On the Vorobyev method of moments

Similar documents
Matching moments and matrix computations

Moments, Model Reduction and Nonlinearity in Solving Linear Algebraic Problems

On the interplay between discretization and preconditioning of Krylov subspace methods

Krylov subspace methods from the analytic, application and computational perspective

Sensitivity of Gauss-Christoffel quadrature and sensitivity of Jacobi matrices to small changes of spectral data

Lanczos tridiagonalization, Krylov subspaces and the problem of moments

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

O jednom starém článku a jeho mnohých souvislostech On an old article and its many connections

Efficient Estimation of the A-norm of the Error in the Preconditioned Conjugate Gradient Method

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

Contribution of Wo¹niakowski, Strako²,... The conjugate gradient method in nite precision computa

Principles and Analysis of Krylov Subspace Methods

Composite convergence bounds based on Chebyshev polynomials and finite precision conjugate gradient computations

Introduction. Chapter One

Efficient Estimation of the A-norm of the Error in the Preconditioned Conjugate Gradient Method

The Lanczos and conjugate gradient algorithms

Orthogonal polynomials

Lanczos tridigonalization and Golub - Kahan bidiagonalization: Ideas, connections and impact

Gene H. Golub and Gérard Meurant Matrices, Moments and Quadrature with Applications

On numerical stability in large scale linear algebraic computations

ON EFFICIENT NUMERICAL APPROXIMATION OF THE BILINEAR FORM c A 1 b

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

On Numerical Stability in Large Scale Linear Algebraic Computations

Introduction to Iterative Solvers of Linear Systems

On the properties of Krylov subspaces in finite precision CG computations

On the accuracy of saddle point solvers

The Conjugate Gradient Method

The following definition is fundamental.

ETNA Kent State University

Reduced Synchronization Overhead on. December 3, Abstract. The standard formulation of the conjugate gradient algorithm involves

ON ORTHOGONAL REDUCTION TO HESSENBERG FORM WITH SMALL BANDWIDTH

On the Superlinear Convergence of MINRES. Valeria Simoncini and Daniel B. Szyld. Report January 2012

ESTIMATES OF THE TRACE OF THE INVERSE OF A SYMMETRIC MATRIX USING THE MODIFIED CHEBYSHEV ALGORITHM

Implicitly Defined High-Order Operator Splittings for Parabolic and Hyperbolic Variable-Coefficient PDE Using Modified Moments

Analysis Preliminary Exam Workshop: Hilbert Spaces

AMSC 600 /CMSC 760 Advanced Linear Numerical Analysis Fall 2007 Krylov Minimization and Projection (KMP) Dianne P. O Leary c 2006, 2007.

MATHEMATICS. Course Syllabus. Section A: Linear Algebra. Subject Code: MA. Course Structure. Ordinary Differential Equations

Math 108b: Notes on the Spectral Theorem

Chebyshev semi-iteration in Preconditioning

Electronic Transactions on Numerical Analysis Volume 50, 2018

1 Extrapolation: A Hint of Things to Come

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

Numerical behavior of inexact linear solvers

On the influence of eigenvalues on Bi-CG residual norms

Preconditioning for Nonsymmetry and Time-dependence

Numerical Solution of Heat Equation by Spectral Method

APPLIED NUMERICAL LINEAR ALGEBRA

The amount of work to construct each new guess from the previous one should be a small multiple of the number of nonzeros in A.

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

A stable variant of Simpler GMRES and GCR

Krylov Subspace Methods for the Evaluation of Matrix Functions. Applications and Algorithms

Hilbert Spaces. Hilbert space is a vector space with some extra structure. We start with formal (axiomatic) definition of a vector space.

Large-scale eigenvalue problems

Total least squares. Gérard MEURANT. October, 2008

Iterative methods for Linear System of Equations. Joint Advanced Student School (JASS-2009)

Index. higher order methods, 52 nonlinear, 36 with variable coefficients, 34 Burgers equation, 234 BVP, see boundary value problems

6.4 Krylov Subspaces and Conjugate Gradients

Preface to the Second Edition. Preface to the First Edition

Vectors in Function Spaces

Iterative methods for Linear System

Stability of Krylov Subspace Spectral Methods

1 Conjugate gradients

Martin Tůma From Moments to Modern Iterative Methods - Historical Connections and Inspirations

Key words. cyclic subspaces, Krylov subspaces, orthogonal bases, orthogonalization, short recurrences, normal matrices.

Applied Linear Algebra in Geoscience Using MATLAB

Contents. Preface for the Instructor. Preface for the Student. xvii. Acknowledgments. 1 Vector Spaces 1 1.A R n and C n 2

Comparison of Fixed Point Methods and Krylov Subspace Methods Solving Convection-Diffusion Equations

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

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

BASIC VON NEUMANN ALGEBRA THEORY

EECS 275 Matrix Computation

AN ITERATIVE METHOD WITH ERROR ESTIMATORS

LARGE SPARSE EIGENVALUE PROBLEMS

Numerical Methods in Matrix Computations

Key words. GMRES method, convergence bounds, worst-case GMRES, ideal GMRES, field of values

Vector Spaces. Vector space, ν, over the field of complex numbers, C, is a set of elements a, b,..., satisfying the following axioms.

How Rutishauser may have found the qd and LR algorithms, t

Weak Formulation of Elliptic BVP s

High performance Krylov subspace method variants

Course Description - Master in of Mathematics Comprehensive exam& Thesis Tracks

Orthogonal Polynomial Ensembles

On solving linear systems arising from Shishkin mesh discretizations

Review and problem list for Applied Math I

1. Introduction. Let µ(t) be a distribution function with infinitely many points of increase in the interval [ π, π] and let

A Method for Constructing Diagonally Dominant Preconditioners based on Jacobi Rotations

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

LINEAR ALGEBRA 1, 2012-I PARTIAL EXAM 3 SOLUTIONS TO PRACTICE PROBLEMS

Characterization of half-radial matrices

Iterative Methods for Sparse Linear Systems

Lecture on: Numerical sparse linear algebra and interpolation spaces. June 3, 2014

Linear Algebra: Matrix Eigenvalue Problems

A Spectral Time-Domain Method for Computational Electrodynamics

HILBERT SPACES AND THE RADON-NIKODYM THEOREM. where the bar in the first equation denotes complex conjugation. In either case, for any x V define

msqm 2011/8/14 21:35 page 189 #197

Chapter 7 Iterative Techniques in Matrix Algebra

On a residual-based a posteriori error estimator for the total error

4.8 Arnoldi Iteration, Krylov Subspaces and GMRES

ITERATIVE METHODS FOR SPARSE LINEAR SYSTEMS

ETNA Kent State University

Applied Mathematics 205. Unit V: Eigenvalue Problems. Lecturer: Dr. David Knezevic

Transcription:

On the Vorobyev method of moments Zdeněk Strakoš Charles University in Prague and Czech Academy of Sciences http://www.karlin.mff.cuni.cz/ strakos Conference in honor of Volker Mehrmann Berlin, May 2015

Thanks, bounds for 1955 Gene Golub, for pushing me to moments Bernd Fischer, for the beautiful book and much more Gérard Meurant, for many moment related joint interests Claude Brezinski, for pointing out the work of Vorobyev Jǒrg Liesen, for sharing interests and many years of collaboration Volker Mehrmann, for lasting inspiration and support in many ways. 1954: Operator orthogonal polynomials and approximation methods for determination of the spectrum of linear operators. 1958 (1965): Method of moments in applied mathematics. Z. Strakoš 2

Broader context of 1955 Euclid (300BC), Hippassus from Metapontum (before 400BC),..., Bhascara II (around 1150), Brouncker and Wallis (1655-56): Three term recurences (for numbers) Euler (1737, 1748),..., Brezinski (1991), Khrushchev (2008) Gauss (1814), Jacobi (1826), Christoffel (1858, 1857),..., Chebyshev (1855, 1859), Markov (1884), Stieltjes (1884, 1893-94): Orthogonal polynomials, quadrature, analytic theory of continued fractions, problem of moments, minimal partial realization, Riemann-Stieltjes integral Gautschi (1981, 2004), Brezinski (1991), Van Assche (1993), Kjeldsen (1993), Hilbert (1906, 1912),..., Von Neumann (1927, 1932), Wintner (1929) resolution of unity, integral representation of operator functions in quantum mechanics Z. Strakoš 3

Broader context of 1955 Krylov (1931), Lanczos (1950, 1952, 1952c), Hestenes and Stiefel (1952), Rutishauser (1953), Henrici (1958), Stiefel (1958), Rutishauser (1959),..., Vorobyev (1954, 1958, 1965), Golub and Welsh (1968),..., Laurie (1991-2001),... Gordon (1968), Schlesinger and Schwartz (1966), Steen (1973), Reinhard (1979),..., Horáček (1983-...), Simon (2007) Paige (1971), Reid (1971), Greenbaum (1989),... Magnus (1962a,b), Gragg (1974), Kalman (1979), Gragg, Lindquist (1983), Gallivan, Grimme, Van Dooren (1994),... Who is Yu. V. Vorobyev? All what we know can be found in Liesen, S, Krylov subspace methods, OUP, 2013, Section 3.7. Z. Strakoš 4

Book (1958, 1965) Z. Strakoš 5

The problem of moments in Hilbert space Let z 0, z 1,...,z n be n+1 linearly independent elements of Hilbert space V. Consider the subspace V n generated by all possible linear combinations of z 0, z 1,...,z n 1 and construct a linear operator B n defined on V n such that z 1 = B n z 0, z 2 = B n z 1,. z n 1 = B n z n 2, E n z n = B n z n 1, where E n z n is the projection of z n onto V n. Z. Strakoš 6

Approximation of bounded linear operators Let B be a bounded linear operator in Hilbert space V. Choosing an element z 0, we first form a sequence of elements z 1,...,z n,... z 0, z 1 = Bz 0, z 2 = Bz 1 = B 2 z 0,..., z n = Bz n 1 = B n z n 1,... For the present z 1,...,z n are assumed to be linearly independent. By solving the moment problem we determine a sequence of operators defined on the sequence of nested subspaces V n such that z 1 = Bz 0 = B n z 0, z 2 = B 2 z 0 = (B n ) 2 z 0,. z n 1 = B n 1 z 0 = (B n ) n 1 z 0, E n z n = E n B n z 0 = (B n ) n z 0. B n Z. Strakoš 7

Approximation of bounded linear operators Using the projection E n onto V n we can write for the operators constructed above (here we need the linearity of B ) B n = E n B E n. The finite dimensional operators B n can be used to obtain approximate solutions to various linear problems. The choice of the elements z 0,...,z n,... as above gives Krylov subspaces that are closely connected with the application (described, e.g. by partial differential equations). Challenges: 1. convergence, 2. computational efficiency. The most important classes of operators to study: completely continuous (compact), self-adjoint. Z. Strakoš 8

Inner product and Riesz map Let V be a real (infinite dimensional) Hilbert space with the inner product (, ) V : V V R, the associated norm V, V # be the dual space of bounded (continuous) linear functionals on V with the duality pairing, : V # V R. For each f V # there exists a unique τf V such that f, v = (τf, v) V for all v V. In this way the inner product (, ) V determines the Riesz map τ : V # V. Z. Strakoš 9

Operator formulation of the PDE BVP Consider a PDE problem described in the form of the functional equation Ax = b, A : V V #, x V, b V #, where the linear, bounded, and coercive operator A is self-adjoint with respect to the duality pairing,. Standard approach to solving boundary-value problems using the preconditioned conjugate gradient method (PCG) preconditions the algebraic problem, A, b, A,b preconditioning PCG applied to Ax = b, i.e., discretization and preconditioning are often considered separately. Z. Strakoš 10

2 Krylov subspaces in Hilbert spaces Using the Riesz map τa : V V, one can form for g V the Krylov sequence g, τag, (τa) 2 g,... in V and define Krylov subspace methods in the Hilbert space operator setting (here CG) such that with r 0 = b Ax 0 V # the approximations x n to the solution x, n = 1, 2,... belong to the Krylov subspaces in V x n x 0 + K n (τa, τr 0 ) x 0 + span{τr 0, τa(τr 0 ), (τa) 2 (τr 0 ),...,(τa) n 1 (τr 0 )}. Approximating the solution x = (τa) 1 τb using Krylov subspaces is not the same as approximating the operator inverse (τa) 1 by the operators I, τa, (τa) 2,... Vorobyev moment problem depends on τb! Z. Strakoš 11

Vorobyev moment problem Using the orthogonal projection E n onto K n with respect to the inner product (, ) V, consider the orthogonally restricted operator τa n : K n K n, τa n E n (τa) E n, by formulating the following equalities τa n (τr 0 ) = τa(τr 0 ), (τa n ) 2 τr 0 = τa n (τa(τr 0 )) = (τa) 2 τr 0,. (τa n ) n 1 τr 0 = τa n ((τa) n 2 τr 0 ) = (τa) n 1 τr 0, (τa n ) n τr 0 = τa n ((τa) n 1 τr 0 ) = E n (τa) n τr 0. Z. Strakoš 12

Lanczos process and Jacobi matrices The n-dimensional approximation τa n of τa matches the first 2n moments ((τa n ) l τr 0, τr 0 ) V = ((τa) l τr 0, τr 0 ) V, l = 0, 1,...,2n 1. Denote symbolically Q n = (q 1,...,q n ) a matrix composed of the columns q 1,...,q n forming an orthonormal basis of K n determined by the Lanczos process τaq n = Q n T n + δ n+1 q n+1 e T n with q 1 = τr 0 / τr 0 V. We get (τa n ) l = Q n T l n Q n, l = 0, 1,... and the matching moments condition e 1 T l n e 1 = q 1(τA) l q 1, l = 0, 1,..., 2n 1, Z. Strakoš 13

Conjugate gradient method - first n steps T n = γ 1 δ 2 δ 2..................... δ n δ n γ n is the Jacobi matrix of the orthogonalization coefficients and the CG method is formulated by T n y n = τr 0 V e 1, x n = x 0 + Q n y n, x n V. Z. Strakoš 14

Spectral representation Since τ A is bounded and self-adjoint, its spectral representation is τa = λu λ L λde λ. The spectral function E λ projections which is of τa represents a family of orthogonal non-decreasing, i.e., if µ > ν, then the subspace onto which E µ projects contains the subspace into which E ν projects; E λl = 0, E λu = I ; E λ is right continuous, i.e. lim λ λ + E λ = E λ. The values of λ where E λ increases by jumps represent the eigenvalues of τa, τaz = λz, z V. Z. Strakoš 15

Representation of the moment problem For the (finite) Jacobi matrix T n we can analogously write T n = n j=1 θ (n) j s (n) j (s (n) j ), λ L < θ (n) 1 < θ (n) 2 < < θ n (n) < λ U, and the operator moment problem turns into the for the 2n unknowns θ (n) j, ω (n) j 2n equations n j=1 ω (n) j {θ (n) j } l = m l λu λ L λ l dω(λ), l = 0, 1,...,2n 1, where dω(λ) = q 1dE λ q 1 represents the Riemann-Stieltjes distribution function associated with τa and q 1. The distribution function ω (n) (λ) approximates ω(λ) in the sense of the nth Gauss-Christoffel quadrature; Gauss (1814), Jacobi (1826), Christoffel (1858). Z. Strakoš 16

Gauss-Christoffel quadrature τa, q 1 = τr 0 / τr 0 V ω(λ), f(λ) dω(λ) T n,e 1 ω (n) (λ), n i=1 ω (n) i f ( θ (n) i ) Using f(λ) = λ 1 gives λu λ L λ 1 dω(λ) = n i=1 ω (n) i ( θ (n) i ) 1 + x x n 2 a τr 0 2 V Continued fraction representation, minimal partial realization etc. Z. Strakoš 17

References J. Málek and Z.S., Preconditioning and the Conjugate Gradient Method in the Context of Solving PDEs. SIAM Spolight Series, SIAM (2015) J. Liesen and Z.S., Krylov Subspace Methods, Principles and Analysis. Oxford University Press (2013) Z.S. and P. Tichý, On efficient numerical approximation of the bilinear form c A 1 b, SIAM J. Sci. Comput., 33 (2011), pp. 565-587 Non self-adjoint compact operators? Z. Strakoš 18

Gauss quadrature in complex plane? Vorobyev moment problem can be based on generalization of the Lanczos process to non self-adjoint operators with starting elements z 0, w 0. Then, however, the tridiagonal matrix of the recurrence coefficients for the properly normalized formal orthogonal polynomials (assuming, for the present, their existence) is complex symmetric but not (in general) Hermitian. Generalization of the n-weight Gauss quadrature representation of the Vorobyev moment problem that eliminates restrictive assumptions on diagonalizability can be based on quasi-definite functionals; see the poster of Stefano Pozza and S. Pozza, M. Pranic and Z.S., Gauss quadrature for quasi-definite linear functionals, submitted (2015). Z. Strakoš 19

Conclusions Vorobyev work was built on the deep knowledge of the previous results. It is amazingly thorough and as to the coverage and references. Published in 1958 (1965), it was much ahead of time. It stimulates new developments for the future. Volker, Many Thanks and Congratulations! Z. Strakoš 20

Whatever we try, does not work Z. Strakoš 21