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Approximate Solution Of Non Symmetric Generalized Eigenvalue Problems And Linear Matrix Equations On Hpc Platforms
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Book Synopsis Approximate Solution of Non-Symmetric Generalized Eigenvalue Problems and Linear Matrix Equations on HPC Platforms by : Martin K"ohler
Download or read book Approximate Solution of Non-Symmetric Generalized Eigenvalue Problems and Linear Matrix Equations on HPC Platforms written by Martin K"ohler and published by Logos Verlag Berlin GmbH. This book was released on 2022-01-18 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: The solution of the generalized eigenvalue problem is one of the computationally most challenging operations in the field of numerical linear algebra. A well known algorithm for this purpose is the QZ algorithm. Although it has been improved for decades and is available in many software packages by now, its performance is unsatisfying for medium and large scale problems on current computer architectures. In this thesis, a replacement for the QZ algorithm is developed. The design of the new spectral divide and conquer algorithms is oriented towards the capabilities of current computer architectures, including the support for accelerator devices. The thesis describes the co-design of the underlying mathematical ideas and the hardware aspects. Closely connected with the generalized eigenvalue value problem, the solution of Sylvester-like matrix equations is the concern of the second part of this work. Following the co-design approach, introduced in the first part of this thesis, a flexible framework covering (generalized) Sylvester, Lyapunov, and Stein equations is developed. The combination of the new algorithms for the generalized eigenvalue problem and the Sylvester-like equation solves problems within an hour, whose solution took several days incorporating the QZ and the Bartels-Stewart algorithm.
Book Synopsis Eigenvalue Algorithms for Symmetric Hierarchical Matrices by : Thomas Mach
Download or read book Eigenvalue Algorithms for Symmetric Hierarchical Matrices written by Thomas Mach and published by Thomas Mach. This book was released on 2012 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis is on the numerical computation of eigenvalues of symmetric hierarchical matrices. The numerical algorithms used for this computation are derivations of the LR Cholesky algorithm, the preconditioned inverse iteration, and a bisection method based on LDL factorizations. The investigation of QR decompositions for H-matrices leads to a new QR decomposition. It has some properties that are superior to the existing ones, which is shown by experiments using the HQR decompositions to build a QR (eigenvalue) algorithm for H-matrices does not progress to a more efficient algorithm than the LR Cholesky algorithm. The implementation of the LR Cholesky algorithm for hierarchical matrices together with deflation and shift strategies yields an algorithm that require O(n) iterations to find all eigenvalues. Unfortunately, the local ranks of the iterates show a strong growth in the first steps. These H-fill-ins makes the computation expensive, so that O(n³) flops and O(n²) storage are required. Theorem 4.3.1 explains this behavior and shows that the LR Cholesky algorithm is efficient for the simple structured Hl-matrices. There is an exact LDLT factorization for Hl-matrices and an approximate LDLT factorization for H-matrices in linear-polylogarithmic complexity. This factorizations can be used to compute the inertia of an H-matrix. With the knowledge of the inertia for arbitrary shifts, one can compute an eigenvalue by bisectioning. The slicing the spectrum algorithm can compute all eigenvalues of an Hl-matrix in linear-polylogarithmic complexity. A single eigenvalue can be computed in O(k²n log^4 n). Since the LDLT factorization for general H-matrices is only approximative, the accuracy of the LDLT slicing algorithm is limited. The local ranks of the LDLT factorization for indefinite matrices are generally unknown, so that there is no statement on the complexity of the algorithm besides the numerical results in Table 5.7. The preconditioned inverse iteration computes the smallest eigenvalue and the corresponding eigenvector. This method is efficient, since the number of iterations is independent of the matrix dimension. If other eigenvalues than the smallest are searched, then preconditioned inverse iteration can not be simply applied to the shifted matrix, since positive definiteness is necessary. The squared and shifted matrix (M-mu I)² is positive definite. Inner eigenvalues can be computed by the combination of folded spectrum method and PINVIT. Numerical experiments show that the approximate inversion of (M-mu I)² is more expensive than the approximate inversion of M, so that the computation of the inner eigenvalues is more expensive. We compare the different eigenvalue algorithms. The preconditioned inverse iteration for hierarchical matrices is better than the LDLT slicing algorithm for the computation of the smallest eigenvalues, especially if the inverse is already available. The computation of inner eigenvalues with the folded spectrum method and preconditioned inverse iteration is more expensive. The LDLT slicing algorithm is competitive to H-PINVIT for the computation of inner eigenvalues. In the case of large, sparse matrices, specially tailored algorithms for sparse matrices, like the MATLAB function eigs, are more efficient. If one wants to compute all eigenvalues, then the LDLT slicing algorithm seems to be better than the LR Cholesky algorithm. If the matrix is small enough to be handled in dense arithmetic (and is not an Hl(1)-matrix), then dense eigensolvers, like the LAPACK function dsyev, are superior. The H-PINVIT and the LDLT slicing algorithm require only an almost linear amount of storage. They can handle larger matrices than eigenvalue algorithms for dense matrices. For Hl-matrices of local rank 1, the LDLT slicing algorithm and the LR Cholesky algorithm need almost the same time for the computation of all eigenvalues. For large matrices, both algorithms are faster than the dense LAPACK function dsyev.
Book Synopsis Variational Methods for Eigenvalue Approximation by : H. F. Weinberger
Download or read book Variational Methods for Eigenvalue Approximation written by H. F. Weinberger and published by SIAM. This book was released on 1974-01-01 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides a common setting for various methods of bounding the eigenvalues of a self-adjoint linear operator and emphasizes their relationships. A mapping principle is presented to connect many of the methods. The eigenvalue problems studied are linear, and linearization is shown to give important information about nonlinear problems. Linear vector spaces and their properties are used to uniformly describe the eigenvalue problems presented that involve matrices, ordinary or partial differential operators, and integro-differential operators.
Book Synopsis Numerical Solution of Eigenvalue Problems by : Open University. Linear Mathematics Course Team
Download or read book Numerical Solution of Eigenvalue Problems written by Open University. Linear Mathematics Course Team and published by . This book was released on 1972 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis ARPACK Users' Guide by : Richard B. Lehoucq
Download or read book ARPACK Users' Guide written by Richard B. Lehoucq and published by SIAM. This book was released on 1998-01-01 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a guide to understanding and using the software package ARPACK to solve large algebraic eigenvalue problems. The software described is based on the implicitly restarted Arnoldi method, which has been heralded as one of the three most important advances in large scale eigenanalysis in the past ten years. The book explains the acquisition, installation, capabilities, and detailed use of the software for computing a desired subset of the eigenvalues and eigenvectors of large (sparse) standard or generalized eigenproblems. It also discusses the underlying theory and algorithmic background at a level that is accessible to the general practitioner.
Book Synopsis Large Scale Eigenvalue Problems by : J. Cullum
Download or read book Large Scale Eigenvalue Problems written by J. Cullum and published by Elsevier. This book was released on 1986-01-01 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Results of research into large scale eigenvalue problems are presented in this volume. The papers fall into four principal categories: novel algorithms for solving large eigenvalue problems, novel computer architectures, computationally-relevant theoretical analyses, and problems where large scale eigenelement computations have provided new insight.
Download or read book Computer & Control Abstracts written by and published by . This book was released on 1996 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Software for Exascale Computing - SPPEXA 2016-2019 by : Hans-Joachim Bungartz
Download or read book Software for Exascale Computing - SPPEXA 2016-2019 written by Hans-Joachim Bungartz and published by Springer Nature. This book was released on 2020-07-30 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest.
Book Synopsis Numerical Methods for Large Eigenvalue Problems by : Yousef Saad
Download or read book Numerical Methods for Large Eigenvalue Problems written by Yousef Saad and published by SIAM. This book was released on 2011-01-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This revised edition discusses numerical methods for computing eigenvalues and eigenvectors of large sparse matrices. It provides an in-depth view of the numerical methods that are applicable for solving matrix eigenvalue problems that arise in various engineering and scientific applications. Each chapter was updated by shortening or deleting outdated topics, adding topics of more recent interest, and adapting the Notes and References section. Significant changes have been made to Chapters 6 through 8, which describe algorithms and their implementations and now include topics such as the implicit restart techniques, the Jacobi-Davidson method, and automatic multilevel substructuring.
Book Synopsis Introduction to High Performance Scientific Computing by : Victor Eijkhout
Download or read book Introduction to High Performance Scientific Computing written by Victor Eijkhout and published by Lulu.com. This book was released on 2010 with total page 536 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a textbook that teaches the bridging topics between numerical analysis, parallel computing, code performance, large scale applications.
Book Synopsis Numerical Computations with GPUs by : Volodymyr Kindratenko
Download or read book Numerical Computations with GPUs written by Volodymyr Kindratenko and published by Springer. This book was released on 2014-07-03 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together research on numerical methods adapted for Graphics Processing Units (GPUs). It explains recent efforts to adapt classic numerical methods, including solution of linear equations and FFT, for massively parallel GPU architectures. This volume consolidates recent research and adaptations, covering widely used methods that are at the core of many scientific and engineering computations. Each chapter is written by authors working on a specific group of methods; these leading experts provide mathematical background, parallel algorithms and implementation details leading to reusable, adaptable and scalable code fragments. This book also serves as a GPU implementation manual for many numerical algorithms, sharing tips on GPUs that can increase application efficiency. The valuable insights into parallelization strategies for GPUs are supplemented by ready-to-use code fragments. Numerical Computations with GPUs targets professionals and researchers working in high performance computing and GPU programming. Advanced-level students focused on computer science and mathematics will also find this book useful as secondary text book or reference.
Book Synopsis Matrix Preconditioning Techniques and Applications by : Ke Chen
Download or read book Matrix Preconditioning Techniques and Applications written by Ke Chen and published by Cambridge University Press. This book was released on 2005-07-14 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to preconditioning techniques, now an essential part of successful and efficient iterative solutions of matrices.
Book Synopsis Solving PDEs in Python by : Hans Petter Langtangen
Download or read book Solving PDEs in Python written by Hans Petter Langtangen and published by Springer. This book was released on 2017-03-21 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a concise and gentle introduction to finite element programming in Python based on the popular FEniCS software library. Using a series of examples, including the Poisson equation, the equations of linear elasticity, the incompressible Navier–Stokes equations, and systems of nonlinear advection–diffusion–reaction equations, it guides readers through the essential steps to quickly solving a PDE in FEniCS, such as how to define a finite variational problem, how to set boundary conditions, how to solve linear and nonlinear systems, and how to visualize solutions and structure finite element Python programs. This book is open access under a CC BY license.
Book Synopsis Vector and Parallel Processing - VECPAR 2000 by : Jose M.L.M. Palma
Download or read book Vector and Parallel Processing - VECPAR 2000 written by Jose M.L.M. Palma and published by Springer. This book was released on 2003-06-29 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the ?nal outcome of VECPAR 2000 – 4th International Meeting on Vector and Parallel Processing. VECPAR constitutes a series of conferences, which have been organized by the Faculty of Engineering of the University of Porto since 1993, with the main objective of disseminating new knowledge on parallel computing. Readership of This Book The book is aimed at an audience of researchers and graduate students in a broad range of scienti?c areas, including not only computer science, but also applied mathematics and numerical analysis, physics, and engineering. Book Plan From a total of 66 papers selected on the basis of extended abstracts for p- sentation at the conference, a subset of 34 papers were chosen during a second review process leading to their inclusion in the book, together with the invited talks. The book contains a total of 40 papers organized into 6 chapters, where each may appeal to people in di?erent but still related scienti?c areas. All ch- ters, with the exception of Chapter 6, are initiated by a short text, providing a quick overview of the organization and papers in the chapter. The 13 papers in Chapter 1 cover the aspects related to the use of multiple processors. Operating systems, languages and software tools for scheduling, and code transformation are the topics included in this chapter, initiated by the talk on computing over the Internet, entitled Grid Computing,byIan Foster.
Book Synopsis A Tutorial on Elliptic PDE Solvers and Their Parallelization by : Craig C. Douglas
Download or read book A Tutorial on Elliptic PDE Solvers and Their Parallelization written by Craig C. Douglas and published by SIAM. This book was released on 2003-01-01 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt: This compact yet thorough tutorial is the perfect introduction to the basic concepts of solving partial differential equations (PDEs) using parallel numerical methods. In just eight short chapters, the authors provide readers with enough basic knowledge of PDEs, discretization methods, solution techniques, parallel computers, parallel programming, and the run-time behavior of parallel algorithms to allow them to understand, develop, and implement parallel PDE solvers. Examples throughout the book are intentionally kept simple so that the parallelization strategies are not dominated by technical details.
Book Synopsis Fractional Dynamics by : Joseph Klafter
Download or read book Fractional Dynamics written by Joseph Klafter and published by World Scientific. This book was released on 2011-10-14 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume provides the latest developments in the field of fractional dynamics, which covers fractional (anomalous) transport phenomena, fractional statistical mechanics, fractional quantum mechanics and fractional quantum field theory. The contributors are selected based on their active and important contributions to their respective topics. This volume is the first of its kind that covers such a comprehensive range of topics in fractional dynamics. It will point out to advanced undergraduate and graduate students, and young researchers the possible directions of research in this subject. In addition to those who intend to work in this field and those already in the field, this volume will also be useful for researchers not directly involved in the field, but want to know the current status and trends of development in this subject. This latter group includes theoretical chemists, mathematical biologists and engineers. Contents:Classical Systems:Anomalous Diffusion and Fractional Transport Equations (R Metzler and J-H Jeon)Stochastic Diffusion and Stable Noise-Induced Phenomena (B Dybiec and E Gudowska-Nowak)Characteristic Times of Anomalous Diffusion in a Potential (W T Coffey, Y P Kalmykov and S V Titov)Reactions in Subdiffusive Media and Associated Fractional Equations (S B Yuste, E Abad and K Lindenberg)Natural and Modified Forms of Distributed-Order Fractional Diffusion Equations (A Chechkin, I M Sokolov and J Klafter)Anomalous Transport in the Presence of Truncated Lévy Flights (D del-Castillo-Negrete)Anomalous Diffusion: From Fractional Master Equations to Path Integrals (R Friedrich)Fractional Feynman–Kac Equation for Anomalous Diffusion Functionals (S Carmi and E Barkai)Foundations of Fractional Dynamics: A Short Account (R Hilfer)Parametric Subordination in Fractional Diffusion Processes (R Gorenflo and F Mainardi)Fractional Calculus, Anomalous Diffusion, and Probability (M M Meerschaert)Fractional Langevin Equation (E Lutz)Subdiffusive Dynamics in Washboard Potentials: Two Different Approaches and Different Univesality Classes (I Goychuk and P Hänggi)Identification and Validation of Fractional Subdiffusion Dynamics (K Burnecki, M Magdziarz and A Weron)A Class of CTRWs: Compound Fractional Poisson Processes (E Scalas)Origin of Allometry Hypothesis (B J West and D West)Quantum Systems:Principles of Fractional Quantum Mechanics (N Laskin)Two Examples of Fractional Quantum Dynamics (A Iomin)Fractional Dynamics of Open Quantum Systems (V E Tarasov)Casimir Effect Associated with Fractional Klein–Gordon Field (S C Lim and L P Teo) Readership: Researchers in mathematical physics. Keywords:Fractional Dynamics;Ergodicity;Bifractional Diffusion Process;FokkerâPlanck
Book Synopsis Large-Scale Scientific Computing by : Ivan Lirkov
Download or read book Large-Scale Scientific Computing written by Ivan Lirkov and published by Springer. This book was released on 2006-02-15 with total page 704 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 5th International Conference on Large-Scale Scientific Computations, LSSC 2005, held in Sozopol, Bulgaria in June 2005. The 75 revised full papers presented together with five invited papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections.
