Join us at the INFORMS Annual Meeting 2025 in Atlanta, Georgia
AMPL is a proud supporter of the 2025 INFORMS Annual Meeting in Atlanta, Georgia. Visit us at Booth 221 for live demos with our technical team, Workshops and Showcases, with free giveaways.
Presented by Gleb Belov and Marcos Dominguez Velad
Optimization has never been more critical – or more accessible. This talk will show how students and applied researchers can bridge the gap between academic learning and real-world impact using AMPL in modern workflows. With free academic licenses now including commercial-grade access to leading linear solvers (Gurobi, CPLEX, Xpress, Mosek, COPT, HiGHS and more), AMPL offers an unmatched opportunity to develop and apply optimization skills directly in industry contexts.
We will explore how AMPL integrates seamlessly with the tools you already know – Python, R, Jupyter, Colab, VS Code, cloud platforms, and modern data pipelines – making it a “set it and forget it” system for building models that stay faithful to the real problems you’re solving. Attendees will see how AMPL powers large-scale, mission-critical applications in energy, finance, transportation, and supply chain, enabling companies to save costs, increase efficiency, and improve decision-making.
By combining intuitive modeling with cutting-edge solver technology, AMPL empowers students and researchers not just to learn optimization, but to contribute immediately to high-impact, industry-grade projects. Join us to discover how AMPL can position you, or your students, at the forefront of applied optimization.
Presented by Jürgen Lentz
AMPLPy is the most popular Python API for AMPL, a powerful algebraic modeling language for large-scale mathematical optimization. As modern optimization workflows increasingly integrate with data-intensive environments, efficient data handling becomes critical. To address this, AMPLPy now incorporates support for Apache Arrow, an open-source, columnar in-memory data format designed for high-performance analytics.
By leveraging Apache Arrow, AMPLPy enables rapid, zero-copy data transfer between AMPL models and Python data structures such as Pandas, Polars and many more data frames. This integration minimizes data transfer overhead and significantly improves the speed and scalability of data exchange in optimization pipelines. Users can seamlessly pass large datasets from Python into AMPL for model instantiation and retrieve results back into Python with minimal latency. Arrow’s cross-language and cross-platform capabilities enhance interoperability with other systems and data science tools, and will also be used to transfer data to and from other AMPL APIs, such as those in Go and Rust, in the future.
The use of Arrow aligns AMPLPy with modern data engineering practices, ensuring compatibility with distributed data processing frameworks and in-memory analytics platforms. This development makes AMPLPy an even more attractive tool for both researchers and practitioners looking to embed optimization within data-centric and performance-critical applications.
Looking to learn how AMPL, solvers and our integration toolkits can support your commercial applications? Get in touch.