Solving Coupled Cluster Equations by the Newton Krylov Method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61388955%3A_____%2F20%3A00538055" target="_blank" >RIV/61388955:_____/20:00538055 - isvavai.cz</a>

Výsledek na webu

<a href="http://hdl.handle.net/11104/0315879" target="_blank" >http://hdl.handle.net/11104/0315879</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fchem.2020.590184" target="_blank" >10.3389/fchem.2020.590184</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Solving Coupled Cluster Equations by the Newton Krylov Method

Popis výsledku v původním jazyce

We describe using the Newton Krylov method to solve the coupled cluster equation. The method uses a Krylov iterative method to compute the Newton correction to the approximate coupled cluster amplitude. The multiplication of the Jacobian with a vector, which is required in each step of a Krylov iterative method such as the Generalized Minimum Residual (GMRES) method, is carried out through a finite difference approximation, and requires an additional residual evaluation. The overall cost of the method is determined by the sum of the inner Krylov and outer Newton iterations. We discuss the termination criterion used for the inner iteration and show how to apply pre-conditioners to accelerate convergence. We will also examine the use of regularization technique to improve the stability of convergence and compare the method with the widely used direct inversion of iterative subspace (DIIS) methods through numerical examples.

Název v anglickém jazyce

Solving Coupled Cluster Equations by the Newton Krylov Method

Popis výsledku anglicky

We describe using the Newton Krylov method to solve the coupled cluster equation. The method uses a Krylov iterative method to compute the Newton correction to the approximate coupled cluster amplitude. The multiplication of the Jacobian with a vector, which is required in each step of a Krylov iterative method such as the Generalized Minimum Residual (GMRES) method, is carried out through a finite difference approximation, and requires an additional residual evaluation. The overall cost of the method is determined by the sum of the inner Krylov and outer Newton iterations. We discuss the termination criterion used for the inner iteration and show how to apply pre-conditioners to accelerate convergence. We will also examine the use of regularization technique to improve the stability of convergence and compare the method with the widely used direct inversion of iterative subspace (DIIS) methods through numerical examples.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10403 - Physical chemistry

Projekt

<a href="/cs/project/GJ19-13126Y" target="_blank" >GJ19-13126Y: Deep learning pro silně korelované systémy v kvantové chemii</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Frontiers in Chemistry

ISSN

2296-2646

e-ISSN

—

Svazek periodika

8

Číslo periodika v rámci svazku

DEC 2020

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

9

Strana od-do

590184

Kód UT WoS článku

000601263300001

EID výsledku v databázi Scopus

2-s2.0-85098203162