amplpy#

AMPL - solve multiple models in parallel#

multiproc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Solve multiple AMPL models in parallel in Python with amplpy and the multiprocessing modules.

AMPL Bin Packing Problem with GCG#

bpp.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Dantzig-Wolfe decomposition for Bin Packing Problem with GCG

AMPL Capacitated p-Median Problem with GCG#

cpmp.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Dantzig-Wolfe decomposition for Capacitated p-Median Problem with GCG

AMPL Christmas Model created by ChatGPT#

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

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

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

3_benders_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

4_benders_in_python_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

5_benders_parallel_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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#

6_benders_ampls_stoch_floc.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: This concluding notebook in our six-part series delves into enhancing the efficiency of our decomposition algorithm by utilizing AMPL Solver Libraries (ampls).

AMPL Model Colaboratory Template#

colab.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic notebook template for the AMPL Colab repository

Aircrew trainee scheduling with seniority constraints#

Containers scheduling#

containers_scheduling.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Scheduling model for harbor operations. It is a problem with dependences between containers, which should be dispatch the fastest possible. We are using the MP solver interfaces to model a complex system using techniques from Constraint Programming, such as indicator constraints, and logical or and forall operators. After the model is written, a couple instances are presented and Highs/Gurobi MIP solvers are used to tackle the problem.

Debugging Model Infeasibility#

debug_infeas.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: This notebook offers a concise guide on troubleshooting model infeasibility using AMPL’s presolve feature and other language capabilities.

Diet model with Google Sheets#

Dynamic routing example#

Dynamic_routing_example.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Example of interactive optimization with GUI using AMPL and Google Maps
Tags: amplpy, gui

Efficient Frontier with Google Sheets#

efficient_frontier.ipynb Open In Colab
Description: Efficient Frontier example using Google Sheets

Employee Scheduling Optimization#

Employee_Scheduling.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Employee scheduling model from the Analytical Decision Modeling course at the Arizona State University.

Financial Portfolio Optimization with amplpy#

amplpyfinance_vs_amplpy.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Financial Portfolio Optimization with amplpy and amplpyfinance

Google Hashcode 2022#

Hospitals-Residents MIP#

hospitals_residents.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: hospitals-residents problem with ties problem solved with ampl and highs

Hydrothermal Scheduling Problem with Conic Programming#

Introduction to Linear and Integer Programming#

intro_to_linear_prorgramming.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic introduction to linear programming and AMPL via a lemonade stand example

Introduction to Mathematical Optimization#

intro_to_optimization.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic introduction to optimization and AMPL via unconstrained optimization

Jupyter Notebook Integration#

magics.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Jupyter Notebook Integration with amplpy

Largest small polygon#

largest_small_polygon.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: lecture about models for the Largest Small Polygon Problem

Logistic Regression with amplpy#

N-Queens#

nqueens.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: How can N queens be placed on an NxN chessboard so that no two of them attack each other?

NFL Team Rating#

NFL_Team_Rating.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: NFL Team Rating problem from the Analytical Decision Modeling course at the Arizona State University.

Network Linear Programs#

network.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic introduction to network linear programms and AMPL via max flow and shortest path problems

Optimal Power Flow with AMPL and Python - Bus Injection Model (BIM)#

Optimal Power Flow with AMPL and Python - Bus Injection Model (BIM) with controllable-phase shifting transformers and tap-changing transformers#

Optimal Power Flow with AMPL and Python - DC Power Flow#

Optimal Power Flow with AMPL and Python - conventional Power Flow#

Optimal Power Flow with AMPL and Python - data management#

Optimization Methods in Finance: Chapter 3#

finance_opt_example_3_1.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Optimization Methods in Finance: Bond Dedication Problem.

Optimize your Christmas Tree to Global Optimality#

Optimizing the number of staff in a chain of stores#

P-Median problem#

p_median.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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.

Pattern Enumeration#

pattern_enumeration.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Pattern enumeration example with amplpy

Pattern Generation#

pattern_generation.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Pattern generation example with amplpy

Production Model: lemonade stand example#

production_model.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Basic introduction to AMPL’s indexed entities and the Pygwalker Python package via a lemonade stand example

Quick Start using Pandas dataframes#

pandasdiet.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Quick Start using Pandas dataframes to load and retrieve data

Quick Start using lists and dictionaries#

nativediet.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Quick Start using lists and dictionaries to load and retrieve data

Roll Cutting - Revision 1 & 2#

pattern_tradeoff.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Pattern tradeoff example with amplpy

Simple sudoku solver using logical constraints (with GUI)#

sudoku.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Simple sudoku model with two formulations: as a Constraint Programming problem using the alldiff operator and as a MIP. Note that the CP formulation is more natural but it needs a solver supporting logical constraints or a MIP solver with automatic reformulation support (see [here](https://mp.ampl.com/) for more information).

Solving simple stochastic optimization problems with AMPL#

newsvendor.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Examples of the Sample Average Approximation method and risk measures in AMPL

Sudoku Generator#

sudoku_gen.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: Generate Sudoku boards with unique solution via iterative method and mip formulation.

Unit Commitment for Electrical Power Generation#

unit_commitment.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
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.

amplpy setup & Quick Start#

quickstart.ipynb Open In Colab Kaggle Gradient Open In SageMaker Studio Lab
Description: amplpy setup and quick start