Guaranteed convergence for a class of coupled-cluster methods based on Arponen's extended theory

03/15/2020
by   Simen Kvaal, et al.
0

A wide class of coupled-cluster methods is introduced, based on Arponen's extended coupled-cluster theory. This class of methods is formulated in terms of a coordinate transformation of the cluster operators. The mathematical framework for the error analysis of coupled-cluster methods based on Arponen's bivariational principle is presented, in which the concept of local strong monotonicity of the flipped gradient of the energy is central. A general mathematical result is presented, describing sufficient conditions for coordinate transformations to preserve the local strong monotonicity. The result is applied to the presented class of methods, which include the standard and quadratic coupled-cluster methods, and also Arponen's canonical version of extended coupled-cluster theory. Some numerical experiments are presented, and the use of canonical coordinates for diagnostics is discussed.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/27/2023

Coupled-Cluster Theory Revisited. Part II: Analysis of the single-reference Coupled-Cluster equations

In a series of two articles, we propose a comprehensive mathematical fra...
research
05/19/2021

Coupled-Cluster Theory Revisited

We propose a comprehensive mathematical framework for Coupled-Cluster-ty...
research
12/24/2022

Analysis of the Single Reference Coupled Cluster Method for Electronic Structure Calculations: The Full Coupled Cluster Equations

The central problem in electronic structure theory is the computation of...
research
02/13/2023

Finite-size effects in periodic coupled cluster calculations

We provide the first rigorous study of the finite-size error in the simp...
research
04/06/2023

Origin of inverse volume scaling in periodic coupled cluster calculations towards thermodynamic limit

Coupled cluster theory is considered to be the “gold standard” ansatz of...
research
01/11/2022

CDNNs: The coupled deep neural networks for coupling of the Stokes and Darcy-Forchheimer problems

In this article, we present an efficient deep learning method called cou...
research
08/16/2022

On GSOR, the Generalized Successive Overrelaxation Method for Double Saddle-Point Problems

We consider the generalized successive overrelaxation (GSOR) method for ...

Please sign up or login with your details

Forgot password? Click here to reset