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A leading commercial solver for linear and mixed-integer optimization, known for speed and robustness.
A high-performance solver for linear programming (LP) and mixed-integer programming (MIP).
A powerful solver for mathematical optimization, offering advanced presolve and parallel capabilities.
A commercial solver optimized for solving large-scale LP and MIP problems efficiently.
Specializes in conic, linear, and quadratic optimization with strong performance in financial and engineering applications.
A nonlinear optimization solver known for its robust handling of large-scale models.
A nonlinear solver well-suited for highly constrained optimization problems.
A solver for quadratic and general nonlinear optimization with an interior-point approach.
A gradient-based solver for large-scale nonlinear optimization with smooth constraints.
Designed for large-scale linear and nonlinear optimization with a focus on stability.
A global optimization solver for handling nonlinear, nonconvex, and mixed-integer problems.
A solver for global optimization problems, particularly useful for nonconvex models.
A solver designed for nonlinear global optimization in various applications.
A global solver specialized in deterministic nonconvex optimization problems.
A GPU-accelerated solver that delivers high-speed optimization for large-scale, real-time decision-making.
A fast open-source solver for LP and MIP problems.
A versatile solver for mixed-integer programming and constraint programming.
An open-source solver for mixed-integer programming with broad application use.
A global solver for mixed-integer nonlinear problems (MINLP).
A decomposition-based solver for large-scale mixed-integer programming problems.
A widely used open-source solver for large-scale nonlinear optimization.
A solver for mixed-integer nonlinear programming (MINLP), combining branch-and-bound with nonlinear solvers.
A constraint programming solver for combinatorial and scheduling problems.
Automate AMPL into custom Python applications, create tailored modeling tools and interfaces, seamlessly utilize python’s data science and visualization tools, and access powerful solvers within Python’s ecosystem.
Use AMPL with Python through the amplpy package, enabling dynamic model interaction, data manipulation, and solution analysis.
Combine AMPL with R for statistical modeling and advanced data analysis.
Use AMPL within C# applications to build enterprise and .NET-based optimization solutions.
Integrate AMPL directly into high-performance C++ applications for fast and efficient optimization.
Leverage AMPL in Java for seamless integration with large-scale business and analytics applications.
Use AMPL within MATLAB for mathematical computing and simulation-driven workflows.
Integrate Pandas with AMPL to effortlessly manipulate and analyze your optimization data, ensuring insightful and data-driven decision-making processes.
Incorporate NumPy with AMPL for efficient numerical computations, optimizing your mathematical modeling and solution analysis workflows.
Connect Excel to AMPL for seamless spreadsheet-based data import and optimization modeling.
Incorporate CSV files into AMPL for lightweight, structured data transfer and optimization workflows.
Incorporate MySQL with AMPL for fast, reliable relational data storage and optimization.
Connect Microsoft SQL Server to AMPL for scalable, secure enterprise data-driven optimization.
AMPL is used across multiple industries and integrates with domain-specific tools:
Integration with power grid modeling and demand forecasting tools.
Use AMPL with financial modeling software for risk analysis and portfolio allocation.
Optimize routing, production, and scheduling by integrating with ERP and supply chain management tools.
Run AMPL models in a cloud-based environment without needing a local installation.
Develop and debug AMPL models within Microsoft Visual Studio Code.
Run AMPL models interactively in Jupyter for education, research, and development.
Run AMPL models in a cloud-based environment without needing a local installation.
Run AMPL on AWS to take advantage of scalable computing resources, elastic storage, and seamless integration with cloud-native services like AWS Lambda and EC2.
Deploy AMPL on Azure for enterprise-grade cloud solutions, enabling seamless integration with Microsoft’s AI and data analytics services for optimization-driven decision-making.
Utilize AMPL on GCP to leverage powerful computing resources, data pipelines, and AI-enhanced tools for large-scale optimization workflows.
Run AMPL models in a cloud-based environment without needing a local installation.
Run AMPL on AWS to take advantage of scalable computing resources, elastic storage, and seamless integration with cloud-native services like AWS Lambda and EC2.
Automate and streamline large-scale optimization runs with batch processing, ensuring efficient execution of multiple scenarios or datasets.
Take advantage of multi-threaded and distributed computing capabilities to accelerate optimization, making it possible to solve complex models faster.
Traditionally, building optimization models and implementing them in real-time were separate challenges, but AMPL and Nextmv bridge this gap by enabling businesses to make faster, smarter decisions and achieve optimal results.
Leverage Streamlit integrations to swiftly build interactive AMPL data applications, enhancing collaboration and decision-making.
Demystify complex AMPL models with DB Gene. Visualize your models for clear communication and deeper understanding between modelers and decision-makers.
Create an account, download a free license, and start integrating today. Or contact us to learn more about key integrations for your workflow.
Learn how AMPL can help you solve your specific challenges – book a demo or discovery call with our experts.