Coupled-Cluster Theory Revisited. Part II: Analysis of the
single-reference Coupled-Cluster equations
- URL: http://arxiv.org/abs/2303.15106v1
- Date: Mon, 27 Mar 2023 11:21:19 GMT
- Title: Coupled-Cluster Theory Revisited. Part II: Analysis of the
single-reference Coupled-Cluster equations
- Authors: Mih\'aly A. Csirik and Andre Laestadius
- Abstract summary: We analyze the nonlinear equations of the single-reference Coupled-Cluster method using topological degree theory.
For the truncated Coupled-Cluster method, we derive an energy error bound for approximate eigenstates of the Schrodinger equation.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In a series of two articles, we propose a comprehensive mathematical
framework for Coupled-Cluster-type methods. In this second part, we analyze the
nonlinear equations of the single-reference Coupled-Cluster method using
topological degree theory. We establish existence results and qualitative
information about the solutions of these equations that also sheds light on the
numerically observed behavior. In particular, we compute the topological index
of the zeros of the single-reference Coupled-Cluster mapping. For the truncated
Coupled-Cluster method, we derive an energy error bound for approximate
eigenstates of the Schrodinger equation.
Related papers
- Algebraic method of group classification for semi-normalized classes of differential equations [0.0]
We prove the important theorems on factoring out symmetry groups and invariance algebras of systems from semi-normalized classes.
Nontrivial examples of classes that arise in real-world applications are provided.
arXiv Detail & Related papers (2024-08-29T20:42:04Z) - HeNCler: Node Clustering in Heterophilous Graphs through Learned Asymmetric Similarity [55.27586970082595]
HeNCler is a novel approach for Heterophilous Node Clustering.
We show that HeNCler significantly enhances performance in node clustering tasks within heterophilous graph contexts.
arXiv Detail & Related papers (2024-05-27T11:04:05Z) - Maximum Likelihood Estimation on Stochastic Blockmodels for Directed Graph Clustering [22.421702511126373]
We formulate clustering as estimating underlying communities in the directed block model.
We introduce two efficient and interpretable directed clustering algorithms, a spectral clustering algorithm and a semidefinite programming based clustering algorithm.
arXiv Detail & Related papers (2024-03-28T15:47:13Z) - Perfect Spectral Clustering with Discrete Covariates [68.8204255655161]
We propose a spectral algorithm that achieves perfect clustering with high probability on a class of large, sparse networks.
Our method is the first to offer a guarantee of consistent latent structure recovery using spectral clustering.
arXiv Detail & Related papers (2022-05-17T01:41:06Z) - Exact solutions of interacting dissipative systems via weak symmetries [77.34726150561087]
We analytically diagonalize the Liouvillian of a class Markovian dissipative systems with arbitrary strong interactions or nonlinearity.
This enables an exact description of the full dynamics and dissipative spectrum.
Our method is applicable to a variety of other systems, and could provide a powerful new tool for the study of complex driven-dissipative quantum systems.
arXiv Detail & Related papers (2021-09-27T17:45:42Z) - Coupled-Cluster Theory Revisited. Part I: Discretization [0.0]
We describe the discretization schemes involved in Coupled-Cluster methods using graph-based concepts.
We derive the single-reference and the Jeziorski-Monkhorst multireference Coupled-Cluster equations in a unified and rigorous manner.
arXiv Detail & Related papers (2021-05-19T16:35:49Z) - Joint Network Topology Inference via Structured Fusion Regularization [70.30364652829164]
Joint network topology inference represents a canonical problem of learning multiple graph Laplacian matrices from heterogeneous graph signals.
We propose a general graph estimator based on a novel structured fusion regularization.
We show that the proposed graph estimator enjoys both high computational efficiency and rigorous theoretical guarantee.
arXiv Detail & Related papers (2021-03-05T04:42:32Z) - Clustering Ensemble Meets Low-rank Tensor Approximation [50.21581880045667]
This paper explores the problem of clustering ensemble, which aims to combine multiple base clusterings to produce better performance than that of the individual one.
We propose a novel low-rank tensor approximation-based method to solve the problem from a global perspective.
Experimental results over 7 benchmark data sets show that the proposed model achieves a breakthrough in clustering performance, compared with 12 state-of-the-art methods.
arXiv Detail & Related papers (2020-12-16T13:01:37Z) - Guaranteed convergence for a class of coupled-cluster methods based on
Arponen's extended theory [0.0]
This class of methods is formulated in terms of a coordinate transformation of the cluster operators.
The concept of local strong monotonicity of the flipped gradient of the energy is central.
Some numerical experiments are presented, and the use of canonical coordinates for diagnostics is discussed.
arXiv Detail & Related papers (2020-03-15T11:24:30Z) - Conjoined Dirichlet Process [63.89763375457853]
We develop a novel, non-parametric probabilistic biclustering method based on Dirichlet processes to identify biclusters with strong co-occurrence in both rows and columns.
We apply our method to two different applications, text mining and gene expression analysis, and demonstrate that our method improves bicluster extraction in many settings compared to existing approaches.
arXiv Detail & Related papers (2020-02-08T19:41:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.