A mathematicians way into STRONGnet

Size: px
Start display at page:

Download "A mathematicians way into STRONGnet"

Transcription

1 A mathematicians way into STRONGnet Nemanja Bozovic Bergische Universität Wuppertal My work is funded by STRONGnet

2 Career so far Nemanja Božović, born on , from Serbia Studied at the University of Novi Sad, Faculty of Sciences, Department of Mathematics and Informatics ( ) Diploma in financial mathematics ( ) Ph.D. student in Novi Sad ( ) Currently, I am a Ph.D. student at the University of Wuppertal (2010-) A mathematicians way into STRONGnet, Nemanja Bozovic 2/13

3 Experiences Work at a German university Part of a research group, good social contact with other fellow Ph.D. scientists Contacts at training events, building a network Bielefeld Summer School; Lattice2011, Squaw Valley, 2011 A look at possible careers in industry (Bielefeld Summer School) Interdisciplinary research Participation at scientific conferences (GAMM Annual Meeting, Graz 2011; ICIAM2011, Vancouver; GAMM Workshop, Bremen 2011) Participation at workshops of the Wuppertal-Regensburg collaboration (SFB-TR 55) A mathematicians way into STRONGnet, Nemanja Bozovic 3/13

4 Get a Ph.D. Further international experience (plan: a few months in Dublin under supervision of Mike Peardon) Helps for a possible career in academia (in Serbia, for example) University systems International network of colleagues Know more about alternatives to the academic career Cooperation with Wuppertal group beyond the end of STRONGnet Improve communication skills in science Gain training experience in English Learn German A mathematicians way into STRONGnet, Nemanja Bozovic 4/13

5 A little bit about my work Working group WG3, University of Wuppertal Algorithmic Innovation A mathematicians way into STRONGnet, Nemanja Bozovic 5/13

6 Restarted GCRO-DR Hybrid Monte Carlo in Lattice QCD Monte Carlo method combined with molecular dynamics Time evolution leads to next configuration in the canonical ensemble Discretized Dirac operator A (i) only changes slightly in each time step Situation: Solve a long sequence of linear systems where A (i) x (i) = b (i), i = 1, 2,... A (i) C n n, b (i) C n change slightly from one system to the next A mathematicians way into STRONGnet, Nemanja Bozovic 6/13

7 Restarted GCRO-DR Krylov subspace methods generate iterates from { } K k (A, b) = span b, Ab,..., A k 1 b minimizes residual norm b Ax for x x 0 + K m While building the Krylov subspace we obtain the Arnoldi relation AV m = V m+1 H m b Ax =... = βe 1 H m y Then we obtain the solution x m = x 0 + V m y m where y m = argmin y βe 1 H m y A mathematicians way into STRONGnet, Nemanja Bozovic 7/13

8 Restarted GCRO-DR Restarted If we are working with systems of large dimensions, is not feasible We have too many vectors to store The total cost of orthogonalization in the Arnoldi process grows We fix m to some value and perform several cycles of Downsides Degradation of the convergence behavior The algorithm can even stagnate A mathematicians way into STRONGnet, Nemanja Bozovic 8/13

9 Restarted GCRO-DR Components of a residual colinear to eigenvectors corresponding to smallest eigenvalues have a big influence on error e = A 1 r We tend to chose augmenting space such that it contains approximations of these eigenvectors This can be done from the Krylov subspace built up in a previous cycle using Harmonic Ritz vectors Harmonic Ritz pair (λ, υ), λ C, υ K, satisfies Aυ λυ AK A mathematicians way into STRONGnet, Nemanja Bozovic 9/13

10 Restarted GCRO-DR GCROT Restarted ignores orthogonality to the discarded space after we restart GCROT retains a subspace between cycles such that the loss of orthogonality with respect to the discarded space is minimized This process is called optimal truncation It maintains matrices U k, C k C n k satisfying the relations AU k = C k C H k C k = I k. A mathematicians way into STRONGnet, Nemanja Bozovic 10/13

11 Restarted GCRO-DR GCROT can be modified to solve a sequence of linear systems by carrying U k to the next system and modifying U k and C k as follows: [Q, R] = reduced QR decomposition of A (i+1) Uk old Ck new = Q Uk new = Uk oldr 1. GCRO-DR combines two ideas of -DR and GCROT A mathematicians way into STRONGnet, Nemanja Bozovic 11/13

12 Preliminary results Preliminary results Wilson discretization of the Dirac operator on a 8 4 lattice, m = 40, k = 20 Restarted GCRO-DR first system second system third system nineth system tenth system eleventh system A mathematicians way into STRONGnet, Nemanja Bozovic 12/13

13 Thank you for your attention A mathematicians way into STRONGnet, Nemanja Bozovic 13/13

arxiv: v1 [hep-lat] 2 May 2012

arxiv: v1 [hep-lat] 2 May 2012 A CG Method for Multiple Right Hand Sides and Multiple Shifts in Lattice QCD Calculations arxiv:1205.0359v1 [hep-lat] 2 May 2012 Fachbereich C, Mathematik und Naturwissenschaften, Bergische Universität

More information

A Jacobi Davidson Method with a Multigrid Solver for the Hermitian Wilson-Dirac Operator

A Jacobi Davidson Method with a Multigrid Solver for the Hermitian Wilson-Dirac Operator A Jacobi Davidson Method with a Multigrid Solver for the Hermitian Wilson-Dirac Operator Artur Strebel Bergische Universität Wuppertal August 3, 2016 Joint Work This project is joint work with: Gunnar

More information

Last Time. Social Network Graphs Betweenness. Graph Laplacian. Girvan-Newman Algorithm. Spectral Bisection

Last Time. Social Network Graphs Betweenness. Graph Laplacian. Girvan-Newman Algorithm. Spectral Bisection Eigenvalue Problems Last Time Social Network Graphs Betweenness Girvan-Newman Algorithm Graph Laplacian Spectral Bisection λ 2, w 2 Today Small deviation into eigenvalue problems Formulation Standard eigenvalue

More information

Algorithms that use the Arnoldi Basis

Algorithms that use the Arnoldi Basis AMSC 600 /CMSC 760 Advanced Linear Numerical Analysis Fall 2007 Arnoldi Methods Dianne P. O Leary c 2006, 2007 Algorithms that use the Arnoldi Basis Reference: Chapter 6 of Saad The Arnoldi Basis How to

More information

Recycling Bi-Lanczos Algorithms: BiCG, CGS, and BiCGSTAB

Recycling Bi-Lanczos Algorithms: BiCG, CGS, and BiCGSTAB Recycling Bi-Lanczos Algorithms: BiCG, CGS, and BiCGSTAB Kapil Ahuja Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements

More information

AMS526: Numerical Analysis I (Numerical Linear Algebra)

AMS526: Numerical Analysis I (Numerical Linear Algebra) AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 23: GMRES and Other Krylov Subspace Methods Xiangmin Jiao SUNY Stony Brook Xiangmin Jiao Numerical Analysis I 1 / 9 Minimizing Residual CG

More information

Final Exam, Linear Algebra, Fall, 2003, W. Stephen Wilson

Final Exam, Linear Algebra, Fall, 2003, W. Stephen Wilson Final Exam, Linear Algebra, Fall, 2003, W. Stephen Wilson Name: TA Name and section: NO CALCULATORS, SHOW ALL WORK, NO OTHER PAPERS ON DESK. There is very little actual work to be done on this exam if

More information

The Lanczos and conjugate gradient algorithms

The Lanczos and conjugate gradient algorithms The Lanczos and conjugate gradient algorithms Gérard MEURANT October, 2008 1 The Lanczos algorithm 2 The Lanczos algorithm in finite precision 3 The nonsymmetric Lanczos algorithm 4 The Golub Kahan bidiagonalization

More information

Eigenvalue problems III: Advanced Numerical Methods

Eigenvalue problems III: Advanced Numerical Methods Eigenvalue problems III: Advanced Numerical Methods Sam Sinayoko Computational Methods 10 Contents 1 Learning Outcomes 2 2 Introduction 2 3 Inverse Power method: finding the smallest eigenvalue of a symmetric

More information

Comparing iterative methods to compute the overlap Dirac operator at nonzero chemical potential

Comparing iterative methods to compute the overlap Dirac operator at nonzero chemical potential Comparing iterative methods to compute the overlap Dirac operator at nonzero chemical potential, Tobias Breu, and Tilo Wettig Institute for Theoretical Physics, University of Regensburg, 93040 Regensburg,

More information

Krylov subspace projection methods

Krylov subspace projection methods I.1.(a) Krylov subspace projection methods Orthogonal projection technique : framework Let A be an n n complex matrix and K be an m-dimensional subspace of C n. An orthogonal projection technique seeks

More information

On the Ritz values of normal matrices

On the Ritz values of normal matrices On the Ritz values of normal matrices Zvonimir Bujanović Faculty of Science Department of Mathematics University of Zagreb June 13, 2011 ApplMath11 7th Conference on Applied Mathematics and Scientific

More information

Krylov Space Methods. Nonstationary sounds good. Radu Trîmbiţaş ( Babeş-Bolyai University) Krylov Space Methods 1 / 17

Krylov Space Methods. Nonstationary sounds good. Radu Trîmbiţaş ( Babeş-Bolyai University) Krylov Space Methods 1 / 17 Krylov Space Methods Nonstationary sounds good Radu Trîmbiţaş Babeş-Bolyai University Radu Trîmbiţaş ( Babeş-Bolyai University) Krylov Space Methods 1 / 17 Introduction These methods are used both to solve

More information

A refined Lanczos method for computing eigenvalues and eigenvectors of unsymmetric matrices

A refined Lanczos method for computing eigenvalues and eigenvectors of unsymmetric matrices A refined Lanczos method for computing eigenvalues and eigenvectors of unsymmetric matrices Jean Christophe Tremblay and Tucker Carrington Chemistry Department Queen s University 23 août 2007 We want to

More information

SF Matrix computations for large-scale systems. Numerical linear algebra for large-scale systems

SF Matrix computations for large-scale systems. Numerical linear algebra for large-scale systems SF2524 - Matrix computations for large-scale systems Numerical linear algebra for large-scale systems Intro lecture, October 31, 2017 KTH Royal Institute of Technology Mathematics Dept. - NA division 1/23

More information

1 Number Systems and Errors 1

1 Number Systems and Errors 1 Contents 1 Number Systems and Errors 1 1.1 Introduction................................ 1 1.2 Number Representation and Base of Numbers............. 1 1.2.1 Normalized Floating-point Representation...........

More information

ANY FINITE CONVERGENCE CURVE IS POSSIBLE IN THE INITIAL ITERATIONS OF RESTARTED FOM

ANY FINITE CONVERGENCE CURVE IS POSSIBLE IN THE INITIAL ITERATIONS OF RESTARTED FOM Electronic Transactions on Numerical Analysis. Volume 45, pp. 133 145, 2016. Copyright c 2016,. ISSN 1068 9613. ETNA ANY FINITE CONVERGENCE CURVE IS POSSIBLE IN THE INITIAL ITERATIONS OF RESTARTED FOM

More information

MULTIGRID ARNOLDI FOR EIGENVALUES

MULTIGRID ARNOLDI FOR EIGENVALUES 1 MULTIGRID ARNOLDI FOR EIGENVALUES RONALD B. MORGAN AND ZHAO YANG Abstract. A new approach is given for computing eigenvalues and eigenvectors of large matrices. Multigrid is combined with the Arnoldi

More information

Introduction to Arnoldi method

Introduction to Arnoldi method Introduction to Arnoldi method SF2524 - Matrix Computations for Large-scale Systems KTH Royal Institute of Technology (Elias Jarlebring) 2014-11-07 KTH Royal Institute of Technology (Elias Jarlebring)Introduction

More information

Deflation for inversion with multiple right-hand sides in QCD

Deflation for inversion with multiple right-hand sides in QCD Deflation for inversion with multiple right-hand sides in QCD A Stathopoulos 1, A M Abdel-Rehim 1 and K Orginos 2 1 Department of Computer Science, College of William and Mary, Williamsburg, VA 23187 2

More information

4.8 Arnoldi Iteration, Krylov Subspaces and GMRES

4.8 Arnoldi Iteration, Krylov Subspaces and GMRES 48 Arnoldi Iteration, Krylov Subspaces and GMRES We start with the problem of using a similarity transformation to convert an n n matrix A to upper Hessenberg form H, ie, A = QHQ, (30) with an appropriate

More information

MATH 304 Linear Algebra Lecture 20: The Gram-Schmidt process (continued). Eigenvalues and eigenvectors.

MATH 304 Linear Algebra Lecture 20: The Gram-Schmidt process (continued). Eigenvalues and eigenvectors. MATH 304 Linear Algebra Lecture 20: The Gram-Schmidt process (continued). Eigenvalues and eigenvectors. Orthogonal sets Let V be a vector space with an inner product. Definition. Nonzero vectors v 1,v

More information

Alternative correction equations in the Jacobi-Davidson method

Alternative correction equations in the Jacobi-Davidson method Chapter 2 Alternative correction equations in the Jacobi-Davidson method Menno Genseberger and Gerard Sleijpen Abstract The correction equation in the Jacobi-Davidson method is effective in a subspace

More information

Matrix Algorithms. Volume II: Eigensystems. G. W. Stewart H1HJ1L. University of Maryland College Park, Maryland

Matrix Algorithms. Volume II: Eigensystems. G. W. Stewart H1HJ1L. University of Maryland College Park, Maryland Matrix Algorithms Volume II: Eigensystems G. W. Stewart University of Maryland College Park, Maryland H1HJ1L Society for Industrial and Applied Mathematics Philadelphia CONTENTS Algorithms Preface xv xvii

More information

Eigenvalue Problems CHAPTER 1 : PRELIMINARIES

Eigenvalue Problems CHAPTER 1 : PRELIMINARIES Eigenvalue Problems CHAPTER 1 : PRELIMINARIES Heinrich Voss voss@tu-harburg.de Hamburg University of Technology Institute of Mathematics TUHH Heinrich Voss Preliminaries Eigenvalue problems 2012 1 / 14

More information

Summary of Iterative Methods for Non-symmetric Linear Equations That Are Related to the Conjugate Gradient (CG) Method

Summary of Iterative Methods for Non-symmetric Linear Equations That Are Related to the Conjugate Gradient (CG) Method Summary of Iterative Methods for Non-symmetric Linear Equations That Are Related to the Conjugate Gradient (CG) Method Leslie Foster 11-5-2012 We will discuss the FOM (full orthogonalization method), CG,

More information

Iterative methods for symmetric eigenvalue problems

Iterative methods for symmetric eigenvalue problems s Iterative s for symmetric eigenvalue problems, PhD McMaster University School of Computational Engineering and Science February 11, 2008 s 1 The power and its variants Inverse power Rayleigh quotient

More information

APPLIED NUMERICAL LINEAR ALGEBRA

APPLIED NUMERICAL LINEAR ALGEBRA APPLIED NUMERICAL LINEAR ALGEBRA James W. Demmel University of California Berkeley, California Society for Industrial and Applied Mathematics Philadelphia Contents Preface 1 Introduction 1 1.1 Basic Notation

More information

Rational Krylov methods for linear and nonlinear eigenvalue problems

Rational Krylov methods for linear and nonlinear eigenvalue problems Rational Krylov methods for linear and nonlinear eigenvalue problems Mele Giampaolo mele@mail.dm.unipi.it University of Pisa 7 March 2014 Outline Arnoldi (and its variants) for linear eigenproblems Rational

More information

On prescribing Ritz values and GMRES residual norms generated by Arnoldi processes

On prescribing Ritz values and GMRES residual norms generated by Arnoldi processes On prescribing Ritz values and GMRES residual norms generated by Arnoldi processes Jurjen Duintjer Tebbens Institute of Computer Science Academy of Sciences of the Czech Republic joint work with Gérard

More information

Simple iteration procedure

Simple iteration procedure Simple iteration procedure Solve Known approximate solution Preconditionning: Jacobi Gauss-Seidel Lower triangle residue use of pre-conditionner correction residue use of pre-conditionner Convergence Spectral

More information

Introduction to Applied Linear Algebra with MATLAB

Introduction to Applied Linear Algebra with MATLAB Sigam Series in Applied Mathematics Volume 7 Rizwan Butt Introduction to Applied Linear Algebra with MATLAB Heldermann Verlag Contents Number Systems and Errors 1 1.1 Introduction 1 1.2 Number Representation

More information

Charles University Faculty of Mathematics and Physics DOCTORAL THESIS. Krylov subspace approximations in linear algebraic problems

Charles University Faculty of Mathematics and Physics DOCTORAL THESIS. Krylov subspace approximations in linear algebraic problems Charles University Faculty of Mathematics and Physics DOCTORAL THESIS Iveta Hnětynková Krylov subspace approximations in linear algebraic problems Department of Numerical Mathematics Supervisor: Doc. RNDr.

More information

Accelerated Solvers for CFD

Accelerated Solvers for CFD Accelerated Solvers for CFD Co-Design of Hardware/Software for Predicting MAV Aerodynamics Eric de Sturler, Virginia Tech Mathematics Email: sturler@vt.edu Web: http://www.math.vt.edu/people/sturler Co-design

More information

Deflation and augmentation techniques in Krylov subspace methods for the solution of linear systems

Deflation and augmentation techniques in Krylov subspace methods for the solution of linear systems arxiv:1303.5692v1 [math.na] 21 Mar 2013 Deflation and augmentation techniques in Krylov subspace methods for the solution of linear systems Olivier Coulaud, Luc Giraud, Pierre Ramet, Xavier Vasseur RESEARCH

More information

Computation of eigenvalues and singular values Recall that your solutions to these questions will not be collected or evaluated.

Computation of eigenvalues and singular values Recall that your solutions to these questions will not be collected or evaluated. Math 504, Homework 5 Computation of eigenvalues and singular values Recall that your solutions to these questions will not be collected or evaluated 1 Find the eigenvalues and the associated eigenspaces

More information

A HARMONIC RESTARTED ARNOLDI ALGORITHM FOR CALCULATING EIGENVALUES AND DETERMINING MULTIPLICITY

A HARMONIC RESTARTED ARNOLDI ALGORITHM FOR CALCULATING EIGENVALUES AND DETERMINING MULTIPLICITY A HARMONIC RESTARTED ARNOLDI ALGORITHM FOR CALCULATING EIGENVALUES AND DETERMINING MULTIPLICITY RONALD B. MORGAN AND MIN ZENG Abstract. A restarted Arnoldi algorithm is given that computes eigenvalues

More information

Principal Components Analysis (PCA)

Principal Components Analysis (PCA) Principal Components Analysis (PCA) Principal Components Analysis (PCA) a technique for finding patterns in data of high dimension Outline:. Eigenvectors and eigenvalues. PCA: a) Getting the data b) Centering

More information

On the influence of eigenvalues on Bi-CG residual norms

On the influence of eigenvalues on Bi-CG residual norms On the influence of eigenvalues on Bi-CG residual norms Jurjen Duintjer Tebbens Institute of Computer Science Academy of Sciences of the Czech Republic duintjertebbens@cs.cas.cz Gérard Meurant 30, rue

More information

Preconditioned inverse iteration and shift-invert Arnoldi method

Preconditioned inverse iteration and shift-invert Arnoldi method Preconditioned inverse iteration and shift-invert Arnoldi method Melina Freitag Department of Mathematical Sciences University of Bath CSC Seminar Max-Planck-Institute for Dynamics of Complex Technical

More information

THE RADAU LANCZOS METHOD FOR MATRIX FUNCTIONS

THE RADAU LANCZOS METHOD FOR MATRIX FUNCTIONS SIAM J. MATRIX ANAL. APPL. Vol. 38, No. 3, pp. 71 732 c 217 Society for Industrial and Applied Mathematics THE RADAU LANCZOS METHOD FOR MATRIX FUNCTIONS ANDREAS FROMMER, KATHRYN LUND, MARCEL SCHWEITZER,

More information

Krylov Subspaces. Lab 1. The Arnoldi Iteration

Krylov Subspaces. Lab 1. The Arnoldi Iteration Lab 1 Krylov Subspaces Lab Objective: Discuss simple Krylov Subspace Methods for finding eigenvalues and show some interesting applications. One of the biggest difficulties in computational linear algebra

More information

Postgraduate studies at the Biozentrum.

Postgraduate studies at the Biozentrum. Postgraduate studies at the Biozentrum. PhD Students & Postdocs. Young talent at the forefront of life science research. With its interdisciplinary PhD program, the Biozentrum offers Master graduates an

More information

CS4495/6495 Introduction to Computer Vision. 8B-L2 Principle Component Analysis (and its use in Computer Vision)

CS4495/6495 Introduction to Computer Vision. 8B-L2 Principle Component Analysis (and its use in Computer Vision) CS4495/6495 Introduction to Computer Vision 8B-L2 Principle Component Analysis (and its use in Computer Vision) Wavelength 2 Wavelength 2 Principal Components Principal components are all about the directions

More information

Hessenberg eigenvalue eigenvector relations and their application to the error analysis of finite precision Krylov subspace methods

Hessenberg eigenvalue eigenvector relations and their application to the error analysis of finite precision Krylov subspace methods Hessenberg eigenvalue eigenvector relations and their application to the error analysis of finite precision Krylov subspace methods Jens Peter M. Zemke Minisymposium on Numerical Linear Algebra Technical

More information

A Newton-Galerkin-ADI Method for Large-Scale Algebraic Riccati Equations

A Newton-Galerkin-ADI Method for Large-Scale Algebraic Riccati Equations A Newton-Galerkin-ADI Method for Large-Scale Algebraic Riccati Equations Peter Benner Max-Planck-Institute for Dynamics of Complex Technical Systems Computational Methods in Systems and Control Theory

More information

Domain decomposition on different levels of the Jacobi-Davidson method

Domain decomposition on different levels of the Jacobi-Davidson method hapter 5 Domain decomposition on different levels of the Jacobi-Davidson method Abstract Most computational work of Jacobi-Davidson [46], an iterative method suitable for computing solutions of large dimensional

More information

CRYSTALLOGRAPHY AND STORYTELLING WITH DATA. President, Association of Women in Science, Bethesda Chapter STEM Consultant

CRYSTALLOGRAPHY AND STORYTELLING WITH DATA. President, Association of Women in Science, Bethesda Chapter STEM Consultant CRYSTALLOGRAPHY AND STORYTELLING WITH DATA President, Association of Women in Science, Bethesda Chapter STEM Consultant MY STORY Passion for Science BS Biology Major MS Biotechnology & Project in Bioinformatics

More information

DELFT UNIVERSITY OF TECHNOLOGY

DELFT UNIVERSITY OF TECHNOLOGY DELFT UNIVERSITY OF TECHNOLOGY REPORT 14-1 Nested Krylov methods for shifted linear systems M. Baumann and M. B. van Gizen ISSN 1389-652 Reports of the Delft Institute of Applied Mathematics Delft 214

More information

AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 23: GMRES and Other Krylov Subspace Methods; Preconditioning

AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 23: GMRES and Other Krylov Subspace Methods; Preconditioning AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 23: GMRES and Other Krylov Subspace Methods; Preconditioning Xiangmin Jiao SUNY Stony Brook Xiangmin Jiao Numerical Analysis I 1 / 18 Outline

More information

A Comparison of Adaptive Algebraic Multigrid and Lüscher s Inexact Deflation

A Comparison of Adaptive Algebraic Multigrid and Lüscher s Inexact Deflation A Comparison of Adaptive Algebraic Multigrid and Lüscher s Inexact Deflation Andreas Frommer, Karsten Kahl, Stefan Krieg, Björn Leder and Matthias Rottmann Bergische Universität Wuppertal April 11, 2013

More information

Arnoldi Methods in SLEPc

Arnoldi Methods in SLEPc Scalable Library for Eigenvalue Problem Computations SLEPc Technical Report STR-4 Available at http://slepc.upv.es Arnoldi Methods in SLEPc V. Hernández J. E. Román A. Tomás V. Vidal Last update: October,

More information

PROJECTED GMRES AND ITS VARIANTS

PROJECTED GMRES AND ITS VARIANTS PROJECTED GMRES AND ITS VARIANTS Reinaldo Astudillo Brígida Molina rastudillo@kuaimare.ciens.ucv.ve bmolina@kuaimare.ciens.ucv.ve Centro de Cálculo Científico y Tecnológico (CCCT), Facultad de Ciencias,

More information

Linear Combination. v = a 1 v 1 + a 2 v a k v k

Linear Combination. v = a 1 v 1 + a 2 v a k v k Linear Combination Definition 1 Given a set of vectors {v 1, v 2,..., v k } in a vector space V, any vector of the form v = a 1 v 1 + a 2 v 2 +... + a k v k for some scalars a 1, a 2,..., a k, is called

More information

Course Notes: Week 1

Course Notes: Week 1 Course Notes: Week 1 Math 270C: Applied Numerical Linear Algebra 1 Lecture 1: Introduction (3/28/11) We will focus on iterative methods for solving linear systems of equations (and some discussion of eigenvalues

More information

Iterative Methods for Linear Systems of Equations

Iterative Methods for Linear Systems of Equations Iterative Methods for Linear Systems of Equations Projection methods (3) ITMAN PhD-course DTU 20-10-08 till 24-10-08 Martin van Gijzen 1 Delft University of Technology Overview day 4 Bi-Lanczos method

More information

ECS231 Handout Subspace projection methods for Solving Large-Scale Eigenvalue Problems. Part I: Review of basic theory of eigenvalue problems

ECS231 Handout Subspace projection methods for Solving Large-Scale Eigenvalue Problems. Part I: Review of basic theory of eigenvalue problems ECS231 Handout Subspace projection methods for Solving Large-Scale Eigenvalue Problems Part I: Review of basic theory of eigenvalue problems 1. Let A C n n. (a) A scalar λ is an eigenvalue of an n n A

More information

Alternative correction equations in the Jacobi-Davidson method. Mathematical Institute. Menno Genseberger and Gerard L. G.

Alternative correction equations in the Jacobi-Davidson method. Mathematical Institute. Menno Genseberger and Gerard L. G. Universiteit-Utrecht * Mathematical Institute Alternative correction equations in the Jacobi-Davidson method by Menno Genseberger and Gerard L. G. Sleijpen Preprint No. 173 June 1998, revised: March 1999

More information

IDR(s) Master s thesis Goushani Kisoensingh. Supervisor: Gerard L.G. Sleijpen Department of Mathematics Universiteit Utrecht

IDR(s) Master s thesis Goushani Kisoensingh. Supervisor: Gerard L.G. Sleijpen Department of Mathematics Universiteit Utrecht IDR(s) Master s thesis Goushani Kisoensingh Supervisor: Gerard L.G. Sleijpen Department of Mathematics Universiteit Utrecht Contents 1 Introduction 2 2 The background of Bi-CGSTAB 3 3 IDR(s) 4 3.1 IDR.............................................

More information

Bindel, Fall 2016 Matrix Computations (CS 6210) Notes for

Bindel, Fall 2016 Matrix Computations (CS 6210) Notes for 1 Arnoldi Notes for 2016-11-16 Krylov subspaces are good spaces for approximation schemes. But the power basis (i.e. the basis A j b for j = 0,..., k 1) is not good for numerical work. The vectors in the

More information

Introduction to Iterative Solvers of Linear Systems

Introduction to Iterative Solvers of Linear Systems Introduction to Iterative Solvers of Linear Systems SFB Training Event January 2012 Prof. Dr. Andreas Frommer Typeset by Lukas Krämer, Simon-Wolfgang Mages and Rudolf Rödl 1 Classes of Matrices and their

More information

6.4 Krylov Subspaces and Conjugate Gradients

6.4 Krylov Subspaces and Conjugate Gradients 6.4 Krylov Subspaces and Conjugate Gradients Our original equation is Ax = b. The preconditioned equation is P Ax = P b. When we write P, we never intend that an inverse will be explicitly computed. P

More information

Krylov subspace methods for linear systems with tensor product structure

Krylov subspace methods for linear systems with tensor product structure Krylov subspace methods for linear systems with tensor product structure Christine Tobler Seminar for Applied Mathematics, ETH Zürich 19. August 2009 Outline 1 Introduction 2 Basic Algorithm 3 Convergence

More information

Krylov Subspace Methods to Calculate PageRank

Krylov Subspace Methods to Calculate PageRank Krylov Subspace Methods to Calculate PageRank B. Vadala-Roth REU Final Presentation August 1st, 2013 How does Google Rank Web Pages? The Web The Graph (A) Ranks of Web pages v = v 1... Dominant Eigenvector

More information

FEM and sparse linear system solving

FEM and sparse linear system solving FEM & sparse linear system solving, Lecture 9, Nov 19, 2017 1/36 Lecture 9, Nov 17, 2017: Krylov space methods http://people.inf.ethz.ch/arbenz/fem17 Peter Arbenz Computer Science Department, ETH Zürich

More information

arxiv: v2 [math.na] 1 Feb 2013

arxiv: v2 [math.na] 1 Feb 2013 A FRAMEWORK FOR DEFLATED AND AUGMENTED KRYLOV SUBSPACE METHODS ANDRÉ GAUL, MARTIN H. GUTKNECHT, JÖRG LIESEN AND REINHARD NABBEN arxiv:1206.1506v2 [math.na] 1 Feb 2013 Abstract. We consider deflation and

More information

Inexact and Nonlinear Extensions of the FEAST Eigenvalue Algorithm

Inexact and Nonlinear Extensions of the FEAST Eigenvalue Algorithm University of Massachusetts Amherst ScholarWorks@UMass Amherst Doctoral Dissertations Dissertations and Theses 2018 Inexact and Nonlinear Extensions of the FEAST Eigenvalue Algorithm Brendan E. Gavin University

More information

Syllabus for the course «Linear Algebra» (Линейная алгебра)

Syllabus for the course «Linear Algebra» (Линейная алгебра) Government of Russian Federation Federal State Autonomous Educational Institution of High Professional Education «National Research University Higher School of Economics» National Research University High

More information

Quantum Physics II (8.05) Fall 2002 Assignment 3

Quantum Physics II (8.05) Fall 2002 Assignment 3 Quantum Physics II (8.05) Fall 00 Assignment Readings The readings below will take you through the material for Problem Sets and 4. Cohen-Tannoudji Ch. II, III. Shankar Ch. 1 continues to be helpful. Sakurai

More information

LINEAR ALGEBRA: NUMERICAL METHODS. Version: August 12,

LINEAR ALGEBRA: NUMERICAL METHODS. Version: August 12, LINEAR ALGEBRA: NUMERICAL METHODS. Version: August 12, 2000 74 6 Summary Here we summarize the most important information about theoretical and numerical linear algebra. MORALS OF THE STORY: I. Theoretically

More information

Index. for generalized eigenvalue problem, butterfly form, 211

Index. for generalized eigenvalue problem, butterfly form, 211 Index ad hoc shifts, 165 aggressive early deflation, 205 207 algebraic multiplicity, 35 algebraic Riccati equation, 100 Arnoldi process, 372 block, 418 Hamiltonian skew symmetric, 420 implicitly restarted,

More information

ABSTRACT. Professor G.W. Stewart

ABSTRACT. Professor G.W. Stewart ABSTRACT Title of dissertation: Residual Arnoldi Methods : Theory, Package, and Experiments Che-Rung Lee, Doctor of Philosophy, 2007 Dissertation directed by: Professor G.W. Stewart Department of Computer

More information

Adiabatic quantum computation a tutorial for computer scientists

Adiabatic quantum computation a tutorial for computer scientists Adiabatic quantum computation a tutorial for computer scientists Itay Hen Dept. of Physics, UCSC Advanced Machine Learning class UCSC June 6 th 2012 Outline introduction I: what is a quantum computer?

More information

Solution of eigenvalue problems. Subspace iteration, The symmetric Lanczos algorithm. Harmonic Ritz values, Jacobi-Davidson s method

Solution of eigenvalue problems. Subspace iteration, The symmetric Lanczos algorithm. Harmonic Ritz values, Jacobi-Davidson s method Solution of eigenvalue problems Introduction motivation Projection methods for eigenvalue problems Subspace iteration, The symmetric Lanczos algorithm Nonsymmetric Lanczos procedure; Implicit restarts

More information

In this section again we shall assume that the matrix A is m m, real and symmetric.

In this section again we shall assume that the matrix A is m m, real and symmetric. 84 3. The QR algorithm without shifts See Chapter 28 of the textbook In this section again we shall assume that the matrix A is m m, real and symmetric. 3.1. Simultaneous Iterations algorithm Suppose we

More information

Augmented GMRES-type methods

Augmented GMRES-type methods Augmented GMRES-type methods James Baglama 1 and Lothar Reichel 2, 1 Department of Mathematics, University of Rhode Island, Kingston, RI 02881. E-mail: jbaglama@math.uri.edu. Home page: http://hypatia.math.uri.edu/

More information

Multigrid absolute value preconditioning

Multigrid absolute value preconditioning Multigrid absolute value preconditioning Eugene Vecharynski 1 Andrew Knyazev 2 (speaker) 1 Department of Computer Science and Engineering University of Minnesota 2 Department of Mathematical and Statistical

More information

M E M O R A N D U M. Faculty Senate approved November 1, 2018

M E M O R A N D U M. Faculty Senate approved November 1, 2018 M E M O R A N D U M Faculty Senate approved November 1, 2018 TO: FROM: Deans and Chairs Becky Bitter, Sr. Assistant Registrar DATE: October 23, 2018 SUBJECT: Minor Change Bulletin No. 5 The courses listed

More information

Advancing Green Chemistry Practices in Business

Advancing Green Chemistry Practices in Business Green Chemistry and Commerce Council: 6 th Annual GC3 Innovators Roundtable Advancing Green Chemistry Practices in Business Barbara Peterson, Ph.D. Marty Mulvihill, Ph.D. Program Director Executive Director,

More information

ABSTRACT OF DISSERTATION. Ping Zhang

ABSTRACT OF DISSERTATION. Ping Zhang ABSTRACT OF DISSERTATION Ping Zhang The Graduate School University of Kentucky 2009 Iterative Methods for Computing Eigenvalues and Exponentials of Large Matrices ABSTRACT OF DISSERTATION A dissertation

More information

LARGE SPARSE EIGENVALUE PROBLEMS. General Tools for Solving Large Eigen-Problems

LARGE SPARSE EIGENVALUE PROBLEMS. General Tools for Solving Large Eigen-Problems LARGE SPARSE EIGENVALUE PROBLEMS Projection methods The subspace iteration Krylov subspace methods: Arnoldi and Lanczos Golub-Kahan-Lanczos bidiagonalization General Tools for Solving Large Eigen-Problems

More information

Key words. conjugate gradients, normwise backward error, incremental norm estimation.

Key words. conjugate gradients, normwise backward error, incremental norm estimation. Proceedings of ALGORITMY 2016 pp. 323 332 ON ERROR ESTIMATION IN THE CONJUGATE GRADIENT METHOD: NORMWISE BACKWARD ERROR PETR TICHÝ Abstract. Using an idea of Duff and Vömel [BIT, 42 (2002), pp. 300 322

More information

Krylov Subspaces. The order-n Krylov subspace of A generated by x is

Krylov Subspaces. The order-n Krylov subspace of A generated by x is Lab 1 Krylov Subspaces Lab Objective: matrices. Use Krylov subspaces to find eigenvalues of extremely large One of the biggest difficulties in computational linear algebra is the amount of memory needed

More information

Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems

Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems Lanczos Bidiagonalization with Subspace Augmentation for Discrete Inverse Problems Per Christian Hansen Technical University of Denmark Ongoing work with Kuniyoshi Abe, Gifu Dedicated to Dianne P. O Leary

More information

Math 2114 Common Final Exam May 13, 2015 Form A

Math 2114 Common Final Exam May 13, 2015 Form A Math 4 Common Final Exam May 3, 5 Form A Instructions: Using a # pencil only, write your name and your instructor s name in the blanks provided. Write your student ID number and your CRN in the blanks

More information

18.06 Problem Set 8 - Solutions Due Wednesday, 14 November 2007 at 4 pm in

18.06 Problem Set 8 - Solutions Due Wednesday, 14 November 2007 at 4 pm in 806 Problem Set 8 - Solutions Due Wednesday, 4 November 2007 at 4 pm in 2-06 08 03 Problem : 205+5+5+5 Consider the matrix A 02 07 a Check that A is a positive Markov matrix, and find its steady state

More information

UNIT 6: The singular value decomposition.

UNIT 6: The singular value decomposition. UNIT 6: The singular value decomposition. María Barbero Liñán Universidad Carlos III de Madrid Bachelor in Statistics and Business Mathematical methods II 2011-2012 A square matrix is symmetric if A T

More information

LARGE SPARSE EIGENVALUE PROBLEMS

LARGE SPARSE EIGENVALUE PROBLEMS LARGE SPARSE EIGENVALUE PROBLEMS Projection methods The subspace iteration Krylov subspace methods: Arnoldi and Lanczos Golub-Kahan-Lanczos bidiagonalization 14-1 General Tools for Solving Large Eigen-Problems

More information

Karhunen-Loève Approximation of Random Fields Using Hierarchical Matrix Techniques

Karhunen-Loève Approximation of Random Fields Using Hierarchical Matrix Techniques Institut für Numerische Mathematik und Optimierung Karhunen-Loève Approximation of Random Fields Using Hierarchical Matrix Techniques Oliver Ernst Computational Methods with Applications Harrachov, CR,

More information

Data Analysis and Manifold Learning Lecture 2: Properties of Symmetric Matrices and Examples

Data Analysis and Manifold Learning Lecture 2: Properties of Symmetric Matrices and Examples Data Analysis and Manifold Learning Lecture 2: Properties of Symmetric Matrices and Examples Radu Horaud INRIA Grenoble Rhone-Alpes, France Radu.Horaud@inrialpes.fr http://perception.inrialpes.fr/ Outline

More information

COST European Cooperation in Science and Technology

COST European Cooperation in Science and Technology COST European Cooperation in Science and Technology Introduction to the COST Framework Programme University of Split, Split, Croatia, May 25 th, 2016. COST is supported by the EU Framework Programme ESF

More information

ITERATIVE PROJECTION METHODS FOR SPARSE LINEAR SYSTEMS AND EIGENPROBLEMS CHAPTER 11 : JACOBI DAVIDSON METHOD

ITERATIVE PROJECTION METHODS FOR SPARSE LINEAR SYSTEMS AND EIGENPROBLEMS CHAPTER 11 : JACOBI DAVIDSON METHOD ITERATIVE PROJECTION METHODS FOR SPARSE LINEAR SYSTEMS AND EIGENPROBLEMS CHAPTER 11 : JACOBI DAVIDSON METHOD Heinrich Voss voss@tu-harburg.de Hamburg University of Technology Institute of Numerical Simulation

More information

Preliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012

Preliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012 Instructions Preliminary/Qualifying Exam in Numerical Analysis (Math 502a) Spring 2012 The exam consists of four problems, each having multiple parts. You should attempt to solve all four problems. 1.

More information

The quadratic eigenvalue problem (QEP) is to find scalars λ and nonzero vectors u satisfying

The quadratic eigenvalue problem (QEP) is to find scalars λ and nonzero vectors u satisfying I.2 Quadratic Eigenvalue Problems 1 Introduction The quadratic eigenvalue problem QEP is to find scalars λ and nonzero vectors u satisfying where Qλx = 0, 1.1 Qλ = λ 2 M + λd + K, M, D and K are given

More information

College of William & Mary Department of Computer Science

College of William & Mary Department of Computer Science Technical Report WM-CS-2009-06 College of William & Mary Department of Computer Science WM-CS-2009-06 Extending the eigcg algorithm to non-symmetric Lanczos for linear systems with multiple right-hand

More information

On Solving Large Algebraic. Riccati Matrix Equations

On Solving Large Algebraic. Riccati Matrix Equations International Mathematical Forum, 5, 2010, no. 33, 1637-1644 On Solving Large Algebraic Riccati Matrix Equations Amer Kaabi Department of Basic Science Khoramshahr Marine Science and Technology University

More information

Data Mining Lecture 4: Covariance, EVD, PCA & SVD

Data Mining Lecture 4: Covariance, EVD, PCA & SVD Data Mining Lecture 4: Covariance, EVD, PCA & SVD Jo Houghton ECS Southampton February 25, 2019 1 / 28 Variance and Covariance - Expectation A random variable takes on different values due to chance The

More information

IDR(s) as a projection method

IDR(s) as a projection method Delft University of Technology Faculty of Electrical Engineering, Mathematics and Computer Science Delft Institute of Applied Mathematics IDR(s) as a projection method A thesis submitted to the Delft Institute

More information

Singular Value Decomposition and Digital Image Compression

Singular Value Decomposition and Digital Image Compression Singular Value Decomposition and Digital Image Compression Chris Bingham December 1, 016 Page 1 of Abstract The purpose of this document is to be a very basic introduction to the singular value decomposition

More information

LEC 2: Principal Component Analysis (PCA) A First Dimensionality Reduction Approach

LEC 2: Principal Component Analysis (PCA) A First Dimensionality Reduction Approach LEC 2: Principal Component Analysis (PCA) A First Dimensionality Reduction Approach Dr. Guangliang Chen February 9, 2016 Outline Introduction Review of linear algebra Matrix SVD PCA Motivation The digits

More information