Remark on “algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound constrained optimization”

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Blitzkriging: Kronecker-structured Stochastic Gaussian Processes (2015, methodology): “Blitzkriging meets these three desiderata: we scale linearly with data, and allow the use of stochastic gradient descent for large data sets; we rely on off-the-shelf optimisation algorithms (limited memory BroydenFletcherGoldfarbShanno (LBFGS) [20] and Adadelta [21]) that reliably find a good param”
Imaging the Schwarzschild-radius-scale Structure of M87 with the Event Horizon Telescope Using Sparse Modeling (2017, unknown): “0; Morales & Nocedal 2011).”
An introduction to continuous optimization for imaging (2016, methodology): “A very popular algorithm is the limited memory BFGS quasi-Newton method (Byrd et al. 1995, Zhu, Byrd, Lu and Nocedal 1997, Morales and Nocedal 2011).”
TAU-REX I: A NEXT GENERATION RETRIEVAL CODE FOR EXOPLANETARY ATMOSPHERES (2014, methodology): “T -REx uses the LM-BFGS (Zhu et al. 1997; Morales & Nocedal 2011) algorithm instead, which being quasiNewtonian uses the inverse Hessian matrix of the χ2 surface to efficiently and robustly converge to the global maximum.”