DOCSLIB.ORG

A. Mathematical Preliminaries

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A. Mathematical Preliminaries A. Mathematical Preliminaries In this appendix, we present some of the miscellaneous bits of mathematical knowledge that are not topics of advanced linear algebra themselves, but are nevertheless useful and might be missing from the reader’s toolbox. We also review some basic linear algebra from an introductory course that the reader may have forgotten. A.1 Review of Introductory Linear Algebra Here we review some of the basics of linear algebra that we expect the reader to be familiar with throughout the main text. We present some of the key results of introductory linear algebra, but we do not present any proofs or much context. For a more thorough presentation of these results and concepts, the reader is directed to an introductory linear algebra textbook like [Joh20]. A.1.1 Systems of Linear Equations One of the first objects typically explored in introductory linear algebra is a system of linear equations (or a linear system for short), which is a collection of 1 or more linear equations (i.e., equations in which variables can only be added to each other and/or multiplied by scalars) in the same variables, like y + 3z = 3 2x + y z = 1 (A.1.1) − x + y + z = 2: Linear systems can have zero, one, or infinitely many solutions, and one particularly useful method of finding these solutions (if they exist) starts by placing the coefficients of the linear system into a rectangular array called a The rows of a matrix matrix. For example, the matrix associated with the linear system (A.1.1) is represent the equations in the 0 1 3 3 linear system, and its 2 1 1 1 : (A.1.2) columns represent 2 − 3 the variables as well 1 1 1 2 as the coefficients 4 5 on the right-hand We then use a method called Gaussian elimination or row reduction, side. which works by using one of the three following elementary row operations to simplify this matrix as much as possible: Multiplication. Multiplying row j by a non-zero scalar c R, which we denote by cR . 2 j © Springer Nature Switzerland AG 2021 415 N. Johnston, Advanced Linear and Matrix Algebra, https://doi.org/10.1007/978-3-030-52815-7 416 Appendix A. Mathematical Preliminaries Swap. Swapping rows i and j, which we denote by R R . i $ j Addition. Replacing row i by (row i) + c(row j), which we denote by Ri + cR j. In particular, we can use these three elementary row operations to put any matrix into reduced row echelon form (RREF), which means that it has the following three properties: Any matrix that has all rows consisting entirely of zeros are below the non-zero rows, the first two of these • in each non-zero row, the first non-zero entry (called the leading entry) three properties is • said to be in is to the left of any leading entries below it, and (not-necessarily- each leading entry equals 1 and is the only non-zero entry in its column. reduced) row • echelon form. For example, we can put the matrix (A.1.2) into reduced row echelon form via the following sequence of elementary row operations: Every matrix can be 0 1 3 3 1 1 1 2 1 1 1 2 converted into one, 2 1 1 1 R1 R3 2 1 1 1 R2 2R1 0 1 3 3 and only one, 2 − 3 $ 2 − 3 − 2 − − − 3 reduced row 1 1 1 2 −−−−! 0 1 3 3 −−−−! 0 1 3 3 echelon form. 4 5 4 1 1 1 2 5 4 1 0 2 1 5 However, there may R1 R2 R − − − be many different 2 0 1 3 3 R3 R2 0 1 3 3 : − 2 3 − 2 3 sequences of row −−! 0 1 3 3 −−−−−! 0 0 0 0 operations that get 4 5 4 5 there. One of the useful features of reduced row echelon form is that the solutions of the corresponding linear system can be read off from it directly. For example, if we interpret the reduced row echelon form above as a linear system, the bottom row simply says 0x+0y+0z = 0 (so we ignore it), the second row says that y + 3z = 3, and the top row says that x 2z = 1. If we just move the “z” − − term in each of these equations over to the other side, we see that every solution of this linear system has x = 2z 1 and y = 3 3z, where z is arbitrary (we − − thus call z a free variable and x and y leading variables). A.1.2 Matrices as Linear Transformations One of the central features of linear algebra is that there is a one-to-one corre- spondence between matrices and linear transformations. That is, every m n n × In fact, vectors and matrix A m;n can be thought of as a function that sends x R to the matrices do not 2 M 2 vector Ax Rm. Conversely, every linear transformation T : Rn Rm (i.e., even need to have 2 ! function with the property that T(x + cy) = T(x) + cT(y) for all x;y Rn and real entries. Their 2 entries can come c R) can be represented by a matrix—there is a unique matrix A 2 2 Mm;n from any “field” (see with the property that Ax = T(x) for all x Rn. We thus think of matrices and the upcoming 2 Appendix A.4). linear transformations as the “same thing”. Linear transformations are special for the fact that they are determined completely by how they act on the standard basis vectors e1;e2;:::;en, which A linear are the vectors with all entries equal to 0, except for a single entry equal to 1 in transformation is also the location indicated by the subscript (e.g., in R3 there are three standard basis completely e = ( ; ; ) e = ( ; ; ) e = ( ; ; ) e e determined by how vectors: 1 1 0 0 , 2 0 1 0 , and 3 0 0 1 ). In particular, A 1, A 2, it acts on any other :::, Aen are exactly the n columns of A, and those n vectors form the sides of basis of Rn. the parallelogram/ parallelepiped/hyperparallelepiped that the unit square/cube/ hypercube is mapped to by A (see Figure A.1). In particular, linear transformations act “uniformly” in the sense that they send a unit square/cube/hypercube grid to a parallelogram/parallelepiped/ hyperparallelepiped grid without distorting any particular region of space more than other regions of space. A.1 Review of Introductory Linear Algebra 417 y y Squares are mapped to Av parallelograms in R2, v cubes are mapped A e to parallelepipeds in e2 A 2 R3, and so on. −−→ Ae1 x x e1 Figure A.1: A matrix A M acts as a linear transformation on R2 that transforms a 2 2 square grid with sides e1 and e2 into a grid made up of parallelograms with sides Ae1 and Ae2 (i.e., the columns of A). Importantly, A preserves which cell of the grid each vector is in (in this case, v is in the 2nd square to the right, 3rd up, and Av is similarly in the 2nd parallelogram in the direction of Ae1 and 3rd in the direction of Ae2. A.1.3 The Inverse of a Matrix 1 The inverse of a square matrix A n is a matrix A− n for which 1 1 2 M 2 M In the n = 1 case, the AA− = A− A = I (the identity matrix). The inverse of a matrix is unique when inverse of a 1 1 it exists, but not all matrices have inverses (even in the n = 1 case, the scalar 0 matrix (i.e., scalar)×a is just 1=a. does not have an inverse). The inverse of a matrix can be computed by using Gaussian elimination to row-reduce the block matrix [ A I ] into its reduced j row echelon form [ I A 1 ]. Furthermore, if the reduced row echelon form of j − [ A I ] has anything other than I in the left block, then A is not invertible. j We show in A one-sided inverse of a matrix is automatically two-sided (that is, if either Remark 1.2.2 that of the equations AB = I or BA = I holds then the other necessarily holds as one-sided inverses 1 are not necessarily well—we can deduce that B = A− based on just one of the two defining two-sided in the equations). We can get some intuition for why this fact is true by thinking infinite-dimensional geometrically—if AB = I then, as linear transformations, A simply undoes case. whatever B does to Rn, and it perhaps seems believable that B similarly undoes whatever A does (see Figure A.2). y y y Av 1 A− Av = v v 1 A A− −−→ −−−→ x x x 1 n Figure A.2: As a linear transformation, A− undoes what A does to vectors in R . That is, A 1Av = v for all v Rn (and AA 1v = v too). − 2 − Row reducing a matrix is equivalent to multiplication on the left by an invertible matrix in the sense that B can be obtained from A 2 Mm;n 2 Mm;n via a sequence of elementary row operations if and only if there is an invertible matrix P such that B = PA. The fact that every matrix can be row- 2 Mm reduced to a (unique) matrix in reduced row echelon form is thus equivalent to the following fact: ! For every A m;n, there exists an invertible P m such that A = PR,2 where M R is the RREF of A.
Load more

Recommended publications
  • MATRIX OPERATORS and the KLEIN FOUR GROUP Ginés R Pérez Teruel
    Palestine Journal of Mathematics Vol. 9(1)(2020) , 402–410 © Palestine Polytechnic University-PPU 2020 MATRIX OPERATORS AND THE KLEIN FOUR GROUP Ginés R Pérez Teruel Communicated by José Luis López Bonilla MSC 2010 Classifications: Primary 15B99,15A24; Secondary 20K99. Keywords and phrases: Klein Four Group; Per-symmetric Matrices; Matrix Algebra. I am in debt to C. Caravello for useful comments and suggestions. Abstract. In this note we show that the set of operators, S = fI; T; P; T ◦ P g that consists of the identity I, the usual transpose T , the per-transpose P and their product T ◦ P , forms a Klein Four-Group with the composition. With the introduced framework, we study in detail the properties of bisymmetric, centrosymmetric matrices and other algebraic structures, and we provide new definitions and results concerning these structures. In particular, we show that the per-tansposition allows to define a degenerate inner product of vectors, a cross product and a dyadic product of vectors with some interesting properties. In the last part of the work, we provide another realization of the Klein Group involving the tensorial product of some 2 × 2 matrices. 1 Introduction and background n×m Definition 1.1. Let A 2 R . If A = [aij] for all 1 ≤ i ≤ n, 1 ≤ j ≤ m, then P (A) is the per-transpose of A, and operation defined by P [aij] = [am−j+1;n−i+1] (1.1) Consequently, P (A) 2 Rm×n. Here, we list some properties: (i) P ◦ P (A) = A (ii) P (A ± B) = P (A) ± P (B) if A, B 2 Rn×m (iii) P (αA) = αP (A) if α 2 R (iv) P (AB) = P (B)P (A) if A 2 Rm×n, B 2 Rn×p (v) P ◦ T (A) = T ◦ P (A) where T (A) is the transpose of A (vi) det(P (A)) = det(A) (vii) P (A)−1 = P (A−1) if det(A) =6 0 The proofs of these properties follow directly from the definition.
    [Show full text]
  • A Complete Bibliography of Publications in Linear Algebra and Its Applications: 2010–2019
    A Complete Bibliography of Publications in Linear Algebra and its Applications: 2010{2019 Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 12 March 2021 Version 1.74 Title word cross-reference KY14, Rim12, Rud12, YHH12, vdH14]. 24 [KAAK11]. 2n − 3[BCS10,ˇ Hil13]. 2 × 2 [CGRVC13, CGSCZ10, CM14, DW11, DMS10, JK11, KJK13, MSvW12, Yan14]. (−1; 1) [AAFG12].´ (0; 1) 2 × 2 × 2 [Ber13b]. 3 [BBS12b, NP10, Ghe14a]. (2; 2; 0) [BZWL13, Bre14, CILL12, CKAC14, Fri12, [CI13, PH12]. (A; B) [PP13b]. (α, β) GOvdD14, GX12a, Kal13b, KK14, YHH12]. [HW11, HZM10]. (C; λ; µ)[dMR12].(`; m) p 3n2 − 2 2n3=2 − 3n [MR13]. [DFG10]. (H; m)[BOZ10].(κ, τ) p 3n2 − 2 2n3=23n [MR14a]. 3 × 3 [CSZ10, CR10c]. (λ, 2) [BBS12b]. (m; s; 0) [Dru14, GLZ14, Sev14]. 3 × 3 × 2 [Ber13b]. [GH13b]. (n − 3) [CGO10]. (n − 3; 2; 1) 3 × 3 × 3 [BH13b]. 4 [Ban13a, BDK11, [CCGR13]. (!) [CL12a]. (P; R)[KNS14]. BZ12b, CK13a, FP14, NSW13, Nor14]. 4 × 4 (R; S )[Tre12].−1 [LZG14]. 0 [AKZ13, σ [CJR11]. 5 Ano12-30, CGGS13, DLMZ14, Wu10a]. 1 [BH13b, CHY12, KRH14, Kol13, MW14a]. [Ano12-30, AHL+14, CGGS13, GM14, 5 × 5 [BAD09, DA10, Hil12a, Spe11]. 5 × n Kal13b, LM12, Wu10a]. 1=n [CNPP12]. [CJR11]. 6 1 <t<2 [Seo14]. 2 [AIS14, AM14, AKA13, [DK13c, DK11, DK12a, DK13b, Kar11a].
    [Show full text]
  • Trends in Field Theory Research
    Complimentary Contributor Copy Complimentary Contributor Copy MATHEMATICS RESEARCH DEVELOPMENTS ADVANCES IN LINEAR ALGEBRA RESEARCH No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services. Complimentary Contributor Copy MATHEMATICS RESEARCH DEVELOPMENTS Additional books in this series can be found on Nova’s website under the Series tab. Additional e-books in this series can be found on Nova’s website under the e-book tab. Complimentary Contributor Copy MATHEMATICS RESEARCH DEVELOPMENTS ADVANCES IN LINEAR ALGEBRA RESEARCH IVAN KYRCHEI EDITOR New York Complimentary Contributor Copy Copyright © 2015 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: [email protected] NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions.
    [Show full text]
  • The General Coupled Matrix Equations Over Generalized Bisymmetric Matrices ∗ Mehdi Dehghan , Masoud Hajarian
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Linear Algebra and its Applications 432 (2010) 1531–1552 Contents lists available at ScienceDirect Linear Algebra and its Applications journal homepage: www.elsevier.com/locate/laa The general coupled matrix equations over generalized bisymmetric matrices ∗ Mehdi Dehghan , Masoud Hajarian Department of Applied Mathematics, Faculty of Mathematics and Computer Science, Amirkabir University of Technology, No. 424, Hafez Avenue, Tehran 15914, Iran ARTICLE INFO ABSTRACT Article history: Received 20 May 2009 In the present paper, by extending the idea of conjugate gradient Accepted 12 November 2009 (CG) method, we construct an iterative method to solve the general Available online 16 December 2009 coupled matrix equations Submitted by S.M. Rump p AijXjBij = Mi,i= 1, 2, ...,p, j=1 AMS classification: 15A24 (including the generalized (coupled) Lyapunov and Sylvester matrix 65F10 equations as special cases) over generalized bisymmetric matrix 65F30 group (X1,X2, ...,Xp). By using the iterative method, the solvability of the general coupled matrix equations over generalized bisym- Keywords: metric matrix group can be determined in the absence of roundoff Iterative method errors. When the general coupled matrix equations are consistent Least Frobenius norm solution group over generalized bisymmetric matrices, a generalized bisymmetric Optimal approximation generalized solution group can be obtained within finite iteration steps in the bisymmetric solution group Generalized bisymmetric matrix absence of roundoff errors. The least Frobenius norm generalized General coupled matrix equations bisymmetric solution group of the general coupled matrix equa- tions can be derived when an appropriate initial iterative matrix group is chosen.
    [Show full text]
  • A. Appendix: Some Mathematical Concepts
    A. APPENDIX: SOME MATHEMATICAL CONCEPTS Overview This appendix on mathematical concepts is simply intended to aid readers who would benefit from a refresher on material they learned in the past or possibly never learned at all. Included here are basic definitions and examples pertaining to aspects of set theory, numerical linear algebra, met- ric properties of sets in real n-space, and analytical properties of functions. Among the latter are such matters as continuity, differentiability, and con- vexity. A.1 Basic terminology and notation from set theory This section recalls some general definitions and notation from set theory. In Section A.4 we move on to consider particular kinds of sets; in the process, we necessarily draw certain functions into the discussion. A set is a collection of objects belonging to some universe of discourse. The objects belonging to the collection (i.e., set) are said to be its members or elements.Thesymbol∈ is used to express the membership of an object in a set. If x is an object and A is a set, the statement that x is an element of A (or x belongs to A) is written x ∈ A. To negate the membership of x in the set A we write x/∈ A. The set of all objects, in the universe of discourse, not belonging to A is called the complement of A. There are many different notations used for the complement of a set A. One of the most popular is Ac. The cardinality of a set is the number of its elements. A set A can have cardinality zero in which case it is said to be empty.
    [Show full text]
  • A New Parametrization of Correlation Matrices∗
    A New Parametrization of Correlation Matrices∗ Ilya Archakova and Peter Reinhard Hansenb† aUniversity of Vienna bUniversity of North Carolina & Copenhagen Business School January 14, 2019 Abstract For the modeling of covariance matrices, the literature has proposed a variety of methods to enforce the positive (semi) definiteness. In this paper, we propose a method that is based on a novel parametrization of the correlation matrix, specifically the off-diagonal elements of the matrix loga- rithmic transformed correlations. This parametrization has many attractive properties, a wide range of applications, and may be viewed as a multivariate generalization of Fisher’s Z-transformation of a single correlation. Keywords: Covariance Modeling, Covariance Regularization, Fisher Transformation, Multivariate GARCH, Stochastic Volatility. JEL Classification: C10; C22; C58 ∗We are grateful for many valuable comments made by Immanuel Bomze, Bo Honoré, Ulrich Müller, Georg Pflug, Werner Ploberger, Rogier Quaedvlieg, and Christopher Sims, as well as many conference and seminar participants. †Address: University of North Carolina, Department of Economics, 107 Gardner Hall Chapel Hill, NC 27599-3305 1 1 Introduction In the modeling of covariance matrices, it is often advantageous to parametrize the model so that the parameters are unrestricted. The literature has proposed several methods to this end, and most of these methods impose additional restrictions beyond the positivity requirement. Only a handful of methods ensure positive definiteness without imposing additional restrictions on the covariance matrix, see Pinheiro and Bates (1996) for a discussion of five parameterizations of this kind. In this paper, we propose a new way to parametrize the covariance matrix that ensures positive definiteness without imposing additional restrictions, and we show that this parametrization has many attractive properties.
    [Show full text]
  • Atom-Light Interactions in Quasi-One-Dimensional Nanostructures: a Green’S-Function Perspective
    PHYSICAL REVIEW A 95, 033818 (2017) Atom-light interactions in quasi-one-dimensional nanostructures: A Green’s-function perspective A. Asenjo-Garcia,1,2,* J. D. Hood,1,2 D. E. Chang,3 and H. J. Kimble1,2 1Norman Bridge Laboratory of Physics MC12-33, California Institute of Technology, Pasadena, California 91125, USA 2Institute for Quantum Information and Matter, California Institute of Technology, Pasadena, California 91125, USA 3ICFO-Institut de Ciencies Fotoniques, Barcelona Institute of Science and Technology, 08860 Castelldefels (Barcelona), Spain (Received 2 November 2016; published 17 March 2017) Based on a formalism that describes atom-light interactions in terms of the classical electromagnetic Green’s function, we study the optical response of atoms and other quantum emitters coupled to one-dimensional photonic structures, such as cavities, waveguides, and photonic crystals. We demonstrate a clear mapping between the transmission spectra and the local Green’s function, identifying signatures of dispersive and dissipative interactions between atoms. We also demonstrate the applicability of our analysis to problems involving three-level atoms, such as electromagnetically induced transparency. Finally we examine recent experiments, and anticipate future observations of atom-atom interactions in photonic band gaps. DOI: 10.1103/PhysRevA.95.033818 I. INTRODUCTION have been interfaced with photonic crystal waveguides. Most of these experiments have been performed in conditions where As already noticed by Purcell in the first half of the past the resonance frequency of the emitter lies outside the band century, the decay rate of an atom can be either diminished gap. However, very recently, the first experiments of atoms [38] or enhanced by tailoring its dielectric environment [1–3].
    [Show full text]
  • On the Exponentials of Some Structured Matrices
    On the Exponentials of Some Structured Matrices Viswanath Ramakrishna & F. Costa Department of Mathematical Sciences and Center for Signals, Systems and Communications University of Texas at Dallas P. O. Box 830688 Richardson, TX 75083 USA email: [email protected] arXiv:math-ph/0407042v1 21 Jul 2004 1 Abstract This note provides explicit techniques to compute the exponentials of a variety of structured 4 4 matrices. The procedures are fully algorithmic and can be used to find the desired × exponentials in closed form. With one exception, they require no spectral information about the matrix being exponentiated. They rely on a mixture of Lie theory and one particular Clifford Algebra isomorphism. These can be extended, in some cases, to higher dimensions when combined with techniques such as Given rotations. PACS Numbers: 03.65.Fd, 02.10.Yn, 02.10.Hh 1 Introduction Finding matrix exponentials is arguably one of the most important goals of mathematical physics. In full generality, this is a thankless task, [1]. However, for matrices with structure, finding exponentials ought to be more tractable. In this note, confirmation of this phenomenon is given for a large class of 4 4 matrices with structure. These include skew-Hamiltonian, × perskewsymmetric, bisymmetric (i.e., simultaneously symmetric and persymmetric -e.g., sym- metric, Toeplitz), symmetric and Hamiltonian etc., Some of the techniques presented extend almost verbatim to some families of complex matrices (see Remark (3.4), for instance). Since such matrices arise in a variety of physical applications in both classical and quantum physics, it is interesting that their exponentials can be calculated algorithmically (these lead to closed form formulae), for the most part, without any auxiliary information about their spectrum.
    [Show full text]
  • Inverses of 2 × 2 Block Matrices
    An Intematiomd Journal computers & mathematics with applications PERGAMON Computers and Mathematics with Applications 43 (2002) 119-129 www.eisevier.com/locate/camwa Inverses of 2 × 2 Block Matrices TZON-TZER LU AND SHENG-HUA SHIOU Department of Applied Mathematics National Sun Yat-sen University Kaohsiung, Taiwan 80424, R.O.C. ttluCmath, nsysu, edu. tv (Received March 2000; revised and accepted October P000) Abstract--ln this paper, the authors give explicit inverse formulae for 2 x 2 block matrices with three differentpartitions. Then these resultsare applied to obtain inversesof block triangular matrices and various structured matrices such as Hamiltonian, per-Hermitian, and centro-Hermitian matrices. (~) 2001 Elsevier Science Ltd. All rights reserved. Keywords--2 x 2 block matrix, Inverse matrix, Structured matrix. 1. INTRODUCTION This paper is devoted to the inverses of 2 x 2 block matrices. First, we give explicit inverse formulae for a 2 x 2 block matrix D ' (1.1) with three different partitions. Then these results are applied to obtain inverses of block triangular matrices and various structured matrices such as bisymmetric, Hamiltonian, per-Hermitian, and centro-Hermitian matrices. In the end, we briefly discuss the completion problems of a 2 x 2 block matrix and its inverse, which generalizes this problem. The inverse formula (1.1) of a 2 x 2 block matrix appears frequently in many subjects and has long been studied. Its inverse in terms of A -1 or D -1 can be found in standard textbooks on linear algebra, e.g., [1-3]. However, we give a complete treatment here. Some papers, e.g., [4,5], deal with its inverse in terms of the generalized inverse of A.
    [Show full text]
  • Potential PCA Interpretation Problems for Volatility Smile Dynamics
    A Service of Leibniz-Informationszentrum econstor Wirtschaft Leibniz Information Centre Make Your Publications Visible. zbw for Economics Reiswich, Dimitri; Tompkins, Robert Working Paper Potential PCA interpretation problems for volatility smile dynamics CPQF Working Paper Series, No. 19 Provided in Cooperation with: Frankfurt School of Finance and Management Suggested Citation: Reiswich, Dimitri; Tompkins, Robert (2009) : Potential PCA interpretation problems for volatility smile dynamics, CPQF Working Paper Series, No. 19, Frankfurt School of Finance & Management, Centre for Practical Quantitative Finance (CPQF), Frankfurt a. M. This Version is available at: http://hdl.handle.net/10419/40185 Standard-Nutzungsbedingungen: Terms of use: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Documents in EconStor may be saved and copied for your Zwecken und zum Privatgebrauch gespeichert und kopiert werden. personal and scholarly purposes. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle You are not to copy documents for public or commercial Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich purposes, to exhibit the documents publicly, to make them machen, vertreiben oder anderweitig nutzen. publicly available on the internet, or to distribute or otherwise use the documents in public. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, If the documents have been made available under an Open gelten abweichend von diesen
    [Show full text]
  • Algebraic Geometry for Computer Vision by Joseph David Kileel Doctor of Philosophy in Mathematics University of California, Berkeley Professor Bernd Sturmfels, Chair
    Algebraic Geometry for Computer Vision by Joseph David Kileel A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Mathematics in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Bernd Sturmfels, Chair Professor Laurent El Ghaoui Assistant Professor Nikhil Srivastava Professor David Eisenbud Spring 2017 Algebraic Geometry for Computer Vision Copyright 2017 by Joseph David Kileel 1 Abstract Algebraic Geometry for Computer Vision by Joseph David Kileel Doctor of Philosophy in Mathematics University of California, Berkeley Professor Bernd Sturmfels, Chair This thesis uses tools from algebraic geometry to solve problems about three- dimensional scene reconstruction. 3D reconstruction is a fundamental task in multi- view geometry, a field of computer vision. Given images of a world scene, taken by cameras in unknown positions, how can we best build a 3D model for the scene? Novel results are obtained for various challenging minimal problems, which are important algorithmic routines in Random Sampling Consensus pipelines for reconstruction. These routines reduce overfitting when outliers are present in image data. Our approach throughout is to formulate inverse problems as structured systems of polynomial equations, and then to exploit underlying geometry. We apply numer- ical algebraic geometry, commutative algebra and tropical geometry, and we derive new mathematical results in these fields. We present simulations on image data as well as an implementation of general-purpose homotopy-continuation software for implicitization in computational algebraic geometry. Chapter1 introduces some relevant computer vision. Chapters2 and3 are de- voted to the recovery of camera positions from images. We resolve an open problem concerning two calibrated cameras raised by Sameer Agarwal, a vision expert at Google Research, by using the algebraic theory of Ulrich sheaves.
    [Show full text]
  • Term Rank Preservers of Bisymmetric Matrices Over Semirings L
    Sassanapitax et al., Cogent Mathematics & Statistics (2018), 5: 1509430 https://doi.org/10.1080/25742558.2018.1509430 PURE MATHEMATICS | RESEARCH ARTICLE Term rank preservers of bisymmetric matrices over semirings L. Sassanapitax1, S. Pianskool1* and A. Siraworakun2 Received: 27 March 2018 Abstract: In this article, we introduce another standard form of linear preservers. Accepted: 30 July 2018 This new standard form provides characterizations of the linear transformations on First Published: 14 August 2018 the set of bisymmetric matrices with zero diagonal and zero antidiagonal over *Corresponding author: S. Pianskool Department of Mathematics and antinegative semirings without zero divisors which preserve some sort of term ranks Computer Science, Faculty of Science, and preserve the matrix that can be determined as the greatest one. The numbers Chulalongkorn University, Thailand E-mail: sajee.pianskool.p.s@gmail. of all possible linear transformations satisfying each condition are also obtained. com Subjects: Science; Mathematics & Statistics; Advanced Mathematics; Algebra; Linear & Reviewing editor: Lishan Liu, Qufu Normal University, Multilinear Algebra China Keywords: linear preserver problem; bisymmetric matrix; Boolean semiring; antinegative Additional information is available at semiring; term rank the end of the article Ams Classification: 15A04; 15A86; 15B34 1. Introduction Linear preserver problems (LPPs) are one of the most active research topics in matrix theory during the past half-century, which have been studied when linear transformations on spaces of matrices leave certain conditions invariant. An excellent reference for LPPs is Pierce et al. (1992). There are many works on LPPs over various algebraic structures. The spaces of matrices over semirings also have been one of them. Rank preserver problems play a pivotal role in investigating questions regarding other preservers.
    [Show full text]