Model logs shows Numerical Issues how to handle this

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Sudheer Reddy

2024年02月21日 13:43

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The model log tells, Consider reformulating model. Here how to find which part of the constraint I need to reformulate. Is there any way to see which constraints are causing this issue? 

Model contains large matrix coefficient range what is this meaning. How to know which one are large? 

It also tells max constraint violation and max general constraint violation, how to know which constraints are violating. 

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• Numerics

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• 正式なコメント

  

  Simranjit Kaur

  • Gurobi Staff

  2026年11月13日 01:29

  This post is more than two 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 try Gurobot, our chatbot interface offering instant, expert-level support.
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  David Torres Sanchez

  • Gurobi Staff

  2024年02月21日 13:44

  Can you try upgrading to v11.0.0 and re-running this test?

  Cheers, 
  David

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  Sudheer Reddy

  2024年02月21日 13:49

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  David Torres Sanchez We do have v11.0 lic I will do. In general just want to understand below points

  Model contains large matrix coefficient range what is this meaning. How to know which one are large?  

  It also tells max constraint violation and max general constraint violation, how to know which constraints are violating. 

  The model log tells, Consider reformulating model. Here how to find which part of the constraint I need to reformulate. Is there any way to see which constraints are causing this issue? 

   

   

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  Riley Clement

  • Gurobi Staff

  2024年02月21日 23:21

  Hi Sudheer,

  This is the code snippet I use (which uses Python and pandas)

  import pandas as pd
  import numpy as np
  
  # assumes your model is "m"
  
  A = np.abs(m.getA()).tocoo() # absolute-value coefficent matrix in scipy sparse coo_matrix form
  cons = np.array(m.getConstrs())
  df = pd.DataFrame({
      "abscoeff":A.data,
      "constr":cons[A.row],
  })
  
  # try to find family of constraints with highest magnitude of coefficients
  #  (you obviously need to tailor this to the coefficients in your model)
  df["name"] = df["constr"].map(lambda c: c.constrname) # add names
  df["family"] = df["name"].map(lambda n: n.split("_")[0]) # assuming name is of the form myconstr_0_1
  df.query("abscoeff > 8e6")["family"].value_counts()
  
  # try to find family of constraints with smallest magnitude of coefficients
  df.query("abscoeff < 1e-3")["family"].value_counts()
  
  # investigate violated constraints
  # note this is only looking at linear constraints
  slacks = m.getAttr("Slack", cons)
  df2 = pd.DataFrame({
      "violation":-slacks[slacks < 0],
      "constr":cons[slacks < 0],
  })
  
  # can sort df2, add names, count family etc

  For guidance on reducing the coefficient range please see the subsections of Tolerances and User Scaling (a section in our Guidelines for Numerical Issues chapter of our reference manual).

  - Riley

   

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