Optimization is part of any educational program in Operations Research or Analytics, but the curriculum must steadily evolve to remain relevant. Following 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 large-scale optimization is taught and learned.
Webinar attendees will learn:
A more natural approach to describing optimization problems. Students can write common logical conditions, “not-quite-linear” functions, and nonlinear functions the way they think about them, without having to learn complicated and error-prone reformulations.
A Python-first alternative to learning AMPL and model-building. New teaching materials leverage the power of Jupyter notebooks and Google Colab to incorporate modern computing concepts and the vast Python ecosystem into the study of optimization.
Faster, easier importing of data and exporting of results. The AMPL Python interface (amplpy) efficiently connects model sets and parameters to Python’s native data structures and Pandas dataframes. An all-new spreadsheet interface reads and writes .xlsx and .csv files, with added support for two-dimensional spreadsheet tables.
Streamlined application development. Python scripts are quickly turned into illustrative applications using amplpy, Pandas, and the Streamlit app framework.
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Speakers
Filipe Brandão and Robert Fourer, AMPL Optimization Inc.