Unified Canonical Forms for Matrices Over a Differential Ring
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1111: Linear Algebra I
1111: Linear Algebra I Dr. Vladimir Dotsenko (Vlad) Lecture 11 Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Lecture 11 1 / 13 Previously on. Theorem. Let A be an n × n-matrix, and b a vector with n entries. The following statements are equivalent: (a) the homogeneous system Ax = 0 has only the trivial solution x = 0; (b) the reduced row echelon form of A is In; (c) det(A) 6= 0; (d) the matrix A is invertible; (e) the system Ax = b has exactly one solution. A very important consequence (finite dimensional Fredholm alternative): For an n × n-matrix A, the system Ax = b either has exactly one solution for every b, or has infinitely many solutions for some choices of b and no solutions for some other choices. In particular, to prove that Ax = b has solutions for every b, it is enough to prove that Ax = 0 has only the trivial solution. Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Lecture 11 2 / 13 An example for the Fredholm alternative Let us consider the following question: Given some numbers in the first row, the last row, the first column, and the last column of an n × n-matrix, is it possible to fill the numbers in all the remaining slots in a way that each of them is the average of its 4 neighbours? This is the \discrete Dirichlet problem", a finite grid approximation to many foundational questions of mathematical physics. Dr. Vladimir Dotsenko (Vlad) 1111: Linear Algebra I Lecture 11 3 / 13 An example for the Fredholm alternative For instance, for n = 4 we may face the following problem: find a; b; c; d to put in the matrix 0 4 3 0 1:51 B 1 a b -1C B C @0:5 c d 2 A 2:1 4 2 1 so that 1 a = 4 (3 + 1 + b + c); 1 8b = 4 (a + 0 - 1 + d); >c = 1 (a + 0:5 + d + 4); > 4 < 1 d = 4 (b + c + 2 + 2): > > This is a system with 4:> equations and 4 unknowns. -
MATH 210A, FALL 2017 Question 1. Let a and B Be Two N × N Matrices
MATH 210A, FALL 2017 HW 6 SOLUTIONS WRITTEN BY DAN DORE (If you find any errors, please email [email protected]) Question 1. Let A and B be two n × n matrices with entries in a field K. −1 Let L be a field extension of K, and suppose there exists C 2 GLn(L) such that B = CAC . −1 Prove there exists D 2 GLn(K) such that B = DAD . (that is, turn the argument sketched in class into an actual proof) Solution. We’ll start off by proving the existence and uniqueness of rational canonical forms in a precise way. d d−1 To do this, recall that the companion matrix for a monic polynomial p(t) = t + ad−1t + ··· + a0 2 K[t] is defined to be the (d − 1) × (d − 1) matrix 0 1 0 0 ··· 0 −a0 B C B1 0 ··· 0 −a1 C B C B0 1 ··· 0 −a2 C M(p) := B C B: : : C B: :: : C @ A 0 0 ··· 1 −ad−1 This is the matrix representing the action of t in the cyclic K[t]-module K[t]=(p(t)) with respect to the ordered basis 1; t; : : : ; td−1. Proposition 1 (Rational Canonical Form). For any matrix M over a field K, there is some matrix B 2 GLn(K) such that −1 BMB ' Mcan := M(p1) ⊕ M(p2) ⊕ · · · ⊕ M(pm) Here, p1; : : : ; pn are monic polynomials in K[t] with p1 j p2 j · · · j pm. The monic polynomials pi are 1 −1 uniquely determined by M, so if for some C 2 GLn(K) we have CMC = M(q1) ⊕ · · · ⊕ M(qk) for q1 j q1 j · · · j qm 2 K[t], then m = k and qi = pi for each i. -
Solutions to Math 53 Practice Second Midterm
Solutions to Math 53 Practice Second Midterm 1. (20 points) dx dy (a) (10 points) Write down the general solution to the system of equations dt = x+y; dt = −13x−3y in terms of real-valued functions. (b) (5 points) The trajectories of this equation in the (x; y)-plane rotate around the origin. Is this dx rotation clockwise or counterclockwise? If we changed the first equation to dt = −x + y, would it change the direction of rotation? (c) (5 points) Suppose (x1(t); y1(t)) and (x2(t); y2(t)) are two solutions to this equation. Define the Wronskian determinant W (t) of these two solutions. If W (0) = 1, what is W (10)? Note: the material for this part will be covered in the May 8 and May 10 lectures. 1 1 (a) The corresponding matrix is A = , with characteristic polynomial (1 − λ)(−3 − −13 −3 λ) + 13 = 0, so that λ2 + 2λ + 10 = 0, so that λ = −1 ± 3i. The corresponding eigenvector v to λ1 = −1 + 3i must be a solution to 2 − 3i 1 −13 −2 − 3i 1 so we can take v = Now we just need to compute the real and imaginary parts of 3i − 2 e3it cos(3t) + i sin(3t) veλ1t = e−t = e−t : (3i − 2)e3it −2 cos(3t) − 3 sin(3t) + i(3 cos(3t) − 2 sin(3t)) The general solution is thus expressed as cos(3t) sin(3t) c e−t + c e−t : 1 −2 cos(3t) − 3 sin(3t) 2 (3 cos(3t) − 2 sin(3t)) dx (b) We can examine the direction field. -
Linear Independence, the Wronskian, and Variation of Parameters
LINEAR INDEPENDENCE, THE WRONSKIAN, AND VARIATION OF PARAMETERS JAMES KEESLING In this post we determine when a set of solutions of a linear differential equation are linearly independent. We first discuss the linear space of solutions for a homogeneous differential equation. 1. Homogeneous Linear Differential Equations We start with homogeneous linear nth-order ordinary differential equations with general coefficients. The form for the nth-order type of equation is the following. dnx dn−1x (1) a (t) + a (t) + ··· + a (t)x = 0 n dtn n−1 dtn−1 0 It is straightforward to solve such an equation if the functions ai(t) are all constants. However, for general functions as above, it may not be so easy. However, we do have a principle that is useful. Because the equation is linear and homogeneous, if we have a set of solutions fx1(t); : : : ; xn(t)g, then any linear combination of the solutions is also a solution. That is (2) x(t) = C1x1(t) + C2x2(t) + ··· + Cnxn(t) is also a solution for any choice of constants fC1;C2;:::;Cng. Now if the solutions fx1(t); : : : ; xn(t)g are linearly independent, then (2) is the general solution of the differential equation. We will explain why later. What does it mean for the functions, fx1(t); : : : ; xn(t)g, to be linearly independent? The simple straightforward answer is that (3) C1x1(t) + C2x2(t) + ··· + Cnxn(t) = 0 implies that C1 = 0, C2 = 0, ::: , and Cn = 0 where the Ci's are arbitrary constants. This is the definition, but it is not so easy to determine from it just when the condition holds to show that a given set of functions, fx1(t); x2(t); : : : ; xng, is linearly independent. -
Toeplitz Nonnegative Realization of Spectra Via Companion Matrices Received July 25, 2019; Accepted November 26, 2019
Spec. Matrices 2019; 7:230–245 Research Article Open Access Special Issue Dedicated to Charles R. Johnson Macarena Collao, Mario Salas, and Ricardo L. Soto* Toeplitz nonnegative realization of spectra via companion matrices https://doi.org/10.1515/spma-2019-0017 Received July 25, 2019; accepted November 26, 2019 Abstract: The nonnegative inverse eigenvalue problem (NIEP) is the problem of nding conditions for the exis- tence of an n × n entrywise nonnegative matrix A with prescribed spectrum Λ = fλ1, . , λng. If the problem has a solution, we say that Λ is realizable and that A is a realizing matrix. In this paper we consider the NIEP for a Toeplitz realizing matrix A, and as far as we know, this is the rst work which addresses the Toeplitz nonnegative realization of spectra. We show that nonnegative companion matrices are similar to nonnegative Toeplitz ones. We note that, as a consequence, a realizable list Λ = fλ1, . , λng of complex numbers in the left-half plane, that is, with Re λi ≤ 0, i = 2, . , n, is in particular realizable by a Toeplitz matrix. Moreover, we show how to construct symmetric nonnegative block Toeplitz matrices with prescribed spectrum and we explore the universal realizability of lists, which are realizable by this kind of matrices. We also propose a Matlab Toeplitz routine to compute a Toeplitz solution matrix. Keywords: Toeplitz nonnegative inverse eigenvalue problem, Unit Hessenberg Toeplitz matrix, Symmetric nonnegative block Toeplitz matrix, Universal realizability MSC: 15A29, 15A18 Dedicated with admiration and special thanks to Charles R. Johnson. 1 Introduction The nonnegative inverse eigenvalue problem (hereafter NIEP) is the problem of nding necessary and sucient conditions for a list Λ = fλ1, λ2, . -
An Introduction to the HESSIAN
An introduction to the HESSIAN 2nd Edition Photograph of Ludwig Otto Hesse (1811-1874), circa 1860 https://upload.wikimedia.org/wikipedia/commons/6/65/Ludwig_Otto_Hesse.jpg See page for author [Public domain], via Wikimedia Commons I. As the students of the first two years of mathematical analysis well know, the Wronskian, Jacobian and Hessian are the names of three determinants and matrixes, which were invented in the nineteenth century, to make life easy to the mathematicians and to provide nightmares to the students of mathematics. II. I don’t know how the Hessian came to the mind of Ludwig Otto Hesse (1811-1874), a quiet professor father of nine children, who was born in Koenigsberg (I think that Koenigsberg gave birth to a disproportionate 1 number of famous men). It is possible that he was studying the problem of finding maxima, minima and other anomalous points on a bi-dimensional surface. (An alternative hypothesis will be presented in section X. ) While pursuing such study, in one variable, one first looks for the points where the first derivative is zero (if it exists at all), and then examines the second derivative at each of those points, to find out its “quality”, whether it is a maximum, a minimum, or an inflection point. The variety of anomalies on a bi-dimensional surface is larger than for a one dimensional line, and one needs to employ more powerful mathematical instruments. Still, also in two dimensions one starts by looking for points where the two first partial derivatives, with respect to x and with respect to y respectively, exist and are both zero. -
The Polar Decomposition of Block Companion Matrices
The polar decomposition of block companion matrices Gregory Kalogeropoulos1 and Panayiotis Psarrakos2 September 28, 2005 Abstract m m¡1 Let L(¸) = In¸ + Am¡1¸ + ¢ ¢ ¢ + A1¸ + A0 be an n £ n monic matrix polynomial, and let CL be the corresponding block companion matrix. In this note, we extend a known result on scalar polynomials to obtain a formula for the polar decomposition of CL when the matrices A0 Pm¡1 ¤ and j=1 Aj Aj are nonsingular. Keywords: block companion matrix, matrix polynomial, eigenvalue, singular value, polar decomposition. AMS Subject Classifications: 15A18, 15A23, 65F30. 1 Introduction and notation Consider the monic matrix polynomial m m¡1 L(¸) = In¸ + Am¡1¸ + ¢ ¢ ¢ + A1¸ + A0; (1) n£n where Aj 2 C (j = 0; 1; : : : ; m ¡ 1; m ¸ 2), ¸ is a complex variable and In denotes the n £ n identity matrix. The study of matrix polynomials, especially with regard to their spectral analysis, has a long history and plays an important role in systems theory [1, 2, 3, 4]. A scalar ¸0 2 C is said to be an eigenvalue of n L(¸) if the system L(¸0)x = 0 has a nonzero solution x0 2 C . This solution x0 is known as an eigenvector of L(¸) corresponding to ¸0. The set of all eigenvalues of L(¸) is the spectrum of L(¸), namely, sp(L) = f¸ 2 C : detL(¸) = 0g; and contains no more than nm distinct (finite) elements. 1Department of Mathematics, University of Athens, Panepistimioupolis 15784, Athens, Greece (E-mail: [email protected]). 2Department of Mathematics, National Technical University, Zografou Campus 15780, Athens, Greece (E-mail: [email protected]). -
Linear Operators Hsiu-Hau Lin [email protected] (Mar 25, 2010)
Linear Operators Hsiu-Hau Lin [email protected] (Mar 25, 2010) The notes cover linear operators and discuss linear independence of func- tions (Boas 3.7-3.8). • Linear operators An operator maps one thing into another. For instance, the ordinary func- tions are operators mapping numbers to numbers. A linear operator satisfies the properties, O(A + B) = O(A) + O(B);O(kA) = kO(A); (1) where k is a number. As we learned before, a matrix maps one vector into another. One also notices that M(r1 + r2) = Mr1 + Mr2;M(kr) = kMr: Thus, matrices are linear operators. • Orthogonal matrix The length of a vector remains invariant under rotations, ! ! x0 x x0 y0 = x y M T M : y0 y The constraint can be elegantly written down as a matrix equation, M T M = MM T = 1: (2) In other words, M T = M −1. For matrices satisfy the above constraint, they are called orthogonal matrices. Note that, for orthogonal matrices, computing inverse is as simple as taking transpose { an extremely helpful property for calculations. From the product theorem for the determinant, we immediately come to the conclusion det M = ±1. In two dimensions, any 2 × 2 orthogonal matrix with determinant 1 corresponds to a rotation, while any 2 × 2 orthogonal HedgeHog's notes (March 24, 2010) 2 matrix with determinant −1 corresponds to a reflection about a line. Let's come back to our good old friend { the rotation matrix, cos θ − sin θ ! cos θ sin θ ! R(θ) = ;RT = : (3) sin θ cos θ − sin θ cos θ It is straightforward to check that RT R = RRT = 1. -
Applications of the Wronskian and Gram Matrices of {Fie”K?
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Applications of the Wronskian and Gram Matrices of {fie”k? Robert E. Hartwig Mathematics Department North Carolina State University Raleigh, North Carolina 27650 Submitted by S. Barn&t ABSTRACT A review is made of some of the fundamental properties of the sequence of functions {t’b’}, k=l,..., s, i=O ,..., m,_,, with distinct X,. In particular it is shown how the Wronskian and Gram matrices of this sequence of functions appear naturally in such fields as spectral matrix theory, controllability, and Lyapunov stability theory. 1. INTRODUCTION Let #(A) = II;,,< A-hk)“‘k be a complex polynomial with distinct roots x 1,.. ., A,, and let m=ml+ . +m,. It is well known [9] that the functions {tieXk’}, k=l,..., s, i=O,l,..., mkpl, form a fundamental (i.e., linearly inde- pendent) solution set to the differential equation m)Y(t)=O> (1) where D = $(.). It is the purpose of this review to illustrate some of the important properties of this independent solution set. In particular we shall show that both the Wronskian matrix and the Gram matrix play a dominant role in certain applications of matrix theory, such as spectral theory, controllability, and the Lyapunov stability theory. Few of the results in this paper are new; however, the proofs we give are novel and shed some new light on how the various concepts are interrelated, and on why certain results work the way they do. LINEAR ALGEBRA ANDITSAPPLlCATIONS43:229-241(1982) 229 C Elsevier Science Publishing Co., Inc., 1982 52 Vanderbilt Ave., New York, NY 10017 0024.3795/82/020229 + 13$02.50 230 ROBERT E. -
NON-SPARSE COMPANION MATRICES∗ 1. Introduction. The
Electronic Journal of Linear Algebra, ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 35, pp. 223-247, June 2019. NON-SPARSE COMPANION MATRICES∗ LOUIS DEAETTy , JONATHAN FISCHERz , COLIN GARNETTx , AND KEVIN N. VANDER MEULEN{ Abstract. Given a polynomial p(z), a companion matrix can be thought of as a simple template for placing the coefficients of p(z) in a matrix such that the characteristic polynomial is p(z). The Frobenius companion and the more recently-discovered Fiedler companion matrices are examples. Both the Frobenius and Fiedler companion matrices have the maximum possible number of zero entries, and in that sense are sparse. In this paper, companion matrices are explored that are not sparse. Some constructions of non-sparse companion matrices are provided, and properties that all companion matrices must exhibit are given. For example, it is shown that every companion matrix realization is non-derogatory. Bounds on the minimum number of zeros that must appear in a companion matrix, are also given. Key words. Companion matrix, Fiedler companion matrix, Non-derogatory matrix, Characteristic polynomial. AMS subject classifications. 15A18, 15B99, 11C20, 05C50. 1. Introduction. The Frobenius companion matrix to the polynomial n n−1 n−2 (1.1) p(z) = z + a1z + a2z + ··· + an−1z + an is the matrix 2 0 1 0 ··· 0 3 6 0 0 1 ··· 0 7 6 7 6 . 7 (1.2) F = 6 . .. 7 : 6 . 7 6 7 4 0 0 0 ··· 1 5 −an −an−1 · · · −a2 −a1 2 In general, a companion matrix to p(z) is an n×n matrix C = C(p) over R[a1; a2; : : : ; an] with n −n entries in R and the remaining entries variables −a1; −a2;:::; −an such that the characteristic polynomial of C is p(z). -
Determinants
12 PREFACECHAPTER I DETERMINANTS The notion of a determinant appeared at the end of 17th century in works of Leibniz (1646–1716) and a Japanese mathematician, Seki Kova, also known as Takakazu (1642–1708). Leibniz did not publish the results of his studies related with determinants. The best known is his letter to l’Hospital (1693) in which Leibniz writes down the determinant condition of compatibility for a system of three linear equations in two unknowns. Leibniz particularly emphasized the usefulness of two indices when expressing the coefficients of the equations. In modern terms he actually wrote about the indices i, j in the expression xi = j aijyj. Seki arrived at the notion of a determinant while solving the problem of finding common roots of algebraic equations. In Europe, the search for common roots of algebraic equations soon also became the main trend associated with determinants. Newton, Bezout, and Euler studied this problem. Seki did not have the general notion of the derivative at his disposal, but he actually got an algebraic expression equivalent to the derivative of a polynomial. He searched for multiple roots of a polynomial f(x) as common roots of f(x) and f (x). To find common roots of polynomials f(x) and g(x) (for f and g of small degrees) Seki got determinant expressions. The main treatise by Seki was published in 1674; there applications of the method are published, rather than the method itself. He kept the main method in secret confiding only in his closest pupils. In Europe, the first publication related to determinants, due to Cramer, ap- peared in 1750. -
Permanental Compounds and Permanents of (O,L)-Circulants*
CORE Metadata, citation and similar papers at core.ac.uk Provided by Elsevier - Publisher Connector Permanental Compounds and Permanents of (O,l)-Circulants* Henryk Mint Department of Mathematics University of California Santa Barbara, Califmia 93106 Submitted by Richard A. Brualdi ABSTRACT It was shown by the author in a recent paper that a recurrence relation for permanents of (0, l)-circulants can be generated from the product of the characteristic polynomials of permanental compounds of the companion matrix of a polynomial associated with (O,l)-circulants of the given type. In the present paper general properties of permanental compounds of companion matrices are studied, and in particular of convertible companion matrices, i.e., matrices whose permanental com- pounds are equal to the determinantal compounds after changing the signs of some of their entries. These results are used to obtain formulas for the limit of the nth root of the permanent of the n X n (0, l)-circulant of a given type, as n tends to infinity. The root-squaring method is then used to evaluate this limit for a wide range of circulant types whose associated polynomials have convertible companion matrices. 1. INTRODUCTION tit Q,,, denote the set of increasing sequences of integers w = (+w2,..., w,), 1 < w1 < w2 < * 9 * < w, < t. If A = (ajj) is an s X t matrix, and (YE Qh,s, p E QkSt, then A[Lx~/~] denotes the h X k submatrix of A whose (i, j) entry is aa,Bj, i = 1,2 ,..., h, j = 1,2,.. ., k, and A(aIP) desig- nates the (s - h) X (t - k) submatrix obtained from A by deleting rows indexed (Y and columns indexed /I.