1680. Large-scale modeling with AMPL in Python: Fast Integration, Cloud Deployment, and Generative AI
Invited abstract in session TD-43: Toolboxes & APIs, stream Software for Optimization.
Tuesday, 14:30-16:00Room: Newlyn GR.07
Authors (first author is the speaker)
| 1. | Marcos Dominguez Velad
|
| AMPL Optimization Inc. |
Abstract
Python and its vast ecosystem are great for data pre-processing, solution analysis, and visualization, but Python's design as a general-purpose programming language makes it less than ideal for expressing the complex optimization problems typical of prescriptive analytics. AMPL is a declarative language that is designed for describing complex optimization problems and that integrates naturally with Python.
In this presentation, we'll explore how the combination of AMPL modeling with Python environments and tools has made optimization software more natural to use, faster to run, and easier to integrate with enterprise systems. We will show how AMPL and Python work together in a range of contexts:
- Installing AMPL and solvers as Python packages anywhere
- Fast data transfer from/to Python data structures such as Pandas and Polars dataframes
- Deploying models to the cloud quickly and easily
You'll also see how Generative AI technology can be used in order to enable a rapid development process for both AMPL and Python, reducing the time and effort to produce a working application that's ready for end-users.
Keywords
- Modeling Systems and Languages
- Optimization Modeling
- Large Scale Optimization
Status: accepted
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