difference between python gurobi and AMPL gurobi

Answered

ahmad alanaqreh

May 13, 2021 17:46

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I have a minimization model as follow :

 

\ LP format - for model browsing. Use MPS format to capture full model detail.
Minimize
  Z + 0 y6 + 0 y17 + 0 y25 + 0 y32 + 0 y37
Subject To
 cut1: - Z - y1 - 3.5 y2 - 2.5 y3 - 4 y4 - y5 - 2 y7 - 1.5 y8 - 2 y9
   - 0.5 y10 - 4.5 y11 - 2 y12 - 3.5 y13 - 4 y14 - 2 y15 - 2.5 y16 - 2 y18
   - 5 y19 - 5.5 y20 - 4 y21 - 1.5 y22 - y23 - 6.5 y24 - 2 y26 - 2 y27
   - 0.5 y28 - 8 y29 - 2.5 y30 - y31 - 4.5 y33 - 4 y34 - 4 y35 - 0.5 y36
   <= 0
Bounds
 y1 <= 1
 y2 <= 1
 y3 <= 1
 y4 <= 1
 y5 <= 1
 y6 <= 1
 y7 <= 1
 y8 <= 1
 y9 <= 1
 y10 <= 1
 y11 <= 1
 y12 <= 1
 y13 <= 1
 y14 <= 1
 y15 <= 1
 y16 <= 1
 y17 <= 1
 y18 <= 1
 y19 <= 1
 y20 <= 1
 y21 <= 1
 y22 <= 1
 y23 <= 1
 y24 <= 1
 y25 <= 1
 y26 <= 1
 y27 <= 1
 y28 <= 1
 y29 <= 1
 y30 <= 1
 y31 <= 1
 y32 <= 1
 y33 <= 1
 y34 <= 1
 y35 <= 1
 y36 <= 1
 y37 <= 1
End

the Z is unrestricted variable , while 0<= y <=1

if I optimize this model on python by using gurobipy package :

MP_model.optimize ()

 the objective is 0, while the same model on AMPL is giving -91 as objective ??!!

 

0

• Official comment

  

  Simranjit Kaur

  • Gurobi Staff

  June 23, 2025 02:54 Edited

  This post is more than three years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum. Or why not try our AI Gurobot?.
• 

  Eli Towle

  • Gurobi Staff

  May 13, 2021 21:21 Edited

  When reading LP files, Gurobi assumes variables have a lower bound of zero unless otherwise specified. If \( \texttt{Z} \) were unbounded, the LP file would contain the line

  Z free

  in the \( \texttt{Bounds} \) section. The LP file contains no such line, so Gurobi interprets the lower bound of \( \texttt{Z} \) to be \( 0 \):

  >>> import gurobipy as gp
  >>> m = gp.read('model.lp')
  Using license file /Users/towle/gurobi.lic
  Read LP format model from file model.lp
  Reading time = 0.00 seconds
  : 1 rows, 38 columns, 33 nonzeros
  >>> m.getVarByName('Z').LB
  0.0

  If \( \texttt{Z} \) were unbounded, then \( -91 \) is the correct optimal objective value:

  >>> m.getVarByName('Z').LB = -gp.GRB.INFINITY
  >>> m.optimize()
  Gurobi Optimizer version 9.1.2 build v9.1.2rc0 (mac64)
  Thread count: 2 physical cores, 4 logical processors, using up to 4 threads
  Optimize a model with 1 rows, 38 columns and 33 nonzeros
  Model fingerprint: 0xddb989e0
  Coefficient statistics:
    Matrix range     [5e-01, 8e+00]
    Objective range  [1e+00, 1e+00]
    Bounds range     [1e+00, 1e+00]
    RHS range        [0e+00, 0e+00]
  
  Presolve removed 1 rows and 38 columns
  Presolve time: 0.02s
  Presolve: All rows and columns removed
  Iteration    Objective       Primal Inf.    Dual Inf.      Time
         0   -9.1000000e+01   0.000000e+00   0.000000e+00      0s
  
  Solved in 0 iterations and 0.03 seconds
  Optimal objective -9.100000000e+01

  If you built this model using the gurobipy package, you did not correctly define \( \texttt{Z} \) to be an unbounded variable. By default, variables added to a model using Model.addVar() or Model.addVars() have a lower bound of \( 0 \). To add an unbounded variable, set its lower bound to \( \texttt{-GRB.INFINITY} \):

  z = MP_model.addVar(lb=-gp.GRB.INFINITY, name='Z')

  1

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