Economical Quasi-Newton Self Consistent Field Solver
Abstract
We present an efficient quasi-Newton orbital solver optimized to reduce the number of gradient (Fock matrix) evaluations. The solver optimizes orthogonal orbitals by sequences of unitary rotations generated by the (preconditioned) limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm incorporating trust-region step restriction. Low-rank structure of the inverse (approximate) Hessian is exploited not only in L-BFGS but also when solving the trust-region problem. The efficiency of the proposed ``Quasi-Newton Unitary Optimization with Trust-Region'' (QUOTR) method is compared to that of the standard Roothaan-Hall approach accelerated by the Direct Inversion of Iterative Subspace (DIIS), and other exact and approximate Newton solvers for mean-field (Hartree-Fock and Kohn-Sham) problems.
Cite
@article{arxiv.2307.00560,
title = {Economical Quasi-Newton Self Consistent Field Solver},
author = {Samuel A. Slattery and Kshitijkumar Surjuse and Edward F. Valeev},
journal= {arXiv preprint arXiv:2307.00560},
year = {2023}
}