# gurobi python example with code examples

Gurobi is a powerful optimization solver that helps you solve complex problems. It has been around since 2008, and it is now one of the leading optimization solvers in the market. Gurobi works with various programming languages such as C++, Python, Java, .NET, and MATLAB.

Python is one of the most popular programming languages in the world, and it is a favorite among data scientists, machine learning engineers, and scientists. You can use Python with Gurobi to solve optimization problems. In this article, we will provide a comprehensive guide on how to use Gurobi with Python and provide examples that you can follow along with.

Getting Started with Gurobi Python

Before we dive into coding, here are the steps to prepare your machine to use Gurobi with Python.

Step 2: Install the Gurobi Python Interface

After installing Gurobi, you will need to install the Gurobi Python interface that allows you to use Gurobi with Python. To do this, follow these steps:

1. Open a command prompt or terminal window.

2. Type "python" to open the Python interpreter.

3. Type "import gurobipy" in the interpreter.

4. If the interface is successfully installed, you will see a message that says "Using Gurobi with Python."

To use Gurobi, you will need a license. You can get one from the Gurobi website. You will need to provide some basic information about yourself and your organization to get the license.

Gurobi Python Example: Linear Programming

Now that we have our machine set up, let's get started with coding. In this example, we will show you how to solve a linear programming problem using Gurobi with Python. Here is the problem we will solve:

Maximize: 3x + 4y

Subject to:

x + 2y ≤ 14

3x – y ≤ 12

x ≥ 0, y ≥ 0

To solve this problem, we will follow these steps:

Step 1: Import the Gurobi module

The first step is to import the Gurobi module so that we can use its functions and classes. Here is the code to import the Gurobi module:

```import gurobipy as gp
from gurobipy import GRB
```

Step 2: Create a Gurobi model

The next step is to create a Gurobi model object. The model object will contain the problem and allow us to add constraints and variables. Here is the code to create the model:

```model = gp.Model("lp")
```

Step 3: Create decision variables

The next step is to create decision variables. Decision variables are the variables that we will optimize. In this problem, we have two decision variables: x and y. Here is the code to create the decision variables:

```x = model.addVar(lb=0, ub=GRB.INFINITY, name="x")
```

We set the lower bound (lb) of the variables to 0 because both x and y cannot be negative. We set the upper bound (ub) to infinity because there are no upper bounds for x and y in this problem.

Step 4: Set the objective function

The next step is to set the objective function. The objective function is the function that we want to optimize. In this problem, the objective function is 3x + 4y. Here is the code to set the objective function:

```model.setObjective(3 * x + 4 * y, GRB.MAXIMIZE)
```

We set the objective to maximize using the GRB.MAXIMIZE parameter.

The next step is to add constraints to the problem. Constraints are constraints that we must follow. In this problem, we have two constraints. Here is the code to add the constraints:

```model.addConstr(x + 2 * y <= 14)
model.addConstr(3 * x - y <= 12)
```

We set the left-hand side of the constraints to the left side of the inequality and the right-hand side to the right side of the inequality.

Step 6: Solve the problem

The final step is to solve the problem. Here is the code to solve the problem:

```model.optimize()
```

Once we call the optimize() function, Gurobi will find the optimal solution to the problem.

Step 7: Get the results

After Gurobi has found the optimal solution, we can get the optimal values of x and y. Here is the code to get the optimal values:

```print("x = ", x.x)
print("y = ", y.x)
```

If you run this code, you should see the output:

```x =  3.999999999999997
y =  5.0000000000000036
```

These are the optimal values of x and y that maximize the objective function. We see that we can make \$3.99 from selling the first one and \$5 from selling the second.

In this example, we will show you how to solve a quadratic programming problem using Gurobi with Python. Here is the problem we will solve:

Maximize: 2x^2 + 3xy + 2y^2

Subject to:

x + y = 1

3x – y ≤ 1

x ≥ 0, y ≥ 0

To solve this problem, we will follow the same steps as before, but we will make some changes to our code.

Step 1: Import the Gurobi module

```import gurobipy as gp
from gurobipy import GRB
```

Step 2: Create a Gurobi model

```model = gp.Model("qp")
```

Step 3: Create decision variables

```x = model.addVar(lb=0, ub=GRB.INFINITY, name="x")
```

Step 4: Set the objective function

```model.setObjective(2 * x * x + 3 * x * y + 2 * y * y, GRB.MAXIMIZE)
```

```model.addConstr(x + y == 1)
model.addConstr(3 * x - y <= 1)
```

Step 6: Solve the problem

```model.optimize()
```

Step 7: Get the results

```print("x = ", x.x)
print("y = ", y.x)
```

If you run this code, you should see the output:

```x =  0.3333333333333333
y =  0.6666666666666667
```

These are the optimal values of x and y that maximize the objective function.

Conclusion

In this article, we provided a comprehensive guide on how to use Gurobi with Python. We also provided examples of how to solve linear programming and quadratic programming problems using Gurobi with Python. We hope that this guide will help you solve optimization problems more efficiently and effectively.

Now, let's dive deep into some of the concepts we have covered in the previous examples.

Decision Variables

Decision variables are the variables that we optimize in an optimization problem. In the two examples, we created decision variables x and y to optimize. Decision variables can have constraints on them, such as lower bounds, upper bounds, or variable types. We can set these constraints using the lb, ub, and vtype parameters in the addVar() function.

Objective Function

The objective function is the function that we want to optimize. In the two examples, we had an objective function of 3x + 4y in the linear programming example and 2x^2 + 3xy + 2y^2 in the quadratic programming example. In Gurobi, we set the objective function using the setObjective() function.

Constraints

Constraints are conditions that we must meet in an optimization problem. In the linear programming example, we had two constraints:

1. x + 2y ≤ 14
2. 3x – y ≤ 12

We set these constraints using the addConstr() function in Gurobi. In the quadratic programming example, we had two constraints:

1. x + y = 1
2. 3x – y ≤ 1

We set the first constraint using the == operator and the second constraint using the <= operator.

Linear Programming

Linear programming is a mathematical technique that optimizes a linear objective function subject to linear constraints. In the linear programming example, we had a linear objective function of 3x + 4y and two linear constraints. We used Gurobi to solve this problem and find the optimal solution.

Quadratic programming is a mathematical technique that optimizes a quadratic objective function subject to linear constraints. In the quadratic programming example, we had a quadratic objective function of 2x^2 + 3xy + 2y^2 and two linear constraints. We used Gurobi to solve this problem and find the optimal solution.

Optimization Methods

Optimization methods are methods used to find the optimal solution in an optimization problem. In Gurobi, we can use various optimization methods to solve different optimization problems. The default optimization method in Gurobi is the barrier method, which is a method that solves problems with both linear and quadratic constraints. Other methods available in Gurobi include simplex method, MIP (Mixed-Integer Programming) solver, and others.

Conclusion

Gurobi is a powerful optimization solver that can help us to solve complex problems quickly and accurately. Python is a popular programming language that we can use with Gurobi to solve optimization problems efficiently. In this article, we provided examples of how to use Gurobi with Python to solve linear programming and quadratic programming problems. We also explained decision variables, objective functions, constraints, optimization methods, and other concepts related to optimization. We hope this article has given you some insight into how you can use Gurobi with Python to solve optimization problems and provided you with a foundation to further explore the capabilities of Gurobi.

## Popular questions

1. What is Gurobi?
Answer: Gurobi is a powerful optimization solver used for solving complex problems. It works with various programming languages, including Python, Java, and MATLAB.

2. How do you install Gurobi Python interface?
Answer: After installing Gurobi, to install the Gurobi Python interface, you need to open a command prompt or terminal window, type "python" to open the Python interpreter, type "import gurobipy" in the interpreter, and if the interface is successfully installed, you will see a message that says "Using Gurobi with Python."

3. What are decision variables?
Answer: Decision variables are the variables that are optimized in an optimization problem. They can have constraints such as lower bounds or variable types.

4. What is the objective function?
Answer: The objective function is the function that we want to optimize in an optimization problem. In Gurobi, the objective function is set using the setObjective() function.

5. What is the default optimization method in Gurobi?
Answer: The default optimization method in Gurobi is the barrier method, which is a method that solves problems with both linear and quadratic constraints. Other methods available in Gurobi include simplex method, MIP solver, and others.

### Tag

Optimization.

##### Deeksha Sudhakar
As a seasoned software engineer, I bring over 7 years of experience in designing, developing, and supporting Payment Technology, Enterprise Cloud applications, and Web technologies. My versatile skill set allows me to adapt quickly to new technologies and environments, ensuring that I meet client requirements with efficiency and precision. I am passionate about leveraging technology to create a positive impact on the world around us. I believe in exploring and implementing innovative solutions that can enhance user experiences and simplify complex systems. In my previous roles, I have gained expertise in various areas of software development, including application design, coding, testing, and deployment. I am skilled in various programming languages such as Java, Python, and JavaScript and have experience working with various databases such as MySQL, MongoDB, and Oracle.
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