Presentation Part 1: Provided by Bob Fourer
Presentation Part 2: Provided by Filipe Brandão
– A more natural approach to describing optimization problems. Students can write many 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 bring modern computing to 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 can be turned quickly into illustrative applications using amplpy, Pandas, and the Streamlit app framework.
All of these features are available free for teaching, in convenient bundles of AMPL and popular solvers called the “AMPL for Courses” bundle. This provides programs with 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.
Courses can also take advantage of our Community Edition, size- limited demos, and short-term full-featured trials.