classify decision variables according their value in the user callback

Answered

Amir Ghiasi Noughaby

March 15, 2021 07:58

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Hi, 

1) For my callback function, I need to get the value of some decision variables rather than all of them and pass them into model.cbGetSolution. The getVars() method return all the values, but I need some of them. How can I solve this?

2) when I get the solution of these specific decision values, I need to classify them according to their values and index and put them into a new list

To illustrate my problem: I have these decision variables which are binary: 

x[0,0], x[0,1], x[0,2],x[1,0],x[1,1],x[1,2]

y[0,0,0],y[0,0,1], y[0,1,1],y[0,2,0],y[1,0,0],y[1,0,1], y[1,1,1],y[1,2,0]

I need the solution value of y variables, and after getting these variables I want to classify them according to the first index, meaning 

C[0,0]: y[0,0,0], y[0,2,0],

C[0,1]:y[0,0,1], y[0,1,1]

C[1,0]:y[1,0,0], y[1,2,0]

C[1,1]:y[1,1,1]

 

0

• Official comment

  

  Simranjit Kaur

  • Gurobi Staff

  June 23, 2025 02:58 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?.
• 

  Jaromił Najman

  • Gurobi Staff

  March 15, 2021 09:19 Edited

  Hi Amir,

  You can access only the \(y\) variables via the cbGetSolution function. To achieve this, you will have to define them as a private model variable

  import gurobipy as gp
  from gurobipy import GRB
  
  def myCallback(model, where):
    if where == GRB.Callback.MIPSOL:
      # get values of the y variables only
      yvals = model.cbGetSolution(model._yvars)
      print(yvals)
      # do something with the values of y variables
          
  m = gp.Model("myModel")
  x = m.addVars(3, 3, vtype = GRB.BINARY, name="x")
  y = m.addVars(3, 3, 3, vtype = GRB.BINARY, name="y")
  
  # construct model
  # [...]
  
  # define the y variables as a private model variable 
  # to get access to them in the callback
  m._yvars = y
  
  m.optimize(myCallback)

  Best regards,
  Jaromił

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