dwave-optimization 0.3.0

Enables the formulation of nonlinear models for industrial optimization problems.

dwave-optimization

dwave-optimization enables the formulation of nonlinear models for industrial optimization problems. The package includes:

• a class for nonlinear models used by the Leap service’s quantum-classical hybrid nonlinear-program solver.

• model generators for common optimization problems.

(For explanations of the terminology, see the Ocean glossary.)

Example Usage

The flow-shop scheduling problem is a variant of the renowned job-shop scheduling optimization problem. Given n jobs to schedule on m machines, with specified processing times for each job per machine, minimize the makespan (the total length of the schedule for processing all the jobs). For every job, the i-th operation is executed on the i-th machine. No machine can perform more than one operation simultaneously.

This small example builds a model for optimizing the schedule for processing two jobs on three machines.

from dwave.optimization.generators import flow_shop_scheduling

processing_times = [[10, 5, 7], [20, 10, 15]]
model = flow_shop_scheduling(processing_times=processing_times)

See the documentation for more examples.

Installation

Installation from PyPI:

pip install dwave-optimization

During package development, it is often convenient to use an editable install. See meson-python’s editible installs for more details.

pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .

License

Released under the Apache License 2.0. See LICENSE file.

Contributing

Ocean’s contributing guide has guidelines for contributing to Ocean packages.

dwave-optimization includes some formatting customization in the .clang-format and setup.cfg files.