# ampl¶

## AMPL - solve multiple models in parallel¶

Description: Solve multiple AMPL models in parallel in Python with amplpy and the multiprocessing modules.

Author: Nicolau Santos (3 notebooks) <nfbvs@ampl.com>

## AMPL - spreadsheet handling with amplxl¶

Description: Basic example of reading/writing data into/from a .xlsx spreadsheet with amplxl

Author: Nicolau Santos (3 notebooks) <nfbvs@ampl.com>

## 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¶

Notebooks > 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.

## AMPL Development Tutorial 6/6 – Implementing Benders Decomposition with *ampls*¶

Description: This concluding notebook in our six-part series delves into enhancing the efficiency of our decomposition algorithm by utilizing

**AMPL Solver Libraries**(*ampls*).## 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

## Network Linear Programs¶

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

## Production Model¶

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

## Solving simple stochastic optimization problems with AMPL¶

Description: Examples of the Sample Average Approximation method and risk measures in AMPL

Author: Nicolau Santos (3 notebooks) <nfbvs@ampl.com>