See algebraic expressions of problem in debug mode or print
AnsweredHello,
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

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
EndBest regards,
Simran0 
Dear Simran,
thanks for the message! This files solves my issue
Best,Paul
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