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Weird behaviour of MixedIntegerLinearProgram

asked 2020-10-12 11:03:01 -0500

Cyrille gravatar image

updated 2020-10-12 13:57:00 -0500

slelievre gravatar image

Not really a question, more of an observation, perhaps a bug, I do not know.

p = MixedIntegerLinearProgram(maximization=False, solver="GLPK")
x = p.new_variable(real=True, nonnegative=True)
p.set_integer(x[2])
p.add_constraint(2*x[0] + 2*x[1] + 3*x[2] - 10*x[3] <= 0)
p.add_constraint(6*x[1] + 4*x[2] - 11*x[3] >= 0)
p.add_constraint(2*x[2] - 6*x[3] <= 0)
p.add_constraint(x[0] - x[1] - x[2] >= 0)
p.add_constraint(x[3] >= 1)
p.set_objective(3*x[1] + 6*x[2] - 3*x[3])
p.show()

This code sets x[0] integer when it should be x[2].

The following code works:

p = MixedIntegerLinearProgram(maximization=False, solver="GLPK")
x = p.new_variable(real=True, nonnegative=True)
p.set_real(x[0])
p.set_real(x[1])
p.set_integer(x[2])
p.add_constraint(2*x[0] + 2*x[1] + 3*x[2] - 10*x[3] <= 0)
p.add_constraint(6*x[1] + 4*x[2] - 11*x[3] >= 0)
p.add_constraint(2*x[2] - 6*x[3] <= 0)
p.add_constraint(x[0] - x[1] - x[2] >= 0)
p.add_constraint(x[3] >= 1)
p.set_objective(3*x[1] + 6*x[2] - 3*x[3])
p.show()

I find this weird. If I code p.set_integer(x[2]), I expect x[2] to be integer. (If I do not add p.set_real(x[1]), x[1] is set to integer). Certainly one more time, somebody will say I did not read the documentation correctly.

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answered 2020-10-13 03:02:29 -0500

updated 2020-10-13 03:05:17 -0500

The names x_i indicate the order in which the variables x[i] are accessed first. You can see it if you set parameter name of method new_variable. So variable x[2] is correctly sets to integer, although it's internal name is x_0.

sage: p = MixedIntegerLinearProgram(maximization=False, solver="GLPK") 
....: x = p.new_variable(real=True, nonnegative=True, name="x") 
....: p.set_integer(x[2]) 
....: p.add_constraint(2*x[0] + 2*x[1] + 3*x[2] - 10*x[3] <= 0) 
....: p.add_constraint(6*x[1] + 4*x[2] - 11*x[3] >= 0) 
....: p.add_constraint(2*x[2] - 6*x[3] <= 0) 
....: p.add_constraint(x[0] - x[1] - x[2] >= 0) 
....: p.add_constraint(x[3] >= 1) 
....: p.set_objective(3*x[1] + 6*x[2] - 3*x[3]) 
....: p.show()                                                                                                                      
Minimization:
  6.0 x[2] + 3.0 x[1] -3.0 x[3] 

Constraints:
  3.0 x[2] + 2.0 x[0] + 2.0 x[1] - 10.0 x[3] <= 0.0
  -4.0 x[2] - 6.0 x[1] + 11.0 x[3] <= 0.0
  2.0 x[2] - 6.0 x[3] <= 0.0
  x[2] - x[0] + x[1] <= 0.0
  - x[3] <= -1.0
Variables:
  x[2] = x_0 is an integer variable (min=0.0, max=+oo)
  x[0] = x_1 is a continuous variable (min=0.0, max=+oo)
  x[1] = x_2 is a continuous variable (min=0.0, max=+oo)
  x[3] = x_3 is a continuous variable (min=0.0, max=+oo)
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Comments

Thanks a lot David, I was not able to imagine such a vicious comportment of the package. I say vicious, because now that I know that I can cope with it, but how can I explain this to economist students who discover Linear programming, Python and Sagemath in the same time. If I want to keep the order I must write its nature for each variable in order it is certainly not the simplest things to do if I have a huge lot of variables.

Cyrille gravatar imageCyrille ( 2020-10-13 08:14:13 -0500 )edit

Trick your students by explaining them that it's easier to read the program if the variables have meaningful names like "price", "benefit", "cost". And so that it's important to define parameter name. Also, use different variables for each type.

David Coudert gravatar imageDavid Coudert ( 2020-10-13 16:30:39 -0500 )edit
3

answered 2020-10-14 03:54:14 -0500

Sébastien gravatar image

If you read the documentation of p.new_variable method, you can see that there is an option indices:

  • "indices" -- (optional) an iterable of keys; components corresponding to these keys are created in order, and access to components with other keys will raise an error; otherwise components of this variable can be indexed by arbitrary keys and are created dynamically on access

Using this option:

p = MixedIntegerLinearProgram(maximization=False, solver="GLPK")
x = p.new_variable(real=True, nonnegative=True, name='x', indices=[0,1,2,3])
p.set_integer(x[2])
p.add_constraint(2*x[0] + 2*x[1] + 3*x[2] - 10*x[3] <= 0)
p.add_constraint(6*x[1] + 4*x[2] - 11*x[3] >= 0)
p.add_constraint(2*x[2] - 6*x[3] <= 0)
p.add_constraint(x[0] - x[1] - x[2] >= 0)
p.add_constraint(x[3] >= 1)
p.set_objective(3*x[1] + 6*x[2] - 3*x[3])

gives the desirable behavior:

sage: p.show()                                                                  
Minimization:
  3.0 x[1] + 6.0 x[2] -3.0 x[3] 

Constraints:
  2.0 x[0] + 2.0 x[1] + 3.0 x[2] - 10.0 x[3] <= 0.0
  -6.0 x[1] - 4.0 x[2] + 11.0 x[3] <= 0.0
  2.0 x[2] - 6.0 x[3] <= 0.0
  - x[0] + x[1] + x[2] <= 0.0
  - x[3] <= -1.0
Variables:
  x[0] = x_0 is a continuous variable (min=0.0, max=+oo)
  x[1] = x_1 is a continuous variable (min=0.0, max=+oo)
  x[2] = x_2 is an integer variable (min=0.0, max=+oo)
  x[3] = x_3 is a continuous variable (min=0.0, max=+oo)
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Comments

Thanks a lot sebastien. I miss this.

Cyrille gravatar imageCyrille ( 2020-10-15 00:09:35 -0500 )edit

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Asked: 2020-10-12 11:03:01 -0500

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Last updated: Oct 15