A python impementation of the famous L-BFGS-B quasi-Newton solver [1].

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] (last update in 2011). Note that this is not a wrapper like 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

[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.

[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.

[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.

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.

• Free software: MIT license
• Documentation: https://lbfgsb.readthedocs.io.