Are you involved in teaching or learning large-scale optimization?
We invite you to check out next week’s INFORMS Sponsored Webinar presented by AMPL:
Date: Wednesday, December 13, 2023
Time: 12:00-1:00 EST (UTC-5)
Speakers: Filipe Brandão and Robert Fourer, AMPL Optimization
Teaching, Learning, and Applying Optimization:
AMPL’s Intuitive Modeling Meets the Python Ecosystem
What we’ll cover:
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:
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.
These features are freely available for teaching, in convenient bundles of AMPL and popular solvers. The AMPL for Courses program provides full-featured, unlimited use by students and staff for the duration of your academic term. Courses can also take advantage of our Community Edition, size-limited demos, and short-term full-featured trials.