am trying to use python IRR function with PULP maximisation but i am getting the following error
TypeError: float() argument must be a string or a number, not 'LpAffineExpression'
TypeError Traceback (most recent call last)
in
11 name[6]*rate[6]*ratesList2[2] + name[7]*rate[7]*ratesList2[2] + name[8]*rate[8]*ratesList2[2] + name[9]*rate[9]*ratesList2[2] + name[10]*rate[10]*ratesList2[2] + name[11]*rate[11]*ratesList2[2] +
12 name[12]*rate[12]*ratesList2[2] + name[13]*rate[13]*ratesList2[2] + name[14]*rate[14]*ratesList2[2] + name[15]*rate[15]*ratesList2[2] + name[16]*rate[16]*ratesList2[2] + name[17]*rate[17]*ratesList2[2] +
---> 13 name[18]*rate[18]*ratesList2[2])])
14
15
problem += np.irr([(-19660528.00),
(name[0]*rate[0] + name[1]*rate[1] + name[2]*rate[2] + name[3]*rate[3] + name[4]*rate[4] + name[5]*rate[5] +
name[6]*rate[6] + name[7]*rate[7] + name[8]*rate[8] + name[9]*rate[9] + name[10]*rate[10] + name[11]*rate[11] +
name[12]*rate[12] + name[13]*rate[13] + name[14]*rate[14] + name[15]*rate[15] + name[16]*rate[16] + name[17]*rate[17] +
name[18]*rate[18]),
(name[0]*rate[0]*ratesList1[1] + name[1]*rate[1]*ratesList2[1] + name[2]*rate[2]*ratesList2[1] + name[3]*rate[3]*ratesList2[1] + name[4]*rate[4]*ratesList2[1] + name[5]*rate[5]*ratesList2[1] +
name[6]*rate[6]*ratesList2[1] + name[7]*rate[7]*ratesList2[1] + name[8]*rate[8]*ratesList2[1] + name[9]*rate[9]*ratesList2[1] + name[10]*rate[10]*ratesList2[1] + name[11]*rate[11]*ratesList2[1] +
name[12]*rate[12]*ratesList2[1] + name[13]*rate[13]*ratesList2[1] + name[14]*rate[14]*ratesList2[1] + name[15]*rate[15]*ratesList2[1] + name[16]*rate[16]*ratesList2[1] + name[17]*rate[17]*ratesList2[1] +
name[18]*rate[18]*ratesList2[1]),
(name[0]*rate[0]*ratesList1[2] + name[1]*rate[1]*ratesList2[2] + name[2]*rate[2]*ratesList2[2] + name[3]*rate[3]*ratesList2[2] + name[4]*rate[4]*ratesList2[2] + name[5]*rate[5]*ratesList2[2] +
name[6]*rate[6]*ratesList2[2] + name[7]*rate[7]*ratesList2[2] + name[8]*rate[8]*ratesList2[2] + name[9]*rate[9]*ratesList2[2] + name[10]*rate[10]*ratesList2[2] + name[11]*rate[11]*ratesList2[2] +
name[12]*rate[12]*ratesList2[2] + name[13]*rate[13]*ratesList2[2] + name[14]*rate[14]*ratesList2[2] + name[15]*rate[15]*ratesList2[2] + name[16]*rate[16]*ratesList2[2] + name[17]*rate[17]*ratesList2[2] +
name[18]*rate[18]*ratesList2[2])])
problem += (name[0] + name[1] + name[2] + name[3] + name[4] + name[5] + name[6] + name[7] + name[8] + name[9] + name[10] +
name[11] + name[12] + name[13] + name[14] + name[15] + name[16] + name[17] + name[18]) <= sum(marketMix['GLA']), "1st constraint"

The numpy function irr() takes as argument a list of values. You are instead passing a list of linear expressions containing variables that are subject to optimization. irr() is not prepared to handle that. It assumes that all arguments can be coerced into a float. Instead of using function irr() you will have to state the respective expression explicitly.

Expand parent prefix number to a given child number

I have a parent prefix number and a child number that begins with the same prefix. I want to expand the parent number up to the target child number
in a way that all prefixes be included and show the target, for example.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ +
+ Example #1 + Example #2 + Example #3 +
+ parent=123, target=1235 + parent=123, target=12354 + parent=123, target=123073 +
+ + + +
+ Expansion would be: + Expansion would be: + Expansion would be: +
+ + + +
+ 123-0 + 123-0 + 123-0-0 +
+ 123-1 + 123-1 + 123-0-1 +
+ 123-2 + 123-2 + 123-0-2 +
+ 123-4 + 123-4 + 123-0-3 +
+ 123-5 --> target + 123-5-0 + 123-0-4 +
+ 123-6 + 123-5-1 + 123-0-5 +
+ 123-7 + 123-5-2 + 123-0-6 +
+ 123-8 + 123-5-4 --> target + 123-0-7-0 +
+ 123-9 + 123-5-5 + 123-0-7-1 +
+ + 123-5-6 + 123-0-7-2 +
+ + 123-5-7 + 123-0-7-3 --> target +
+ + 123-5-8 + 123-0-7-4 +
+ + 123-5-9 + 123-0-7-5 +
+ + 123-6 + 123-0-7-6 +
+ + 123-7 + 123-0-7-7 +
+ + 123-8 + 123-0-7-8 +
+ + 123-9 + 123-0-7-9 +
+ + + 123-0-8 +
+ + + 123-0-9 +
+ + + 123-1 +
+ + + 123-2 +
+ + + 123-3 +
+ + + 123-4 +
+ + + 123-5 +
+ + + 123-6 +
+ + + 123-7 +
+ + + 123-8 +
+ + + 123-9 +
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Like you see,
In example #1, target is 4 digit length and only one level expansion
was needed
In example #2, target is 5 digit length and two level
expansion was needed
In example #3, target is 6 digit length and three level expansion
was needed
In my current code shown below, I'm only able to print one level expansion and only for one target. I'm stuck in how to expand the parent number when target length is greater in 2 or more digits compared with length of parent and if input targets are more than one. Help would be appreciated.
parent="1312314"
child="13123147674"
l = len(parent)
target=child[l:]
t=int(target[0])
for k in range(t):
print(parent + str(k))
print(parent + str(t) + " --> target")
for k in range(t+1,10):
print(parent + str(k))
Current output:
13123140
13123141
13123142
13123143
13123144
13123145
13123146
13123147 --> target
13123148
13123149
Update
Example #4
parent=12345, target1=1234538, target2=1234570924, target3=123459
Expansion would be:
123450
123451
123452
1234530
1234531
1234532
1234533
1234534
1234535
1234536
1234537
1234538 --> target
1234539
123454
123455
123456
12345700
12345701
12345702
12345703
12345704
12345705
12345706
12345707
12345708
123457090
123457091
1234570920
1234570921
1234570922
1234570923
1234570924 --> target
1234570925
1234570926
1234570927
1234570928
1234570929
123457093
123457094
123457095
123457096
123457097
123457098
123457099
1234571
1234572
1234573
1234574
1234575
1234576
1234577
1234578
1234579
123458
123459 --> target

you can try this. the trick is result.sort()
parent="123"
child= "12354"
p = len(parent)
c = len(child)
result = []
for i in range(p, c):
for x in range(0, 10):
y = child[0:i] + str(x)
result.append(y)
result.sort()
for a in result:
print (a + ("--> target" if a == child else ""))
Result:
1230
1231
1232
1233
1234
1235
12350
12351
12352
12353
12354--> target
12355
12356
12357
12358
12359
1236
1237
1238
1239
for multiple targets:
parent="12345"
target1="1234538"
target2="1234570924"
target3="123459"
targets = [target1, target2, target3]
result = []
for target in targets:
for i in range(len(parent), len(target)):
for x in range(0, 10):
y = target[0:i] + str(x)
result.append(y)
result = list(set(result))
result.sort()
for a in result:
print (a + ("--> target" if a in targets else ""))

u can do as below:
parent='123'
child='123073'
if len(child)> len(parent):
tuple_list =[]
nums_to_append=child[len(parent):]
l = len(nums_to_append)
for i,num in enumerate(nums_to_append):
for j in range(10):
if num == str(j):
tuple_list.append((parent,j+1))
parent = parent+num
if i==(l-1):
print(parent, "--> target")
break
else:
print(parent+str(j))
for parent,num in tuple_list[::-1]:
for j in range(num,10):
print(parent+str(j))
12300
12301
12302
12303
12304
12305
12306
123070
123071
123072
123073 --> target
123074
123075
123076
123077
123078
123079
12308
12309
1231
1232
1233
1234
1235
1236
1237
1238
1239

Generate a Bode-form transfer function using ratsimp

I have to simplify a transfer function using sympy. I am used to maxima and I am looking for advice to get similar performances in a python environment.
Using the following Maxima code:
A:-Avol0/(1+s/(2*pi*fp));
Zph:Rsh/(1+Rsh*Cj*s);
Zf:Rf/(1+Rf*Cf*s);
alpha:Zf*Zph/(Zf+Zph);
beta:Zph/(Zf+Zph);
BetaA:ratsimp(beta*A,s);
H:ratsimp(alpha*A/(1-BetaA),s);
I get the following:
(H)-> -(2*Avol0*Rf*Rsh*fp*pi)/((Cj+Cf)*Rf*Rsh*s^2+((2*Cj+(2*Avol0+2)*Cf)*Rf*Rsh*fp*pi+Rsh+Rf)*s+((2*Avol0+2)*Rsh+2*Rf)*fp*pi)
The same opertions in sympy do not get to such a nice result:
import numpy as np
import sympy as sy
"""
Formulas
"""
s, Rf, Cf, Rsh, Cj, Cd, Ccm, GBP, Avol0, fp, w = \
sy.symbols("s Rf Cf Rsh Cj Cd Ccm GBP Avol0 fp w")
A = -Avol0/(1+s/(2*np.pi*fp))
Zph = Rsh/(1+Rsh*Cj*s)
Zf = Rf/(1+Rf*Cf*s)
alpha = Zf*Zph/(Zf+Zph)
beta = Zph/(Zf+Zph)
Gloop = sy.ratsimp(beta*A)
H = alpha*A/(1-Gloop)
sy.ratsimp(H)
returns an unreadable result:
-1.0*(1.0*Avol0*Cf**2*Cj*Rf**3*Rsh**3*fp**2*s**3 + 0.159154943091895*Avol0*Cf**2*Cj*Rf**3*Rsh**3*fp*s**4 + 1.0*Avol0*Cf**2*Rf**3*Rsh**2*fp**2*s**2 + 0.159154943091895*Avol0*Cf**2*Rf**3*Rsh**2*fp*s**3 + 1.0*Avol0*Cf*Cj**2*Rf**3*Rsh**3*fp**2*s**3 + 0.159154943091895*Avol0*Cf*Cj**2*Rf**3*Rsh**3*fp*s**4 + 2.0*Avol0*Cf*Cj*Rf**3*Rsh**2*fp**2*s**2 + 0.318309886183791*Avol0*Cf*Cj*Rf**3*Rsh**2*fp*s**3 + 2.0*Avol0*Cf*Cj*Rf**2*Rsh**3*fp**2*s**2 + 0.318309886183791*Avol0*Cf*Cj*Rf**2*Rsh**3*fp*s**3 + 1.0*Avol0*Cf*Rf**3*Rsh*fp**2*s + 0.159154943091895*Avol0*Cf*Rf**3*Rsh*fp*s**2 + 2.0*Avol0*Cf*Rf**2*Rsh**2*fp**2*s + 0.318309886183791*Avol0*Cf*Rf**2*Rsh**2*fp*s**2 + 1.0*Avol0*Cj**2*Rf**2*Rsh**3*fp**2*s**2 + 0.159154943091895*Avol0*Cj**2*Rf**2*Rsh**3*fp*s**3 + 2.0*Avol0*Cj*Rf**2*Rsh**2*fp**2*s + 0.318309886183791*Avol0*Cj*Rf**2*Rsh**2*fp*s**2 + 1.0*Avol0*Cj*Rf*Rsh**3*fp**2*s + 0.159154943091895*Avol0*Cj*Rf*Rsh**3*fp*s**2 + 1.0*Avol0*Rf**2*Rsh*fp**2 + 0.159154943091895*Avol0*Rf**2*Rsh*fp*s + 1.0*Avol0*Rf*Rsh**2*fp**2 + 0.159154943091895*Avol0*Rf*Rsh**2*fp*s)/(1.0*Avol0*Cf**3*Cj*Rf**3*Rsh**3*fp**2*s**4 + 0.159154943091895*Avol0*Cf**3*Cj*Rf**3*Rsh**3*fp*s**5 + 1.0*Avol0*Cf**3*Rf**3*Rsh**2*fp**2*s**3 + 0.159154943091895*Avol0*Cf**3*Rf**3*Rsh**2*fp*s**4 + 1.0*Avol0*Cf**2*Cj**2*Rf**3*Rsh**3*fp**2*s**4 + 0.159154943091895*Avol0*Cf**2*Cj**2*Rf**3*Rsh**3*fp*s**5 + 2.0*Avol0*Cf**2*Cj*Rf**3*Rsh**2*fp**2*s**3 + 0.318309886183791*Avol0*Cf**2*Cj*Rf**3*Rsh**2*fp*s**4 + 3.0*Avol0*Cf**2*Cj*Rf**2*Rsh**3*fp**2*s**3 + 0.477464829275686*Avol0*Cf**2*Cj*Rf**2*Rsh**3*fp*s**4 + 1.0*Avol0*Cf**2*Rf**3*Rsh*fp**2*s**2 + 0.159154943091895*Avol0*Cf**2*Rf**3*Rsh*fp*s**3 + 3.0*Avol0*Cf**2*Rf**2*Rsh**2*fp**2*s**2 + 0.477464829275686*Avol0*Cf**2*Rf**2*Rsh**2*fp*s**3 + 2.0*Avol0*Cf*Cj**2*Rf**2*Rsh**3*fp**2*s**3 + 0.318309886183791*Avol0*Cf*Cj**2*Rf**2*Rsh**3*fp*s**4 + 4.0*Avol0*Cf*Cj*Rf**2*Rsh**2*fp**2*s**2 + 0.636619772367581*Avol0*Cf*Cj*Rf**2*Rsh**2*fp*s**3 + 3.0*Avol0*Cf*Cj*Rf*Rsh**3*fp**2*s**2 + 0.477464829275686*Avol0*Cf*Cj*Rf*Rsh**3*fp*s**3 + 2.0*Avol0*Cf*Rf**2*Rsh*fp**2*s + 0.318309886183791*Avol0*Cf*Rf**2*Rsh*fp*s**2 + 3.0*Avol0*Cf*Rf*Rsh**2*fp**2*s + 0.477464829275686*Avol0*Cf*Rf*Rsh**2*fp*s**2 + 1.0*Avol0*Cj**2*Rf*Rsh**3*fp**2*s**2 + 0.159154943091895*Avol0*Cj**2*Rf*Rsh**3*fp*s**3 + 2.0*Avol0*Cj*Rf*Rsh**2*fp**2*s + 0.318309886183791*Avol0*Cj*Rf*Rsh**2*fp*s**2 + 1.0*Avol0*Cj*Rsh**3*fp**2*s + 0.159154943091895*Avol0*Cj*Rsh**3*fp*s**2 + 1.0*Avol0*Rf*Rsh*fp**2 + 0.159154943091895*Avol0*Rf*Rsh*fp*s + 1.0*Avol0*Rsh**2*fp**2 + 0.159154943091895*Avol0*Rsh**2*fp*s + 1.0*Cf**3*Cj*Rf**3*Rsh**3*fp**2*s**4 + 0.318309886183791*Cf**3*Cj*Rf**3*Rsh**3*fp*s**5 + 0.0253302959105844*Cf**3*Cj*Rf**3*Rsh**3*s**6 + 1.0*Cf**3*Rf**3*Rsh**2*fp**2*s**3 + 0.318309886183791*Cf**3*Rf**3*Rsh**2*fp*s**4 + 0.0253302959105844*Cf**3*Rf**3*Rsh**2*s**5 + 2.0*Cf**2*Cj**2*Rf**3*Rsh**3*fp**2*s**4 + 0.636619772367581*Cf**2*Cj**2*Rf**3*Rsh**3*fp*s**5 + 0.0506605918211689*Cf**2*Cj**2*Rf**3*Rsh**3*s**6 + 4.0*Cf**2*Cj*Rf**3*Rsh**2*fp**2*s**3 + 1.27323954473516*Cf**2*Cj*Rf**3*Rsh**2*fp*s**4 + 0.101321183642338*Cf**2*Cj*Rf**3*Rsh**2*s**5 + 3.0*Cf**2*Cj*Rf**2*Rsh**3*fp**2*s**3 + 0.954929658551372*Cf**2*Cj*Rf**2*Rsh**3*fp*s**4 + 0.0759908877317533*Cf**2*Cj*Rf**2*Rsh**3*s**5 + 2.0*Cf**2*Rf**3*Rsh*fp**2*s**2 + 0.636619772367581*Cf**2*Rf**3*Rsh*fp*s**3 + 0.0506605918211689*Cf**2*Rf**3*Rsh*s**4 + 3.0*Cf**2*Rf**2*Rsh**2*fp**2*s**2 + 0.954929658551372*Cf**2*Rf**2*Rsh**2*fp*s**3 + 0.0759908877317533*Cf**2*Rf**2*Rsh**2*s**4 + 1.0*Cf*Cj**3*Rf**3*Rsh**3*fp**2*s**4 + 0.318309886183791*Cf*Cj**3*Rf**3*Rsh**3*fp*s**5 + 0.0253302959105844*Cf*Cj**3*Rf**3*Rsh**3*s**6 + 3.0*Cf*Cj**2*Rf**3*Rsh**2*fp**2*s**3 + 0.954929658551372*Cf*Cj**2*Rf**3*Rsh**2*fp*s**4 + 0.0759908877317533*Cf*Cj**2*Rf**3*Rsh**2*s**5 + 4.0*Cf*Cj**2*Rf**2*Rsh**3*fp**2*s**3 + 1.27323954473516*Cf*Cj**2*Rf**2*Rsh**3*fp*s**4 + 0.101321183642338*Cf*Cj**2*Rf**2*Rsh**3*s**5 + 3.0*Cf*Cj*Rf**3*Rsh*fp**2*s**2 + 0.954929658551372*Cf*Cj*Rf**3*Rsh*fp*s**3 + 0.0759908877317533*Cf*Cj*Rf**3*Rsh*s**4 + 8.0*Cf*Cj*Rf**2*Rsh**2*fp**2*s**2 + 2.54647908947033*Cf*Cj*Rf**2*Rsh**2*fp*s**3 + 0.202642367284676*Cf*Cj*Rf**2*Rsh**2*s**4 + 3.0*Cf*Cj*Rf*Rsh**3*fp**2*s**2 + 0.954929658551372*Cf*Cj*Rf*Rsh**3*fp*s**3 + 0.0759908877317533*Cf*Cj*Rf*Rsh**3*s**4 + 1.0*Cf*Rf**3*fp**2*s + 0.318309886183791*Cf*Rf**3*fp*s**2 + 0.0253302959105844*Cf*Rf**3*s**3 + 4.0*Cf*Rf**2*Rsh*fp**2*s + 1.27323954473516*Cf*Rf**2*Rsh*fp*s**2 + 0.101321183642338*Cf*Rf**2*Rsh*s**3 + 3.0*Cf*Rf*Rsh**2*fp**2*s + 0.954929658551372*Cf*Rf*Rsh**2*fp*s**2 + 0.0759908877317533*Cf*Rf*Rsh**2*s**3 + 1.0*Cj**3*Rf**2*Rsh**3*fp**2*s**3 + 0.318309886183791*Cj**3*Rf**2*Rsh**3*fp*s**4 + 0.0253302959105844*Cj**3*Rf**2*Rsh**3*s**5 + 3.0*Cj**2*Rf**2*Rsh**2*fp**2*s**2 + 0.954929658551372*Cj**2*Rf**2*Rsh**2*fp*s**3 + 0.0759908877317533*Cj**2*Rf**2*Rsh**2*s**4 + 2.0*Cj**2*Rf*Rsh**3*fp**2*s**2 + 0.636619772367581*Cj**2*Rf*Rsh**3*fp*s**3 + 0.0506605918211689*Cj**2*Rf*Rsh**3*s**4 + 3.0*Cj*Rf**2*Rsh*fp**2*s + 0.954929658551372*Cj*Rf**2*Rsh*fp*s**2 + 0.0759908877317533*Cj*Rf**2*Rsh*s**3 + 4.0*Cj*Rf*Rsh**2*fp**2*s + 1.27323954473516*Cj*Rf*Rsh**2*fp*s**2 + 0.101321183642338*Cj*Rf*Rsh**2*s**3 + 1.0*Cj*Rsh**3*fp**2*s + 0.318309886183791*Cj*Rsh**3*fp*s**2 + 0.0253302959105844*Cj*Rsh**3*s**3 + 1.0*Rf**2*fp**2 + 0.318309886183791*Rf**2*fp*s + 0.0253302959105844*Rf**2*s**2 + 2.0*Rf*Rsh*fp**2 + 0.636619772367581*Rf*Rsh*fp*s + 0.0506605918211689*Rf*Rsh*s**2 + 1.0*Rsh**2*fp**2 + 0.318309886183791*Rsh**2*fp*s + 0.0253302959105844*Rsh**2*s**2)

There is a difference between the maxima code and the python one: const 'pi' is kept symbolic in the first case, and approximated to a floating-point value in the second. Replacing np.pi with pi solves the problem, anyway it is weird how sympy tries to simplify the expression when pi is numeric.

Error , Infeasible No value for uninitialized NumericValue object

I'm trying to optmize a problem of clients/facility allocation and I'm using pyomo to do so. I have the following problem that appear and cannot solve it even if I initialize all the values of Model.z to 0; the problems is still infeasible
from __future__ import division
import pyomo.environ as pyo
#from pyomo.environ import *
from pyomo.opt import SolverFactory
import sys
import numpy as np
import sys
import os
from time import perf_counter
from pulp import *
from pyomo.util.infeasible import log_infeasible_constraints
#print(sys.path)
opt = pyo.SolverFactory('glpk')
opt.options['threads'] =4
#import instance
model = pyo.ConcreteModel()
# n is the number of facility related to J
# m is the number of clients related to I
# t is the number of periods relatdd to T
# H is the maximum number of clients that can be served
[m,n,t,H] = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, max_rows = 1)
f_values = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, max_rows = 4,skip_header=1)
c_values = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, skip_footer = 1,skip_header=5)
p_values = np.genfromtxt(fname = r'./Instances/data-100-10-4-3-0.txt', dtype = None, skip_header=405)
model.J = pyo.Set(initialize=[int(i)+1 for i in range(n)])
model.T= pyo.Set(initialize=[int(i)+1 for i in range(t)])
model.T.pprint()
model.I = pyo.Set(initialize=[int(i)+1 for i in range(m)])
model.H = pyo.Set(initialize=[int(i)+1 for i in range(H)])
# Generation of Subsets of all clients
client_list = [int(i)+1 for i in range(100)]
list_H = [int(i)+1 for i in range(H)]
possible_groups_of_clients=[]
for i in list_H:
possible_groups_of_clients += [list(elem) for elem in itertools.combinations(client_list,i)]
possible_groups_of_clients = possible_groups_of_clients[0:5] #Only select the first 5 subsets
model.K = pyo.Set(initialize=[int(i)+1 for i in range(len(possible_groups_of_clients))])
p_val = np.zeros((len(model.K),len(model.J),len(model.T)))
for t in model.T:
t=t-1 #Pyomo starts at 1
for j in model.J:
j=j-1
count = 0
for k in possible_groups_of_clients:
#k=k+1
c_sum=0;
for p in k:
if t==0:
c_sum = c_sum +c_values[p][j]
elif t==1:
c_sum = c_sum +c_values[p+100][j]
elif t==2:
c_sum = c_sum +c_values[p+200][j]
elif t==3:
c_sum = c_sum +c_values[p+300][j]
#print(f_values[t][j])
p_val[count][j][t] = f_values[t][j]+ c_sum
count=count+1
model.p_val = pyo.Param(model.K,model.J,model.T, initialize = lambda model,k,j,t: p_val[k-1][j-1][t-1])
#Matrice Aik
A = [[0 for k in range(len(possible_groups_of_clients))] for i in range(100)]
#model.p_val = pyo.Param(model.T,model.J,model.K, initialize = lambda model,t,j,k: A[t-1][j-1][k-1])
model.A = pyo.Param(model.I, model.K, mutable= True,initialize= lambda model,i,k: A[i-1][k-1])
count=1
for i in possible_groups_of_clients:
for k in range(100):
k=k+1
#print(k)
if k in i:
model.A[k,count]=1
else:
model.A[k,count]=0
count=count+1
model.z = pyo.Var(model.K,model.J,model.T,initialize= 0,within=pyo.NonNegativeReals,bounds=(0,1))
a=range(len(possible_groups_of_clients))
print(p_values)
# Minization of objective functions
# Constraint unique client
model.uniq_client = pyo.Constraint(
model.I,
rule=lambda model, i: sum(model.A[i,k]*model.z[k,j,t] for t in model.T for j in model.J for k in model.K) == 1
)
model.unique_service_groupclients = pyo.Constraint(
model.J, model.T,
rule=lambda model,j,t: sum(model.z[k,j,t] for k in model.K)<=1
)
model.min_factory_perperiod = pyo.Constraint(
model.T,
rule=lambda model, t: sum(model.z[k,j,t] for j in model.J for k in model.K )>=p_values[t-1]
)
model.obj = pyo.Objective(rule= lambda model:sum(model.p_val[k,j,t]*model.z[k,j,t] for t in model.T for j in model.J for k in model.K ), sense= pyo.minimize)
model.pprint()
Constraint Group Client
sol = pyo.SolverFactory('glpk',).solve(model).write()
log_infeasible_constraints(model)
# ==========================================================
# = Solver Results =
# ==========================================================
# ----------------------------------------------------------
# Problem Information
# ----------------------------------------------------------
Problem:
- Name: unknown
Lower bound: -inf
Upper bound: inf
Number of objectives: 1
Number of constraints: 145
Number of variables: 201
Number of nonzeros: 601
Sense: minimize
# ----------------------------------------------------------
# Solver Information
# ----------------------------------------------------------
Solver:
- Status: ok
Termination condition: infeasible
Statistics:
Branch and bound:
Number of bounded subproblems: 0
Number of created subproblems: 0
Error rc: 0
Time: 0.008042335510253906
ERROR: evaluating object as numeric value: z[1,1,1]
(object: <class 'pyomo.core.base.var._GeneralVarData'>)
No value for uninitialized NumericValue object z[1,1,1]
ERROR: evaluating object as numeric value: 1.0 - (A[1,1]*z[1,1,1] +
A[1,2]*z[2,1,1] + A[1,3]*z[3,1,1] + A[1,4]*z[4,1,1] + A[1,5]*z[5,1,1] +
A[1,1]*z[1,2,1] + A[1,2]*z[2,2,1] + A[1,3]*z[3,2,1] + A[1,4]*z[4,2,1] +
A[1,5]*z[5,2,1] + A[1,1]*z[1,3,1] + A[1,2]*z[2,3,1] + A[1,3]*z[3,3,1] +
A[1,4]*z[4,3,1] + A[1,5]*z[5,3,1] + A[1,1]*z[1,4,1] + A[1,2]*z[2,4,1] +
A[1,3]*z[3,4,1] + A[1,4]*z[4,4,1] + A[1,5]*z[5,4,1] + A[1,1]*z[1,5,1] +
A[1,2]*z[2,5,1] + A[1,3]*z[3,5,1] + A[1,4]*z[4,5,1] + A[1,5]*z[5,5,1] +
A[1,1]*z[1,6,1] + A[1,2]*z[2,6,1] + A[1,3]*z[3,6,1] + A[1,4]*z[4,6,1] +
A[1,5]*z[5,6,1] + A[1,1]*z[1,7,1] + A[1,2]*z[2,7,1] + A[1,3]*z[3,7,1] +
A[1,4]*z[4,7,1] + A[1,5]*z[5,7,1] + A[1,1]*z[1,8,1] + A[1,2]*z[2,8,1] +
A[1,3]*z[3,8,1] + A[1,4]*z[4,8,1] + A[1,5]*z[5,8,1] + A[1,1]*z[1,9,1] +
A[1,2]*z[2,9,1] + A[1,3]*z[3,9,1] + A[1,4]*z[4,9,1] + A[1,5]*z[5,9,1] +
A[1,1]*z[1,10,1] + A[1,2]*z[2,10,1] + A[1,3]*z[3,10,1] + A[1,4]*z[4,10,1]
+ A[1,5]*z[5,10,1] + A[1,1]*z[1,1,2] + A[1,2]*z[2,1,2] + A[1,3]*z[3,1,2] +
A[1,4]*z[4,1,2] + A[1,5]*z[5,1,2] + A[1,1]*z[1,2,2] + A[1,2]*z[2,2,2] +
A[1,3]*z[3,2,2] + A[1,4]*z[4,2,2] + A[1,5]*z[5,2,2] + A[1,1]*z[1,3,2] +
A[1,2]*z[2,3,2] + A[1,3]*z[3,3,2] + A[1,4]*z[4,3,2] + A[1,5]*z[5,3,2] +
A[1,1]*z[1,4,2] + A[1,2]*z[2,4,2] + A[1,3]*z[3,4,2] + A[1,4]*z[4,4,2] +
A[1,5]*z[5,4,2] + A[1,1]*z[1,5,2] + A[1,2]*z[2,5,2] + A[1,3]*z[3,5,2] +
A[1,4]*z[4,5,2] + A[1,5]*z[5,5,2] + A[1,1]*z[1,6,2] + A[1,2]*z[2,6,2] +
A[1,3]*z[3,6,2] + A[1,4]*z[4,6,2] + A[1,5]*z[5,6,2] + A[1,1]*z[1,7,2] +
A[1,2]*z[2,7,2] + A[1,3]*z[3,7,2] + A[1,4]*z[4,7,2] + A[1,5]*z[5,7,2] +
A[1,1]*z[1,8,2] + A[1,2]*z[2,8,2] + A[1,3]*z[3,8,2] + A[1,4]*z[4,8,2] +
A[1,5]*z[5,8,2] + A[1,1]*z[1,9,2] + A[1,2]*z[2,9,2] + A[1,3]*z[3,9,2] +
A[1,4]*z[4,9,2] + A[1,5]*z[5,9,2] + A[1,1]*z[1,10,2] + A[1,2]*z[2,10,2] +
A[1,3]*z[3,10,2] + A[1,4]*z[4,10,2] + A[1,5]*z[5,10,2] + A[1,1]*z[1,1,3] +
A[1,2]*z[2,1,3] + A[1,3]*z[3,1,3] + A[1,4]*z[4,1,3] + A[1,5]*z[5,1,3] +
A[1,1]*z[1,2,3] + A[1,2]*z[2,2,3] + A[1,3]*z[3,2,3] + A[1,4]*z[4,2,3] +
A[1,5]*z[5,2,3] + A[1,1]*z[1,3,3] + A[1,2]*z[2,3,3] + A[1,3]*z[3,3,3] +
A[1,4]*z[4,3,3] + A[1,5]*z[5,3,3] + A[1,1]*z[1,4,3] + A[1,2]*z[2,4,3] +
A[1,3]*z[3,4,3] + A[1,4]*z[4,4,3] + A[1,5]*z[5,4,3] + A[1,1]*z[1,5,3] +
A[1,2]*z[2,5,3] + A[1,3]*z[3,5,3] + A[1,4]*z[4,5,3] + A[1,5]*z[5,5,3] +
A[1,1]*z[1,6,3] + A[1,2]*z[2,6,3] + A[1,3]*z[3,6,3] + A[1,4]*z[4,6,3] +
A[1,5]*z[5,6,3] + A[1,1]*z[1,7,3] + A[1,2]*z[2,7,3] + A[1,3]*z[3,7,3] +
A[1,4]*z[4,7,3] + A[1,5]*z[5,7,3] + A[1,1]*z[1,8,3] + A[1,2]*z[2,8,3] +
A[1,3]*z[3,8,3] + A[1,4]*z[4,8,3] + A[1,5]*z[5,8,3] + A[1,1]*z[1,9,3] +
A[1,2]*z[2,9,3] + A[1,3]*z[3,9,3] + A[1,4]*z[4,9,3] + A[1,5]*z[5,9,3] +
A[1,1]*z[1,10,3] + A[1,2]*z[2,10,3] + A[1,3]*z[3,10,3] + A[1,4]*z[4,10,3]
+ A[1,5]*z[5,10,3] + A[1,1]*z[1,1,4] + A[1,2]*z[2,1,4] + A[1,3]*z[3,1,4] +
A[1,4]*z[4,1,4] + A[1,5]*z[5,1,4] + A[1,1]*z[1,2,4] + A[1,2]*z[2,2,4] +
A[1,3]*z[3,2,4] + A[1,4]*z[4,2,4] + A[1,5]*z[5,2,4] + A[1,1]*z[1,3,4] +
A[1,2]*z[2,3,4] + A[1,3]*z[3,3,4] + A[1,4]*z[4,3,4] + A[1,5]*z[5,3,4] +
A[1,1]*z[1,4,4] + A[1,2]*z[2,4,4] + A[1,3]*z[3,4,4] + A[1,4]*z[4,4,4] +
A[1,5]*z[5,4,4] + A[1,1]*z[1,5,4] + A[1,2]*z[2,5,4] + A[1,3]*z[3,5,4] +
A[1,4]*z[4,5,4] + A[1,5]*z[5,5,4] + A[1,1]*z[1,6,4] + A[1,2]*z[2,6,4] +
A[1,3]*z[3,6,4] + A[1,4]*z[4,6,4] + A[1,5]*z[5,6,4] + A[1,1]*z[1,7,4] +
A[1,2]*z[2,7,4] + A[1,3]*z[3,7,4] + A[1,4]*z[4,7,4] + A[1,5]*z[5,7,4] +
A[1,1]*z[1,8,4] + A[1,2]*z[2,8,4] + A[1,3]*z[3,8,4] + A[1,4]*z[4,8,4] +
A[1,5]*z[5,8,4] + A[1,1]*z[1,9,4] + A[1,2]*z[2,9,4] + A[1,3]*z[3,9,4] +
A[1,4]*z[4,9,4] + A[1,5]*z[5,9,4] + A[1,1]*z[1,10,4] + A[1,2]*z[2,10,4] +
A[1,3]*z[3,10,4] + A[1,4]*z[4,10,4] + A[1,5]*z[5,10,4])
(object: <class 'pyomo.core.expr.numeric_expr.SumExpression'>)
No value for uninitialized NumericValue object z[1,1,1]
Traceback (most recent call last):
File "main.py", line 246, in <module>
log_infeasible_constraints(model)
File "/home/john/.local/lib/python3.6/site-packages/pyomo /util/infeasible.py", line 25, in log_infeasible_constraints
if constr.equality and fabs(value(constr.lower - constr.body)) >= tol:
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/numvalue.py", line 226, in value
tmp = obj(exception=True)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/numeric_expr.py", line 222, in __call__
return evaluate_expression(self, exception)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/visitor.py", line 973, in evaluate_expression
return visitor.dfs_postorder_stack(exp)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/visitor.py", line 518, in dfs_postorder_stack
flag, value = self.visiting_potential_leaf(_sub)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/visitor.py", line 893, in visiting_potential_leaf
return True, value(node)
File "/home/john/.local/lib/python3.6/site-packages/pyomo/core/expr/numvalue.py", line 230, in value
% (obj.name,))
ValueError: No value for uninitialized NumericValue object z[1,1,1]
I got this error whatever I try and I don't understand why Z should be initialized as it is the variable I'm trying to minimize

Naming LDA topics in Python

I am new to python and trying to implement topic modelling. I am successful in implementing LDA in pything using gensim , but I am not able to give any label/name to these topics.
How do we name these topics? please help out with the best way to implement in python.
My LDA output is somewhat like this(please let me know if you need the code) :-
0.024*research + 0.021*students + 0.019*conference + 0.019*chi + 0.017*field + 0.014*work + 0.013*student + 0.013*hci + 0.013*group + 0.013*researchers
0.047*research + 0.034*students + 0.020*ustars + 0.018*underrepresented + 0.017*participants + 0.012*researchers + 0.012*mathematics + 0.012*graduate + 0.012*mathematical + 0.012*conference
0.027*students + 0.026*research + 0.018*conference + 0.017*field + 0.015*new + 0.014*participants + 0.013*chi + 0.012*robotics + 0.010*researchers + 0.010*student
0.023*students + 0.019*robotics + 0.018*conference + 0.017*international + 0.016*interact + 0.016*new + 0.016*ph.d. + 0.016*meet + 0.016*ieee + 0.015*u.s.
0.033*research + 0.030*flow + 0.028*field + 0.023*visualization + 0.020*challenges + 0.017*students + 0.015*project + 0.013*shape + 0.013*visual + 0.012*data
0.044*research + 0.020*mathematics + 0.017*program + 0.014*june + 0.014*conference + 0.014*- + 0.013*mathematicians + 0.013*conferences + 0.011*field + 0.011*mrc
0.023*research + 0.021*students + 0.015*field + 0.014*hovering + 0.014*mechanisms + 0.014*dpiv + 0.013*aerodynamic + 0.012*unsteady + 0.012*conference + 0.012*hummingbirds
0.031*research + 0.018*mathematics + 0.016*program + 0.014*flow + 0.014*mathematicians + 0.012*conferences + 0.011*field + 0.011*june + 0.010*visualization + 0.010*communities
0.028*students + 0.028*research + 0.018*ustars + 0.018*mathematics + 0.015*underrepresented + 0.010*program + 0.010*encouraging + 0.010*'', + 0.010*participants + 0.010*conference
0.049*research + 0.021*conference + 0.021*program + 0.020*mathematics + 0.014*mathematicians + 0.013*field + 0.013*- + 0.011*conferences + 0.010*areas

Labeling topics is completely distinct from topic modeling. Here's an article that describes using a keyword extraction technique (KERA) to apply meaningful labels to topics: http://arxiv.org/abs/1308.2359

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