AMPL Python API

amplpy is an interface that allows developers to access the features of AMPL from within Python. For a quick introduction to AMPL see Quick Introduction to AMPL.

In the same way that AMPL’s syntax matches naturally the mathematical description of the model, the input and output data matches naturally Python lists, sets, dictionaries, pandas and numpy objects.

All model generation and solver interaction is handled directly by AMPL, which leads to great stability and speed; the library just acts as an intermediary, and the added overhead (in terms of memory and CPU usage) depends mostly on how much data is sent and read back from AMPL, the size of the expanded model as such is irrelevant.

With amplpy you can model and solve large scale optimization problems in Python with the performance of heavily optimized C code without losing model readability. The same model can be deployed on applications built on different languages by just switching the API used.

Quick Start using Pandas dataframes

Data can be loaded in various forms, one of which is pandas.DataFrame objects.

Quick Start using lists and dictionaries

Data can be loaded in various forms, including Python lists and dictionaries.

More notebooks with examples available on the AMPL Model Colaboratory.

Contents

• Introduction
  • What is AMPL API?
  • Who can use AMPL API
  • System requirements
  • About this manual
• Initial Setup
  • Installation
  • amplpy.modules
  • Google Colab
  • Initial test
  • Development
  • Deployment
• Quick start
  • Complete listing
  • Needed modules and AMPL environment creation
  • Load model and data from files
  • Load model using eval
  • Load the data using Pandas objects
  • Load the data using lists and dictionaries
  • Solve a problem
  • Get an AMPL entity in the programming environment (get objective value)
  • Modify model data (assign values to parameters)
  • Get numeric values from variables
  • Get arbitrary values via ampl expressions
• Class structure
  • AMPL class
  • Modelling entities classes
  • Relation between entities and data
• Python API Reference
  • AMPL classes
  • Algebraic entities classes
  • Exceptions
• Python API Examples
  • Example 1: First steps
  • Example 2: Get and set AMPL options
  • Example 3: Assign all data to a model and solve it
  • Example 4: Build an efficient frontier
  • Example 5: Simple heuristic

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Introduction