See algebraic expressions of problem in debug mode or print

回答済み

Paul-Niklas Kandora

2024年06月05日 14:21

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Hello,

I have the following toy problem:

import gurobipy as gp
from gurobipy import GRB

# Create a new model
model = gp.Model("quadratic_constraint_problem")

# Add variables
x1 = model.addVar(name="x1", lb=0)
x2 = model.addVar(name="x2", lb=0)

# Set objective function
model.setObjective(x1 + 2 * x2, GRB.MINIMIZE)

# Define the quadratic constraint matrix Q
Q = [[2, 1],
     [1, 2]]

# Add quadratic constraint: x^T Q x <= 1
quad_expr = gp.QuadExpr()
quad_expr += Q[0][0] * x1 * x1 + Q[0][1] * x1 * x2
quad_expr += Q[1][0] * x2 * x1 + Q[1][1] * x2 * x2

model.addQConstr(quad_expr <= 1, "quad_constr")

# Optimize model
model.optimize()

# Print the results
if model.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"x1 = {x1.x}")
    print(f"x2 = {x2.x}")
    print(f"Objective value = {model.objVal}")
else:
    print("No optimal solution found.")

How can I see the algebraic expression of this problem? This might be very useful, specifically when we have larger scale Q and x.
I mean by algebraic expression something like:

min x[0] + 2 * x[1]
s.t. Q[0][0] * x[0] * x[0] + Q[0][1] * x[0] * x[1] + Q[1][0] * x[1] * x[0] + Q[1][1] * x[1] * x[1] <= 1
x[0] >= 0, x[1] >= 0

Thank you and best regards

Paul

関連

• Model Files

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• 正式なコメント

  

  Simranjit Kaur

  • Gurobi Staff

  2026年11月13日 01:24

  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.
• 

  Simranjit Kaur

  • Gurobi Staff

  2024年06月05日 23:27

  Hi Paul,

  You can export the model in an LP file format using model.write("mymodel.lp") just before the optimize command. The LP file format provides a readable representation of the optimization model, including the objective function, constraints, and variable bounds. For your example model, the LP file will contain:

  \ Model quadratic_constraint_problem
  \ LP format - for model browsing. Use MPS format to capture full model detail.
  Minimize
    x1 + 2 x2
  Subject To
   quad_constr: [ 2 x1 ^2 + 2 x1 * x2 + 2 x2 ^2 ] <= 1
  Bounds
  End  

  Best regards,
  Simran

  0
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  Paul-Niklas Kandora

  2024年06月06日 06:50

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  Dear Simran,

  thanks for the message! This files solves my issue
  
  Best,

  Paul

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