Gurobi’s outstanding performance has been demonstrated through leadership in public benchmark tests and dramatic improvement in solve times year after year. Built using the latest algorithmic developments and large-scale computing techniques, Gurobi’s extremely robust code ensures correctness and scalability of results.
Gurobi solves linear and quadratic programming problems with continuous and integer variables, handling both convex and nonconvex cases. It also supports logical and nonlinear expressions, enabling more flexible and accurate modeling of real-world constraints.
Gurobi detects problem structures to improve performance, automatically recognizing convex and nonconvex quadratic expressions, conic quadratic constraints, and logical or nonlinear operators. These optimizations streamline problem-solving and enhance efficiency.
Gurobi applies advanced algorithms to different problem types, using simplex and interior-point methods for continuous problems, branch-and-cut for integer programming, and spatial branch-and-bound for nonconvex optimization. These techniques ensure fast and effective solutions.
Gurobi offers global optimization for select nonlinear problems, parallel processing, distributed optimization, and seamless cloud deployment. Multi-objective optimization and infeasibility diagnosis tools further enhance problem-solving capabilities.
Gurobi for AMPL is a specialized version of the solver, designed exclusively for use within the AMPL environment. It includes several key enhancements that make the solver even more powerful, enabling it to handle larger, more complex optimization problems with greater efficiency and speed.
Gurobi for AMPL is more than just a solver—it’s an enhanced optimization experience. AMPL provides a powerful solver interface that allows Gurobi to handle larger, more complex problems with greater efficiency. By leveraging AMPL’s purpose-built modeling language, users gain faster insights, improved workflow efficiency, and a better return on their solver investment—all without sacrificing power or flexibility.
AMPL’s solver interface is designed to optimize problem structures before they reach the solver, reducing computation time and improving solver efficiency. By automatically structuring mathematical programs in a way that minimizes complexity, AMPL enables Gurobi to solve models faster and more effectively, making large-scale decision-making more practical and scalable.
One of AMPL’s key advantages is its ability to automatically reformulate problems into solver-friendly formats. This means that even highly complex or nonlinear models can be structured in a way that Gurobi can process more efficiently, avoiding bottlenecks and improving convergence speed. Whether it’s handling logical constraints, simplifying expressions, or restructuring large-scale models, AMPL ensures that Gurobi operates at its full potential.
To maximize solver performance, AMPL offers consulting services and dedicated support plans that help users fine-tune both their models and their solver settings. Our experts provide solver tuning, model diagnostics, and advanced optimization strategies to ensure that users achieve the best possible performance from Gurobi. Whether you’re looking to refine your problem formulation, improve computation times, or troubleshoot solver behavior, AMPL’s specialized services help you get the most out of your solver investment.
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")For learning optimization and benchmarking solvers – a one-time 1 month free trial of Gurobi is available with a free Community Edition license.
For building and testing optimization models with commercial solver integration and full support, ensuring a smooth path to enterprise deployment.
AMPL starting at
$300 / month
Billed annually, includes updates and basic support.
Gurobi licenses and pricing all built to your specific needs. Contact sales for more.
Build a custom plan – designed for large teams, multiple processes, high computational demands, or unique workflows.
All custom licenses built to your specific needs.
AMPL empowers businesses across diverse industries to make smarter decisions, improve efficiency, and maximize performance through optimization. From supply chain logistics to financial modeling, our expertise helps organizations tackle real-world challenges with precision and speed.
Gurobi and AMPL enable financial institutions to build robust portfolio optimization models, balancing risk and return while considering regulatory constraints. With advanced solver enhancements, investment firms can efficiently allocate capital, optimize asset selection, and perform scenario analysis at scale.
Gurobi and AMPL power large-scale supply chain models, optimizing transportation, warehousing, and inventory management. Businesses use our solver-enhanced modeling interface to minimize costs, reduce delays, and improve resilience in dynamic logistics environments.
From renewable energy integration to demand forecasting, Gurobi and AMPL help energy providers optimize production schedules, grid management, and market bidding strategies. Our solver-driven approach ensures efficient resource allocation while meeting regulatory and sustainability goals.
Get the most out of Gurobi with these essential resources—documentation, tutorials, and expert guides to help you optimize efficiently.
Gurobi for AMPL Documentation
Your go-to reference for using Gurobi, covering everything from installation to advanced solver techniques.
Gurobi Parameters & Options
Fine-tune Gurobi’s performance with a full list of solver parameters and configuration options.
MP Modeling Guide
Learn how to use the AMPL MP library to create flexible, solver-agnostic optimization models that work seamlessly with Gurobi.
Gurobi for AMPL on Google Colab
Run Gurobi with AMPL directly in your browser using pre-configured Google Colab notebooks—no installation required.
Gurobi for AMPL Change Log
Stay up to date with the latest improvements, bug fixes, and feature updates for the Gurobi-AMPL interface.
How to Use Gurobi for AMPL
Best practices for setting up Gurobi with AMPL, running models, and optimizing solver performance.
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.
Access a complete optimization application building platform with simple pricing, or work with our team to design a custom package specifically for your business needs.
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From startups to Fortune 500s, explore how industry-leading companies use AMPL to optimize complex problems.