DOCSLIB.ORG

Sparse Sets in Time and Frequency with Diophantine Problems and Integrable Systems

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Sparse Sets in Time and Frequency with Diophantine Problems and Integrable Systems Sparse Sets in Time and Frequency with Diophantine Problems and Integrable Systems D. V. Chudnovsky, G.V. Chudnovsky, T. Morgan IMAS, Polytechnic University 6 MetroTech Center Brooklyn, NY 11201 Overview of sparseness in space and fre- quency • Space and frequency domains - setting and motivation • Reconstruction and uncertainty in sparse signals • Ill conditioning–effects on reconstruction • Diophantine meaning of ill conditioning • Band limiting and related integrable systems • Combinatorial description of singular sparse sets The spaces over which we operate In this introduction, we give examples of the duality between conventional spatial and frequency representations of ‘sparse objects’, signals sparse in both space and frequency representations. We have a space of signals, and two basis representing signals. One basis consists of confined functions (delta functions),representing atoms of space and a second basis,consisiting of widely distributed functions (sines and cosines), having unbounded support, and the operator, , mapping between the basis. F Then we consider the behavior of the operator, , and the consequences of projecting F signals onto subspaces simultaneously in space and in frequency. The examples that follow will focus on Fourier representations; however other basis sharing the property of confined/unconfined support can be used as well For example, the standard basis can be paired with a polynomial basis. The Fourier matrix 00 10 (n 1)0 ζn ζn ... ζn − 0 01 11 1 1 ζn ζn ... ... n = √ B C F n B ... ... ... ... C B C B 0(n 1) 1(n 1) (n 1)(n 1) C B ζn − ζn − ... ζn − − C @ A 11 1 1 1 1 1 0 2iπ 4iπ 6iπ 6iπ 4iπ 2iπ 1 1 e 7 e 7 e 7 e− 7 e− 7 e− 7 B 4iπ 6iπ 2iπ 2iπ 6iπ 4iπ C B 1 e 7 e− 7 e− 7 e 7 e 7 e− 7 C 1 B 6iπ 2iπ 4iπ 4iπ 2iπ 6iπ C B 7 7 7 7 7 7 C √7 B 1 e e− e e− e e− C B 6iπ 2iπ 4iπ 4iπ 2iπ 6iπ C B C B 1 e− 7 e 7 e− 7 e 7 e− 7 e 7 C B 4iπ 6iπ 2iπ 2iπ 6iπ 4iπ C B 1 e 7 e 7 e 7 e 7 e 7 e 7 C B − − − C B 2iπ 4iπ 6iπ 6iπ 4iπ 2iπ C B 1 e− 7 e− 7 e− 7 e 7 e 7 e 7 C @ A The world (of human kind) is full of band limited signals F SF ←→ Sδ RW ↓ ↓ PT is in here: ? W ↔ T In the block diagram of a system, it’s not at all unusual for an early on block to be a band pass filter Further, there’s an enormous ’filter’ at any system’s outputs – namely, what’s interesting to the observer? Some examples of signals and their representations 500 400 ' Ordinary' images F 300 512 x512 have most energy ® 200 near zero 100 0 0 100 200 300 400 500 500 400 300 F512 x512 ¾¾¾¾¾¾¾¾¾¾® 200 100 0 0 100 200 300 400 500 500 400 F 300 One of a few ways to 51 x512 produce high ¾¾¾¾¾¾¾¾¾¾® 200 frequencies 100 0 0 100 200 300 400 500 Reconstruction/concentration S F S F ←→ δ R P W ↓ ↓ T ? W T ↔ • If the Fourier operator limited to the W, T subspaces is invertible, then signals can be reconstructed from partial frequency data. • If the Fourier operator limited to the W, T subspaces is *not* invertible, then it has a null space Vectors in this null space are interesting, since they exhibit concentration in both space (T) and frequency ( S W ) These are curious vectors, ones which are F \ running up against uncertainty principle limits. A visual example of reconstruction RW F ¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾® SF ←→ Sδ PT RW ↓ ↓ PT ? Reconstruct W ↔ T ¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾¾® The notation for the operators of in- terest, and what are their actions S F S F ←→ δ R P W ↓ ↓ T ? W T ↔ RW operator which projects to W W a subspace of frequencies − PT operator which projects to T T a subspace of spatial points − RWT the composition of RW PT The action of RWT , concretely, is straighforward: 00 10 (n 1)0 ζn ζn ... ζn − 0 01 11 1 1 ζn ζn ... ... n = B C F √n B ... ... ... ... C B C B 0(n 1) 1(n 1) (n 1)(n 1) C B ζn − ζn − ... ζn − − C @ A Non zero deteriminant of certain sub- matrices of the Fourier matrix A little more about the case with invertibility This opens an interesting possibility. For example, you might be able to quickly recover spatial data, if you know that the spatial data was of limited support (but not where it was) For this to work, more than ’non - zero’ is required to be known about the determinant of the the Fourier submatrices Some estimates of the conditioning of the Fourier submatrices are needed Statement of Chebotarev’s Theorem Chebotarev found a modular reduction of the value of the determinant of matrices of the form: a1x1 a1x2 a1xk ζp ζp ... ζp 0 a2x1 a2x2 a2xk 1 ζp ζp ... ζp det B C = B ... ... ... ... C B C B akx1 akx2 akxk C B ζp ζp ... ζp C @ A k(k−1) k(k−1) (xi xj ) (ai aj ) +1 Q − Q − 2 2 1!2!...(k 1)! π mod π − „ « Where π =(ζp 1), p is a prime and a and x are integers − i j In outline the proof proceeds by expanding the matrix entries in a series for the formal variable π , gathering the coefficients of the like terms of π and finding values for the coefficients for various powers of π in the expansion of the determinant. More about the reconstruction opera- tion S F S F ←→ δ R P W ↓ ↓ T ? W T ↔ Reconstruction consists of inverting the operator RWT on the subspace W We look at a signal, s, that is band limited to W (that is,the Fourier coefficients of S are zero outside of W). We want to reconstruct the signal, s everywhere, from it’s values on a subset T. For this to succeed, T must be at least as great as W . | | | | As a practical matter, the more observations points available in T, the better the reconstruction. For convenience, instead of reconstructing s, we reconstruct f= (s) ,knowing that f is F non zero only on W and recover s from f. 1 Writing explicitly the identity: s = − (s) 1 ( u x) F we have: − · √n f(u) ζn = s(x) x ǫ T u W · P∈ the sum may be restricted to W because f = (s) is zero outside W. In matrix notation, F this expression becomes: RWT f = s The pseudo-inverse and it’s applica- tion to recontruction Given a matrix,the Moore-Penrose generalized matrix inverse is a unique matrix pseudoinverse.This matrix was independently defined by Moore in 1920 and Penrose (1955),and variously known as the generalized inverse, pseudoinverse,or Moore-Penrose inverse. The psuedo inverse for a system A x = y : apply AH to both sides: AHA x = AH y AHA is square; invert it to find: 1 x = AH A − AH y The` psuedo´ inverse produces a useful result, even with inconsistent data; it provides a minimal L2 fit to the system of equations. For our system of equations, reconstruction depends on the conditioning of the matrix W x W matrix, Q = RWT RWTH t(u v) | | | | ( Q)u,v= tǫT ζn − for (u,v) in W The largerPT , the better conditioned the matrix Q. In particular, for T = n, Q isthe | | | | identity matrix of order W . | | The engineers point of view, part 1 The engineer imagines ’n’ as the discretization scale. The extent to which the number theoretic properties of ’n’ are involved is unsatisfactory, and surprising. Further ’non-singularity’ isn’t good enough. For practical reconstruction, well conditioning of the matrix Q is necessary. For these purposes, matrix conditioning is measured in a variety of ways 1.The size of the determinant 2.The size of the smallest eigenvalue 3.Ratio of the largest to the smallest eigenvalue 4.The size of the Frobenius norm of the inverted matrix 3 is a standard measure of matrix conditioning; 4 has an immediate consequence for the processing of quantized values Related by inequalities, any of these means provides an indication of reconstruction problems The distribution of the eigenvalues of the ma- trix Q shows extraordinary outliers Numerical evidence - subset pairs for n=23,d1=5 ,d2=6 Sidebar - on programming to gather numerical evidence • Problems amount to exhaustive search of all (most) subsets • Processing of equivalence classes to extend the search space • Optimizing programming execution time vs total time to result • Compiled versus interpretitive environments + very basic parallelism And then – How do you look at a billion numbers? A bound on det Q by means of Cheb- otarev’s theorem a1x1 a1x2 a1xk ζp ζp ... ζp 0 a2x1 a2x2 a2xk 1 ζp ζp ... ζp det B C = B ... ... ... ... C B C B akx1 akx2 akxk C B ζp ζp ... ζp C @ A k(k−1) k(k−1) (xi xj ) (ai aj ) +1 Q − Q − 2 2 1!2!...(k 1)! π modπ − „ « By Chebotarev’s theorm,the determinant of = w t CW,T ζn · w W,t T ∈ ∈ is non zero` for´ n a prime with W = T . We have: detQ = detC 2 | | | | | W,T | | W,T | How small can this determinant be for a given W and T and n a prime? (n 3)/2 It can be shown that: det C nd(d 1)/4 dd/2 − − W,T ≥ − · ˛ ` ´˛ ` ´ ˛ ˛ The engineers point of view, part 2 (n 3)/2 det C nd(d 1)/4 dd/2 − − W,T ≥ − · When˛ ` d is small´˛ and n is large,` this´ bound is exponential in n.
Load more

Recommended publications
  • Extremely Accurate Solutions Using Block Decomposition and Extended Precision for Solving Very Ill-Conditioned Equations
    Extremely accurate solutions using block decomposition and extended precision for solving very ill-conditioned equations †Kansa, E.J.1, Skala V.2, and Holoborodko, P.3 1Convergent Solutions, Livermore, CA 94550 USA 2Computer Science & Engineering Dept., Faculty of Applied Sciences, University of West Bohemia, University 8, CZ 301 00 Plzen, Czech Republic 3Advanpix LLC, Maison Takashima Bldg. 2F, Daimachi 10-15-201, Yokohama, Japan 221-0834 †Corresponding author: [email protected] Abstract Many authors have complained about the ill-conditioning associated the numerical solution of partial differential equations (PDEs) and integral equations (IEs) using as the continuously differentiable Gaussian and multiquadric continuously differentiable (C ) radial basis functions (RBFs). Unlike finite elements, finite difference, or finite volume methods that lave compact local support that give rise to sparse equations, the C -RBFs with simple collocation methods give rise to full, asymmetric systems of equations. Since C RBFs have adjustable constent or variable shape parameters, the resulting systems of equations that are solve on single or double precision computers can suffer from “ill-conditioning”. Condition numbers can be either the absolute or relative condition number, but in the context of linear equations, the absolute condition number will be understood. Results will be presented that demonstrates the combination of Block Gaussian elimination, arbitrary arithmetic precision, and iterative refinement can give remarkably accurate numerical salutations to large Hilbert and van der Monde equation systems. 1. Introduction An accurate definition of the condition number, , of the matrix, A, is the ratio of the largest to smallest absolute value of the singular values, { i}, obtained from the singular value decomposition (SVD) method, see [1]: (A) = maxjjminjj (1) This definition of condition number will be used in this study.
    [Show full text]
  • A Study of Convergence of the PMARC Matrices Applicable to WICS Calculations
    A Study of Convergence of the PMARC Matrices applicable to WICS Calculations /- by Amitabha Ghosh Department of Mechanical Engineering Rochester Institute of Technology Rochester, NY 14623 Final Report NASA Cooperative Agreement No: NCC 2-937 Presented to NASA Ames Research Center Moffett Field, CA 94035 August 31, 1997 Table of Contents Abstract ............................................ 3 Introduction .......................................... 3 Solution of Linear Systems ................................... 4 Direct Solvers .......................................... 5 Gaussian Elimination: .................................. 5 Gauss-Jordan Elimination: ................................ 5 L-U Decompostion: ................................... 6 Iterative Solvers ........................................ 6 Jacobi Method: ..................................... 7 Gauss-Seidel Method: .............................. .... 7 Successive Over-relaxation Method: .......................... 8 Conjugate Gradient Method: .............................. 9 Recent Developments: ......................... ....... 9 Computational Efficiency .................................... 10 Graphical Interpretation of Residual Correction Schemes .................... 11 Defective Matrices ............................. , ......... 15 Results and Discussion ............ ......................... 16 Concluding Remarks ...................................... 19 Acknowledgements ....................................... 19 References ..........................................
    [Show full text]
  • Applied Linear Algebra
    APPLIED LINEAR ALGEBRA Giorgio Picci November 24, 2015 1 Contents 1 LINEAR VECTOR SPACES AND LINEAR MAPS 10 1.1 Linear Maps and Matrices . 11 1.2 Inverse of a Linear Map . 12 1.3 Inner products and norms . 13 1.4 Inner products in coordinate spaces (1) . 14 1.5 Inner products in coordinate spaces (2) . 15 1.6 Adjoints. 16 1.7 Subspaces . 18 1.8 Image and kernel of a linear map . 19 1.9 Invariant subspaces in Rn ........................... 22 1.10 Invariant subspaces and block-diagonalization . 23 2 1.11 Eigenvalues and Eigenvectors . 24 2 SYMMETRIC MATRICES 25 2.1 Generalizations: Normal, Hermitian and Unitary matrices . 26 2.2 Change of Basis . 27 2.3 Similarity . 29 2.4 Similarity again . 30 2.5 Problems . 31 2.6 Skew-Hermitian matrices (1) . 32 2.7 Skew-Symmetric matrices (2) . 33 2.8 Square roots of positive semidefinite matrices . 36 2.9 Projections in Rn ............................... 38 2.10 Projections on general inner product spaces . 40 3 2.11 Gramians. 41 2.12 Example: Polynomial vector spaces . 42 3 LINEAR LEAST SQUARES PROBLEMS 43 3.1 Weighted Least Squares . 44 3.2 Solution by the Orthogonality Principle . 46 3.3 Matrix least-Squares Problems . 48 3.4 A problem from subspace identification . 50 3.5 Relation with Left- and Right- Inverses . 51 3.6 The Pseudoinverse . 54 3.7 The Euclidean pseudoinverse . 63 3.8 The Pseudoinverse and Orthogonal Projections . 64 3.9 Linear equations . 66 4 3.10 Unfeasible linear equations and Least Squares . 68 3.11 The Singular value decomposition (SVD) .
    [Show full text]
  • Matrix Computations
    Chapter 5 Matrix Computations §5.1 Setting Up Matrix Problems §5.2 Matrix Operations §5.3 Once Again, Setting Up Matrix Problems §5.4 Recursive Matrix Operations §5.5 Distributed Memory Matrix Multiplication The next item on our agenda is the linear equation problem Ax = b. However, before we get into algorithmic details, it is important to study two simpler calculations: matrix-vector mul- tiplication and matrix-matrix multiplication. Both operations are supported in Matlab so in that sense there is “nothing to do.” However, there is much to learn by studying how these com- putations can be implemented. Matrix-vector multiplications arise during the course of solving Ax = b problems. Moreover, it is good to uplift our ability to think at the matrix-vector level before embarking on a presentation of Ax = b solvers. The act of setting up a matrix problem also deserves attention. Often, the amount of work that is required to initialize an n-by-n matrix is as much as the work required to solve for x. We pay particular attention to the common setting when each matrix entry aij is an evaluation of a continuous function f(x, y). A theme throughout this chapter is the exploitation of structure. It is frequently the case that there is a pattern among the entries in A which can be used to reduce the amount of work. The fast Fourier transform and the fast Strassen matrix multiply algorithm are presented as examples of recursion in the matrix computations. The organization of matrix-matrix multiplication on a ring of processors is also studied and gives us a nice snapshot of what algorithm development is like in a distributed memory environment.
    [Show full text]
  • Contributions of Issai Schur to Analysis
    Contributions of Issai Schur to Analysis Harry Dym and Victor Katsnelson∗ November 1, 2018 The name Schur is associated with many terms and concepts that are widely used in a number of diverse fields of mathematics and engineering. This survey article focuses on Schur’s work in analysis. Here too, Schur’s name is commonplace: The Schur test and Schur-Hadamard multipliers (in the study of estimates for Hermitian forms), Schur convexity, Schur complements, Schur’s results in summation theory for sequences (in particular, the fundamental Kojima-Schur theorem), the Schur-Cohn test, the Schur al- gorithm, Schur parameters and the Schur interpolation problem for functions that are holomorphic and bounded by one in the unit disk. In this survey, we shall discuss all of the above mentioned topics and then some, as well as some of the generalizations that they inspired. There are nine sections of text, each of which is devoted to a separate theme based on Schur’s work. Each of these sections has an independent bibliography. There is very little overlap. A tenth section presents a list of the papers of Schur that focus on topics that are commonly considered to be analysis. We shall begin with a review of Schur’s less familiar papers on the theory of commuting differential operators. Acknowledgement: The authors extend their thanks to Bernd Kirstein for carefully reading the manuscript and spotting a number of misprints. 1 . Permutable differential operators and fractional powers of differential operators. Let arXiv:0706.1868v1 [math.CA] 13 Jun 2007 dny dn−1y P (y)= p (x) + p (x) + + p (x)y (1.1) n dxn n−1 dxn−1 ··· 0 and dmy dm−1y Q(y)= q (x) + q (x) + + q (x)y, (1.2) m dxm m−1 dxm−1 ··· 0 ∗HD thanks Renee and Jay Weiss and VK thanks Ruth and Sylvia Shogam for endowing the chairs that support their respective research.
    [Show full text]
  • Package 'Matrixcalc'
    Package ‘matrixcalc’ July 28, 2021 Version 1.0-5 Date 2021-07-27 Title Collection of Functions for Matrix Calculations Author Frederick Novomestky <[email protected]> Maintainer S. Thomas Kelly <[email protected]> Depends R (>= 2.0.1) Description A collection of functions to support matrix calculations for probability, econometric and numerical analysis. There are additional functions that are comparable to APL functions which are useful for actuarial models such as pension mathematics. This package is used for teaching and research purposes at the Department of Finance and Risk Engineering, New York University, Polytechnic Institute, Brooklyn, NY 11201. Horn, R.A. (1990) Matrix Analysis. ISBN 978-0521386326. Lancaster, P. (1969) Theory of Matrices. ISBN 978-0124355507. Lay, D.C. (1995) Linear Algebra: And Its Applications. ISBN 978-0201845563. License GPL (>= 2) Repository CRAN Date/Publication 2021-07-28 08:00:02 UTC NeedsCompilation no R topics documented: commutation.matrix . .3 creation.matrix . .4 D.matrix . .5 direct.prod . .6 direct.sum . .7 duplication.matrix . .8 E.matrices . .9 elimination.matrix . 10 entrywise.norm . 11 1 2 R topics documented: fibonacci.matrix . 12 frobenius.matrix . 13 frobenius.norm . 14 frobenius.prod . 15 H.matrices . 17 hadamard.prod . 18 hankel.matrix . 19 hilbert.matrix . 20 hilbert.schmidt.norm . 21 inf.norm . 22 is.diagonal.matrix . 23 is.idempotent.matrix . 24 is.indefinite . 25 is.negative.definite . 26 is.negative.semi.definite . 28 is.non.singular.matrix . 29 is.positive.definite . 31 is.positive.semi.definite . 32 is.singular.matrix . 34 is.skew.symmetric.matrix . 35 is.square.matrix . 36 is.symmetric.matrix .
    [Show full text]
  • Math 448/548 Cpt S 430/530 Assignment 2 Due February 9 1. Let
    Math 448/548 Cpt S 430/530 Assignment 2 due February 9 1. Let A be a real n × n matrix. n (a) Let x 2 R and y = Ax. Show that for i = 1, . , n, yi (the ith component of y) satisfies n ! X jyi j ≤ jaij j kxk1: j=1 (b) Deduce that n ! X ky k ≤ max jaij j kxk 1 i 1 j=1 and n kAxk1 X max ≤ max jaij j: (1) x6=0 kxk i 1 j=1 (c) Show that equality holds in (1) by exhibiting an x for which n kAxk1 X = max jaij j: kxk i 1 j=1 (Hint: Choose an x whose entries are all ±1, with the signs care- fully chosen.) n ! X (d) Deduce that kAk = max jaij j 1 i j=1 2. Let 2 1 2 3 3 A = 4 4 5 6 5 : 7 8 9 (a) Prove by hand computation that A is singular. For example, you can show that the rows of A are linearly dependent. (b) Demonstrate that MATLAB, which uses floating-point arithmetic, does not notice that A is singular (although it does suspect that something is wrong). Use MATLAB commands like inv (inverse) and cond (condition number). 3. The Hilbert matrices are the most famous family of ill-conditioned ma- trices. The (i; j) entry of the n×n Hilbert matrix is hij = 1=(i+j −1). Using MATLAB, do the following for n = 4, 8, and 12. Generate the n×n Hilbert matrix. (MATLAB has a convenient command hilb that generates a Hilbert matrix.) Let z denote the vector of all ones, and let b = Az, where A is the Hilbert matrix.
    [Show full text]
  • Math 240 Summer 2019 Notes
    Math 240 Summer 2019 Notes Yao-Rui Lecture notes might contain typos. Contents 1 Lecture 1 { Introduction 4 1.1 Brief Overview of the Course . 4 1.2 Separable ODEs . 6 1.3 First Order Linear ODEs . 7 1.4 Bernoulli ODEs . 8 1.5 Ricatti ODEs . 8 1.6 Reduction of Order . 9 2 Lecture 2 { Matrices 10 2.1 Row Echelon Form . 10 2.2 Determinants and Invertible Matrices . 11 2.3 More Determinant Facts . 13 3 Lecture 3 { Linear Maps 15 3.1 Basis . 15 3.2 Matrix Representation of Linear Maps . 15 3.3 Change of Basis . 17 3.4 Subspaces and the Rank-Nullity Theorem . 18 3.5 An Overview of Abstract Vector Spaces . 19 4 Lecture 4 { Eigenvalues and Eigenvectors 21 4.1 Eigenvalues and Eigenvectors . 21 4.2 The Cayley-Hamilton Theorem . 24 5 Lecture 5 { Matrix Exponentials 26 5.1 Definition and Example Computations . 26 5.2 Analytic Function on Matrices . 28 5.3 Nilpotent Matrices and Exponentiation . 29 6 Lecture 6 { First Order Constant Linear ODEs Part 1 30 6.1 The General Solution . 30 6.2 Examples on Diagonalizable Matrices . 30 6.3 Duhamel's Formula . 31 7 Lecture 7 { First Order Constant Linear ODEs Part 2 33 7.1 Generalized Eigenvectors . 33 7.2 The Jordan Canonical Form for Nilpotent Matrices . 35 7.3 The Jordan Canonical Form in General . 36 1 8 Lecture 8 { First Order Constant Linear ODEs Part 3 42 8.1 An Application of Invariant Subspaces . 42 8.2 Buchheim's Algorithm and Some Formulas for Exponentiating Matrices .
    [Show full text]
  • Arxiv:Math/9905079V1 [Math.RA] 12 May 1999 Ento 1.1
    THE FILBERT MATRIX THOMAS M. RICHARDSON DEPARTMENT OF MATHEMATICS AND STATISTICS WESTERN MICHIGAN UNIVERSITY KALAMAZOO, MI 49008 Abstract. A Filbert matrix is a matrix whose (i, j) entry is 1/Fi+j−1, where th Fn is the n Fibonacci number. The inverse of the n × n Filbert matrix resembles the inverse of the n × n Hilbert matrix, and we prove that it shares the property of having integer entries. We prove that the matrix formed by replacing the Fibonacci numbers with the Fibonacci polynomials has entries which are integer polynomials. We also prove that certain Hankel matrices of reciprocals of binomial coefficients have integer entries, and we conjecture that the corresponding matrices based on Fibonomial coefficients have integer entries. Our method is to give explicit formulae for the inverses. 1. Introduction 1 The n × n Hilbert matrix is the n × n matrix whose (i, j)-entry is i+j−1 . In [2], Man-Duen Choi explores many fascinating properties of the Hilbert matrix, including the fact the the (i, j)-entry of its inverse is n + i − 1 n + j − 1 i + j − 2 2 α = (−1)i+j (i + j − 1) . (1) ij n − j n − i i − 1 Choi asks what sort of coincidence it is if the inverse of a matrix of reciprocals of integers has integer entries. In this paper we show that the inverses of the Hankel matrices based on the reciprocals of the Fibonacci numbers, the reciprocals of the i+j i+j+2 binomial coefficients 2 , and the reciprocals of the binomial coeffiencts 3 all have integer entries.
    [Show full text]
  • Complexity of Computations with Matrices and Polynomials
    Complexity of Computations with Matrices and Polynomials Victor Pan Department of Computer Science, Columbia University, New York, NY 10027 Computer Science Department State University of New York at Albany Albany, New York 12222 and Mathematics and Computer Science Department Lehman College, CUNY Bronx, NY 10468 CUCS-031-90 Summary. We review the complexity of polynomial and matrix computations, as well as their various correlations to each other and some major techniques for the design of algebraic and numerical algo- rithms. 0. Introduction. Polynomial and matrix computations are highly important classical subjects. They have been thoroughly revised during the last decades due to the development of computer technology, whose latest notable progress was the advent of parallel computers. The current polynomial and matrix algorithms perform far better than their classical predecessors, and one of our major objectives is to review the computational cost of such modern algorithms, together with other aspects of their design and analysis, including their various correlations to each other and to the algorithms for other algebraic and numerical computations. In this review we will present some highlights of computations with univariate polynomials and matrices over the complex, real or rational fields; some further treatment can be found in [BP,a] (which is the most updated coverage) and, to some extent, in the earlier books [AHU], [BM] and (for polyno- - 2 - mial computations) [Kn]. Many of the techniques and the algorithms that we review can be extended to computations over finite fields too (see [BP,a]). Reviewing matrix computations, we will avoid overlaps with the excellent presentations in such books as [Wilk], [GL] and [GeLi] by focusing on distinct but still important issues, in particular, on the complexity of general matrix computations, on some very recent algorithms, on the complexity of computations with dense structured matrices and on their correlations to polynomial computations.
    [Show full text]
  • A Remark on Hilbert's Matrix* F. Albert0 Griinbaum Mathematics
    A Remark on Hilbert’s Matrix* F. Albert0 Griinbaum Mathematics Department University of California at Berkeley Berkeley, California 94720 Sulmlittetl by Richard .4. Brualdi ABSTRACT We exhibit a Jacobi matrix T which has simple spectrum and integer entries, and commutes with Hilbert’s matrix. As an application we replace the computation of the eigenvectors of Hilbert’s matrix (a very ill-conditioned problem) by the computation of the eigenvectors of T (a nicely stable numerical problem). 1. INTRODUCTION The purpose of this note is to exhibit a tridiagonal matrix T,(B) with integer coefficients which commutes with Hilbert’s matrix (H,(8))ii=(i+i+fl)-‘, lGi,jGn. The matrix T,(O) has a simple spectrum, and thus its eigenvectors are eigenvectors of the Hilbert matrix. In the same vein, H,(B) can be written as a polynomial in T,,(B). Both of these points are taken up below. The class of Hankel matrices, i.e. those satisfying that have a tridiagonal matrix-with simple spectrum-in their commutator is very limited, and thus the result reported here is quite surprising. The situation resembles that of Toeplitz matrices, among which a complete description of those with a tridiagonal matrix in their commutator has been given in [2]. *Suppor&l by NSF Grant MCS 78-06718. LINEAR ALGEBRA AND ITS APPLICATIONS 43: 119- 124 (1982) 119 G ElsevierScience PublishingCo., Inc., 1982 52 Vanderbilt Ave., New York, NY 10017 0024379.5/82/020119 + 6$02.75 120 F. ALBERT0 GRiiNBAUM As a general reference on the Hilbert matrix one can consult [3] and the references mentioned there.
    [Show full text]
  • Numerical Matrix Analysis
    Copyright ©2009 by the Society for Industrial and Applied Mathematics This electronic version is for personal use and may not be duplicated or distributed. Numerical Matrix Analysis Mathematics Applied and Industrial for Society Buy this book from SIAM at www.ec-securehost.com/SIAM/OT113.html Copyright ©2009 by the Society for Industrial and Applied Mathematics This electronic version is for personal use and may not be duplicated or distributed. Mathematics Applied and Industrial for Society Buy this book from SIAM at www.ec-securehost.com/SIAM/OT113.html Copyright ©2009 by the Society for Industrial and Applied Mathematics This electronic version is for personal use and may not be duplicated or distributed. Numerical Matrix Analysis Linear Systems and LeastMathematics Squares Applied Ilse C. F. Ipsenand North Carolina State University Raleigh, NorthIndustrial Carolina for Society Society for Industrial and Applied Mathematics Philadelphia Buy this book from SIAM at www.ec-securehost.com/SIAM/OT113.html Copyright ©2009 by the Society for Industrial and Applied Mathematics This electronic version is for personal use and may not be duplicated or distributed. Copyright © 2009 by the Society for Industrial and Applied Mathematics 10 9 8 7 6 5 4 3 2 1 All rights reserved. Printed in the United States of America. No part of this book may be reproduced, stored, or transmitted in any manner without the writtenMathematics permission of the publisher. For information, write to the Society for Industrial and Applied Mathematics, 3600 Market Street, 6th Floor, Philadelphia, PA, 19104-2688 USA. Trademarked names may be used in this book withoutApplied the inclusion of a trademark symbol.
    [Show full text]