Decision Optimization notebooks

You can create and run Decision Optimization models in Python notebooks by using DOcplex, a native Python API for Decision Optimization. Several Decision Optimization notebooks are already available for you to use.

The Decision Optimization environment currently supports Python 3.10. The following Python environments give you access to the Community Edition of the CPLEX engines. The Community Edition is limited to solving problems with up to 1000 constraints and 1000 variables, or with a search space of 1000 X 1000 for Constraint Programming problems.

Runtime 23.1 on Python 3.10 S/XS/XXS
Runtime 22.2 on Python 3.10 S/XS/XXS

To run larger problems, select a runtime that includes the full CPLEX commercial edition. The Decision Optimization environment (DOcplex) is available in the following runtimes (full CPLEX commercial edition):

NLP + DO runtime 23.1 on Python 3.10 with CPLEX 22.1.1.0
DO + NLP runtime 22.2 on Python 3.10 with CPLEX 20.1.0.1

You can easily change environments (runtimes and Python version) inside a notebook by using the Environment tab (see Changing the environment of a notebook). Thus, you can formulate optimization models and test them with small data sets in one environment. Then, to solve models with bigger data sets, you can switch to a different environment, without having to rewrite or copy the notebook code.

Multiple examples of Decision Optimization notebooks are available in the Resource hub, including:

The Sudoku example, a Constraint Programming example in which the objective is to solve a 9x9 Sudoku grid.
The Pasta Production Problem example, a Linear Programming example in which the objective is to minimize the production cost for some pasta products and to ensure that the customers' demand for the products is satisfied.

These and more examples are also available in the jupyter folder of the DO-samples

All Decision Optimization notebooks use DOcplex.

DOcplex

The Decision Optimization notebooks use DOcplex, a native Python API for modeling and solving Decision Optimization problems. The API is available by default as part of the Python environment.

It is composed of two modules:

Mathematical Programming Modeling for Python that uses docplex.mp
Constraint Programming Modeling for Python that uses docplex.cp

In your code you can specify the library you want to use as follows, for example for Mathematical Programming libraries:

from docplex.mp.model import Model

The API is licensed under the Apache License V2.0 and is numpy/pandas friendly.

You can read the full DOcplex API documentation on rawgit. You can find DOcplex examples on the Decision Optimization GitHub.

Decision Optimization client API

In addition to DOcplex, a Decision Optimization client API is available for you to create scenarios and handle models that are made in the Decision Optimization experiment UI. For example, see Generating multiple scenarios.

See the Decision Optimization client API documentation. You can also find the previous example in the jupyter folder of the DO-samples.

Running Decision Optimization notebooks

Depending on whether you are interested in Constraint Programming or Linear Programming, choose one of the two notebooks presented earlier and run it as follows.

If you already have a project in Cloud Pak for Data as a Service:

From the Resource hub, open the notebook you want to work with.
If you have already created a project in Cloud Pak for Data as a Service, click Add to project.
Select an existing project in the drop-down list, and select a runtime, for example Runtime 23.1 on Python 3.10 XS (or for larger models that require the Commercial Edition of CPLEX engines, select DO + NLP Runtime 23.1 on Python 3.10 XS), and click Create. The notebook is added to your project.

If you do not already have a project in Cloud Pak for Data as a Service, click the Download button to download the example onto your computer.

Create a new project: select Projects > View all Projects from the menu and click the New Project button.
Select Create an empty project and in the window that opens enter a name and click Create.
Select the Assets tab.
Select New asset > Jupyter notebook editor in the Code editors section.
Choose Local file. Then click Drag and drop files here or upload and browse to the notebook onto your computer.
Click Create Notebook.The notebook is added to your project.

Your notebook automatically opens.

To run your notebook, click Cell > Run All.

Example Python notebooks are provided in the Decision Optimization GitHub. To use these notebooks, see Jupyter notebook samples. These examples do not use the experiment UI.

Also a Python notebook that shows you how to generate multiple scenarios and that uses randomized data, is provided in the jupyter folder of the DO-samples. This approach can be useful to test a model made in the experiment UI with different data sets. For more information, see Generating multiple scenarios.

Decision Optimization tutorials

You can find more DOcplex examples that introduce you to the DOcplex Python API on the Decision Optimization GitHub:

Linear Programming: You can read a detailed description of this notebook in this Linear Programming (CPLEX Part 1) tutorial. You can clone or download this Decision Optimization Linear Programming notebook from Github.
Beyond Linear Programming: You can read a detailed description of this notebook in this Linear Programming (CPLEX Part 2) tutorial. You can clone or download this Decision Optimization Beyond Linear Programming notebook from Github.
Getting started with Scheduling in CPLEX for Python: You can read a detailed description of this notebook in this Scheduling in CPLEX for Python tutorial. You can clone or download this Getting started with Scheduling in CPLEX for Python notebook from Github.