https://github.com/emmt/vmlmb
VMLMB algorithm for Python, Matlab, GNU Octave, and Yorick
https://github.com/emmt/vmlmb
Last synced: about 1 year ago
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VMLMB algorithm for Python, Matlab, GNU Octave, and Yorick
- Host: GitHub
- URL: https://github.com/emmt/vmlmb
- Owner: emmt
- License: other
- Created: 2022-03-27T21:55:24.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2024-11-13T16:16:14.000Z (over 1 year ago)
- Last Synced: 2025-03-26T20:55:41.142Z (about 1 year ago)
- Language: SWIG
- Homepage:
- Size: 554 KB
- Stars: 9
- Watchers: 3
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE.md
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README
# The VMLMB algorithm for large scale optimization with bound constraints
This repository provides implementations of the `VMLMB` algorithm and
preconditioned linear conjugate gradient method in various high-level
programming languages.
`VMLMB` (for *Variable Metric Limited Memory with Bounds*) is a quasi-Newton
optimization method with small memory requirements and which may take into
account separable bound constraints. This algorithm is of particular interest
for minimizing a smooth cost function of a potentially very large number of
variables (millions or billions) possibly under bound constraints. `VMLMB` has
been successfully used to solve many different kinds of problems notably image
restoration in *inverse problems* framework.
The objective of this repository is to provide algorithms that:
* run out of the box (no additional libraries needed);
* are efficient (although maybe not as fast as if implemented in a low level
compiled language) and usable for serious applications;
* are well documented;
* have readable code;
* can be easily modified.
## Contents
The repository is organized as follows:
- Directory [`matlab`](./matlab) contains a pure
[Matlab](https://www.mathworks.com)/[GNU
Octave](https://www.gnu.org/software/octave) version of `VMLMB` and of a
preconditioned linear conjugate gradient method. See file
[`matlab/README.md`](./matlab/README.md) for installation and usage
instructions.
- Directory [`python`](./python) contains a pure [`NumPy`](https://numpy.org/)
version of `VMLMB` and of a preconditioned linear conjugate gradient method.
See file [`python/README.md`](./python/README.md) for installation and usage
instructions.
- Directory [`yorick`](./yorick) contains a pure
[Yorick](https://github.com/LLNL/yorick) version of `VMLMB` and of a
preconditioned linear conjugate gradient method. See file
[`yorick/README.md`](./yorick/README.md) for installation and usage
instructions.
## References
- Hestenes, M. R. & Stiefel, E. "*Methods of Conjugate Gradients for Solving
Linear Systems*," in Journal of Research of the National Bureau of Standards,
**49**, pp. 409-436 (1952).
- É. Thiébaut, "*Optimization issues in blind deconvolution algorithms*," in
Astronomical Data Analysis II, SPIE Proc. **4847**, 174-183 (2002).