The Fibonacci Identities of Orthogonality

Similar documents
An Application of Matricial Fibonacci Identities to the Computation of Spectral Norms

PAijpam.eu ON THE BOUNDS FOR THE NORMS OF R-CIRCULANT MATRICES WITH THE JACOBSTHAL AND JACOBSTHAL LUCAS NUMBERS Ş. Uygun 1, S.

MATH 423 Linear Algebra II Lecture 33: Diagonalization of normal operators.

ON QUADRAPELL NUMBERS AND QUADRAPELL POLYNOMIALS

Chapter 3 Transformations

arxiv: v2 [math.co] 8 Oct 2015

The generalized order-k Fibonacci Pell sequence by matrix methods

Ir O D = D = ( ) Section 2.6 Example 1. (Bottom of page 119) dim(v ) = dim(l(v, W )) = dim(v ) dim(f ) = dim(v )

On the Hadamard Product of Fibonacci Q n matrix and Fibonacci Q n matrix

MATH 20F: LINEAR ALGEBRA LECTURE B00 (T. KEMP)

The spectral decomposition of near-toeplitz tridiagonal matrices

k-pell, k-pell-lucas and Modified k-pell Numbers: Some Identities and Norms of Hankel Matrices

Chapter 6: Orthogonality

GENERALIZED LUCAS NUMBERS OF THE FORM 5kx 2 AND 7kx 2

Math 443 Differential Geometry Spring Handout 3: Bilinear and Quadratic Forms This handout should be read just before Chapter 4 of the textbook.

GOLDEN SEQUENCES OF MATRICES WITH APPLICATIONS TO FIBONACCI ALGEBRA

Quantum Computing Lecture 2. Review of Linear Algebra

Graph fundamentals. Matrices associated with a graph

Math 350 Fall 2011 Notes about inner product spaces. In this notes we state and prove some important properties of inner product spaces.

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

The k-fibonacci matrix and the Pascal matrix

Review of some mathematical tools

4. Linear transformations as a vector space 17

Balancing sequences of matrices with application to algebra of balancing numbers

Lecture 15, 16: Diagonalization

SOME FORMULAE FOR THE FIBONACCI NUMBERS

MATH 1120 (LINEAR ALGEBRA 1), FINAL EXAM FALL 2011 SOLUTIONS TO PRACTICE VERSION

1 Last time: least-squares problems

Throughout these notes we assume V, W are finite dimensional inner product spaces over C.

ABSOLUTELY FLAT IDEMPOTENTS

j=1 u 1jv 1j. 1/ 2 Lemma 1. An orthogonal set of vectors must be linearly independent.

MATH 235. Final ANSWERS May 5, 2015

Math 108b: Notes on the Spectral Theorem

arxiv: v1 [math.nt] 9 May 2017

Chapter 7: Symmetric Matrices and Quadratic Forms

c 1 v 1 + c 2 v 2 = 0 c 1 λ 1 v 1 + c 2 λ 1 v 2 = 0

q-counting hypercubes in Lucas cubes

SCHUR IDEALS AND HOMOMORPHISMS OF THE SEMIDEFINITE CONE

Remark 1 By definition, an eigenvector must be a nonzero vector, but eigenvalue could be zero.

Recall the convention that, for us, all vectors are column vectors.

The Spectral Norms of Geometric Circulant Matrices with Generalized Tribonacci Sequence

12x + 18y = 30? ax + by = m

Orthogonal Symmetric Toeplitz Matrices

Orthogonality. Orthonormal Bases, Orthogonal Matrices. Orthogonality

TRUNCATED TOEPLITZ OPERATORS ON FINITE DIMENSIONAL SPACES

Linear Algebra: Characteristic Value Problem

EXAM. Exam 1. Math 5316, Fall December 2, 2012

Spectral Theorem for Self-adjoint Linear Operators

= W z1 + W z2 and W z1 z 2

Pascal Eigenspaces and Invariant Sequences of the First or Second Kind

Math 3191 Applied Linear Algebra

DS-GA 1002 Lecture notes 0 Fall Linear Algebra. These notes provide a review of basic concepts in linear algebra.

Homework 11 Solutions. Math 110, Fall 2013.

Computing the Determinant and Inverse of the Complex Fibonacci Hermitian Toeplitz Matrix

Divisibility in the Fibonacci Numbers. Stefan Erickson Colorado College January 27, 2006

Diagonalizing Matrices

UNDERSTANDING THE DIAGONALIZATION PROBLEM. Roy Skjelnes. 1.- Linear Maps 1.1. Linear maps. A map T : R n R m is a linear map if

On identities with multinomial coefficients for Fibonacci-Narayana sequence

Glossary of Linear Algebra Terms. Prepared by Vince Zaccone For Campus Learning Assistance Services at UCSB

Review of Linear Algebra

The Spectral Theorem for normal linear maps

No books, no notes, no calculators. You must show work, unless the question is a true/false, yes/no, or fill-in-the-blank question.

22m:033 Notes: 7.1 Diagonalization of Symmetric Matrices

Linear Algebra 2 Spectral Notes

Summary of Week 9 B = then A A =

There are two things that are particularly nice about the first basis

Review problems for MA 54, Fall 2004.

The following definition is fundamental.

Linear Algebra using Dirac Notation: Pt. 2

MATH 23a, FALL 2002 THEORETICAL LINEAR ALGEBRA AND MULTIVARIABLE CALCULUS Solutions to Final Exam (in-class portion) January 22, 2003

Tutorial on Principal Component Analysis

A classification of sharp tridiagonal pairs. Tatsuro Ito, Kazumasa Nomura, Paul Terwilliger

THE MINIMAL POLYNOMIAL AND SOME APPLICATIONS

On the adjacency matrix of a block graph

Edexcel GCE A Level Maths Further Maths 3 Matrices.

Math 3191 Applied Linear Algebra

spring, math 204 (mitchell) list of theorems 1 Linear Systems Linear Transformations Matrix Algebra

IMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET

MATH 324 Summer 2011 Elementary Number Theory. Notes on Mathematical Induction. Recall the following axiom for the set of integers.

POINT VALUES AND NORMALIZATION OF TWO-DIRECTION MULTIWAVELETS AND THEIR DERIVATIVES

MTH 5102 Linear Algebra Practice Final Exam April 26, 2016

MATH 320: PRACTICE PROBLEMS FOR THE FINAL AND SOLUTIONS

MAT 445/ INTRODUCTION TO REPRESENTATION THEORY

a 11 a 12 a 11 a 12 a 13 a 21 a 22 a 23 . a 31 a 32 a 33 a 12 a 21 a 23 a 31 a = = = = 12

Math Camp Lecture 4: Linear Algebra. Xiao Yu Wang. Aug 2010 MIT. Xiao Yu Wang (MIT) Math Camp /10 1 / 88

Math 408 Advanced Linear Algebra

4.2. ORTHOGONALITY 161

Final Exam - Take Home Portion Math 211, Summer 2017

Large Scale Data Analysis Using Deep Learning

A matrix over a field F is a rectangular array of elements from F. The symbol

Matrix functions that preserve the strong Perron- Frobenius property

MATHEMATICS 23a/E-23a, Fall 2015 Linear Algebra and Real Analysis I Module #1, Week 4 (Eigenvectors and Eigenvalues)

IMPORTANT DEFINITIONS AND THEOREMS REFERENCE SHEET

Computational math: Assignment 1

5.) For each of the given sets of vectors, determine whether or not the set spans R 3. Give reasons for your answers.

arxiv: v1 [math.rt] 16 Jun 2015

A linear algebra proof of the fundamental theorem of algebra

Math Camp II. Basic Linear Algebra. Yiqing Xu. Aug 26, 2014 MIT

Tridiagonal pairs in algebraic graph theory

Thus, the integral closure A i of A in F i is a finitely generated (and torsion-free) A-module. It is not a priori clear if the A i s are locally

Transcription:

The Fibonacci Identities of Orthogonality Kyle Hawins, Ursula Hebert-Johnson and Ben Mathes January 14, 015 Abstract In even dimensions, the orthogonal projection onto the two dimensional space of second order recurrence sequences is particularly nice: it is a scaled Hanel matrix whose entries consist of the classical Fibonacci sequence A new proof is given of this result, and new Fibonacci identities are derived from it Examples are given showing that familiar Fibonacci identities can be viewed as special cases We show that the projection in odd dimensions can be written as a ran one Lucas perturbation of a scaled Lucas Hanel matrix, from which more Fibonacci identities are derived 1 The Fibonacci projection Let R n denote the n dimensional vector space of all real n-tuples, and let R n (, l) denote the vector subspace consisting of the (s i ) R n for which s i s i 1 + ls i, for i 3,,n (See [7] for a delightful article on general Fibonacci sequences) The element of R n (, l) whose first two coordinates are 0 and 1 (respectively) will be denoted (f i ) and is called the general Fibonacci sequence, and the sequence beginning with and is denoted (l i ) and is called the general Lucas sequence Matrices with Fibonacci and Lucas entries were studied in [1],[3],[4],[5],[6], [8],[9], and [10], Motivated by these wors, in [5] it was shown that, when n is even, the orthogonal projection onto R n (, 1) 0 AMS 000 Classification numbers: Primary: 15B36 Secondary: 65F35 0 Key Words: Orthogonal projection, Fibonacci numbers, Fibonacci identities 1

is the Hanel matrix f n+1 f n+ f 1 f 0 f n+ f n+3 f 0 f 1 f 1 f 0 3 f 0 f 1 1 (A proof of this in the special case of R n (1, 1) may now be found in []) We begin with a new proof of this fact Let E denote the matrix E 0 1 1 It follows that for all integers m E m fm 1 f m f m f m+1, Lemma 1 For any integer t and any nonnegative integer s, we have s i0 E t+4i f (s+1) E t+s Proof It suffices to prove the scalar identity s i0 f t+4i f (s+1) f t+s, and we proceed via induction on s Since f, the identity holds when s 0, so we assume the induction hypothesis s 1 f t+4i f s f t+(s 1) i0 Substituting this in for the first s summands of s i0 f t+4i, we see that the equality holds s f t+4i f (s+1) f t+s i0

if and only if The equality f s f t+s + f t+4s f (s+1) f t+s E t+4s E t+s E s yields the identity f t+4s f s f t+s 1 + f s+1 f t+s from which we derive f s+ f t+s f s f t+s + f s+1 f t+s f s f t+s + f s f t+s 1 + f s+1 f t+s f s f t+s + f t+4s Let H f denote the n n matrix f n+1 f n+ f 1 f 0 f n+ f n+3 f 0 f 1 H f f 1 f 0 3 f 0 f 1 1 We will call H f the central Fibonacci Hanel matrix Replacing f i with l i yields H l,thecentral Lucas Hanel matrix Theorem For even n, thematrix H f is the orthogonal projection onto R(, 1) Proof It is clear that H f is selfadjoint, and its range is R(, 1), so we need only prove H f is idempotent, which is equivalent to proving H f H f Write n m and notice that E n+ E n+4 E E 0 E n+4 E n+6 E 0 E H f E E 0 E n 6 E n 4 E 0 E E n 4 E n E (i+j) (m+1) m i,j1 3

Using our lemma, it follows that Hf m r1 E(i+r) (m+1) E (r+j) (m+1) m i,j1 m r1 E(i+j) 4m+4(r 1) m i,j1 m 1 f m fn fn H f s0 E(i+j) 4m+4s m i,j1 E (i+j) 4m+(m 1) m E (i+j) (m+1) m i,j1 i,j1 The theme of this paper is the interplay between the linear algebraic equations of orthogonality and Fibonacci identities To illustrate with an example, observe that the idempotence of H f leads immediately to the identity f i+j n+1 n 1 fn f i+ n+1 f +j n+1, for all even n and n +1 i, j n 1 This proves the following 0 Corollary 3 Assume that n is even and n +1 i, j n 1 We have f i u f j u f i+j n+1 n 1 u0 Assume that n is even and s (s i ) n 1 i0 R(, 1), which we will thin of as a column vector We then let s denote the transpose of s, whichisa row vector We define s ( s n 1,s n, s n 3, s 1,s 0 ) (( 1) i+1 s n i 1 ) n 1 i0, and observe that this is also an element of R(, 1), and {s, s } forms an orthogonal basis of R(, 1) for each non-zero s R(, 1) Normalizing, and denoting u s s and v s, then, by elementary linear s algebra, the orthogonal projection onto R(, 1) is the matrix uu + vv Equating this matrix with H f establishes the following identity (Note that s s 1 (s n 1s n s 0 s 1 )) 4

Theorem 4 Assume that n is even Then, for all 0 i, j n 1 we have ie we have s n 1 s n s 0 s 1 (s i s j +( 1) i+j s n i 1 s n j 1 ) f i+j n+1 (s i s j +( 1) i+j s n i 1 s n j 1 ) (s n 1 s n s 0 s 1 )f i+j n+1 The preceding identity has many famous identities as special cases Example 1 Tae s to be the Fibonacci sequence itself, and let i j 0 The result is that 1 f n 1 f n+1 for all even n In other words, f q f q for odd subscripts q Example Tae s to be the Fibonacci sequence again, and this time let i 1 and j 0 The result is that 1 ( 1 ) f n+ for all even n In other words, f q f q for all even subscripts q Example 3 With s the Fibonacci sequence again, let i n/ and j n/ to obtain the identity f q + f q 1 f q 1 for all positive integers q Let φ denote the positive solution of the equation x x 10, which we call the -golden ratio We let ψ denote the negative solution Verify that φψ 1, from which we see that when s (1,φ,,φ n 1 ) and t (1,ψ,,ψ n 1 ), we have another orthogonal basis {s, t} of R(, 1) Of course, in this case t is just a multiple of s We equate the entries of the matrix 1 s ss + 1 t tt with those of the projection in Theorem to obtain the following 5

Theorem 5 With n even we have 1 φ 1 φ n φi+j + 1 ψ 1 ψ n ψi+j f i+j n+1 for all 0 i, j n 1 Alternatively, this can be written φ i+j+1 φ n 1 + ψi+j+1 ψ n 1 f i+j n+1 Example 4 Letting j n 1 and i 1 in the equation above yields a slightly convoluted formulation of the Binet equation: 1 φn+1 φ n 1 + ψn+1 ψ n 1 Using the fact that n is even and φψ 1, one calculates φ n+1 φ n 1 + ψn+1 ψ n (φ n ψ n )φ ψ, 1 from which the familiar Binet formula appears as 1 (φ n ψ n )φ ψ The Lucas Projection In this section we consider odd values o, withn m + 1 We now let f and l denote the central Fibonacci and Lucas sequences, which are the transposes of (f m,,f 0,,f m ) and (l m,,l 0,,l m ), respectively A computation reveals that f f mf m+1 and l l ml m+1 Recalling that f 0 0, f i ( 1) i+1 f i, and l i ( 1) i l i, you see that m i m f i l i m f i l i + f 0 l 0 + i1 6 m f i l i 0, i1

so the pair {f, l} forms an orthogonal basis of R(, 1) One expression for the orthogonal projection onto R(, 1) is then given by ff + ll f m f m+1 l m l m+1 Our second expression for this projection gives it as a linear combination of the two Lucas matrices H l and ll Theorem 6 The orthogonal projection onto R(, 1) is the matrix ( 1) m+1 (4 + ll + H l )f m f m+1 l m l m+1 Proof The Hanel matrix H l has eigenvectors f and l with corresponding eigenvalues (4+ )f mf m+1 and lml m+1 (respectively) It follows that H l (4+ )f mf m+1 f mf m+1 ff + lml m+1 l ml m+1 ll (4+ ) ff + 1 ll, and, letting P denote our projection, we compute P f mf m+1 ff + l ml m+1 ll f mf m+1 ( (4+ ) H l 1 (4+ ) ll )+ l ml m+1 ll Simplifying the above equation while using the identity l m l m+1 (4 + )f m f m+1 ( 1) m gives the expression ( 1) m+1 P (4 + ll + H l )f m f m+1 l m l m+1 Corollary 7 I is odd with n m +1, then f i f j + l il j ( 1) m+1 f m f m+1 l m l m+1 (4 + l i l j + l i+j )f m f m+1 l m l m+1 for all m i, j m 7

Example 5 The identity l m l m+1 (4 + )f m f m+1 ( 1) m used in the derivation of the above is recovered when we let i j 0, when i j 1 and again when we let one of the indices equal zero, and the other equal m This suggests to us that this identity is intrinsically an orthogonality relation Example 6 the identity Let i j m in the equation above and simplify to obtain (4 + )f m + l m l m There is nothing sacred about the Lucas numbers in the above expression for our projection, aside from the fact that the resulting formula is the simplest we have found Indeed, if s is any element of R(, 1) and H s is the corresponding central Hanel matrix, then H s is diagonalizable with eigenvectors in R(, 1) corresponding to non-zero eigenvalues If s is ran two (as most of the H s are), then choosing an orthonormal eigenbasis {u, v} of R(, 1) gives us an expression for both H s and P : P uu + vv and H s α uu + β vv One can then choose to solve for one of the orthogonal projections in the expression for H s and substitute into the expression for P, giving P in terms of H s and the other projection, for example P ( 1 α H s β α vv )+vv Let us express P in terms of the central Fibonacci Hanel matrix H f illustrate this method Recall that H f is defined by to H f (f i+j ) m ij m Verify that H f (f) fmf m+1 l and H f (l) lml m+1 f It is convenient to write a fmf m+1 f and b lml m+1 l, from which we see that H f ( abf + al) ab( abf + al), 8

and H f ( abf al) ab( abf al) Since H f is symmetric, it is reassuring to chec that the eigenvector abf +al is orthogonal to abf al As f is orthogonal to l, we get abf + al abf al ab f + a l f 4 l We let u ( abf+al) f l and v ( abf al),withα ab f l f l and β ab f l, in the equation above to prove the following Theorem 8 Assume that n is odd, and n m +1 The orthogonal projection onto R(, 1) is the matrix (p ij ), where p ij f l f i+j +v i v j and i, j m,, m f l f i+j + 1 f f i f j + 1 l l i l j 1 f l (f il j + l i f j ) Example 7 For all i, j m,, m one has f i+j f i l j + l i f j, which can be seen following from Theorem 8 by remembering that and hence p ij so f l f i+j 1 f l (f il j + l i f j ) References p ij 1 f f if j + 1 l l il j, f l f 1 i+j + p ij f l (f il j + l i f j ), [1] M Bahsi and S Sola, On the spectral norms of Hanel matrices with Fibonacci and Lucas numbers, Selcu J Appl Math, 1 no 1 (011), 71-76 9

[] O Bretscher, Linear Algebra with Applications, Fifth Edition, Pearson Education, New Jersey [3] D Bozurt and M Abula, On the norms of Toeplitz matrices involving Fibonacci and Lucas numbers, Hacet J Math Stat, 37 ()(008), 89-95 [4] J Dixon, B Mathes, and D Wheeler, An application of matricial Fibonacci identities to the computation of spectral norms, RocyMtJ Math, to appear [5] E Dupree and B Mathes, Singular values of -Fibonacci and -Lucas Hanel Matrices, Int J Contemp Math Sciences, to appear [6] A Ipe, On the spectral norms of circulant matrices with classical Fibonacci and Lucas numbers entries, Appl Math Comput, 17 (011), 6011-601 [7] D Kalman and R Mena, The Fibonacci numbers exposed, Mathematics Magazine, vol 76, no 3 (003) [8] S Shen, On the norms of Toeplitz matrices involving -Fibonacci and -Lucas numbers, Int J Contemp Math Sciences, 7 (8) (01), 363-368 [9] S Shen, J Cen, On the spectral norms of r-circulant matrices with the -Fibonacci and -Lucas numbers, Int J Contemp Math Sciences, 5 (1) (010), 569-578 [10] S Sola, On the norms of circulant matrices with the Fibonacci and Lucas numbers, Appl Math Comput, 160 (005), 15-13 10