As mentioned above, the number of geometry cycles required to converge to the optimized geometry can be reduced with a concomitant increase in computational time depending on the Hessian used. Bakken and Helgaker, for instance, already addressed this problem, where it is noted that utilizing precise Hessians at each step reduces the number of required iterations while compromising on total computation time. Only 125 and 111 steps are required for the Baker test set with HF/STO-3G using Cartesian and redundant internal coordinates, respectively, when using exact Hessians at each step. Baker, in Ref. 20, used natural internal coordinates and the initial Hessian provided by the CVFF force field in combination with an eigenvector following (EF) approach to reduce the number of iterations required for HF/STO-3G geometry optimization to 240 steps. Bakken and Helgaker managed to optimize the Baker test set in 185 steps using a combination of extra-redundant internal coordinates, a good approximate initial Hessian with BFGS updates, and optimized step size. A slightly larger number of 198 steps was reported when using the more flexible 6-31G* basis set. Swart and Bickelhaupt managed to further reduce the number of geometry optimization steps using the delocalized coordinates setup of Baker, a combination of quasi-Newton steps with GDIIS, and a modification of Lindh's force constant model to generate initial Hessians, resulting in an impressive 173 steps for the Baker test set using the PW91 density functional and a large triple zeta Slater type basis set with polarization functions (TZP). It therefore stands to reason that replacing the unit matrix with a better initial Hessian approximation should also reduce the number of geometry optimization steps required by the L-BFGS optimization employed by DL-FIND.