Overview

As we enter into the era of 5G, the telecommunications industry is undergoing profound and rapid change. Companies and organizations across the telecommunications value chain – from telecom services providers to telecom equipment manufacturers to government regulators and other key players such as vendors and consultants – must be able to transform their businesses to cope with the changes, overcome the challenges, and capitalize on the opportunities created by the emergence of 5G.

The Solver That Does More

Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.

  • Unmatched Performance
  • Continuous Innovation
  • Responsive, Expert Support
  • Unmatched Performance
  • Continuous Innovation
  • Responsive, Expert Support
  • Gurobi Optimizer Delivers Unmatched Performance

    Unmatched Performance

    Gurobi’s powerful MIP algorithm allows you to add complexity to your model to better represent the real world, and still solve your model within the available time.

    • Public benchmarks consistently show that Gurobi finds proven-optimal solutions faster than competing solvers.
    • The performance gap grows as model size and difficulty increase.
    • Gurobi has a history of making continual improvements across a range of problem types.
    • Gurobi is tuned to optimize performance over a wide range of instances.
    • Gurobi is tested thoroughly for numerical stability and correctness using an internal library of over 10,000 models from industry and academia.
     

  • Gurobi Optimizer Delivers Continuous Innovation

    Continuous Innovation

    Our development team includes the brightest minds in decision-intelligence technology--and they're continually raising the bar in terms of solver speed and functionality.

    • Our code is fundamentally parallel—not sequential code that was parallelized—so you can make the most of parallelism and run sequentially.
    • We go beyond cutting-edge MIP cutting planes, with new classes of cuts you can find only with Gurobi.
    • Our advanced MIP heuristics identify feasible, good quality solutions, fast—where other solvers fall flat.
    • Our barrier algorithms fully exploit the features of the latest computer architectures.
    • Our APIs are lightweight, modern, and intuitive—to minimize your learning curve while maximizing your productivity.

  • Gurobi Optimizer Delivers Responsive, Expert Support

    Responsive, Expert Support

    Our PhD-level experts are here when you need them—ready to provide comprehensive guidance and technical support. They bring deep expertise in working with commercial models and are there to assist you throughout the process of implementing and using Gurobi.

    • Tap into our team’s deep expertise—from implementation to tuning and more.
    • We respond to customer inquiries in hours not days, helping to quickly resolve any issues you’re facing.
    • We can help you fit and adapt your mathematical optimization application to your changing requirements.

Peek Under the Hood

Dive deep into sample models, built with our Python API.

  • Customer Assignment
  • Decentralization Planning
  • Facility Location
  • Cell Tower Problem
  • Customer Assignment
  • Decentralization Planning
  • Facility Location
  • Cell Tower Problem
  • Customer Assignment

    Customer Assignment

    Sharpen your mathematical optimization modeling skills with this example, in which you will learn how to select the location of facilities based on their proximity to customers. We’ll demonstrate how you can construct a mixed-integer programming (MIP) model of this facility location problem, implement this model in the Gurobi Python API, and generate an optimal solution using the Gurobi Optimizer. This modeling example is at the intermediate level, where we assume that you know Python and are familiar with the Gurobi Python API. In addition, you should have some knowledge about building mathematical optimization models.

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  • Decentralization Planning

    Decentralization Planning

    Ready for a mathematical optimization modeling challenge? Put your skills to the test with this example, where you’ll learn how to model and solve a decentralization planning problem. You’ll have to figure out – given a set of departments of a company, and potential cities where these departments can be located – the “best” location for each department in order to maximize gross margins. This model is example 10 from the fifth edition of Model Building in Mathematical Programming by H. Paul Williams on pages 265 and 317-319. This modeling example is at the advanced level, where we assume that you know Python and the Gurobi Python API and that you have advanced knowledge of building mathematical optimization models. Typically, the objective function and/or constraints of these examples are complex or require advanced features of the Gurobi Python API.

     Learn More
  • Facility Location Problem

    Facility Location

    Facility location problems can be commonly found in many industries, including logistics and telecommunications. In this example, we’ll show you how to tackle a facility location problem that involves determining the number and location of warehouses that are needed to supply a group of supermarkets. We’ll demonstrate how to construct a mixed-integer programming (MIP) model of this problem, implement this model in the Gurobi Python API, and then use the Gurobi Optimizer to find an optimal solution. This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.

     Learn More
  • Cell Tower Coverage Problem

    Cell Tower Problem

    Want to learn how to configure a network of cell towers to provide signal coverage to the largest number of people possible? In this example, you’ll learn how to solve this simple covering problem. We’ll show you how to construct a mixed-integer programming (MIP) model of the problem, implement this model in the Gurobi Python API, and find an optimal solution using the Gurobi Optimizer. This modeling example is at the beginner level, where we assume that you know Python and that you have some knowledge about building mathematical optimization models.

     Learn More

Frequently Asked Questions

Mathematical optimization uses the power of math to find the best possible solution to a complex, real-life problem. You input the details of your problem—the goals you want to achieve, the limitations you’re facing, and the variables you control—and the mathematical optimization solver will calculate your optimal set of decisions.

85% of the world’s leading companies use mathematical optimization to make optimal business decisions. For example, Air France uses it to build the most efficient schedule for its entire fleet, in order to save on fuel and operational costs, while reducing delay propagation.

Descriptive and predictive analytics show you what has happened in the past, why it happened, and what’s likely to happen next. But to decide what to do with that information, you need human input—which can introduce bias.

With mathematical optimization, you receive a decision recommendation based on your goals, constraints, and variables alone. You can, of course, involve human input when it comes to whether or not to act on that recommendation. Or you can bypass human input altogether and automate your decision-making.

Additional Insight

Guidance for Your Journey

Gurobi: Always Free for Academics

We make it easy for students, faculty, and researchers to work with mathematical optimization.

Trusted Partners, at Your Service

When you face complex optimization challenges, you can trust our Gurobi Alliance partners for expert services.

We’ve Got Your Back

Our global team of helpful, PhD-level experts are here to support you—with responses in hours, not days.

What's
New at Gurobi

News
Gurobi 10.0 Delivers Blazing-Fast Speed, Innovative Data Science Integration, and an Enterprise Development and Deployment Experience
Latest release enables data professionals to easily integrate machine learning models into optimization models to solve new types of problems.
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Event
Webinar: What’s New in Gurobi 10.0
In this webinar, attendees will get a first look at our upcoming product release, Gurobi 10.0. We will summarize the performance improvements and highlight some of the underlying algorithmic advances, such as the network simplex algorithm, enhancements in concurrent LP, and optimization based bound tightening.
 Learn More
new content
Cost Savings & Business Benefits for Gurobi Customers
2022 Total Economic Impact™ Study Reveals A 518% ROI with Gurobi
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