Looking for Gurobi? As an authorized partner, we make it easy to get the right license, understand pricing, and access expert support.
Trusted by thousands of industry leading teams
A streamlined Gurobi license tailored for use with AMPL delivering full MIP/LP/QP solving power without extra tooling. Get exactly what you need at a better price.
AMPL’s solver interface handles large, complex models with no custom scripting or solver-specific rewrites.
Desktop, cloud, or on-prem – license what you need, when you need it.
AMPL and Gurobi are trusted in energy, finance, logistics, and other high-stakes industries.
As a Premier Partner of Gurobi since 2012, AMPL gives you a modeling environment built for large-scale, long-term optimization. Design, test, and deploy your Gurobi-based models with greater speed and fewer internal resources.
We make it faster, easier, and more cost-effective to get started with Gurobi.
Full-featured solver access, configured for desktop, server, or cloud
A robust, declarative interface to simplify and scale your model development
For Python, Jupyter, AMPL MP, or command-line workflows, get solving fast
Optimization experts available for use-case benchmarks, tuning, and scaling
AMPL connects with Python, APIs, and your existing data pipelines with no workflow disruption
Access to solver engineers who understand both your model and your mission
Access this world-class solver in the AMPL License Portal, available on Windows, Linux, and macOS
Gurobi can be used with AMPL from various programming languages using our dedicated APIs
For Python enthusiasts, Gurobi is also accessible as a module with amplpy, blending seamlessly with your Python projects.
How to install using amplpy:
# Install Python API for AMPL:
$ python -m pip install amplpy --upgrade
# Install AMPL & solver modules:
$ python -m amplpy.modules install gurobi # install Gurobi
# Activate your license (e.g., free ampl.com/ce or ampl.com/courses licenses):
$ python -m amplpy.modules activate <your-license-uuid>How to use:
from amplpy import AMPL
ampl = AMPL()
...
ampl.solve(solver="gurobi", gurobi_options="option1=value1 option2=value2")Choose a complete, optimized licensing solution or work with us to design a custom package that fits your environment and goals. We’ll help you configure, deploy, and scale faster.
Gurobi is designed to solve various types of mathematical optimization problems, including Linear Programming (LP), Mixed-Integer Linear Programming (MILP), Quadratic Programming (QP), Mixed-Integer Quadratic Programming (MIQP), and Mixed-Integer Nonlinear Programming (MINLP). It is highly effective in handling large-scale, complex optimization problems across different industries.
Learn more about Gurobi with our training notebooks and it’s applications with mixed integer programming (learn more here).
Combining AMPL’s modeling simplicity with Gurobi’s powerful optimization engine creates an ideal environment for newcomers to optimization. AMPL’s intuitive modeling language allows users to express optimization problems naturally and concisely, which is particularly beneficial for those just starting out. This simplicity, paired with AMPL’s extensive range of interfaces supporting popular programming languages like Python, C++, Java, .NET, and MATLAB, opens up accessibility to a wider audience. While beginners might find optimization challenging initially, the joint use of AMPL and Gurobi eases this learning curve. Both offer comprehensive documentation, illustrative examples, and vibrant user communities for support. Furthermore, their commitment to education is evident through the provision of educational licenses and resources, making AMPL and Gurobi a popular combination in academic circles. Together, they provide a more integrated and user-friendly approach to learning and applying optimization.
AMPL and Gurobi are designed to work in tandem, offering a powerful and seamless integration with a wide range of software and systems. AMPL’s strength in model formulation and Gurobi’s robust optimization capabilities complement each other perfectly. This synergy is further enhanced by their support for APIs in several popular programming languages, including Python. This allows users to embed AMPL models directly within custom applications that utilize Gurobi as the solver. This level of flexibility is particularly advantageous in various industrial applications, where they can serve as the core of larger decision-support systems or data analytics platforms. The combination of AMPL’s modeling ease and Gurobi’s optimization strength ensures that users can not only develop solutions efficiently but also integrate these solutions effortlessly with existing systems and workflows, providing a comprehensive and versatile toolset for optimization challenges.
Gurobi is widely used in various sectors for LP and MIP applications. Common uses include supply chain optimization, logistics planning, production scheduling, financial portfolio optimization, and energy distribution. In these applications, Gurobi helps in optimizing resources, minimizing costs, and improving decision-making processes by finding the best possible solutions to complex linear and integer-constrained problems.
Gurobi is designed for high performance and can handle large-scale LP problems efficiently. It utilizes advanced algorithms and techniques such as dual simplex, barrier methods, and concurrent optimization to solve problems quickly. Gurobi’s ability to provide parallel processing also significantly speeds up the solution of large-scale problems by utilizing multiple cores and processors.
While Gurobi is primarily known for linear and mixed-integer linear programming, it can also handle mixed-integer quadratic programming (MIQP) and mixed-integer nonlinear programming (MINLP) to a certain extent. For non-linear constraints, Gurobi can linearize certain types of non-linear functions, enabling the solver to handle a wider range of MIP problems. However, the capability to solve complex non-linear problems might be limited compared to specialized non-linear solvers.
Gurobi provides several features to enhance MIP modeling and solving. This includes advanced presolving to reduce problem size, cutting planes to improve solution bounds, and heuristics for finding good feasible solutions quickly. Gurobi also offers tuning tools to optimize solver parameters for specific problem types, and its branch-and-cut algorithm is highly effective for solving MIPs efficiently.
Gurobi aids decision-making by providing optimal solutions to complex problems in various industries. For instance, in logistics and supply chain management, it helps in route optimization and inventory management. In finance, Gurobi is used for asset allocation and risk management. In manufacturing, it assists in production planning and resource allocation. These solutions help businesses make data-driven decisions, reduce costs, and improve operational efficiency.