Iterative subspace algorithms for finite-temperature solution of Dyson equation
Pavel Pokhilko, Chia-Nan Yeh, Dominika Zgid

TL;DR
This paper develops and compares iterative acceleration techniques for solving the Dyson equation at finite temperature, demonstrating improved convergence especially for strongly correlated systems in Green's function methods.
Contribution
It introduces a generalized residual concept and applies acceleration methods like DIIS, LCIIS, and KAIN to enhance Dyson equation solutions, enabling convergence in challenging cases.
Findings
Commutator residuals outperform difference residuals in convergence.
Generalized CDIIS and LCIIS methods successfully converge restricted GF2 for strongly correlated systems.
Practical guidelines are provided for achieving convergence in difficult scenarios.
Abstract
One-particle Green's functions obtained from the self-consistent solution of the Dyson equation can be employed in evaluation of spectroscopic and thermodynamic properties for both molecules and solids. However, typical acceleration techniques used in the traditional quantum chemistry self-consistent algorithms cannot be easily deployed for the Green's function methods, because of non-convex grand potential functional and non-idempotent density matrix. Moreover, the inclusion of correlation effects in the form of the self-energy matrix and changing chemical potential or fluctuations in the number of particles can make the optimization problem more difficult. In this paper, we study acceleration techniques to target the self-consistent solution of the Dyson equation directly. We use the direct inversion in the iterative subspace (DIIS), the least-squared commutator in the iterative…
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