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.
Gurobi delivers blazing speeds and advanced features—backed by brilliant innovators and expert support.
Public benchmarks consistently show that Gurobi finds both feasible and proven optimal solutions faster than competing solvers. With our powerful MIP algorithm, you can add complexity to your model to better represent the real world, and still solve your model within the available time.
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 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.
Dive deep into sample models, built with our Python API.
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
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.
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