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Eric Polizzi

Publications and source records attributed to Eric Polizzi.

At least 19 recordsLinked to original sources
arXiv2026

Nonlinear eigenvalue algorithm for $GW$ quasiparticle equations

The use of the Green's function in quantum many-body theory often leads to nonlinear eigenvalue problems, as the Green's function needs to be defined in the energy domain. The GW approximation method is one typical example. In this article, we introduce a method based on the FEAST eigenvalue algorithm for accurately solving the nonlinear eigenvalue G0W0 quasiparticle equation. Based on the FEAST contour integral method for nonlinear eigenvalue problems, the energy (eigenvalue) domain is extended to the complex plane. An analytic continuation is introduced to the calculation of the GW self-energy to carry the imaginary parts of both Green's functions and FEAST contour quadrature nodes. Calculation results for various molecules are presented and compared with the more conventional graphical solution approximation method and spectral functions method. It is confirmed that the highest occupied molecular orbital from the Kohn-Sham (and Hartree-Fock) equation is very close to that of GW, while the lowest unoccupied molecular orbital shows noticeable differences.

physics.comp-ph↗
arXiv2026

LDA-1/2 for Molecular Systems: A Real-Space Finite-Element Benchmark on the GW100 Set

The LDA-1/2 method provides an efficient correction to semilocal density functional theory for improving ionization energies and band gaps, yet its application to molecular systems has remained limited. In this work, we present an all-electron finite-element implementation of LDA-1/2 within the NESSIE electronic-structure framework and apply it to the GW100 molecular benchmark set with systematically controllable numerical accuracy. The self-energy correction is constructed explicitly from neutral and half-ionized calculations for each molecule, avoiding the use of precomputed atomic correction potentials. The real-space finite-element formulation enables systematic convergence with respect to the discretization and provides a controlled assessment of LDA-1/2 performance. For the GW100 set, the present implementation yields a mean absolute error of 0.472 eV and a root-mean-square error of 0.645 eV relative to CCSD(T) reference ionization energies, substantially improving upon conventional LDA and the previously reported LAPW implementation of LDA-1/2, while achieving accuracy comparable to G0W0@PBE. Convergence tests show that third-order finite elements are sufficient to reach or approach chemical accuracy relative to higher-order calculations for the representative systems considered. The resulting corrected Hamiltonian also improves several lower lying valence states relative to LDA, although the improvement becomes less systematic away from the HOMO. This work provides accurate LDA-1/2 benchmark data for molecular systems and establishes a rigorous finite-element foundation for future molecular GW calculations.

cond-mat.mtrl-sci↗
arXiv2025

Low-Rank SPIKE Framework for Solving Large Sparse Linear Systems with Applications

The SPIKE family of linear system solvers provides parallelism using a block tridiagonal partitioning. Typically SPIKE-based solvers are applied to banded systems, resulting in structured off-diagonal blocks with non-zeros elements restricted to relatively small submatrices comprising the band of the original matrix. In this work, a low-rank SVD based approximation of the off-diagonal blocks is investigated. This produces a representation which more effectively handles matrices with large, sparse bands. A set of flexible distributed solvers, the LR-SPIKE variants, are implemented. There are applicable to a wide range of applications -- from use as a "black-box" preconditioner which straightforwardly improves upon the classic Block Jacobi preconditioner, to use as a specialized "approximate direct solver." An investigation of the effectiveness of the new preconditioners for a selection of SuiteSparse matrices is performed, particularly focusing on matrices derived from 3D finite element simulations. In addition, the SPIKE approximate linear system solvers are also paired with the FEAST eigenvalue solver, where they are shown to be particularly effective due to the former's rapid convergence, and the latter's acceptance of loose linear system solver convergence, resulting in a combination which requires very few solver iterations.

math.NA↗
arXiv2024

One-dimensional Plasmons and Hybridized Coupled Polaritons in Carbon Nanotubes

This paper presents real-time time-dependent density functional theory (TDDFT) ab-initio simulations of selected armchair carbon nanotubes (CNTs). By scaling the lengths of CNTs, we provide a comprehensive analysis of the Tomonaga-Luttinger (T-L) 1-D plasmon velocities, confirming consistency with theoretical predictions and experimental observations. Our findings include detailed visual representations of excitation densities at various resonances. Furthermore, we explore the coupling between T-L plasmons and single electron excitations, identifying distinct 1-D polariton behaviors, such as strong harmonic generation due to nonlinearities, as well as energy gaps that differ from conventional 2-D polaritons. The study highlights the unique properties of armchair SWCNTs as low-loss nanocavity resonators, demonstrating potential applications in strong light-matter coupling and other nanophotonic devices. The simulation framework employed here opens avenues for further research into 1-D plasmonic phenomena and electronic spectroscopy in complex nanostructures.

cond-mat.mes-hall↗
arXiv2023

A method of calculating bandstructure in real-space with application to all-electron and full potential

We introduce a practical and efficient approach for calculating the all-electron full potential bandstructure in real space, employing a finite element basis. As an alternative to the k-space method, the method involves the self-consistent solution of the Kohn-Sham equation within a larger finite system that encloses the unit-cell. It is based on the fact that the net potential of the unit-cell converges at a certain radius point. Bandstructure results are then obtained by performing non-self-consistent calculations in the Brillouin zone. Numerous numerical experiments demonstrate that the obtained valence and conduction bands are in excellent agreement with the pseudopotential k-space method. Moreover, we successfully observe the band bending of core electrons.

cond-mat.mtrl-sci↗
arXiv2020

An Iterative Method for Contour-Based Nonlinear Eigensolvers

Contour integration techniques have become a popular choice for solving the linear and non-linear eigenvalue problems. They principally include the Sakurai-Sugiura methods, the Beyn's algorithm, the FEAST/NLFEAST algorithms and other rational filtering techniques. While these methods can result in effective 'black-box' approach for solving linear eigenvalue problems, they still present several shortcomings for addressing nonlinear eigenvalue problems which are both mathematically and practically far more challenging. In this paper, we introduce a new hybrid algorithm that advantageously combines the iterative nature of NLFEAST with the effectiveness of Beyn's approach to deal with general non-linearity. In doing so, this NLFEAST-Beyn hybrid algorithm can overcome current limitations of both algorithms taken separately. After presenting its derivation from both a Beyn's and NLFEAST's perspective, several numerical examples are discussed to demonstrate the efficiency of the new technique.

math.NA↗
arXiv2020

FEAST Eigenvalue Solver v4.0 User Guide

The FEAST library package represents an unified framework for solving various family of eigenvalue problems and achieving accuracy, robustness, high-performance and scalability on parallel architectures. Its originality lies with a new transformative numerical approach to the traditional eigenvalue algorithm design - the FEAST algorithm. The algorithm gathers key elements from complex analysis, numerical linear algebra and approximation theory, to construct an optimal subspace iteration technique using approximate spectral projectors. FEAST can be used for solving both standard and generalized forms of the Hermitian or non-Hermitian problems (linear or non-linear), and it belongs to the family of contour integration eigensolvers. FEAST's main computational task consists of a numerical quadrature computation that involves solving independent linear systems along a complex contour, each with multiple right hand sides. In v4.0, FEAST has been reimplemented using an inverse residual iteration algorithm which enables the linear systems to be solved with very low accuracy (in single precision) with no impact on the FEAST double precision convergence rate. As a result, v4.0 is on average 3-4 times faster than v2.1 and v3.0 using new default optimization parameters (v2.1 has been featured as Intel-MKL's principal HPC eigensolver since 2013). v4.0 also implements new important features such as IFEAST (using Inexact Iterative solver), Non-linear polynomial FEAST, and PFEAST with its 3-MPI levels of parallelism. FEAST is both a comprehensive library package, and an easy to use software. It includes flexible reverse communication interfaces and ready to use driver interfaces for dense, banded and sparse systems.

cs.MS↗
arXiv2018

A Feature Complete SPIKE Banded Algorithm and Solver

New features and enhancements for the SPIKE banded solver are presented. Among all the SPIKE algorithm versions, we focus our attention on the recursive SPIKE technique which provides the best trade-off between generality and parallel efficiency, but was known for its lack of flexibility. Its application was essentially limited to power of two number of cores/processors. This limitation is successfully addressed in this paper. In addition, we present a new transpose solve option, a standard feature of most numerical solver libraries which has never been addressed by the SPIKE algorithm so far. A pivoting recursive SPIKE strategy is finally presented as an alternative to non-pivoting scheme for systems with large condition numbers. All these new enhancements participate to create a feature complete SPIKE algorithm and a new black-box SPIKE-OpenMP package that significantly outperforms the performance and scalability obtained with other state-of-the-art banded solvers.

math.NA↗
arXiv2018

FEAST Eigensolver for Nonlinear Eigenvalue Problems

The linear FEAST algorithm is a method for solving linear eigenvalue problems. It uses complex contour integration to calculate the eigenvectors whose eigenvalues that are located inside some user-defined region in the complex plane. This makes it possible to parallelize the process of solving eigenvalue problems by simply dividing the complex plane into a collection of disjoint regions and calculating the eigenpairs in each region independently of the eigenpairs in the other regions. In this paper we present a generalization of the linear FEAST algorithm that can be used to solve nonlinear eigenvalue problems. Like its linear progenitor, the nonlinear FEAST algorithm can be used to solve nonlinear eigenvalue problems for the eigenpairs whose eigenvalues lie in a user-defined region in the complex plane, thereby allowing for the calculation of large numbers of eigenpairs in parallel. We describe the nonlinear FEAST algorithm, and use several physically-motivated examples to demonstrate its properties.

physics.comp-ph↗
arXiv2017

An improved Krylov eigenvalue strategy using the FEAST algorithm with inexact system solves

The FEAST eigenvalue algorithm is a subspace iteration algorithm that uses contour integration in the complex plane to obtain the eigenvectors of a matrix for the eigenvalues that are located in any user-defined search interval. By computing small numbers of eigenvalues in specific regions of the complex plane, FEAST is able to naturally parallelize the solution of eigenvalue problems by solving for multiple eigenpairs simultaneously. The traditional FEAST algorithm is implemented by directly solving collections of shifted linear systems of equations; in this paper, we describe a variation of the FEAST algorithm that uses iterative Krylov subspace algorithms for solving the shifted linear systems inexactly. We show that this iterative FEAST algorithm (which we call IFEAST) is mathematically equivalent to a block Krylov subspace method for solving eigenvalue problems. By using Krylov subspaces indirectly through solving shifted linear systems, rather than directly for projecting the eigenvalue problem, IFEAST is able to solve eigenvalue problems using very large dimension Krylov subspaces, without ever having to store a basis for those subspaces. IFEAST thus combines the flexibility and power of Krylov methods, requiring only matrix-vector multiplication for solving eigenvalue problems, with the natural parallelism of the traditional FEAST algorithm. We discuss the relationship between IFEAST and more traditional Krylov methods, and provide numerical examples illustrating its behavior.

math.NA↗
arXiv2016

Enhancing the Performance and Robustness of the FEAST Eigensolver

The FEAST algorithm is a subspace iteration method that uses a spectral projector as a rational filter in order to efficiently solve interior eigenvalue problems in parallel. Although the solutions from the FEAST algorithm converge rapidly in many cases, convergence can be slow in situations where the eigenvalues of a matrix are densely populated near the edges of the search interval of interest, which can be detrimental to parallel load balancing. This work introduces two methods that allow one to improve the convergence robustness of the FEAST algorithm in these situations without having to increase the amount of computation. Selected numerical examples are presented and discussed

math.NA↗
arXiv2015

FEAST Eigenvalue Solver v3.0 User Guide

The FEAST eigensolver package is a free high-performance numerical library for solving the Hermitian and non-Hermitian eigenvalue problems, and obtaining all the eigenvalues and (right/left) eigenvectors within a given search interval or arbitrary contour in the complex plane. Its originality lies with a new transformative numerical approach to the traditional eigenvalue algorithm design - the FEAST algorithm. The FEAST eigensolver combines simplicity and efficiency and it offers many important capabilities for achieving high performance, robustness, accuracy, and scalability on parallel architectures. FEAST is both a comprehensive library package, and an easy to use software. It includes flexible reverse communication interfaces and ready to use predefined interfaces for dense, banded and sparse systems. The current version v3.0 of the FEAST package can address both Hermitian and non-Hermitian eigenvalue problems (real symmetric, real non-symmetric, complex Hermitian, complex symmetric, or complex general systems) on both shared-memory and distributed memory architectures (i.e contains both FEAST-SMP and FEAST-MPI packages). This User's guide provides instructions for installation setup, a detailed description of the FEAST interfaces and a large number of examples.

cs.MS↗
arXiv2015

FEAST Eigensolver for non-Hermitian Problems

A detailed new upgrade of the FEAST eigensolver targeting non-Hermitian eigenvalue problems is presented and thoroughly discussed. It aims at broadening the class of eigenproblems that can be addressed within the framework of the FEAST algorithm. The algorithm is ideally suited for computing selected interior eigenvalues and their associated right/left bi-orthogonal eigenvectors,located within a subset of the complex plane. It combines subspace iteration with efficient contour integration techniques that approximate the left and right spectral projectors. We discuss the various algorithmic choices that have been made to improve the stability and usability of the new non-Hermitian eigensolver. The latter retains the convergence property and multi-level parallelism of Hermitian FEAST, making it a valuable new software tool for the scientific community.

math.NA↗
arXiv2015

Universal nature of collective plasmonic excitations in finite 1-D carbon-based nanostructures

Tomonaga-Luttinger (T-L) theory predicts collective plasmon resonances in 1-D nanostructure conductors of finite length, that vary roughly in inverse proportion to the length of the structure. In-depth quantitative understanding of such resonances which have not been clearly identified in experiments so far, would be invaluable for future generations of nano-photonic and nano-electronic devices that employ 1-D conductors. Here we provide evidence of the plasmon resonances in a number of representative 1-D finite carbon-based nanostructures using first-principle computational electronic spectroscopy studies. Our special purpose real-space/real-time all-electron Time-Dependent Density-Functional Theory (TDDFT) simulator can perform excited-states calculations to obtain correct frequencies for known optical transitions, and capture various nanoscopic effects including collective plasmon excitations. The presence of 1-D plasmons is universally predicted by the various numerical experiments, which also demonstrate a phenomenon of resonance splitting. For the metallic carbon nanotubes under study, the plasmons are expected to be related to the T-L plasmons of infinitely long 1-D structures.

cond-mat.mtrl-sci↗
arXiv2014

Efficient estimation of eigenvalue counts in an interval

Estimating the number of eigenvalues located in a given interval of a large sparse Hermitian matrix is an important problem in certain applications and it is a prerequisite of eigensolvers based on a divide-and-conquer paradigm. Often an exact count is not necessary and methods based on stochastic estimates can be utilized to yield rough approximations. This paper examines a number of techniques tailored to this specific task. It reviews standard approaches and explores new ones based on polynomial and rational approximation filtering combined with a stochastic procedure.

math.NA↗
arXiv2014

Zolotarev Quadrature Rules and Load Balancing for the FEAST Eigensolver

The FEAST method for solving large sparse eigenproblems is equivalent to subspace iteration with an approximate spectral projector and implicit orthogonalization. This relation allows to characterize the convergence of this method in terms of the error of a certain rational approximant to an indicator function. We propose improved rational approximants leading to FEAST variants with faster convergence, in particular, when using rational approximants based on the work of Zolotarev. Numerical experiments demonstrate the possible computational savings especially for pencils whose eigenvalues are not well separated and when the dimension of the search space is only slightly larger than the number of wanted eigenvalues. The new approach improves both convergence robustness and load balancing when FEAST runs on multiple search intervals in parallel.

math.NA↗