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We discuss a particular source of error in the Numerical Renormalization Group (NRG) method for quantum impurity problems, which is related to a renormalization of impurity parameters due to the bath propagator. At any step of the NRG…

Statistical Mechanics · Physics 2010-02-25 Matthias Vojta , Ralf Bulla , Fabian Guettge , Frithjof Anders

The application of renormalization group methods to microscopic nuclear many-body calculations is discussed. We present the solution of the renormalization group equations in the particle-hole channels for neutron matter and the application…

Nuclear Theory · Physics 2007-05-23 A. Schwenk , B. Friman , G. E. Brown

We present a nonperturbative derivation of effective valence-shell Hamiltonians in the framework of the recently developed in-medium similarity renormalization group (IM-SRG). As a first application, we calculate the spectra of $p$- and…

Nuclear Theory · Physics 2012-07-03 K. Tsukiyama , S. K. Bogner , A. Schwenk

We investigate several fundamental characteristics of the multiloop functional renormalization group (mfRG) flow by hands of its application to a prototypical many-electron system: the Anderson impurity model (AIM). We first analyze the…

Strongly Correlated Electrons · Physics 2022-04-22 Patrick Chalupa-Gantner , Fabian B. Kugler , Cornelia Hille , Jan von Delft , Sabine Andergassen , Alessandro Toschi

For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown quantities. While most existing algorithms for solving those kind of ill-posed…

Numerical Analysis · Mathematics 2022-07-28 Neil K. Chada , Marco A. Iglesias , Shuai Lu , Frank Werner

This work performs a comparative numerical study of the impurity average self-frequency $\tilde{\omega}$ in an unconventional superconducting alloy with non-magnetic impurities. Two methods are used: the Levenberg-Marquardt algorithm as a…

Superconductivity · Physics 2021-05-17 P. Contreras , Juan Moreno

The Density Matrix Renormalization Group (DMRG) has become a powerful numerical method that can be applied to low-dimensional strongly correlated fermionic and bosonic systems. It allows for a very precise calculation of static, dynamical…

Condensed Matter · Physics 2007-05-23 Karen Hallberg

We systematically study a numerical procedure that reveals the asymptotically self-similar dynamics of solutions of partial differential equations (PDEs). This procedure, based on the renormalization group (RG) theory for PDEs, appeared…

Numerical Analysis · Mathematics 2018-07-12 Gastão A. Braga , Federico C. Furtado , Vincenzo Isaia , Long Lee

We describe a low cost alternative to the standard variational DMRG (density matrix renormalization group) algorithm that is analogous to the combination of selected configuration interaction plus perturbation theory (SCI+PT). We denote the…

Chemical Physics · Physics 2018-03-28 Sheng Guo , Zhendong Li , Garnet Kin-Lic Chan

The Density Matrix Renormalization Group (DMRG) method is developed for application to realistic nuclear systems. Test results are reported for 24Mg.

Nuclear Theory · Physics 2007-05-23 S. S. Dimitrova , S. Pittel , J. Dukelsky , M. V. Stoitsov

The full-density-matrix numerical renormalization group (NRG) has evolved as a systematic and transparent setting for the cal- culation of thermodynamical quantities at arbitrary temperatures within the NRG framework. It directly evaluates…

Strongly Correlated Electrons · Physics 2013-05-30 Andreas Weichselbaum

We apply Renormalization Group (RG) techniques to Classical Information Theory, in the limit of large codeword size $n$. In particular, we apply RG techniques to (1) noiseless coding (i.e., a coding used for compression) and (2) noisy…

Quantum Physics · Physics 2007-05-23 Robert R. Tucci

The purpose of this paper is (i) to present a generic and fully functional implementation of the density-matrix renormalization group (DMRG) algorithm, and (ii) to describe how to write additional strongly-correlated electron models and…

Strongly Correlated Electrons · Physics 2015-05-13 G. Alvarez

The Kondo effect in quantum dots (QDs) - artificial magnetic impurities - attached to ferromagnetic leads is studied with the numerical renormalization group (NRG) method. It is shown that the QD level is spin-split due to presence of…

Mesoscale and Nanoscale Physics · Physics 2007-05-23 J. Martinek , M. Sindel , L. Borda , J. Barnaś , J. König , G. Schön , J. von Delft

The density matrix renormalization group (DMRG) of White 1992 remains to this day an integral component of many state-of-the-art methods for efficiently simulating strongly correlated quantum systems. In quantum chemistry, QC-DMRG became a…

Quantum Physics · Physics 2021-03-16 Mazen Ali

A new numerical method for the solution of the Dynamical Mean Field Theory's self-consistent equations is introduced. The method uses the Density Matrix Renormalization Group technique to solve the associated impurity problem. The new…

Strongly Correlated Electrons · Physics 2016-08-31 Daniel J. Garcia , Karen Hallberg , Marcelo J. Rozenberg

A practical algorithm for many-electron systems based on the path-integral renormalization group (PIRG) method is proposed in the real-space finite-difference (RSFD) approach. The PIRG method, developed for investigating strongly correlated…

Strongly Correlated Electrons · Physics 2007-11-30 Masashi Kojo , Kikuji Hirose

The density matrix renormalization group (DMRG) method is applied to the interaction round a face (IRF) model. When the transfer matrix is asymmetric, singular-value decomposition of the density matrix is required. A trial numerical…

Condensed Matter · Physics 2009-10-28 Tomotoshi Nishino

We describe a large disorder renormalization group (LDRG) method for the Anderson model of localization in one dimension which decimates eigenstates based on the size of their wavefunctions rather than their energy. We show that our LDRG…

Disordered Systems and Neural Networks · Physics 2014-11-04 Sonika Johri , R. N. Bhatt

Discrete inverse problems correspond to solving a system of equations in a stable way with respect to noise in the data. A typical approach to enforce uniqueness and select a meaningful solution is to introduce a regularizer. While for most…

Optimization and Control · Mathematics 2022-04-22 Cristian Vega , Cesare Molinari , Lorenzo Rosasco , Silvia Villa
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