Optimization can feel intimidating at first—but it doesn’t have to be. With Gurobi’s intuitive Python interface, you can build powerful models using simple, readable code. In this post, we’ll walk through a basic Gurobi Python example, helping you understand each step and how to tailor it to your own decision-making problems.
Whether you’re a student learning linear programming or a developer exploring prescriptive analytics, this hands-on tutorial will get you started quickly.
Learning by example is one of the most effective ways to master a new tool. Gurobi’s Python API (gurobipy) is widely used across industries for solving everything from scheduling to logistics and portfolio optimization. By starting with a simple example, you’ll build a foundation you can apply to more complex scenarios.
Before you run your first model, make sure you have everything installed:
Let’s look at a simple linear programming model:
Objective: Maximize x + y
Constraint: x + 2y ≤ 4
Here’s how to model and solve it in Python using Gurobi:
from gurobipy import Model, GRB
# Create a model
m = Model(“basic_example”)
# Add decision variables
x = m.addVar(name=”x”)
y = m.addVar(name=”y”)
# Set objective: Maximize x + y
m.setObjective(x + y, GRB.MAXIMIZE)
# Add constraint: x + 2y ≤ 4
m.addConstr(x + 2 * y <= 4, “c1”)
# Solve the model
m.optimize()
# Display results
for v in m.getVars():
print(f”{v.varName}: {v.x}”)
print(f”Objective Value: {m.objVal}”)
This model showcases Gurobi’s clean, Pythonic syntax. Every line closely mirrors the mathematical structure of the problem.
Understanding the Code Step by Step
Once solved, you’ll see the optimal values for x, y, and the objective.
Solved in 0 iterations and 0.01 seconds (0.00 work units)
Optimal objective 4.000000000e+00
x: 4.0
y: 0.0
Objective Value: 4.0
Customizing Examples for Your Own Use Case
This is where the real value kicks in. You can modify the above example to suit your own business constraints and objectives. Whether you’re scheduling workers, assigning resources, or optimizing routes, Gurobi’s API makes it easy to define new variables, constraints, and goals.
Check out the Gurobi Examples Repository for models covering:
These templates are ideal for both learning and rapid prototyping.
If you encounter errors while running a Gurobi Python example, don’t worry. Gurobi provides extensive logging, and the Gurobi support portal is packed with articles, community Q&A, and expert advice.
Gurobi offers free academic licenses that are perfect for learning and teaching. These licenses support full-featured functionality and are ideal for coursework, research, or capstone projects.
Many Gurobi users start with a basic Python example and go on to deploy large-scale optimization solutions in finance, logistics, energy, and AI decision-making systems. The flexibility of Python and the power of Gurobi allow you to scale as your needs grow.
Ready to take your first step? Try this example and see how easily you can solve real-world problems using Gurobi and Python.
Looking for more tutorials? Visit gurobi.com to explore our documentation, case studies, and industry applications.
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