| _version_ | 1866902136369971200 |
|---|---|
| author | Collet, Antoine |
| author_facet | Collet, Antoine |
| contents | <div> <div>A python impementation of the famous L-BFGS-B quasi-Newton solver [1].</div> <br> <div>This code is a python port of the famous implementation of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), algorithm 778 written in Fortran [2,3]</div> <div>(last update in 2011). Note that this is not a wrapper such as `minimize`` in scipy but a complete reimplementation (pure python). The original Fortran code can be found here: https://dl.acm.org/doi/10.1145/279232.279236</div> </div> <div> <div> </div> <div>The aim of this reimplementation was threefold. First, familiarize ourselves with the code, its logic and inner optimizations. Second, gain access to certain parameters that are hard-coded in the Fortran code and cannot be modified (typically wolfe conditions parameters for the line search). Third, implement additional functionalities that require significant modification of the code core.</div> </div> <div><br> <div>References</div> <div>----------</div> <div>[1] R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing, 16, 5, pp. 1190-1208.</div> <div>[2] C. Zhu, R. H. Byrd and J. Nocedal. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (1997), ACM Transactions on Mathematical Software, 23, 4, pp. 550 - 560.</div> <div>[3] J.L. Morales and J. Nocedal. L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (2011), ACM Transactions on Mathematical Software, 38, 1.</div> <br> <div> </div> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18108903 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | lbfgsb Collet, Antoine <div> <div>A python impementation of the famous L-BFGS-B quasi-Newton solver [1].</div> <br> <div>This code is a python port of the famous implementation of Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), algorithm 778 written in Fortran [2,3]</div> <div>(last update in 2011). Note that this is not a wrapper such as `minimize`` in scipy but a complete reimplementation (pure python). The original Fortran code can be found here: https://dl.acm.org/doi/10.1145/279232.279236</div> </div> <div> <div> </div> <div>The aim of this reimplementation was threefold. First, familiarize ourselves with the code, its logic and inner optimizations. Second, gain access to certain parameters that are hard-coded in the Fortran code and cannot be modified (typically wolfe conditions parameters for the line search). Third, implement additional functionalities that require significant modification of the code core.</div> </div> <div><br> <div>References</div> <div>----------</div> <div>[1] R. H. Byrd, P. Lu and J. Nocedal. A Limited Memory Algorithm for Bound Constrained Optimization, (1995), SIAM Journal on Scientific and Statistical Computing, 16, 5, pp. 1190-1208.</div> <div>[2] C. Zhu, R. H. Byrd and J. Nocedal. L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (1997), ACM Transactions on Mathematical Software, 23, 4, pp. 550 - 560.</div> <div>[3] J.L. Morales and J. Nocedal. L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization (2011), ACM Transactions on Mathematical Software, 38, 1.</div> <br> <div> </div> </div> |
| title | lbfgsb |
| url | https://doi.org/10.5281/zenodo.18108903 |