Join us October 14-16, 2023 in Phoenix, Arizona.
AMPL will be a Gold Sponsor for the 2023 event and has sponsored the Networking Lounge. Visit us at booth 303 next to the Networking Lounge for a live demo.
Our conference presentations will feature an introduction to Python and AMPL for Optimization in Prescriptive Analytics and Operations Research. The Technology Workshop and Tutorial will show you how to build analytics applications quickly, from prototyping to deployment, with Pandas, Colab, Streamlit, and amplpy.
Location: CC-North 121 A
Teaching, Learning, and Applying Optimization: New Developments in the AMPL Modeling System
Presented by: Filipe Brandão and Robert Fourer
Optimization is the most widely adopted technology of Operations Research and Analytics, yet it must steadily evolve to remain relevant. After an introductory example, this presentation takes you on a tour through new developments in the AMPL modeling language and system that have been changing the ways that people learn and apply large-scale optimization:
We conclude with deployment examples, showing how Python scripts can be turned quickly into OR and Prescriptive Analytics applications using amplpy, Pandas, and the Streamlit app framework. Deployments are supported on traditional servers and in a variety of modern virtual environments including containers, clusters, and cloud machines.
Location: CC-North 231B
Advances in Model-Based Optimization with AMPL
Presented by: Filipe Brandão
The ideal of model-based optimization is to describe your problem the way you think about it, and then let the computer do the work of getting a solution. Recent enhancements aim to bring the AMPL modeling language and system closer to this ideal. Using a variety of modeling language extensions, common formulations are described more naturally, with the AMPL translator, the AMPL-solver interface, or the solver itself doing most of the needed transformations. Extensions described in this presentation include quadratic expressions, logical operators and constraints, simple near-linear and nonlinear functions, and combinations of these together with linear terms. All are supported by a new C++ AMPL-solver interface library that can be adapted to handle the multiple detection and transformation strategies required by large-scale solvers.
Location: CC-North 120 D
Python and AMPL: Build Prescriptive Analytics applications quickly with Pandas, Colab, Streamlit, and amplpy
Presented by: Filipe Brandão and Robert Fourer
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 OR and prescriptive analytics. AMPL is a declarative language that is designed for describing optimization problems, and that integrates naturally with Python.
In this presentation, you’ll learn 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. Following a quick introduction to model based optimization, we will show how AMPL and Python work together in a range of contexts:
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