How to query constraints in callback function?

回答済み

Wissal Guedri

• 2021年09月22日 14:09

Hello,

Is it possible to query the constraints, defined by the user or added by Gurobi, that are in process or have been satisfied in the callback function?

Best,

Wissal

 

 

1

• 正式なコメント

  

  Simranjit Kaur

  Gurobi Staff

  • 2024年12月06日 00:25

  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

  • 2021年09月28日 12:51

  Hi Wissal,

  This is not possible.

  However, with a bit of hacking and the use of callbacks it can be achieved.You can create a copy of your model for which you fix the lower and upper bounds to the feasible point value obtained via cbGetSolution and found in the MIPSOL callback. You then optimize this copy (this is a trivial solve, since all variables are fixed) and finally retrieve the information you need. Please note that you cannot access user cuts/lazy constraints.

  An example code would be

  import gurobipy as gp
  from gurobipy import GRB
  
  def mycallback(model,where):
    if where == GRB.Callback.MIPSOL:
      # get feasible point values
      vals = model.cbGetSolution(model._vars)
      # get copy model's variable and fix them
      vars = model._copy.getVars()
      for i in range(len(vars)):
        vars[i].lb = vals[i]
        vars[i].ub = vals[i]
      # run a trivial optimize and access constraint values of copy model
      model._copy.optimize()
      for c in model._copy.getConstrs():
        print("constraint %s: %f %s= %f"%(c.ConstrName,model._copy.getRow(c).getValue(),c.Sense,c.RHS))
      # reset all copy model bounds (not neccessary)
      model._copy.reset()	
  
  # read example model
  model = gp.read("/Library/gurobi912/mac64/examples/data/glass4.mps")
  # save original vars to access them in callback
  model._vars = model.getVars()
  # create copy of the original model and suppress output
  model._copy = model.copy()
  model._copy.setParam("OutputFlag",0)
  model.optimize(mycallback)

  Best regards,
  Jaromił

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