Decision Optimization notebooks
You can create and run Decision Optimization models in Python notebooks using DOcplex, a native Python API for Decision Optimization. There are several Decision Optimization notebooks already available for you to use.
The Decision Optimization for Watson Studio environment
currently supports Python 3.7. All
Watson Studio Python environments give you access to the
Community Edition of the CPLEX engines. This makes it possible to solve problems with up to 1000
constraints and 1000 variables, or with a search space of 1000 X 1000 for Constraint Programming
problems. To run larger problems, select the
Default Python 3.7 XS + DO environment which includes the
full CPLEX commercial edition.
You can easily change environments inside a notebook using the Environment tab (see Changing the environment). This means that you can formulate optimization models and test them with small data sets in one environment, and then, to solve with bigger data sets, you can just switch to a different environment, without having to rewrite or copy the notebook code.
- 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 ensure that the customers' demand for the products is satisfied.
All Decision Optimization notebooks use DOcplex.
The Decision Optimization notebooks use DOcplex, a native Python API for modeling and solving Decision Optimization problems. The API is available by default in Watson Studio as part of the Python environment.
- Mathematical Programming Modeling for Python using
- Constraint Programming Modeling for Python using
from docplex.mp.model import Model
The API is licensed under the Apache License V2.0 and is
Python scenario API
In addition to DOcplex, a scenario API is available for you to create scenarios in Watson Studio and handle models made in the Decision Optimization model builder. For example, see Generating multiple scenarios.
Running Decision Optimization notebooks
Depending on whether you are interested in Constraint Programming or Linear Programming, choose one of the two notebooks presented earlier in this section and run it in Watson Studio as follows.
- From the Gallery, open the notebook you want to work with.
- If you have already created a project in Watson Studio, click the Copy button .
- Select an existing project in the drop-down list, and select a runtime, for example Default Python 3.7 XS (or for larger models which require the Commercial Edition of CPLEX engines, select Default Python 3.7 XS + DO) and click Create Notebook. The notebook is added to your project.
If you have not already created a project in Watson Studio, click the Download button to download the example on your machine.
- 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.
- To create a new notebook, click the Add to Project button and select Notebook.
- Choose From File. Then click Choose file and browse to the notebook on your machine.
- Click Create Notebook.
To run your notebook, click Cell > Run All.
Example Python notebooks are provided in the Decision Optimization GitHub. To use these, see Jupyter notebook samples. These examples do not use the model builder.
Also a Python notebook that shows you how to generate multiple scenarios using randomized data is provided in the jupyter folder of the DO-samples. This can be useful to test a model made in the model builder with different data sets. See Generating multiple scenarios for how to do this.
Decision Optimization tutorials
You can find more DOcplex examples that will introduce you to the DOcplex Python API on the Decision Optimization GitHub:
Beyond Linear Programming
Getting started with Scheduling in CPLEX for Python