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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.

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.

fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)

For explanations of the terminology, see the Ocean glossary.

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 editable installs for more details.

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

Testing

All code should be thoroughly tested and all pull requests should include tests.

To run the Python tests, first install the package using an editable install as described above. The tests can then be run with unittest.

python -m unittest

To run the C++ tests, first install the project dependencies, then setup a meson build directory. You must configure the build as a debug build for the tests to run.

pip install -r requirements.txt
meson setup build -Dbuildtype=debug

You can then run the tests using meson's test framework.

meson test -Cbuild

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.

Release Notes

dwave-optimization makes use of reno to manage its release notes.

When making a contribution to dwave-optimization that will affect users, create a new release note file by running

reno new your-short-descriptor-here

You can then edit the file created under releasenotes/notes/. Remove any sections not relevant to your changes. Commit the file along with your changes.

See reno's user guide for details.

About

Enables the formulation of nonlinear models for industrial optimization problems.

Resources

Readme
License
Activity

Stars

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Watchers

0 watching

Forks

0 forks
Report repository

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GitHub - k8culver/dwave-optimization: Enables the formulation of nonlinear models for industrial optimization problems. · GitHub
Skip to content
BranchesTags
Repository files navigation
https://circleci.com/gh/dwavesystems/dwave-optimization.svg?style=svg
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.
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.
fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)
For explanations of the terminology, see the
Ocean glossary.
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 editable installs
for more details.
pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .
Testing
All code should be thoroughly tested and all pull requests should include tests.
To run the Python tests, first install the package using an editable install
as described above. The tests can then be run with
unittest.
python -m unittest
To run the C++ tests, first install the project dependencies, then setup a
meson build directory. You must configure the build as a debug build for
the tests to run.
pip install -r requirements.txt
meson setup build -Dbuildtype=debug
You can then run the tests using
meson's test framework.
meson test -Cbuild
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.
Release Notes
dwave-optimization makes use of reno
to manage its release notes.
When making a contribution to dwave-optimization that will affect users,
create a new release note file by running
reno new your-short-descriptor-here
You can then edit the file created under releasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.
See reno's user guide
for details.
About
Enables the formulation of nonlinear models for industrial optimization problems.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
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var __re = new RegExp('^' + ".*" + '
GitHub - k8culver/dwave-optimization: Enables the formulation of nonlinear models for industrial optimization problems. · GitHub
Skip to content
BranchesTags
Repository files navigation
https://circleci.com/gh/dwavesystems/dwave-optimization.svg?style=svg
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.
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.
fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)
For explanations of the terminology, see the
Ocean glossary.
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 editable installs
for more details.
pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .
Testing
All code should be thoroughly tested and all pull requests should include tests.
To run the Python tests, first install the package using an editable install
as described above. The tests can then be run with
unittest.
python -m unittest
To run the C++ tests, first install the project dependencies, then setup a
meson build directory. You must configure the build as a debug build for
the tests to run.
pip install -r requirements.txt
meson setup build -Dbuildtype=debug
You can then run the tests using
meson's test framework.
meson test -Cbuild
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.
Release Notes
dwave-optimization makes use of reno
to manage its release notes.
When making a contribution to dwave-optimization that will affect users,
create a new release note file by running
reno new your-short-descriptor-here
You can then edit the file created under releasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.
See reno's user guide
for details.
About
Enables the formulation of nonlinear models for industrial optimization problems.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
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Contributors
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - k8culver/dwave-optimization: Enables the formulation of nonlinear models for industrial optimization problems. · GitHub
Skip to content
BranchesTags
Repository files navigation
https://circleci.com/gh/dwavesystems/dwave-optimization.svg?style=svg
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.
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.
fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)
For explanations of the terminology, see the
Ocean glossary.
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 editable installs
for more details.
pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .
Testing
All code should be thoroughly tested and all pull requests should include tests.
To run the Python tests, first install the package using an editable install
as described above. The tests can then be run with
unittest.
python -m unittest
To run the C++ tests, first install the project dependencies, then setup a
meson build directory. You must configure the build as a debug build for
the tests to run.
pip install -r requirements.txt
meson setup build -Dbuildtype=debug
You can then run the tests using
meson's test framework.
meson test -Cbuild
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.
Release Notes
dwave-optimization makes use of reno
to manage its release notes.
When making a contribution to dwave-optimization that will affect users,
create a new release note file by running
reno new your-short-descriptor-here
You can then edit the file created under releasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.
See reno's user guide
for details.
About
Enables the formulation of nonlinear models for industrial optimization problems.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
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0 forks
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, 'i');
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try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - k8culver/dwave-optimization: Enables the formulation of nonlinear models for industrial optimization problems. · GitHub
Skip to content
BranchesTags
Repository files navigation
https://circleci.com/gh/dwavesystems/dwave-optimization.svg?style=svg
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.
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.
fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)
For explanations of the terminology, see the
Ocean glossary.
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 editable installs
for more details.
pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .
Testing
All code should be thoroughly tested and all pull requests should include tests.
To run the Python tests, first install the package using an editable install
as described above. The tests can then be run with
unittest.
python -m unittest
To run the C++ tests, first install the project dependencies, then setup a
meson build directory. You must configure the build as a debug build for
the tests to run.
pip install -r requirements.txt
meson setup build -Dbuildtype=debug
You can then run the tests using
meson's test framework.
meson test -Cbuild
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.
Release Notes
dwave-optimization makes use of reno
to manage its release notes.
When making a contribution to dwave-optimization that will affect users,
create a new release note file by running
reno new your-short-descriptor-here
You can then edit the file created under releasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.
See reno's user guide
for details.
About
Enables the formulation of nonlinear models for industrial optimization problems.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - k8culver/dwave-optimization: Enables the formulation of nonlinear models for industrial optimization problems. · GitHub
Skip to content
BranchesTags
Repository files navigation
https://circleci.com/gh/dwavesystems/dwave-optimization.svg?style=svg
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.
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.
fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)
For explanations of the terminology, see the
Ocean glossary.
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 editable installs
for more details.
pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .
Testing
All code should be thoroughly tested and all pull requests should include tests.
To run the Python tests, first install the package using an editable install
as described above. The tests can then be run with
unittest.
python -m unittest
To run the C++ tests, first install the project dependencies, then setup a
meson build directory. You must configure the build as a debug build for
the tests to run.
pip install -r requirements.txt
meson setup build -Dbuildtype=debug
You can then run the tests using
meson's test framework.
meson test -Cbuild
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.
Release Notes
dwave-optimization makes use of reno
to manage its release notes.
When making a contribution to dwave-optimization that will affect users,
create a new release note file by running
reno new your-short-descriptor-here
You can then edit the file created under releasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.
See reno's user guide
for details.
About
Enables the formulation of nonlinear models for industrial optimization problems.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
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(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - k8culver/dwave-optimization: Enables the formulation of nonlinear models for industrial optimization problems. · GitHub
Skip to content
BranchesTags
Repository files navigation
https://circleci.com/gh/dwavesystems/dwave-optimization.svg?style=svg
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.
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.
fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)
For explanations of the terminology, see the
Ocean glossary.
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 editable installs
for more details.
pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .
Testing
All code should be thoroughly tested and all pull requests should include tests.
To run the Python tests, first install the package using an editable install
as described above. The tests can then be run with
unittest.
python -m unittest
To run the C++ tests, first install the project dependencies, then setup a
meson build directory. You must configure the build as a debug build for
the tests to run.
pip install -r requirements.txt
meson setup build -Dbuildtype=debug
You can then run the tests using
meson's test framework.
meson test -Cbuild
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.
Release Notes
dwave-optimization makes use of reno
to manage its release notes.
When making a contribution to dwave-optimization that will affect users,
create a new release note file by running
reno new your-short-descriptor-here
You can then edit the file created under releasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.
See reno's user guide
for details.
About
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GitHub - k8culver/dwave-optimization: Enables the formulation of nonlinear models for industrial optimization problems. · GitHub
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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.
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.
fromdwave.optimization.generatorsimportflow_shop_schedulingprocessing_times= [[10, 5, 7], [20, 10, 15]]
model=flow_shop_scheduling(processing_times=processing_times)
For explanations of the terminology, see the
Ocean glossary.
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 editable installs
for more details.
pip install -r requirements.txt
pip install --no-build-isolation --config-settings=editable-verbose=true --editable .
Testing
All code should be thoroughly tested and all pull requests should include tests.
To run the Python tests, first install the package using an editable install
as described above. The tests can then be run with
unittest.
python -m unittest
To run the C++ tests, first install the project dependencies, then setup a
meson build directory. You must configure the build as a debug build for
the tests to run.
pip install -r requirements.txt
meson setup build -Dbuildtype=debug
You can then run the tests using
meson's test framework.
meson test -Cbuild
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.
Release Notes
dwave-optimization makes use of reno
to manage its release notes.
When making a contribution to dwave-optimization that will affect users,
create a new release note file by running
reno new your-short-descriptor-here
You can then edit the file created under releasenotes/notes/.
Remove any sections not relevant to your changes.
Commit the file along with your changes.
See reno's user guide
for details.
About
Enables the formulation of nonlinear models for industrial optimization problems.
Resources
Readme
License
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages