highlights#

A Party Scheduling Problem with FICO Xpress#

party1.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In Gradient Open In SageMaker Studio Lab
Description: A scheduling problem for visitor-host assignments. Feasibility version (no objective function). Demonstrates high-level modeling in AMPL MP, AMPL Python API, and tuning in FICO Xpress

AMPL Bin Packing Problem with GCG#

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

AMPL Christmas Model created by ChatGPT#

AMPL Model Colaboratory Template#

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

Inspecting AMPL Models: expand and show with amplpy#

Notebooks > Inspecting AMPL Models: `expand and show with amplpy <../notebooks/inspecting-ampl-models-expand-and-show-with-amplpy.html>`_
expand_show.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In Gradient Open In SageMaker Studio Lab
Description: Demonstrates the ampl.show(), ampl.expand(), and entity-level expand() methods introduced in amplpy 0.17.0, using the classic diet problem as a running example.

Logistic Regression with amplpy#

Minimize the Pairwise Distance Ratio for N Points#

Multi-Objective Knapsack Problem with AMPLPY#

knapsack.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In Gradient Open In SageMaker Studio Lab
Description: knapsack problem using multiple objectives, setting objective-specific options

N-Queens#

nqueens.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In 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?

Optimize your Christmas Tree to Global Optimality#

Quick Start using Pandas dataframes#

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

Robust Linear Programming with Ellipsoidal Uncertainty#

tip6_robust_linear_programming.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In Gradient Open In SageMaker Studio Lab
Description: AMPL Modeling Tips #6: Robust Linear Programming

Simple sudoku solver using logical constraints (with GUI)#

sudoku.ipynb Open In Colab Open In Deepnote Open In Kaggle Open In 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).