# Gyorgy Matyasfalvi (15 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#

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*).## Capacity expansion of power generation#

Description: Models the extensive form of a deterministic multi-stage capacity expansion problem. In this model we can have multiple resources of the same type which have identical properties. The model can be further developed into a stochastic one.

## 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

## Plot feasible region#

Description: Plot the feasible region and optimal solution for a simple two variable model using AMPL’s Python API.

## Pricing and target-market#

Description: Formulate a pricing optimization and target-market problem as a MILP.

## Production Model#

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

## Unit Commitment for Electrical Power Generation#

Description: This notebook illustrates the power generation problem using AMPL. The original version featured the Gurobi solver. By default, this notebook uses the HiGHS and CBC solvers.