https://github.com/otvam/global_optim_fitting_matlab

MATLAB Toolbox for Global Fitting/Optimization
https://github.com/otvam/global_optim_fitting_matlab

cache fitting genetic-algorithm matlab optimization parallel

Last synced: 10 months ago
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MATLAB Toolbox for Global Fitting/Optimization

• Host: GitHub
• URL: https://github.com/otvam/global_optim_fitting_matlab
• Owner: otvam
• License: bsd-2-clause
• Created: 2022-05-05T00:13:15.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2024-12-18T04:46:13.000Z (about 1 year ago)
• Last Synced: 2025-03-28T00:41:25.826Z (10 months ago)
• Topics: cache, fitting, genetic-algorithm, matlab, optimization, parallel
• Language: MATLAB
• Homepage:
• Size: 280 KB
• Stars: 6
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

          
# MATLAB Toolbox for Global Fitting/Optimization

![license - BSD](https://img.shields.io/badge/license-BSD-green)

![language - MATLAB](https://img.shields.io/badge/language-MATLAB-blue)

![category - science](https://img.shields.io/badge/category-science-lightgrey)

![status - unmaintained](https://img.shields.io/badge/status-unmaintained-red)

This **MATLAB toolbox** can be used for the following problems:

* finding **global minimum** of a function

* **fitting a function** to a dataset

This toolbox is specially adapted to the **following problems**:

* **non-smooth** error function

* **non-convex** error function

* **computationally heavy** error function

* error function with **local minima**

* error function with **many input variables**

This toolbox provides a common interface for **different solvers**:

* **gradient**: fminunc / fmincon

* **simplex**: fminsearch

* **surrogate**: surrogateopt

* **evolutionary**: particleswarm / ga

* the aforementioned solvers can be **combined**

Customized **error function**:

* custom **weights** for the dataset points

* choice of the **error metric** (norm, average, percentile, etc.)

* recover from **undefined values**

* **vectorized evaluation** of the error function

* **parallel evaluation** of the error function

* **caching** of the error function

Advanced **variable handling**:

* **abstraction layer** for the variables

* **initial** values

* **scalar or vector** variables

* variable **transformation** (linear, quadratic, logarithmic, etc.)

* variable **normalization**

* **constraints** (lower and upper bounds)

* **sine transformation** for handling constraints

Advanced **monitoring** capabilities:

* compute various error metrics

* compute solver figures of merit

* plot/display the solver progress

* plot/display the final results

## Limitations

* All the provided features have a **computational cost**.

* Therefore, this library is mostly adapted to **time-consuming error functions**.

* For simple error functions, the **overhead is non-negligible**.

## Examples

* [run_example_fitting.m](run_example_fitting.m) - Simple fitting of a model with respect to a dataset.

* [run_example_optim.m](run_example_optim.m) - Find the global minimum of a function.



    

    



## Compatibility

* Tested with MATLAB R2021a.

* The `gads_toolbox` is required (for the MATLAB solvers).

* The `optimization_toolbox` is required (for the MATLAB solvers).

* The `distrib_computing_toolbox` is required (for parfor loops)

* Compatibility with GNU Octave not tested but probably problematic.

## Author

**Thomas Guillod** - [GitHub Profile](https://github.com/otvam)

## License

This project is licensed under the **BSD License**, see [LICENSE.md](LICENSE.md).