Discrete Ill Posed and Rank Deficient Problems. Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 1

Size: px
Start display at page:

Download "Discrete Ill Posed and Rank Deficient Problems. Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 1"

Transcription

1 Discrete Ill Posed and Rank Deficient Problems Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 1

2 Definitions Overview Inversion, SVD, Picard Condition, Rank Deficient, Ill-Posed Classical Regularization Tikhonov and the Discrete Smoothing Norms Discretization Quadrature and Galerkin Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 2

3 Discretization Overview 2 Discrete Picard Condition Quadrature Galerkin Other Techniques TSVD CG Interation Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 3

4 Inversion Ax=b Solve for x, when A and b are known (or for A, when x and b are known) via Inverse, Psuedo-Inverse x= A 1 b x=a t b Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 4

5 SVD SVD(A) σ in non-increasing order or GSVD(A,L) n A=U V T = i=1 U T U =VV T =I n u i i v i T A T A=V 2 V T AA T =U 2 U T Singular Values Singular Vectors Left & Right (2) Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 5

6 Rank Deficient Gap in the Singular Values Approx. Zero Noise! Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 6

7 No gap in values Ill Posed Lots of small values Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 7

8 (Discrete) Picard Condition 1 0 Continuous K s,t f t =g s K s, t = i=1 f t = i=1 i=1 i u i s v i t SVE SVD Discrete Ax=b n A=U V T = i=1 n u i, g v i t x=a t i b= i=1 u i, g i 2... u i T b i u i i v i T v i denominator must decrease faster than numerator or we get an unbounded solution Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 8

9 Regularization First regularize, then discretize. Minimize residual norm with Constrained Values, with Constrained Size, Minimize Size, with Constrained Residual Minimize Residual and Size 1 r= 0 K s,t f t g s min r 2 2, x S min r 2 2, w min w 2 2, r 2 2 Tikhonov! min{ r w 2 2 } Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 9

10 Tikhonov Regularization min{ Ax b L x x } Solved by Least Squares using SVD Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 10

11 Discrete Smoothing Norms Choices for L : Identity Matrix Weighted Diagonal Discrete Derivative Approximations Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 11

12 L = I The identity matrix... Additional constraint: Minimize the absolute value of the solution min{ Ax b I x x } Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 12

13 L = diag(w) A diagonal matrix of weights on x... Additional constraint: Minimize a weighted selection of values of the solution min{ Ax b W x x } W =diag w Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 13

14 L = L1 or L2 A banded matrix approximating a derivative operator... Additional constraint: Minimize total variation in values of the solution (but still allow steep gradients) min{ Ax b L 1 x x } Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 14

15 Priors Allow the addition of a priori information about the result min{ Ax b L x x } Using no prior is the same as a zero prior Weight solution towards expectation Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 15

16 L curve under-smoothing over-smoothing (fake plot) Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 16

17 Discretization of Integral Quadrature 1 0 n t dt j=1 Equations choose w j w j t j a ij =w j K s,t b i =g s i Galerkin 1 a ij = 0 choose, 1 K s,t i s j t ds dt b i = 0 g s i s ds Raliegh-Ritz If =, K is symetric, and nodes are co-located Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 17

18 Solution (fake plots) Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 18

19 Discussion [ Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 19

20 References P. C. Hansen, Rank-Deficient and Discrete Ill-Posed Problems, SIAM, 1998 Wikipedia: Inverse Problems, visited Jan 28, 2009 J Kaipio, E Somersalo, Statistical and Computational Inverse Problems, Springer, 2005 Alistair Boyle, Feb 2009, SYS5906: Directed Studies Inverse Problems 20

Tikhonov Regularization in General Form 8.1

Tikhonov Regularization in General Form 8.1 Tikhonov Regularization in General Form 8.1 To introduce a more general formulation, let us return to the continuous formulation of the first-kind Fredholm integral equation. In this setting, the residual

More information

Regularization methods for large-scale, ill-posed, linear, discrete, inverse problems

Regularization methods for large-scale, ill-posed, linear, discrete, inverse problems Regularization methods for large-scale, ill-posed, linear, discrete, inverse problems Silvia Gazzola Dipartimento di Matematica - Università di Padova January 10, 2012 Seminario ex-studenti 2 Silvia Gazzola

More information

Preconditioning. Noisy, Ill-Conditioned Linear Systems

Preconditioning. Noisy, Ill-Conditioned Linear Systems Preconditioning Noisy, Ill-Conditioned Linear Systems James G. Nagy Emory University Atlanta, GA Outline 1. The Basic Problem 2. Regularization / Iterative Methods 3. Preconditioning 4. Example: Image

More information

Preconditioning. Noisy, Ill-Conditioned Linear Systems

Preconditioning. Noisy, Ill-Conditioned Linear Systems Preconditioning Noisy, Ill-Conditioned Linear Systems James G. Nagy Emory University Atlanta, GA Outline 1. The Basic Problem 2. Regularization / Iterative Methods 3. Preconditioning 4. Example: Image

More information

Golub-Kahan iterative bidiagonalization and determining the noise level in the data

Golub-Kahan iterative bidiagonalization and determining the noise level in the data Golub-Kahan iterative bidiagonalization and determining the noise level in the data Iveta Hnětynková,, Martin Plešinger,, Zdeněk Strakoš, * Charles University, Prague ** Academy of Sciences of the Czech

More information

Singular value decomposition. If only the first p singular values are nonzero we write. U T o U p =0

Singular value decomposition. If only the first p singular values are nonzero we write. U T o U p =0 Singular value decomposition If only the first p singular values are nonzero we write G =[U p U o ] " Sp 0 0 0 # [V p V o ] T U p represents the first p columns of U U o represents the last N-p columns

More information

linearly indepedent eigenvectors as the multiplicity of the root, but in general there may be no more than one. For further discussion, assume matrice

linearly indepedent eigenvectors as the multiplicity of the root, but in general there may be no more than one. For further discussion, assume matrice 3. Eigenvalues and Eigenvectors, Spectral Representation 3.. Eigenvalues and Eigenvectors A vector ' is eigenvector of a matrix K, if K' is parallel to ' and ' 6, i.e., K' k' k is the eigenvalue. If is

More information

Singular value decomposition

Singular value decomposition Singular value decomposition The eigenvalue decomposition (EVD) for a square matrix A gives AU = UD. Let A be rectangular (m n, m > n). A singular value σ and corresponding pair of singular vectors u (m

More information

Dimensionality Reduction: PCA. Nicholas Ruozzi University of Texas at Dallas

Dimensionality Reduction: PCA. Nicholas Ruozzi University of Texas at Dallas Dimensionality Reduction: PCA Nicholas Ruozzi University of Texas at Dallas Eigenvalues λ is an eigenvalue of a matrix A R n n if the linear system Ax = λx has at least one non-zero solution If Ax = λx

More information

Structural Damage Detection Using Time Windowing Technique from Measured Acceleration during Earthquake

Structural Damage Detection Using Time Windowing Technique from Measured Acceleration during Earthquake Structural Damage Detection Using Time Windowing Technique from Measured Acceleration during Earthquake Seung Keun Park and Hae Sung Lee ABSTRACT This paper presents a system identification (SI) scheme

More information

A MODIFIED TSVD METHOD FOR DISCRETE ILL-POSED PROBLEMS

A MODIFIED TSVD METHOD FOR DISCRETE ILL-POSED PROBLEMS A MODIFIED TSVD METHOD FOR DISCRETE ILL-POSED PROBLEMS SILVIA NOSCHESE AND LOTHAR REICHEL Abstract. Truncated singular value decomposition (TSVD) is a popular method for solving linear discrete ill-posed

More information

Linear Least Squares. Using SVD Decomposition.

Linear Least Squares. Using SVD Decomposition. Linear Least Squares. Using SVD Decomposition. Dmitriy Leykekhman Spring 2011 Goals SVD-decomposition. Solving LLS with SVD-decomposition. D. Leykekhman Linear Least Squares 1 SVD Decomposition. For any

More information

ITERATIVE REGULARIZATION WITH MINIMUM-RESIDUAL METHODS

ITERATIVE REGULARIZATION WITH MINIMUM-RESIDUAL METHODS BIT Numerical Mathematics 6-3835/3/431-1 $16. 23, Vol. 43, No. 1, pp. 1 18 c Kluwer Academic Publishers ITERATIVE REGULARIZATION WITH MINIMUM-RESIDUAL METHODS T. K. JENSEN and P. C. HANSEN Informatics

More information

Regularization Parameter Estimation for Least Squares: A Newton method using the χ 2 -distribution

Regularization Parameter Estimation for Least Squares: A Newton method using the χ 2 -distribution Regularization Parameter Estimation for Least Squares: A Newton method using the χ 2 -distribution Rosemary Renaut, Jodi Mead Arizona State and Boise State September 2007 Renaut and Mead (ASU/Boise) Scalar

More information

Linear Inverse Problems

Linear Inverse Problems Linear Inverse Problems Ajinkya Kadu Utrecht University, The Netherlands February 26, 2018 Outline Introduction Least-squares Reconstruction Methods Examples Summary Introduction 2 What are inverse problems?

More information

Why the QR Factorization can be more Accurate than the SVD

Why the QR Factorization can be more Accurate than the SVD Why the QR Factorization can be more Accurate than the SVD Leslie V. Foster Department of Mathematics San Jose State University San Jose, CA 95192 foster@math.sjsu.edu May 10, 2004 Problem: or Ax = b for

More information

CS6964: Notes On Linear Systems

CS6964: Notes On Linear Systems CS6964: Notes On Linear Systems 1 Linear Systems Systems of equations that are linear in the unknowns are said to be linear systems For instance ax 1 + bx 2 dx 1 + ex 2 = c = f gives 2 equations and 2

More information

THE SINGULAR VALUE DECOMPOSITION MARKUS GRASMAIR

THE SINGULAR VALUE DECOMPOSITION MARKUS GRASMAIR THE SINGULAR VALUE DECOMPOSITION MARKUS GRASMAIR 1. Definition Existence Theorem 1. Assume that A R m n. Then there exist orthogonal matrices U R m m V R n n, values σ 1 σ 2... σ p 0 with p = min{m, n},

More information

MATH 3795 Lecture 10. Regularized Linear Least Squares.

MATH 3795 Lecture 10. Regularized Linear Least Squares. MATH 3795 Lecture 10. Regularized Linear Least Squares. Dmitriy Leykekhman Fall 2008 Goals Understanding the regularization. D. Leykekhman - MATH 3795 Introduction to Computational Mathematics Linear Least

More information

MARN 5898 Regularized Linear Least Squares.

MARN 5898 Regularized Linear Least Squares. MARN 5898 Regularized Linear Least Squares. Dmitriy Leykekhman Spring 2010 Goals Understanding the regularization. D. Leykekhman - MARN 5898 Parameter estimation in marine sciences Linear Least Squares

More information

Numerical Methods in Matrix Computations

Numerical Methods in Matrix Computations Ake Bjorck Numerical Methods in Matrix Computations Springer Contents 1 Direct Methods for Linear Systems 1 1.1 Elements of Matrix Theory 1 1.1.1 Matrix Algebra 2 1.1.2 Vector Spaces 6 1.1.3 Submatrices

More information

Numerical Methods I Singular Value Decomposition

Numerical Methods I Singular Value Decomposition Numerical Methods I Singular Value Decomposition Aleksandar Donev Courant Institute, NYU 1 donev@courant.nyu.edu 1 MATH-GA 2011.003 / CSCI-GA 2945.003, Fall 2014 October 9th, 2014 A. Donev (Courant Institute)

More information

Multi-Linear Mappings, SVD, HOSVD, and the Numerical Solution of Ill-Conditioned Tensor Least Squares Problems

Multi-Linear Mappings, SVD, HOSVD, and the Numerical Solution of Ill-Conditioned Tensor Least Squares Problems Multi-Linear Mappings, SVD, HOSVD, and the Numerical Solution of Ill-Conditioned Tensor Least Squares Problems Lars Eldén Department of Mathematics, Linköping University 1 April 2005 ERCIM April 2005 Multi-Linear

More information

Uncertainty Quantification for Inverse Problems. November 7, 2011

Uncertainty Quantification for Inverse Problems. November 7, 2011 Uncertainty Quantification for Inverse Problems November 7, 2011 Outline UQ and inverse problems Review: least-squares Review: Gaussian Bayesian linear model Parametric reductions for IP Bias, variance

More information

Inverse problems Total Variation Regularization Mark van Kraaij Casa seminar 23 May 2007 Technische Universiteit Eindh ove n University of Technology

Inverse problems Total Variation Regularization Mark van Kraaij Casa seminar 23 May 2007 Technische Universiteit Eindh ove n University of Technology Inverse problems Total Variation Regularization Mark van Kraaij Casa seminar 23 May 27 Introduction Fredholm first kind integral equation of convolution type in one space dimension: g(x) = 1 k(x x )f(x

More information

Krylov subspace iterative methods for nonsymmetric discrete ill-posed problems in image restoration

Krylov subspace iterative methods for nonsymmetric discrete ill-posed problems in image restoration Krylov subspace iterative methods for nonsymmetric discrete ill-posed problems in image restoration D. Calvetti a, B. Lewis b and L. Reichel c a Department of Mathematics, Case Western Reserve University,

More information

ON THE DYNAMICAL SYSTEMS METHOD FOR SOLVING NONLINEAR EQUATIONS WITH MONOTONE OPERATORS

ON THE DYNAMICAL SYSTEMS METHOD FOR SOLVING NONLINEAR EQUATIONS WITH MONOTONE OPERATORS PROCEEDINGS OF THE AMERICAN MATHEMATICAL SOCIETY Volume, Number, Pages S -9939(XX- ON THE DYNAMICAL SYSTEMS METHOD FOR SOLVING NONLINEAR EQUATIONS WITH MONOTONE OPERATORS N. S. HOANG AND A. G. RAMM (Communicated

More information

5 Linear Algebra and Inverse Problem

5 Linear Algebra and Inverse Problem 5 Linear Algebra and Inverse Problem 5.1 Introduction Direct problem ( Forward problem) is to find field quantities satisfying Governing equations, Boundary conditions, Initial conditions. The direct problem

More information

Introduction. Chapter One

Introduction. Chapter One Chapter One Introduction The aim of this book is to describe and explain the beautiful mathematical relationships between matrices, moments, orthogonal polynomials, quadrature rules and the Lanczos and

More information

Basic Linear Inverse Method Theory - DRAFT NOTES

Basic Linear Inverse Method Theory - DRAFT NOTES Basic Linear Inverse Method Theory - DRAFT NOTES Peter P. Jones 1 1 Centre for Compleity Science, University of Warwick, Coventry CV4 7AL, UK (Dated: 21st June 2012) BASIC LINEAR INVERSE METHOD THEORY

More information

Linear Algebra - Part II

Linear Algebra - Part II Linear Algebra - Part II Projection, Eigendecomposition, SVD (Adapted from Sargur Srihari s slides) Brief Review from Part 1 Symmetric Matrix: A = A T Orthogonal Matrix: A T A = AA T = I and A 1 = A T

More information

Numerical Methods for Separable Nonlinear Inverse Problems with Constraint and Low Rank

Numerical Methods for Separable Nonlinear Inverse Problems with Constraint and Low Rank Numerical Methods for Separable Nonlinear Inverse Problems with Constraint and Low Rank Taewon Cho Thesis submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial

More information

Solving discrete ill posed problems with Tikhonov regularization and generalized cross validation

Solving discrete ill posed problems with Tikhonov regularization and generalized cross validation Solving discrete ill posed problems with Tikhonov regularization and generalized cross validation Gérard MEURANT November 2010 1 Introduction to ill posed problems 2 Examples of ill-posed problems 3 Tikhonov

More information

Mathematical Beer Goggles or The Mathematics of Image Processing

Mathematical Beer Goggles or The Mathematics of Image Processing How Mathematical Beer Goggles or The Mathematics of Image Processing Department of Mathematical Sciences University of Bath Postgraduate Seminar Series University of Bath 12th February 2008 1 How 2 How

More information

Analysis of Error Produced by Truncated SVD and Tikhonov Regularization Met hods *

Analysis of Error Produced by Truncated SVD and Tikhonov Regularization Met hods * Analysis of Error Produced by Truncated SVD and Tikhonov Regularization Met hods * Irina F. Gorodnitsky and Bhaskar D. Rao Dept. of Electrical and Computer Engineering University of California, San Diego

More information

REGULARIZATION PARAMETER SELECTION IN DISCRETE ILL POSED PROBLEMS THE USE OF THE U CURVE

REGULARIZATION PARAMETER SELECTION IN DISCRETE ILL POSED PROBLEMS THE USE OF THE U CURVE Int. J. Appl. Math. Comput. Sci., 007, Vol. 17, No., 157 164 DOI: 10.478/v10006-007-0014-3 REGULARIZATION PARAMETER SELECTION IN DISCRETE ILL POSED PROBLEMS THE USE OF THE U CURVE DOROTA KRAWCZYK-STAŃDO,

More information

A study on regularization parameter choice in Near-field Acoustical Holography

A study on regularization parameter choice in Near-field Acoustical Holography Acoustics 8 Paris A study on regularization parameter choice in Near-field Acoustical Holography J. Gomes a and P.C. Hansen b a Brüel & Kjær Sound and Vibration Measurement A/S, Skodsborgvej 37, DK-285

More information

Discrete ill posed problems

Discrete ill posed problems Discrete ill posed problems Gérard MEURANT October, 2008 1 Introduction to ill posed problems 2 Tikhonov regularization 3 The L curve criterion 4 Generalized cross validation 5 Comparisons of methods Introduction

More information

Unfolding Methods in Particle Physics

Unfolding Methods in Particle Physics Unfolding Methods in Particle Physics Volker Blobel University of Hamburg, Hamburg, Germany 1 Inverse problems Abstract Measured distributions in particle physics are distorted by the finite resolution

More information

COMPUTATIONAL ISSUES RELATING TO INVERSION OF PRACTICAL DATA: WHERE IS THE UNCERTAINTY? CAN WE SOLVE Ax = b?

COMPUTATIONAL ISSUES RELATING TO INVERSION OF PRACTICAL DATA: WHERE IS THE UNCERTAINTY? CAN WE SOLVE Ax = b? COMPUTATIONAL ISSUES RELATING TO INVERSION OF PRACTICAL DATA: WHERE IS THE UNCERTAINTY? CAN WE SOLVE Ax = b? Rosemary Renaut http://math.asu.edu/ rosie BRIDGING THE GAP? OCT 2, 2012 Discussion Yuen: Solve

More information

Problem set 5: SVD, Orthogonal projections, etc.

Problem set 5: SVD, Orthogonal projections, etc. Problem set 5: SVD, Orthogonal projections, etc. February 21, 2017 1 SVD 1. Work out again the SVD theorem done in the class: If A is a real m n matrix then here exist orthogonal matrices such that where

More information

Rank-Deficient Nonlinear Least Squares Problems and Subset Selection

Rank-Deficient Nonlinear Least Squares Problems and Subset Selection Rank-Deficient Nonlinear Least Squares Problems and Subset Selection Ilse Ipsen North Carolina State University Raleigh, NC, USA Joint work with: Tim Kelley and Scott Pope Motivating Application Overview

More information

Vector and Matrix Norms. Vector and Matrix Norms

Vector and Matrix Norms. Vector and Matrix Norms Vector and Matrix Norms Vector Space Algebra Matrix Algebra: We let x x and A A, where, if x is an element of an abstract vector space n, and A = A: n m, then x is a complex column vector of length n whose

More information

c 1999 Society for Industrial and Applied Mathematics

c 1999 Society for Industrial and Applied Mathematics SIAM J. MATRIX ANAL. APPL. Vol. 21, No. 1, pp. 185 194 c 1999 Society for Industrial and Applied Mathematics TIKHONOV REGULARIZATION AND TOTAL LEAST SQUARES GENE H. GOLUB, PER CHRISTIAN HANSEN, AND DIANNE

More information

Linear Algebra Review. Vectors

Linear Algebra Review. Vectors Linear Algebra Review 9/4/7 Linear Algebra Review By Tim K. Marks UCSD Borrows heavily from: Jana Kosecka http://cs.gmu.edu/~kosecka/cs682.html Virginia de Sa (UCSD) Cogsci 8F Linear Algebra review Vectors

More information

σ 11 σ 22 σ pp 0 with p = min(n, m) The σ ii s are the singular values. Notation change σ ii A 1 σ 2

σ 11 σ 22 σ pp 0 with p = min(n, m) The σ ii s are the singular values. Notation change σ ii A 1 σ 2 HE SINGULAR VALUE DECOMPOSIION he SVD existence - properties. Pseudo-inverses and the SVD Use of SVD for least-squares problems Applications of the SVD he Singular Value Decomposition (SVD) heorem For

More information

14 Singular Value Decomposition

14 Singular Value Decomposition 14 Singular Value Decomposition For any high-dimensional data analysis, one s first thought should often be: can I use an SVD? The singular value decomposition is an invaluable analysis tool for dealing

More information

Iterative Methods for Ill-Posed Problems

Iterative Methods for Ill-Posed Problems Iterative Methods for Ill-Posed Problems Based on joint work with: Serena Morigi Fiorella Sgallari Andriy Shyshkov Salt Lake City, May, 2007 Outline: Inverse and ill-posed problems Tikhonov regularization

More information

Introduction to Numerical Linear Algebra II

Introduction to Numerical Linear Algebra II Introduction to Numerical Linear Algebra II Petros Drineas These slides were prepared by Ilse Ipsen for the 2015 Gene Golub SIAM Summer School on RandNLA 1 / 49 Overview We will cover this material in

More information

Matrices and Vectors. Definition of Matrix. An MxN matrix A is a two-dimensional array of numbers A =

Matrices and Vectors. Definition of Matrix. An MxN matrix A is a two-dimensional array of numbers A = 30 MATHEMATICS REVIEW G A.1.1 Matrices and Vectors Definition of Matrix. An MxN matrix A is a two-dimensional array of numbers A = a 11 a 12... a 1N a 21 a 22... a 2N...... a M1 a M2... a MN A matrix can

More information

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

Bindel, Fall 2016 Matrix Computations (CS 6210) Notes for 1 A cautionary tale Notes for 2016-10-05 You have been dropped on a desert island with a laptop with a magic battery of infinite life, a MATLAB license, and a complete lack of knowledge of basic geometry.

More information

Experiences with Model Reduction and Interpolation

Experiences with Model Reduction and Interpolation Experiences with Model Reduction and Interpolation Paul Constantine Stanford University, Sandia National Laboratories Qiqi Wang (MIT) David Gleich (Purdue) Emory University March 7, 2012 Joe Ruthruff (SNL)

More information

Orthonormal Transformations and Least Squares

Orthonormal Transformations and Least Squares Orthonormal Transformations and Least Squares Tom Lyche Centre of Mathematics for Applications, Department of Informatics, University of Oslo October 30, 2009 Applications of Qx with Q T Q = I 1. solving

More information

Iterative Methods for Smooth Objective Functions

Iterative Methods for Smooth Objective Functions Optimization Iterative Methods for Smooth Objective Functions Quadratic Objective Functions Stationary Iterative Methods (first/second order) Steepest Descent Method Landweber/Projected Landweber Methods

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

Linear Algebra for Machine Learning. Sargur N. Srihari

Linear Algebra for Machine Learning. Sargur N. Srihari Linear Algebra for Machine Learning Sargur N. srihari@cedar.buffalo.edu 1 Overview Linear Algebra is based on continuous math rather than discrete math Computer scientists have little experience with it

More information

Inverse Ill Posed Problems in Image Processing

Inverse Ill Posed Problems in Image Processing Inverse Ill Posed Problems in Image Processing Image Deblurring I. Hnětynková 1,M.Plešinger 2,Z.Strakoš 3 hnetynko@karlin.mff.cuni.cz, martin.plesinger@tul.cz, strakos@cs.cas.cz 1,3 Faculty of Mathematics

More information

Newton s Method for Estimating the Regularization Parameter for Least Squares: Using the Chi-curve

Newton s Method for Estimating the Regularization Parameter for Least Squares: Using the Chi-curve Newton s Method for Estimating the Regularization Parameter for Least Squares: Using the Chi-curve Rosemary Renaut, Jodi Mead Arizona State and Boise State Copper Mountain Conference on Iterative Methods

More information

The Normal Equations. For A R m n with m > n, A T A is singular if and only if A is rank-deficient. 1 Proof:

The Normal Equations. For A R m n with m > n, A T A is singular if and only if A is rank-deficient. 1 Proof: Applied Math 205 Homework 1 now posted. Due 5 PM on September 26. Last time: piecewise polynomial interpolation, least-squares fitting Today: least-squares, nonlinear least-squares The Normal Equations

More information

Statistical Geometry Processing Winter Semester 2011/2012

Statistical Geometry Processing Winter Semester 2011/2012 Statistical Geometry Processing Winter Semester 2011/2012 Linear Algebra, Function Spaces & Inverse Problems Vector and Function Spaces 3 Vectors vectors are arrows in space classically: 2 or 3 dim. Euclidian

More information

Linear inversion methods and generalized cross-validation

Linear inversion methods and generalized cross-validation Online supplement to Using generalized cross-validation to select parameters in inversions for regional caron fluxes Linear inversion methods and generalized cross-validation NIR Y. KRAKAUER AND TAPIO

More information

IV. Matrix Approximation using Least-Squares

IV. Matrix Approximation using Least-Squares IV. Matrix Approximation using Least-Squares The SVD and Matrix Approximation We begin with the following fundamental question. Let A be an M N matrix with rank R. What is the closest matrix to A that

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

ECE295, Data Assimila0on and Inverse Problems, Spring 2015

ECE295, Data Assimila0on and Inverse Problems, Spring 2015 ECE295, Data Assimila0on and Inverse Problems, Spring 2015 1 April, Intro; Linear discrete Inverse problems (Aster Ch 1 and 2) Slides 8 April, SVD (Aster ch 2 and 3) Slides 15 April, RegularizaFon (ch

More information

The Application of Discrete Tikhonov Regularization Inverse Problem in Seismic Tomography

The Application of Discrete Tikhonov Regularization Inverse Problem in Seismic Tomography The Application of Discrete Tikhonov Regularization Inverse Problem in Seismic Tomography KAMBIZ TEIMOORNEGAD, NEDA POROOHAN 2, Geology Department Islamic Azad University, Lahijan Branch 2 Islamic Azad

More information

Matrix Differentiation

Matrix Differentiation Matrix Differentiation CS5240 Theoretical Foundations in Multimedia Leow Wee Kheng Department of Computer Science School of Computing National University of Singapore Leow Wee Kheng (NUS) Matrix Differentiation

More information

EIGENVALE PROBLEMS AND THE SVD. [5.1 TO 5.3 & 7.4]

EIGENVALE PROBLEMS AND THE SVD. [5.1 TO 5.3 & 7.4] EIGENVALE PROBLEMS AND THE SVD. [5.1 TO 5.3 & 7.4] Eigenvalue Problems. Introduction Let A an n n real nonsymmetric matrix. The eigenvalue problem: Au = λu λ C : eigenvalue u C n : eigenvector Example:

More information

1. Introduction. We consider linear discrete ill-posed problems of the form

1. Introduction. We consider linear discrete ill-posed problems of the form AN EXTRAPOLATED TSVD METHOD FOR LINEAR DISCRETE ILL-POSED PROBLEMS WITH KRONECKER STRUCTURE A. BOUHAMIDI, K. JBILOU, L. REICHEL, AND H. SADOK Abstract. This paper describes a new numerical method for the

More information

C&O367: Nonlinear Optimization (Winter 2013) Assignment 4 H. Wolkowicz

C&O367: Nonlinear Optimization (Winter 2013) Assignment 4 H. Wolkowicz C&O367: Nonlinear Optimization (Winter 013) Assignment 4 H. Wolkowicz Posted Mon, Feb. 8 Due: Thursday, Feb. 8 10:00AM (before class), 1 Matrices 1.1 Positive Definite Matrices 1. Let A S n, i.e., let

More information

We first repeat some well known facts about condition numbers for normwise and componentwise perturbations. Consider the matrix

We first repeat some well known facts about condition numbers for normwise and componentwise perturbations. Consider the matrix BIT 39(1), pp. 143 151, 1999 ILL-CONDITIONEDNESS NEEDS NOT BE COMPONENTWISE NEAR TO ILL-POSEDNESS FOR LEAST SQUARES PROBLEMS SIEGFRIED M. RUMP Abstract. The condition number of a problem measures the sensitivity

More information

2. LINEAR ALGEBRA. 1. Definitions. 2. Linear least squares problem. 3. QR factorization. 4. Singular value decomposition (SVD) 5.

2. LINEAR ALGEBRA. 1. Definitions. 2. Linear least squares problem. 3. QR factorization. 4. Singular value decomposition (SVD) 5. 2. LINEAR ALGEBRA Outline 1. Definitions 2. Linear least squares problem 3. QR factorization 4. Singular value decomposition (SVD) 5. Pseudo-inverse 6. Eigenvalue decomposition (EVD) 1 Definitions Vector

More information

A Hybrid LSQR Regularization Parameter Estimation Algorithm for Large Scale Problems

A Hybrid LSQR Regularization Parameter Estimation Algorithm for Large Scale Problems A Hybrid LSQR Regularization Parameter Estimation Algorithm for Large Scale Problems Rosemary Renaut Joint work with Jodi Mead and Iveta Hnetynkova SIAM Annual Meeting July 10, 2009 National Science Foundation:

More information

CHAPTER 11. A Revision. 1. The Computers and Numbers therein

CHAPTER 11. A Revision. 1. The Computers and Numbers therein CHAPTER A Revision. The Computers and Numbers therein Traditional computer science begins with a finite alphabet. By stringing elements of the alphabet one after another, one obtains strings. A set of

More information

Tikhonov Regularization of Large Symmetric Problems

Tikhonov Regularization of Large Symmetric Problems NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS Numer. Linear Algebra Appl. 2000; 00:1 11 [Version: 2000/03/22 v1.0] Tihonov Regularization of Large Symmetric Problems D. Calvetti 1, L. Reichel 2 and A. Shuibi

More information

The Singular Value Decomposition

The Singular Value Decomposition The Singular Value Decomposition Philippe B. Laval KSU Fall 2015 Philippe B. Laval (KSU) SVD Fall 2015 1 / 13 Review of Key Concepts We review some key definitions and results about matrices that will

More information

Terascale Statistics Tools School Spring 2010 Tuesday 23 March Friday 26 March Data unfolding. Volker Blobel Universität Hamburg

Terascale Statistics Tools School Spring 2010 Tuesday 23 March Friday 26 March Data unfolding. Volker Blobel Universität Hamburg Terascale Statistics Tools School Spring 21 Tuesday 23 March Friday 26 March 21 Data unfolding Volker Blobel Universität Hamburg Unfolding is required, due to migration effects, for the measurement of

More information

Lecture 2: Tikhonov-Regularization

Lecture 2: Tikhonov-Regularization Lecture 2: Tikhonov-Regularization Bastian von Harrach harrach@math.uni-stuttgart.de Chair of Optimization and Inverse Problems, University of Stuttgart, Germany Advanced Instructional School on Theoretical

More information

RESCALING THE GSVD WITH APPLICATION TO ILL-POSED PROBLEMS

RESCALING THE GSVD WITH APPLICATION TO ILL-POSED PROBLEMS RESCALING THE GSVD WITH APPLICATION TO ILL-POSED PROBLEMS L. DYKES, S. NOSCHESE, AND L. REICHEL Abstract. The generalized singular value decomposition (GSVD) of a pair of matrices expresses each matrix

More information

AMS526: Numerical Analysis I (Numerical Linear Algebra)

AMS526: Numerical Analysis I (Numerical Linear Algebra) AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 7: More on Householder Reflectors; Least Squares Problems Xiangmin Jiao SUNY Stony Brook Xiangmin Jiao Numerical Analysis I 1 / 15 Outline

More information

Total least squares. Gérard MEURANT. October, 2008

Total least squares. Gérard MEURANT. October, 2008 Total least squares Gérard MEURANT October, 2008 1 Introduction to total least squares 2 Approximation of the TLS secular equation 3 Numerical experiments Introduction to total least squares In least squares

More information

Let A an n n real nonsymmetric matrix. The eigenvalue problem: λ 1 = 1 with eigenvector u 1 = ( ) λ 2 = 2 with eigenvector u 2 = ( 1

Let A an n n real nonsymmetric matrix. The eigenvalue problem: λ 1 = 1 with eigenvector u 1 = ( ) λ 2 = 2 with eigenvector u 2 = ( 1 Eigenvalue Problems. Introduction Let A an n n real nonsymmetric matrix. The eigenvalue problem: EIGENVALE PROBLEMS AND THE SVD. [5.1 TO 5.3 & 7.4] Au = λu Example: ( ) 2 0 A = 2 1 λ 1 = 1 with eigenvector

More information

COMP 558 lecture 18 Nov. 15, 2010

COMP 558 lecture 18 Nov. 15, 2010 Least squares We have seen several least squares problems thus far, and we will see more in the upcoming lectures. For this reason it is good to have a more general picture of these problems and how to

More information

Regularizing inverse problems. Damping and smoothing and choosing...

Regularizing inverse problems. Damping and smoothing and choosing... Regularizing inverse problems Damping and smoothing and choosing... 141 Regularization The idea behind SVD is to limit the degree of freedom in the model and fit the data to an acceptable level. Retain

More information

be a Householder matrix. Then prove the followings H = I 2 uut Hu = (I 2 uu u T u )u = u 2 uut u

be a Householder matrix. Then prove the followings H = I 2 uut Hu = (I 2 uu u T u )u = u 2 uut u MATH 434/534 Theoretical Assignment 7 Solution Chapter 7 (71) Let H = I 2uuT Hu = u (ii) Hv = v if = 0 be a Householder matrix Then prove the followings H = I 2 uut Hu = (I 2 uu )u = u 2 uut u = u 2u =

More information

Comparison of A-Posteriori Parameter Choice Rules for Linear Discrete Ill-Posed Problems

Comparison of A-Posteriori Parameter Choice Rules for Linear Discrete Ill-Posed Problems Comparison of A-Posteriori Parameter Choice Rules for Linear Discrete Ill-Posed Problems Alessandro Buccini a, Yonggi Park a, Lothar Reichel a a Department of Mathematical Sciences, Kent State University,

More information

A Note on the Pin-Pointing Solution of Ill-Conditioned Linear System of Equations

A Note on the Pin-Pointing Solution of Ill-Conditioned Linear System of Equations A Note on the Pin-Pointing Solution of Ill-Conditioned Linear System of Equations Davod Khojasteh Salkuyeh 1 and Mohsen Hasani 2 1,2 Department of Mathematics, University of Mohaghegh Ardabili, P. O. Box.

More information

Recovery of Sparse Signals from Noisy Measurements Using an l p -Regularized Least-Squares Algorithm

Recovery of Sparse Signals from Noisy Measurements Using an l p -Regularized Least-Squares Algorithm Recovery of Sparse Signals from Noisy Measurements Using an l p -Regularized Least-Squares Algorithm J. K. Pant, W.-S. Lu, and A. Antoniou University of Victoria August 25, 2011 Compressive Sensing 1 University

More information

SINGULAR VALUE DECOMPOSITION AND LEAST SQUARES ORBIT DETERMINATION

SINGULAR VALUE DECOMPOSITION AND LEAST SQUARES ORBIT DETERMINATION SINGULAR VALUE DECOMPOSITION AND LEAST SQUARES ORBIT DETERMINATION Zakhary N. Khutorovsky & Vladimir Boikov Vympel Corp. Kyle T. Alfriend Texas A&M University Outline Background Batch Least Squares Nonlinear

More information

CS 143 Linear Algebra Review

CS 143 Linear Algebra Review CS 143 Linear Algebra Review Stefan Roth September 29, 2003 Introductory Remarks This review does not aim at mathematical rigor very much, but instead at ease of understanding and conciseness. Please see

More information

Gene Golub Day Memorial Workshop HKBU. SVD the Swiss Army Knife of Linear Algebra. Walter Gander. February 7, 2015

Gene Golub Day Memorial Workshop HKBU. SVD the Swiss Army Knife of Linear Algebra. Walter Gander. February 7, 2015 1 Gene Golub Day Memorial Workshop HKBU SVD the Swiss Army Knife of Linear Algebra Walter Gander February 7, 15 2 Swiss Army Knife? Dianne P. O'Leary Matrix Factorizations for Information Retrieval (06)

More information

AN ANALYSIS OF MULTIPLICATIVE REGULARIZATION

AN ANALYSIS OF MULTIPLICATIVE REGULARIZATION Michigan Technological University Digital Commons @ Michigan Tech Dissertations, Master's Theses and Master's Reports - Open Dissertations, Master's Theses and Master's Reports 2015 AN ANALYSIS OF MULTIPLICATIVE

More information

A NEW L-CURVE FOR ILL-POSED PROBLEMS. Dedicated to Claude Brezinski.

A NEW L-CURVE FOR ILL-POSED PROBLEMS. Dedicated to Claude Brezinski. A NEW L-CURVE FOR ILL-POSED PROBLEMS LOTHAR REICHEL AND HASSANE SADOK Dedicated to Claude Brezinski. Abstract. The truncated singular value decomposition is a popular method for the solution of linear

More information

PHYSTAT2011 Workshop on Unfolding. Unfolding methods for particle physics

PHYSTAT2011 Workshop on Unfolding. Unfolding methods for particle physics PHYSTAT2011 Workshop on Unfolding Thursday Jan 20th 2011 Volker Blobel Universität Hamburg Unfolding methods for particle physics 1. Unfolding direct and inverse process 2. Naive unfolding 3. Convolution/deconvolution

More information

Applied Mathematics 205. Unit II: Numerical Linear Algebra. Lecturer: Dr. David Knezevic

Applied Mathematics 205. Unit II: Numerical Linear Algebra. Lecturer: Dr. David Knezevic Applied Mathematics 205 Unit II: Numerical Linear Algebra Lecturer: Dr. David Knezevic Unit II: Numerical Linear Algebra Chapter II.3: QR Factorization, SVD 2 / 66 QR Factorization 3 / 66 QR Factorization

More information

The Chi-squared Distribution of the Regularized Least Squares Functional for Regularization Parameter Estimation

The Chi-squared Distribution of the Regularized Least Squares Functional for Regularization Parameter Estimation The Chi-squared Distribution of the Regularized Least Squares Functional for Regularization Parameter Estimation Rosemary Renaut DEPARTMENT OF MATHEMATICS AND STATISTICS Prague 2008 MATHEMATICS AND STATISTICS

More information

1. Let a(x) > 0, and assume that u and u h are the solutions of the Dirichlet problem:

1. Let a(x) > 0, and assume that u and u h are the solutions of the Dirichlet problem: Mathematics Chalmers & GU TMA37/MMG800: Partial Differential Equations, 011 08 4; kl 8.30-13.30. Telephone: Ida Säfström: 0703-088304 Calculators, formula notes and other subject related material are not

More information

Regularization of the Inverse Laplace Transform with Applications in Nuclear Magnetic Resonance Relaxometry Candidacy Exam

Regularization of the Inverse Laplace Transform with Applications in Nuclear Magnetic Resonance Relaxometry Candidacy Exam Regularization of the Inverse Laplace Transform with Applications in Nuclear Magnetic Resonance Relaxometry Candidacy Exam Applied Mathematics, Applied Statistics, & Scientific Computation University of

More information

A Meshless Method for the Laplace and Biharmonic Equations Subjected to Noisy Boundary Data

A Meshless Method for the Laplace and Biharmonic Equations Subjected to Noisy Boundary Data Copyright c 4 Tech Science Press CMES, vol.6, no.3, pp.3-61, 4 A Meshless Method for the Laplace and Biharmonic Equations Subjected to Noisy Boundary Data B. Jin 1, Abstract: In this paper, we propose

More information

Parameter estimation: A new approach to weighting a priori information

Parameter estimation: A new approach to weighting a priori information c de Gruyter 27 J. Inv. Ill-Posed Problems 5 (27), 2 DOI.55 / JIP.27.xxx Parameter estimation: A new approach to weighting a priori information J.L. Mead Communicated by Abstract. We propose a new approach

More information

SIGNAL AND IMAGE RESTORATION: SOLVING

SIGNAL AND IMAGE RESTORATION: SOLVING 1 / 55 SIGNAL AND IMAGE RESTORATION: SOLVING ILL-POSED INVERSE PROBLEMS - ESTIMATING PARAMETERS Rosemary Renaut http://math.asu.edu/ rosie CORNELL MAY 10, 2013 2 / 55 Outline Background Parameter Estimation

More information