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#
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.
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.
Tags: constraint-programming, educational, mp, sequences, arithmetic, reinforced-formulations, highs, gecode, cbc, mip
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: lemonade stand example#
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.