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Removes NLOpt Methods #172

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Removes NLOpt Methods #172

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57399ae
Remove NLOpt functionality, dependancy, examples
Jan 23, 2024
5c09e07
Updt. Changelog, remove notebook, updt. README
Jan 23, 2024
94ec16e
Merge branch 'develop' into 63-bug-nlopt-install-error-for-apple-silicon
NicolaCourtier Jan 26, 2024
e331ba5
Update choice of examples in Readme
NicolaCourtier Jan 26, 2024
4c1269d
Update test_standalone.py
NicolaCourtier Jan 26, 2024
cb103cb
Update test_standalone.py
NicolaCourtier Jan 26, 2024
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1 change: 1 addition & 0 deletions CHANGELOG.md
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## Bug Fixes

- [#63](https://github.com/pybop-team/PyBOP/issues/63) - Removes NLOpt Optimiser from future releases. This is to support deployment to the Apple M-Series platform.
- [#164](https://github.com/pybop-team/PyBOP/issues/164) - Fixes convergence issues with gradient-based optimisers, changes default `model.check_params()` to allow infeasible solutions during optimisation iterations. Adds a feasibility check on the optimal parameters.

# [v23.12](https://github.com/pybop-team/PyBOP/tree/v23.12) - 2023-12-19
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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -111,13 +111,13 @@ These general cases encompass a wide variety of optimisation problems that requi

PyBOP comes with a number of [example notebooks and scripts](https://github.com/pybop-team/PyBOP/blob/develop/examples) which can be found in the examples folder.

The [spm_descent.py](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_descent.py) script illustrates a straightforward example that starts by generating artificial data from a single particle model (SPM). The unknown parameter values are identified by implementing a sum-of-square error cost function using the terminal voltage as the observed signal and a gradient descent optimiser. To run this example:
The [spm_pso.py](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/spm_pso.py) script illustrates a straightforward example that starts by generating artificial data from a single particle model (SPM). The unknown parameter values are identified by employing a sum-of-squared errors cost function using the terminal voltage as the observed signal and a particle swarm optimisation algorithm. To run this example:

```bash
python examples/scripts/spm_descent.py
python examples/scripts/spm_pso.py
```

In addition, [spm_CMAES.ipynb](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/spm_CMAES.ipynb) provides a second example in notebook form. This example estimates the SPM parameters based on an RMSE cost function and a BOBYQA optimiser.
Alternatively, [spm_CMAES.ipynb](https://github.com/pybop-team/PyBOP/blob/develop/examples/notebooks/spm_CMAES.ipynb) provides an example in notebook form. This example estimates SPM parameters based on a sum-of-squared errors cost function and a CMA-ES optimiser.

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## Code of Conduct
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