Losing multiobjective behavior after feasibility relaxation?

回答済み

Vineet Jagadeesan Nair

• 2021年07月28日 21:18

When I run model.feasRelax on my multiobjective model (with minrelax = 0), it seems to automatically turn the model into a single objective model (model.IsMultiObj = 0 and model.NumObj =1). Thus, I'm no longer able to access the values of each of my individual objective functions with the updated solutions. Is there any way to get around this so that I can record the objective values?

The other option I have is to manually recalculate each of the terms in my objective function after the fact, using the solutions. But this is a bit tedious so I'm wondering if there's an alternative.

0

• 正式なコメント

  

  Simranjit Kaur

  Gurobi Staff

  • 2024年12月06日 00:28

  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

  • 2021年07月28日 22:47
  • 編集済み

  This is expected behavior - the feasibility relaxation creates a single objective to minimize the solution's violation of the original constraints with respect to the specified metric. Note that only \( \texttt{minrelax=False} \) is currently supported for multi-objective models.

  To evaluate your multiple objectives at the feasibility relaxation's solution, you can store each objective function as a LinExpr object, then call LinExpr.getValue() once the feasibility relaxation finishes solving. For example, the following code

  import gurobipy as gp
  
  # Build simple model
  m = gp.Model()
  x = m.addVars(3, name='x')
  m.addConstr(x[0] >= 20)
  m.addConstr(x[1] >= 10)
  m.addConstr(x[2] >= 5)
  
  # Store objectives as LinExpr objects
  obj1 = x[0] + x[2]
  obj2 = 2*x[1]
  
  # Solve feasibility relaxation (minrelax=False)
  # For illustrative purposes - our model is feasible
  m.feasRelaxS(0, False, True, True)
  m.optimize()
  
  # Evaluate objectives at current solution
  print(f'obj1 = {obj1.getValue()}')
  print(f'obj2 = {obj2.getValue()}')

  evaluates the two linear expressions given the feasibility relaxation solution:

  obj1 = 25.0
  obj2 = 20.0

  1
• 

  Vineet Jagadeesan Nair

  • 2021年07月29日 03:48

  

  

  

  Yes, this is the kind of solution I was looking for. Thank you!

  0

投稿コメントは受け付けていません。