| Name | amplify-bbopt JSON |
| Version |
0.2.3
JSON |
| download |
| home_page | None |
| Summary | An Amplify-based library to facilitate black-box optimization |
| upload_time | 2024-09-09 05:33:28 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | MIT License Copyright (c) 2024 Fixstars Amplify Corporation Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
amplify
black-box optimization
bayesian optimization
|
| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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# Amplify-BBOpt
Amplify-BBOpt is a Python package to facilitate the execution of black-box optimization developed by [Fixstars Amplify](https://amplify.fixstars.com/en).
- Documentation:
https://amplify.fixstars.com/docs/amplify-bbopt/v0/
- Contributing (bug reports, improvements):
https://amplify.fixstars.com/en/contact
Amplify-BBOpt is built around the [Amplify SDK](https://amplify.fixstars.com/en/docs/amplify/). Annealing-based black-box optimization methods, FMQA and kernel-QA, are available in conjunction with quantum annealing machines and Ising machines (hence using Amplify SDK). Important features include (but not limited to):
* Easy to run and change methods
* Applicability to high-dimensional problems
* Consideration of constraints
* Multi-objective optimization
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