AMPL

SERIES INTRO! # This is the first of eighteen videos over which we build an app for the awesome Rebrickable® community, which finds multiple LEGO® sets buildable concurrently from a user’s part inventory.

We establish a connection via an import script (written in the AMPL scripting syntax, later to be replaced by a Python script) to a collection of CSV and flat text data files.

We discuss why grouping sets of single entities into aggregate sets of tuples (aka ‘cartesian products’, ‘cross-joins’) dramatically increases the performance of our optimization app. Also, we discuss the accompanying increase in data memory footprint.

We add a simple but functional, attractive text-based user interface by way of a flat data file formatted for good readability and manual data input.

One-by-one we convert our natural language comments representing model vars and constraints into actual model entities. In the process, we add display statements for health-checking the memory size and cardinality of our new model element.

With constraints in place which ensure that selected LEGO® sets are complete, we now implement constraints limiting the search space to sets that the user desires. We then observe that, while functional, a faster-computing way to implement such ‘problem shrinking’ is via set manipulation rather than constraint imposition.

A close look at large-scale optimization modeling tools and languages, and linear and nonlinear solvers.

We add functionality to our script that drops unneeded constraints per user-specified options, effectively changing the form of our optimization model on-the-fly. We also discuss extreme approaches to such run-time model construction, in light of the modular nature of optimization model components.

Having worked thus far in only the AMPL modeling and scripting languages respectively, we jettison the AMPL scripting language and replace it with Python. This transforms our model into a component callable from within Python via the AMPL-Python API.

Our new Streamlit interface makes it easy to imagine and implement an important new feature: the ability to ‘pin’ one or more suggested LEGO® sets, discarding the others, and resolving, so that the user can iteratively arrive at an attractive collection of buildable sets.