# open (14 notebooks)¶

## AMPL Development Tutorial 1/6 – Capacitated Facility Location Problem¶

Description: This notebook marks the beginning of a six-part series.

## AMPL Development Tutorial 2/6 – Stochastic Capacitated Facility Location Problem¶

Description: This notebook continues our six-part series as the second installment.

## AMPL Development Tutorial 3/6 – Benders Decomposition via AMPL scripting¶

Description: In this third installment of our six-part series, we continue our exploration by addressing the complexities introduced by the stochastic programming formulation presented in part two.

## AMPL Development Tutorial 4/6 – Benders Decomposition via PYTHON scripting¶

Description: In this fourth installment of our six-part series, we advance our exploration by demonstrating how to adapt our AMPL script for use with AMPL’s Python API.

## AMPL Development Tutorial 5/6 – Parallelizing Subproblem Solves in Benders Decomposition¶

Description: In the fifth installment of our six-part series, we delve deeper by showing how to evolve our Benders decomposition Python script from a serial execution to one that solves subproblems in parallel.

## Debugging Model Infeasibility¶

Description: This notebook offers a concise guide on troubleshooting model infeasibility using AMPL’s presolve feature and other language capabilities.

## Introduction to Linear and Integer Programming¶

Description: Basic introduction to linear programming and AMPL via a lemonade stand example

## Introduction to Mathematical Optimization¶

Description: Basic introduction to optimization and AMPL via unconstrained optimization

## Largest small polygon¶

Description: lecture about models for the Largest Small Polygon Problem

## Magic sequences¶

Description: Solving magic sequences through reinforced formulations and constrained programming. Some comparison between models and solvers is done, and we look into the “Another solution” problem for these sequences.

## Network Linear Programs¶

Description: Basic introduction to network linear programms and AMPL via max flow and shortest path problems

## P-Median problem¶

Description: this notebook states the p-median problem with a simple example, and a MIP formulation in amplpy. The problem is parametrized with a class, so it is easier to sample and replicate experiments. A graphical solution is plotted.

## Production Model¶

Description: Basic introduction to AMPL’s indexed entities and the Pygwalker Python package via a lemonade stand example

## Supply chain network¶

Description: Compute optimal routes to connect suppliers/demanding nodes in a network. Routes have an associated fixed and variable cost. There are different products to ship. The problem is formulated as a MIP with binary variables. Python data structures are used to load the data into the model.