Hankel Hyperdeterminants, Rectangular Jack Polynomials and Even Powers of the Vandermonde Hacene Belbachir, Adrien Boussicault, Jean-Gabriel Luque
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Cofactors and Eigenvectors of Banded Toeplitz Matrices: Trench Formulas
Cofactors and eigenvectors of banded Toeplitz matrices: Trench formulas via skew Schur polynomials ∗ Egor A. Maximenko, Mario Alberto Moctezuma-Salazar June 12, 2017 Abstract The Jacobi–Trudi formulas imply that the minors of the banded Toeplitz matrices can be written as certain skew Schur polynomials. In 2012, Alexandersson expressed the corresponding skew partitions in terms of the indices of the struck-out rows and columns. In the present paper, we develop the same idea and obtain some new ap- plications. First, we prove a slight generalization and modification of Alexandersson’s formula. Then, we deduce corollaries about the cofactors and eigenvectors of banded Toeplitz matrices, and give new simple proofs to the corresponding formulas published by Trench in 1985. MSC 2010: 05E05 (primary), 15B05, 11C20, 15A09, 15A18 (secondary). Keywords: Toeplitz matrix, skew Schur function, minor, cofactor, eigenvector. Contents 1 Introduction 2 2 Main results 3 arXiv:1705.08067v2 [math.CO] 9 Jun 2017 3 Skew Schur polynomials: notation and facts 6 4 Minors of banded Toeplitz matrices 10 5 Cofactors of banded Toeplitz matrices 16 6 Eigenvectors of banded Toeplitz matrices 19 ∗The research was partially supported by IPN-SIP project, Instituto Polit´ecnico Nacional, Mexico, and by Conacyt, Mexico. 1 1 Introduction The first exact formulas for banded Toeplitz determinants were found by Widom [31] and by Baxter and Schmidt [3]. Trench [29, 30] discovered another equivalent formula for the determinants and exact formulas for the inverse matrices and eigenvectors. Among many recent investigations on Toeplitz matrices and their generalizations we mention [2, 5, 6, 10, 15–18]. See also the books [8, 11, 12, 20] which employ an analytic approach and contain asymptotic results on Toeplitz determinants, inverse matrices, eigenvalue distribution, etc. -
Linear Projections of the Vandermonde Polynomial
Linear Projections of the Vandermonde Polynomial C. Ramya B. V. Raghavendra Rao Department of Computer Science and Engineering IIT Madras, Chennai, INDIA ramya,bvrr @cse.iitm.ac.in f g Abstract An n-variate Vandermonde polynomial is the determinant of the n n matrix 2 n−1 T × where the ith column is the vector (1; xi; xi ; : : : ; xi ) . Vandermonde polynomials play a crucial role in the theory of alternating polynomials and occur in Lagrangian polynomial interpolation as well as in the theory of error correcting codes. In this work we study structural and computational aspects of linear projections of Van- dermonde polynomials. Firstly, we consider the problem of testing if a given polynomial is linearly equiv- alent to the Vandermonde polynomial. We obtain a deterministic polynomial time algorithm to test if f is linearly equivalent to the Vandermonde polynomial when f is given as product of linear factors. In the case when f is given as a black-box our algorithm runs in randomized polynomial time. Exploring the structure of pro- jections of Vandermonde polynomials further, we describe the group of symmetries of a Vandermonde polynomial and show that the associated Lie algebra is simple. Finally, we study arithmetic circuits built over projections of Vandermonde poly- nomials. We show universality property for some of the models and obtain a lower bounds against sum of projections of Vandermonde determinant. 1 Introduction The n n symbolic Vandermonde matrix is given by × 2 1 1 1 1 1 3 arXiv:1705.03188v1 [cs.CC] 9 May 2017 6 x1 x2 xn 7 6 2 2 ······ 2 7 6 x1 x2 xn 7 6 . -
Extreme Points of the Vandermonde Determinant and Phenomenological Modelling with Power Exponential Functions 2019 Isbn 978-91-7485-431-2 Issn 1651-4238 P.O
Mälardalen University Doctoral Dissertation 293 Karl Lundengård Lundengård Karl Extreme points of the Vandermonde determinant and phenomenological EXTREME POINTS OF THE VANDERMONDE DETERMINANT AND PHENOMENOLOGICAL MODELLING WITH POWER EXPONENTIAL FUNCTIONS modelling with power exponential functions Karl Lundengård Address: P.O. Box 883, SE-721 23 Västerås. Sweden ISBN 978-91-7485-431-2 Address: P.O. Box 325, SE-631 05 Eskilstuna. Sweden E-mail: [email protected] Web: www.mdh.se ISSN 1651-4238 2019 1 Mälardalen University Press Dissertations No. 293 EXTREME POINTS OF THE VANDERMONDE DETERMINANT AND PHENOMENOLOGICAL MODELLING WITH POWER EXPONENTIAL FUNCTIONS Karl Lundengård 2019 School of Education, Culture and Communication 2 Copyright © Karl Lundengård, 2019 ISBN 978-91-7485-431-2 ISSN 1651-4238 Printed by E-Print AB, Stockholm, Sweden 3 Mälardalen University Press Dissertations No. 293 EXTREME POINTS OF THE VANDERMONDE DETERMINANT AND PHENOMENOLOGICAL MODELLING WITH POWER EXPONENTIAL FUNCTIONS Karl Lundengård Akademisk avhandling som för avläggande av filosofie doktorsexamen i matematik/tillämpad matematik vid Akademin för utbildning, kultur och kommunikation kommer att offentligen försvaras torsdagen den 26 september 2019, 13.15 i Delta, Mälardalens högskola, Västerås. Fakultetsopponent: Professor Palle Jorgensen, University of Iowa Akademin för utbildning, kultur och kommunikation 4 Abstract This thesis discusses two topics, finding the extreme points of the Vandermonde determinant on various surfaces and phenomenological modelling using power-exponential functions. The relation between these two problems is that they are both related to methods for curve-fitting. Two applications of the mathematical models and methods are also discussed, modelling of electrostatic discharge currents for use in electromagnetic compatibility and modelling of mortality rates for humans. -
Extreme Points of the Vandermonde Determinant in Numerical
Mälardalen University Doctoral Dissertation 327 Asaph Keikara Muhumuza Extreme points of the Vandermonde determinant in numerical EXTREME POINTS OF THE VANDERMONDE DETERMINANT IN NUMERICAL APPROXIMATION, RANDOM MATRIX THEORY AND FINANCIAL MATHEMATICS approximation, random matrix theory and financial mathematics Asaph Keikara Muhumuza Address: P.O. Box 883, SE-721 23 Västerås. Sweden ISBN 978-91-7485-484-8 2020 Address: P.O. Box 325, SE-631 05 Eskilstuna. Sweden E-mail: [email protected] Web: www.mdh.se ISSN 1651-4238 1 Mälardalen University Press Dissertations No. 327 EXTREME POINTS OF THE VANDERMONDE DETERMINANT IN NUMERICAL APPROXIMATION, RANDOM MATRIX THEORY AND FINANCIAL MATHEMATICS Asaph Keikara Muhumuza 2020 School of Education, Culture and Communication 2 Copyright © Asaph Keikara Muhumuza, 2020 ISBN 978-91-7485-484-8 ISSN 1651-4238 Printed by E-Print AB, Stockholm, Sweden 3 Mälardalen University Press Dissertations No. 327 EXTREME POINTS OF THE VANDERMONDE DETERMINANT IN NUMERICAL APPROXIMATION, RANDOM MATRIX THEORY AND FINANCIAL MATHEMATICS Asaph Keikara Muhumuza Akademisk avhandling som för avläggande av filosofie doktorsexamen i matematik/tillämpad matematik vid Akademin för utbildning, kultur och kommunikation kommer att offentligen försvaras måndagen den 14 december 2020, 15.15 i Lambda +(digitalt Zoom), Mälardalens Högskola, Västerås. Fakultetsopponent: Docent Olga Liivapuu, Estonian University of Life Sciences Akademin för utbildning, kultur och kommunikation 4 Abstract This thesis discusses the extreme points of the Vandermonde determinant on various surfaces, their applications in numerical approximation, random matrix theory and financial mathematics. Some mathematical models that employ these extreme points such as curve fitting, data smoothing, experimental design, electrostatics, risk control in finance and method for finding the extreme points on certain surfaces are demonstrated. -
A Determinantal Identity for the Permanent of a Rank 2 Matrix
A determinantal identity for the permanent of a rank 2 matrix Adam W. Marcus∗ Princeton University August 30, 2016 Abstract We prove a relationship between the permanent of a rank 2 matrix and the determinants of its Hadamard powers. The proof uses a decomposition of the determinant in terms of Schur polynomials. n×n Let F be a field. For a matrix M 2 F with entries m(i; j) and integer p, let Mp be the matrix with p Mp(i; j) = m(i; j) : When M has rank at most 2 and no nonzero entries, Carlitz and Levine showed [1]: det [M−2] = det [M−1] perm [M−1] (1) where n n X Y X Y perm [M] = m(i; σ(i)) and det [M] = (−1)jσj m(i; σ(i)) σ2Sn i=1 σ2Sn i=1 are the usual permanent and determinant matrix functions. The proof in [1] is elementary, using little more than the definitions and some facts concerning the cycle structure of permutations. In this paper we prove an identity in a similar spirit, but using an entirely different means. Our main result (Theorem 2.5) shows that when M has rank at most 2, 2 n (n!) det [Mn] = (n ) det [Mn−1] perm [M1] : (2) The proof uses a decomposition of the determinant into Schur polynomials (Lemma 2.1). 1 Preliminaries For a set S and a function f, we will write S Y a := ai and f(S) = ff(i)gi2S: i2S [n] We use the customary notation that [n] = f1; 2; : : : ; ng and that k denotes the collection of subsets of [n] size k. -
Cumulative Probability for the Sum of Exponentially-Distributed Variables
Cumulative probability for the sum of exponentially-distributed variables Cecilia Chirenti and M. Coleman Miller March 19, 2018 Abstract Exponential distributions appear in a wide range of applications in- cluding chemistry, nuclear physics, time series analyses, and stock market trends. There are conceivable circumstances in which one would be in- terested in the cumulative probability distribution of the sum of some number of exponential variables, with potentially differing constants in their exponents. In this article we present a pedagogical derivation of the cumulative distribution, which reproduces the known formula from power density analyses in the limit that all of the constants are equal, and which assumes no prior knowledge of combinatorics except for some of the properties of a class of symmetric polynomials in n variables (Schur polynomials). 1 Introduction In many circumstances, a system can be considered to have no \memory", in the sense that, for example, the expected amount of time to the next radioactive decay, or chemical reaction, or failure of a machine part, does not depend on how much time has already elapsed. The unique memoryless and continuous probability distribution is the exponential distribution; if a variable P has an expected value Pexp, then the normalized differential probability density is 1 prob(P ) = e−P=Pexp (1) Pexp and the probability that P will be P0 or larger is −P0=Pexp prob(P ≥ P0) = e : (2) Alternatively, the exponential distribution is the solution to the differential equation dy = −y=P : (3) dP exp There may be occasions in which one wishes to compute the probability distribution of the sum of several independently-sampled exponential variables, 1 which could have different decay constants. -
Symmetric Functions and Hall Polynomials
OXFORD EliV"1'III 11V1 ICAI. AIU\OGR;APIIs Symmetric Functions and Hall Polynomials SECOND EDITION I. G. MACDONALD OXFORD SCIENCE PUBLICATIONS OXFORD MATHEMATICAL MONOGRAPHS Series Editors J. M. BallE. M. FriedlanderI. G. Macdonald L. NirenbergR. PenroseJ. T. Stuart OXFORD MATHEMATICAL MONOGRAPHS A. Belleni-Moranti: Applied semigroups and evolution equations A. M. Arthurs: Complementary variational principles 2nd edition M. Rosenblum and J. Rovnyak: Hardy classes and operator theory J. W. P. Hirschfeld: Finite projective spaces of three dimensions A. Pressley and G. Segal: Loop groups D. E. Edmunds and W. D. Evans: Spectral theory and differential operators Wang Jianhua: The theory of games S. Omatu and J. H. Seinfeld: Distributed parameter systems: theory and applications J. Hilgert, K. H. Hofmann, and J. D. Lawson: Lie groups, convex cones, and semigroups S. Dineen: The Schwarz lemma S. K. Donaldson and P. B. Kronheimer The geometry offour-manifolds D. W. Robinson: Elliptic operators and Lie groups A. G. Werschulz: The computational complexity of differential and integral equations L. Evens: Cohomology of groups G. Effinger and D. R. Hayes: Additive number theory of polynomials J. W. P. Hirschfeld and J. A. Thas: General Galois geometries P. N. Hoffman and J. F. Humphreys: Projective representations of the symmetric groups 1. GyOri and G. Ladas: The oscillation theory of delay differential equations J. Heinonen, T. Kilpelainen, and O. Martio: Non-linear potential theory B. Amberg, S. Franciosi, and F. de Giovanni: Products of groups M. E. Gurtin: Thermomechanics of evolving phase boundaries in the plane I. lonescu and M. F. Sofonea: Functional and numerical methods in viscoplasticity N. -
Generalized Vandermonde Matrices and Determinants in Electromagnetic Compatibility
Mälardalen University Press Licentiate Theses No. 253 Mälardalen University Press Licentiate Theses No. 253 GENERALIZED VANDERMONDE MATRICES AND DETERMINANTS IN ELECTROMAGNETIC COMPATIBILITY GENERALIZED VANDERMONDE MATRICES AND DETERMINANTS IN ELECTROMAGNETIC COMPATIBILITY Karl Lundengård Karl2017 Lundengård 2017 School of Education, Culture and Communication School of Education, Culture and Communication Abstract Matrices whose rows (or columns) consists of monomials of sequential powers are called Vandermonde matrices and can be used to describe several useful concepts and have properties that can be helpful for solving many kinds of problems. In this thesis we will discuss this matrix and some of its properties as well as a generalization of it and how it can be applied to curve fitting discharge current for the purpose of ensuring electromagnetic compatibility. In the first chapter the basic theory for later chapters is introduced. This includes the Vandermonde matrix and some of its properties, history, appli- cations and generalizations, interpolation and regression problems, optimal experiment design and modelling of electrostatic discharge currents with the purpose to ensure electromagnetic compatibility. The second chapter focuses on finding the extreme points for the deter- minant for the Vandermonde matrix on various surfaces including spheres, ellipsoids, cylinders and tori. The extreme points are analysed in three dimensions or more. The third chapter discusses fitting a particular model called the p-peaked Analytically Extended Function (AEF) to data taken either from a stan- dard for electromagnetic compatibility or experimental measurements. More specifically the AEF will be fitted to discharge currents from the IEC 62305-1 and IEC 61000-4-2 standards for lightning protection and electrostatic dis- charge immunity as well as some experimentally measured data of similar phenomena. -
Polynomials Afg from Wikipedia, the Free Encyclopedia Contents
Polynomials afg From Wikipedia, the free encyclopedia Contents 1 Abel polynomials 1 1.1 Examples ............................................... 1 1.2 References ............................................... 1 1.3 External links ............................................. 2 2 Abel–Ruffini theorem 3 2.1 Interpretation ............................................. 3 2.2 Lower-degree polynomials ....................................... 3 2.3 Quintics and higher .......................................... 4 2.4 Proof ................................................. 4 2.5 History ................................................ 5 2.6 See also ................................................ 6 2.7 Notes ................................................. 6 2.8 References .............................................. 6 2.9 Further reading ............................................ 6 2.10 External links ............................................. 6 3 Actuarial polynomials 7 3.1 See also ................................................ 7 3.2 References ............................................... 7 4 Additive polynomial 8 4.1 Definition ............................................... 8 4.2 Examples ............................................... 8 4.3 The ring of additive polynomials ................................... 9 4.4 The fundamental theorem of additive polynomials .......................... 9 4.5 See also ................................................ 9 4.6 References ............................................... 9 4.7 External