Decision Optimization Notebooks
Introduces the Decision Optimization notebooks available, how to create and run them in Watson Studio.
- The Sudoku example, a Constraint Programming example in which the objective is to solve a 9x9 Sudoku grid.
- The Balance in-house and external production of pasta example 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.
Running Decision Optimization Notebooks
- From the Community page, open the Notebook you want to work with.
- Click the Download button to download the example on your machine.
- If you have already created a project in Watson Studio, open your existing project. Else create a new project: select Projects> View all Projects from the menu and click the Add Project button (or select Create Project from the Create New menu).
- To create a new Notebook, select Notebooks and click the Add Notebook button .
- Choose From File. Then click Browse… and browse to the Notebook on your machine.
- Click Create Notebook.
To run your Notebook, click Cell > Run All.
See requirements.html#installation__section_modelsize for the size of problems that can be solved without subscription.
Decision Optimization tutorials
DOcplex is a native Python API for modeling and solving optimization problems. The API is available by default in Watson Studio as part of the Python environment. See Installation, requirements and versions for the currently supported version of Python.
You can read the full API documentation on RawGit.
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