Self-Dual Z4-Codes of Type IV Generated by Skew-Hadamard Matrices and Conference Matrices

Total Page:16

File Type:pdf, Size:1020Kb

Self-Dual Z4-Codes of Type IV Generated by Skew-Hadamard Matrices and Conference Matrices AUSTRALASIAN JOURNAL OF COMBINATORICS Volume 42 (2008), Pages 177–188 Self-dual Z4-codes of Type IV generated by skew-Hadamard matrices and conference matrices Mieko Yamada Division of Mathematical and Physical Sciences Graduate School of Natural Science and Technology Kanazawa University Japan [email protected] Abstract In this paper, we give families of self-dual Z4-codes of Type IV-I and Type IV-II generated by conference matrices and skew-Hadamard matri- ces. Furthermore, we give a family of self-dual Z4-codes of Type IV-I generated by bordered skew-Hadamard matrices. 1 Introduction In 1994, Hammons et al. showed that certain binary nonlinear codes are the binary image of linear codes over the Galois ring GR(4,m), an extension ring of Z4 = Z/4Z [7]. Active research on Z4-codes has been undertaken since their paper was published. The distinct rows of an Hadamard matrix are orthogonal. If we recognize the entries 1 and −1 of an Hadamard matrix H4n of order 4n as the elements of Z4,then the Z4-code generated by H4n is self-orthogonal. In 1999, Charnes proved that if H1 and H2 are H-equivalent, then the Z4-codes generated by H1 and H2 are equivalent [3]. Sol´e showed that if an Hadamard matrix H4n has order 4n and n is odd, then the Z4-code generated by H4n is self-dual and equivalent to Klemm’s code [3]. Charnes and Seberry considered the Z4-code generated by a weighing matrix W (n, 4). They proved that if n is even, then it is a tetrad code and if it has type 4(n−4)/224,thenit is a self-dual code [4]. Self-dual Z4-codes of lengths up to 20 are classified [2, 5, 6, 8, 9]. A Type II Z4-code is a self-dual code which has the property that all Euclidean weights are divisible by 8. A self-dual Z4-code which is not a Type II code is called a Type I Z4-code. A Type IV Z4-code is a self-dual code with all codewords of even Hamming weight. A type IV code which is also Type I or Type II, is called a TypeIV-I, or a Type IV-II code respectively. Two infinite families of Type IV codes are known, that is, Klemm’s codes and Cm,r codes [1, 5]. 178 MIEKO YAMADA In this paper, we give families of self-dual Z4-codes of Type IV-I and Type IV-II generated by conference matrices and skew-Hadamard matrices. Furthermore we give a family of self-dual Z4-codes of Type IV-I generated by bordered skew-Hadamard matrices. 2 Self-dual Z4-codes n An additive subgroup of Z4 is called a Z4-code of length n. We define an inner n n product on Z4 by a · b = i=1 ai · bi for vectors a =(a1,a2, ··· ,an)andb = ⊥ ⊥ n (b1,b2, ··· ,bn). The dual code C of a Z4-code C is defined as C = {x ∈ Z4 |x·y = 0 for all y ∈ C}.IfC ⊆ C⊥,acodeC is called self-orthogonal and if C = C⊥, C is called self-dual. Two codes are permutation-equivalent if one can be obtained from the other by permuting coordinates. Any Z4-code is permutation-equivalent to a code with generator matrix of the form Ik AB G = 1 O 2Ik2 2D where the entries of A and D are in Z2 = {0, 1} and the entries of B are in Z4.Then it contains 4k1 2k2 codewords. We say that the code C has type 4k1 2k2 .Itisknown k1 k2 ⊥ n−k1−k2 k2 that if Z4-code C has type 4 2 , then the dual code C has type 4 2 . Let ni(a) be the number of components of a vector a that are congruent to i (mod 4),i =0, 1, 2, 3. The Hamming weight wtH (a)ofa is defined by wtH (a)= n1(a)+n2(a)+n3(a) and the Euclidean weight wtE(a)ofa is defined by wtE(a)= n1(a)+4n2(a)+n3(a). The Hammimg distance dH(a, b) is defined by wtH (a − b) and the Euclidean distance dE(a, b) is defined by wtE(a − b). The minimum Hamming distance dH of a Z4-code C is min{dH(a, b)|a, b ∈ C, a = b} and the minimum Euclidean distance dE of C is min{dE(a, b)|a, b ∈ C, a = b}. The highest minimum Hamming weights and the highest minimum Euclidean weights of Type IV self-dual codes of lengths up to 40, Type IV-I codes of length 56 and Type IV-II codes of lengths 48,56,64 were determined [2]. Klemm’s code Kn is given as Kn = Rn +2Pn =2Pn ∪ (e +2Pn) where Rn is a repetition code, Pn is its dual code, and e is the all-one vector. For 3r ≤ m − 1, Cm,r code is given as Cm,r = RM(r, m)+2RM(m − r − 1,m) where RM(r, m) is a Reed-Muller code. SELF-DUAL Z4-CODES 179 3 Self-dual Z4-codes of Type IV generated by conference matrices If a square matrix H of order n with entries ±1satisfiesHHt = nI, then it is called t an Hadamard matrix. An Hadamard matrix H = H0 + I such that H0 = −H0 is called a skew-Hadamard matrix. The distinct rows of an Hadamard matrix are orthogonal. If we recognize the entries 1 and −1 of an Hadamard matrix H4m of order 4m as the elements of Z4, then the Z4-code generated by rows of H4m is self-orthogonal. In this section, we give families of self-dual Z4-codes of Type IV-I and Type IV-II generated by conference matrices and skew-Hadamard matrices. Let q be an odd prime power and GF(q) be a finite field with q elements. Let χ be a quadratic character of GF(q). We let χ(0) = 0. A Paley matrix P =(pij )of order q is the matrix whose entry pij is defined by pij = χ(ai − aj) where ai and aj (0 ≤ i, j ≤ q −1) are elements of GF(q). Denote a conference matrix of order q +1byQ,thatis 0 e Q = χ(−1)et P where e is the all-one vector. The followings relations are well-known. QQt = qI, Qt = χ(−1)Q (1) where I is the unit matrix. In what follows, we recognize the entries 1, −1 and 0 of a matrix as the elements of Z4. We construct families of self-dual Z4-codes of Type IV-I and of Type IV-II. Theorem 1. Put N = Q +2I. Define the matrix GQ as follows: ⎛ ⎞ IN N I ⎝ ⎠ GQ = O 2I 2(J − I)2J OO 2I 2(J − I) where J is the all-one matrix and O is the zero matrix. Then CQ with generator matrix GQ is a self-dual Z4-codeofTypeIV. Proof. From the relations (1), we have NNt =(Q+2I)(Qt +2I)=QQt +2(Q+Qt)= qI,2NNt +2I = O,2N =2(J −I), 2N +2(J −I)=O and 2N +2n(J −I)+2J = O. 180 MIEKO YAMADA Hence we have, ⎛ ⎞ ⎛ ⎞ IO O IN N I ⎜ t ⎟ t ⎝ ⎠ ⎜N 2IO⎟ GQG = O 2I 2(J − I)2J Q ⎝N t 2(J − I)2I ⎠ OO 2I 2(J − I) I 2J 2(J − I) ⎛ ⎞ 2I +2NNt 2N +2N(J − I)+2J 2N +2(J − I) = ⎝2N t +2(J − I)N t +2JO O⎠ 2N t +2(J − I) OO = O. q+1 2(q+1) Since the number of codewords of CQ is 4 2 , the number of codewords of ⊥ 4(q+1)−(q+1)−2(q+1) 2(q+1) q+1 2(q+1) the dual code CQ is 4 2 =4 2 . Hence CQ is a self-dual Z4-code. ⎛ ⎞ G1 ⎝ ⎠ Next we shall prove CQ is a Type IV code. Put n = q +1. LetGQ = G2 G3 where G1 =(I,N,N,I), G2 =(O, 2I,⎛2(J −⎞I), 2J)and⎛ ⎞ ⎛ ⎞ u1 v1 w1 ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜u2⎟ ⎜v2⎟ ⎜w2⎟ G3 =(O, O, 2I,2(J − I)). Put G1 = ⎜ . ⎟, G2 = ⎜ . ⎟ and G3 = ⎜ . ⎟.Then ⎝ . ⎠ ⎝ . ⎠ ⎝ . ⎠ un vn wn the codeword c of CQ is written as n n n c = αkuk + βkvk + γkwk k=1 k=1 k=1 where αk ∈ Z4 and βk,γk ∈ Z2 (1 ≤ k ≤ n). Let s = |{αk : αk = ±1}| and t = |{αk : αk =2}|. Wemayassumethefirst s coefficients α1, ··· ,αs are odd, and the next t coefficients αs+1, ··· ,αs+t are all 2, and other coefficients are all zero by permuting rows and columns of G1.The matrices G2 and G3 do not change by further suitable permuting rows and columns of G2 and G3. Then the codeword is written as s t n n c = αkuk + αkuk+s + βkvk + γkwk k=1 k=1 k=1 k=1 ± ≤ ≤ ≤ ≤ where αk = 1, (1 k s)andαk =2(1 k t). Denote the (k,l) entry of the s t matrix N by N(k,l). Let the vector g1 = k=1 αkuk+ k=1 αkuk =(g1,g2, ··· ,g4n). Then we have • gi = αi, (1 ≤ i ≤ s), SELF-DUAL Z4-CODES 181 • gi+s = αi, (1 ≤ i ≤ t), • gi+s+t =0, (1 ≤ i ≤ n − (s + t)), s t • gi+n = gi+2n = k=1 αkN(k,i)+ k=1 αkN(s + k,i), (1 ≤ i ≤ n), • gi+3n = αi, (1 ≤ i ≤ s), • gi+3n+s = αi, (1 ≤ i ≤ t), • gi+3n+s+t =0, (1 ≤ i ≤ n − (s + t)).
Recommended publications
  • Matrix Algebra and Control
    Appendix A Matrix Algebra and Control Boldface lower case letters, e.g., a or b, denote vectors, boldface capital letters, e.g., A, M, denote matrices. A vector is a column matrix. Containing m elements (entries) it is referred to as an m-vector. The number of rows and columns of a matrix A is nand m, respectively. Then, A is an (n, m)-matrix or n x m-matrix (dimension n x m). The matrix A is called positive or non-negative if A>, 0 or A :2:, 0 , respectively, i.e., if the elements are real, positive and non-negative, respectively. A.1 Matrix Multiplication Two matrices A and B may only be multiplied, C = AB , if they are conformable. A has size n x m, B m x r, C n x r. Two matrices are conformable for multiplication if the number m of columns of the first matrix A equals the number m of rows of the second matrix B. Kronecker matrix products do not require conformable multiplicands. The elements or entries of the matrices are related as follows Cij = 2::;;'=1 AivBvj 'Vi = 1 ... n, j = 1 ... r . The jth column vector C,j of the matrix C as denoted in Eq.(A.I3) can be calculated from the columns Av and the entries BVj by the following relation; the jth row C j ' from rows Bv. and Ajv: column C j = L A,vBvj , row Cj ' = (CT),j = LAjvBv, (A. 1) /1=1 11=1 A matrix product, e.g., AB = (c: c:) (~b ~b) = 0 , may be zero although neither multipli­ cand A nor multiplicator B is zero.
    [Show full text]
  • LCD Codes from Weighing Matrices
    LCD codes from weighing matrices Dean Crnkovi´c, Ronan Egan, B. G. Rodrigues and Andrea Svobˇ Abstract Linear codes with complementary duals are linear codes whose intersection with their duals are trivial, shortly named LCD codes. In this paper we outline a construction for LCD codes over finite fields of order q using weighing matrices and their orbit matrices. The LCD codes constructed can be of any length dimension according to the choice of matrices used in their construction. As a special case, LCD codes of length 2n and dimension n are constructed which also have the property of being formally self-dual. Alternatively, under a condition depending on q that the codes are not LCD, this method constructs self-dual codes. To illustrate the method we construct LCD codes from weighing matrices, including the Paley conference matrices and Hadamard matrices. We also extend the construction to Hermitian LCD codes over the finite field of order 4. In addition, we propose a decoding algorithm that can be feasible for the LCD codes obtained from some of the given methods. 2010 Mathematics Subject Classification: 05B20, 05B30, 94B05, 12E20. Keywords: Weighing matrix, orbit matrix, LCD code. 1 Introduction In this paper we introduce a method for constructing linear codes with complementary duals, or LCD codes. LCD codes are linear codes whose intersection with their duals are trivial; they were introduced by Massey in [23] and have been widely applied in information protection, arXiv:1812.00368v2 [math.CO] 28 Nov 2019 electronics and cryptography. They provide an optimum linear coding solution for the two user binary adder channel.
    [Show full text]
  • Orr Hunter K 202104 Msc.Pdf (5.035Mb)
    Modelling Human Behaviour Based on Similarity Measurements Between Event Sequences by Hunter Orr A thesis submitted to the School of Computing in conformity with the requirements for the degree of Master of Science Queen's University Kingston, Ontario, Canada May 2021 Copyright c Hunter Orr, 2021 Abstract From a set of sequences, individual's behavioural patterns can be identified. Using these sequences of events, the metadata available can be processed into a weighted format to improve the meaningfulness of the sequence comparisons. The usefulness of this process, identifying users' behavioural patterns, is important in a number of areas such as cybersecurity. This work examines the properties a cybersecurity dataset might contain and demonstrates its effectiveness on a dataset with those properties. Building on the existing sequence comparison method, Damerau-levenshtein dis- tance, this work develops a pipeline of steps that can be used to transform the meta- data and integrate this weighted format into the sequence comparison calculation. In this pipeline, one of the most significant transformations that is applied to the meta- data is based on previous work by Brand. This transformation reduces the impact of high popularity pairwise relationships. This pipeline is shown to incorporate the metadata information into the resulting distance values. Thus, producing meaningful changes which demonstrate the benefit of these extra steps. i Acknowledgments I would like to thank Dr. David Skillicorn for his guidance and thoughtful discussions throughout this entire work. The discussions we had were invaluable and continue to encourage me to improve my thinking process. I appreciate the enthusiasm and encouragement you have provided throughout this research.
    [Show full text]
  • Circulant Weighing Matrices
    Circulant Weighing Matrices by Goldwyn Millar A Thesis submitted to the Faculty of Graduate Studies In Partial Fulfillment of the Requirements for the Degree of MASTER OF SCItrNCtr Department of Mathematics University of Manitoba Winnipeg, Manitoba Copyright @ 2009 by Goldwyn Millar THE UNIVBRSTTY OF MANTTOBA FACULTY OF GRADUATB STUDIBS COPYRIGHT PBRMISSION Circulant Weighing Matrices B--v Goldwyn Millar A Thesis/Practicum submitted to thc Faculty of Graduate Studies of The Univer.sity of Mnnitob¿r in ¡rartitl fulfillure nt of thc requirernent of the degree of Master of Science Goldrvvn MillarO2009 Permission h¿rs been grirntetl to the Univcl'sit],of M¿rnitoba Libraries to le¡rd a copy of this thesis/¡rracticttm, to Librar¡' antl Arctrives C¿rn¿rtla (LAC) to lencl a copy of this thesiii¡rracticum, and to LAC's âgent (UMIiProQuest) to microfilm, sellcopies and to pi t lirtr an abstr.act of this thesis/prncticum. This reproductiotl or copy of this tllesis h¿rs been rn:rde available by authority of the copyright owner solely for the pur¡rose of ¡rrivate study and reseârch, *nd may onl¡, 1¡" reprottucert aria copiea as perrnitted b¡' co¡rvright larvs or rvith ext)rcss n'l'itten ¿ruthoriz¿rtion frour thà copyrig¡t on,nér. Abstract A ci,rculant matrin is a matrix such that each of its rows, after the first, can be obtained from the row above it by a right cyclic shift. A ci,rculant wei,gh'ing matri,r of order n and weight w (a CW(n,w)) is a n x n circuiant (0, t1)-matrix trZ such that WWr : wI.
    [Show full text]
  • Trace, Symmetry and Orthogonality
    Canad. Math. Bull. Vol. 37 (4), 1994 pp. 461-467 TRACE, SYMMETRY AND ORTHOGONALITY R. CRAIGEN ABSTRACT. Does there exist a circulant conference matrix of order > 2? When is there a symmetric Hadamard matrix with constant diagonal? How many pairwise disjoint, amicable weighing matrices of order n can there be? These are questions concerning which the trace function gives a great deal of insight. We offer easy proofs of the known solutions to the first two, the first being new, and develop new results regarding the latter question. It is shown that there are 2* disjoint amicable weighing matrices of order 2*p, where p is odd, and that this is an upper bound for t < 1. An even stronger bound is obtained for certain cases. 1. Introduction and preliminaries. Weighing matrices A, B are said to be amicable if ABl = BAl (and antiamicable if ABf + BAl = 0). Clearly A and B are amicable (antiamicable) if and only if At and Bl are as well. We denote by A C\B the Hadamard, or entry-wise product of matrices A, B having the same size. These are disjoint if A HB = 0. One of the most powerful means by which Hadamard matrices are constructed is with orthogonal designs (see [7]). An orthogonal design OD{n\s\,... ,ty) corresponds to k pairwise disjoint, antiamicable weighing matrices of order n, with weights si,... ,^. Similar constructions use disjoint, amicable weighing matrices (see [3, Theorem 4.2], [5]). Even without the disjointness property, amicable weighing matrices are of consid­ erable interest (see pp. 217-219 of [7]).
    [Show full text]
  • Discrete Mathematics Generalized Weighing Matrices and Self
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Elsevier - Publisher Connector Discrete Mathematics 309 (2009) 4697–4699 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: www.elsevier.com/locate/disc Note Generalized weighing matrices and self-orthogonal codes Vladimir D. Tonchev Department of Mathematical Sciences, Michigan Technological University, Houghton, MI 49931, USA article info a b s t r a c t Article history: It is shown that the row spaces of certain generalized weighing matrices, Bhaskar Rao Received 11 October 2006 designs, and generalized Hadamard matrices over a finite field of order q are hermitian Accepted 28 May 2008 self-orthogonal codes. Certain matrices of minimum rank yield optimal codes. In the special Available online 1 July 2008 case when q D 4, the codes are linked to quantum error-correcting codes, including some codes with optimal parameters. Keywords: ' 2008 Elsevier B.V. All rights reserved. Generalized weighing matrix Generalized Hadamard matrix Bhaskar Rao design Self-orthogonal code Quantum code 1. Introduction A generalized weighing matrix over a multiplicative group G of order g is a v × b matrix M D .mij/ with entries from G [ f0g such that for every two rows .mi1;:::; mib/; .mj1;:::; mjb/, i 6D j, the multi-set f −1 j ≤ ≤ 6D g mismjs 1 s b; mjs 0 (1) contains every element of G the same number of times. A generalized weighing matrix with the additional properties that every row contains precisely r nonzero entries, each column contains exactly k nonzero entries, and for every two distinct rows the multi-set (1) contains every group element exactly λ/g times is known as a generalized Bhaskar Rao design GBRD.v; b; r; k; λI G/ [10].
    [Show full text]
  • Download Download
    Applying balanced generalized weighing matrices to construct block designs Yury J. Ionin Department of Mathematics Central Michigan University Mt. Pleasant, MI 48858, USA [email protected] Submitted: October 10, 2000; Accepted: January 27, 2001. MR Subject Classifications: 05B05 Abstract Balanced generalized weighing matrices are applied for constructing a family of symmetric designs with parameters (1 + qr(rm+1 − 1)/(r − 1),rm,rm−1(r − 1)/q), where m is any positive integer and q and r =(qd − 1)/(q − 1) are prime powers, and a family of non-embeddable quasi-residual 2−((r +1)(rm+1 −1)/(r −1),rm(r + 1)/2,rm(r − 1)/2) designs, where m is any positive integer and r =2d − 1, 3 · 2d − 1 or 5 · 2d − 1 is a prime power, r ≥ 11. 1 Introduction A balanced incomplete block design (BIBD) with parameters (v, b, r,k, λ)ora2-(v, k, λ) design is a pair D =(V,B), where V is a set (of points) of cardinality v and B is a collection of bk-subsets of V (blocks) such that each point is contained in exactly r blocks and each 2-subset of V is contained in exactly λ blocks. If V = {x1,x2,... ,xv} and B = {B1,B2,... ,Bb}, then the v ×b matrix, whose (i, j)-entry is equal to 1 if xi ∈ Bj and is equal to 0 otherwise, is the incidence matrix of the design. A (0, 1) matrix X of size t v × b is the incidence matrix of a (v, b, r,k, λ) BIBD if and only if XX =(r − λ)Iv + λJv and JvX = kJv×b,whereIv, Jv,andJv×b are the identity matrix of order v,thev × v all-one matrix, and the v × b all-one matrix, respectively.
    [Show full text]
  • Some Non-Existence and Asymptotic Existence Results for Weighing
    Some non-existence and asymptotic existence results for weighing matrices Ebrahim Ghaderpour 1 Lassonde School of Engineering York University, Canada Abstract Orthogonal designs and weighing matrices have many applications in areas such as coding theory, cryptography, wireless networking and communication. In this paper, we first show that if positive integer k cannot be written as the sum of three integer squares, then there does not exist any skew-symmetric weighing matrix of order 4n and weight k, where n is an odd positive integer. Then we show that for any square k, there is an integer N(k) such that for each n N(k), there is a symmetric weighing matrix of order n and weight k.≥ Moreover, we improve some of the asymptotic existence results for weighing matrices obtained by Eades, Geramita and Seberry. Keywords: Asymptotic existence; Orthogonal design; Skew-symmetric weighing matrix; Symmetric weighing matrix; Weighing matrix. 1 Introduction An orthogonal design (OD) [3, Chapter 1] of order n and type (s1,...,sℓ), arXiv:1602.07794v1 [math.CO] 23 Feb 2016 denoted OD(n; s1,...,sℓ), is a square matrix X of order n with entries from 0, x ,..., xℓ , where the xj’s are commuting variables, that satisfies { ± 1 ± } ℓ T 2 XX = sjxj In, j ! X=1 T where X is the transpose of X, and In is the identity matrix of order n. An OD with no zero entry is called a full OD. Equating all variables to 1 1E-mail: [email protected]. 1 in any full OD results in a Hadamard matrix. Equating all variables to 1 in any OD of order n results a weighing matrix, denoted W (n, k), where k is the weight that is the number of nonzero entries in each row (column) of the weighing matrix.
    [Show full text]
  • On Cocyclic Weighing Matrices and the Regular Group Actions of Certain
    Discrete Applied Mathematics 102 (2000) 63{101 View metadata, citation and similar papers at core.ac.uk brought to you by CORE On cocyclic weighing matrices and the regular group provided by Elsevier - Publisher Connector actions of certain paley matrices Warwick de Launey∗, Richard M. Sta ord Center for Communications Research, 4320 Westerra Court, San Diego, California, CA92121, USA Received 1 September 1998; revised 7 June 1999; accepted 2 August 1999 Abstract In this paper we consider cocyclic weighing matrices. Cocyclic development of a weighing matrix is shown to be related to regular group actions on the points of the associated group divisible design. We show that a cocyclic weighing matrix is equivalent to a relative di erence set with central forbidden subgroup of order two. We then set out an agenda for studying a known cocyclic weighing matrix and carry it out for the Paley conference matrix and for the type I Paley Hadamard matrix. Using a connection with certain near ÿelds, we determine all the regular group actions on the group divisible design associated to such a Paley matrix. It happens that all the regular actions of the Paley type I Hadamard matrix have already been described in the literature, however, new regular actions are identiÿed for the Paley conference matrix. This allows us to determine all the extension groups and indexing groups for the cocycles of the aforementioned Paley matrices, and gives new families of normal and non-normal relative di erence sets with forbidden subgroup of size two. ? 2000 Elsevier Science B.V. All rights reserved.
    [Show full text]
  • Optimization Problems with Orthogonal Matrix Constraints
    NUMERICAL ALGEBRA, doi:10.3934/naco.2018026 CONTROL AND OPTIMIZATION Volume 8, Number 4, December 2018 pp. 413–440 OPTIMIZATION PROBLEMS WITH ORTHOGONAL MATRIX CONSTRAINTS K. T. Arasu Department of Mathematics and Statistics Wright State University 3640 Colonel Glenn Highway Dayton, OH 45435, U.S.A. Manil T. Mohan∗† Department of Mathematics and Statistics Air Force Institute of Technology, 2950 Hobson Way Wright Patterson Air Force Base, OH 45433, USA (Communicated by Zhong Wan) Abstract. The optimization problems involving orthogonal matrices have been formulated in this work. A lower bound for the number of stationary points of such optimization problems is found and its connection to the num- ber of possible partitions of natural numbers is also established. We obtained local and global optima of such problems for different orders and showed their connection with the Hadamard, conference and weighing matrices. The appli- cation of general theory to some concrete examples including maximization of Shannon, Rény, Tsallis and Sharma-Mittal entropies for orthogonal matrices, minimum distance orthostochastic matrices to uniform van der Waerden matri- ces, Cressie-Read and K-divergence functions for orthogonal matrices, etc are also discussed. Global optima for all orders has been found for the optimization problems involving unitary matrix constraints. 1. Introduction. Optimization problems involving constraints like orthogonal ma- trices and unitary matrices play an important role in several applications of science, engineering and technology. Some examples include linear and nonlinear eigenvalue problems, electronic structures computations, low-rank matrix optimization, poly- nomial optimization, subspace tracking, combinatorial optimization, sparse princi- pal component analysis, etc (see [8,1, 18, 24, 25] and references therein).
    [Show full text]
  • Signed Groups, Sequences, and the Asymptotic Existence of Hadamard Matrices
    JOURNAL OF COMBINATORIALTHEORY, Series A 71, 241-254 (1995) Signed Groups, Sequences, and the Asymptotic Existence of Hadamard Matrices R. CRAIGEN * Department of Mathematics and Computer Science, University of Lethbridge, Lethbridge, Alberta, Canada T1K 3M4 Communicated by Vera Pless Received December 15, 1993 We use the newly developed theory of signed groups and some known sequences with zero autocorrelation to derive new results on the asymptotic existence of Hadamard matrices. New values of t are obtained such that, for any odd number p, there exists an Hadamard matrix of order 2tp. These include: t = 2N, where N is the number of nonzero digits in the binary expansion of p, and t = 4[-~ log2((p- 1)/2)7 + 2. Both numbers improve on all previous general results, but neither uses the full power of our method. We also discuss some of the implica- tions of our method in terms of signed group Hadarnard matrices and signed group weighing matrices: There exists a circulant signed group Hadamard matrix of every even order n, using a suitable signed group. This result stands in striking contrast to the known results for Hadamard matrices and complex Hadamard matrices, and the circulant Hadamard matrix conjecture. Signed group weighing matrices of even order n always exist, with any specified weight w ~< n. © 1995 AcademicPress, Inc. 1. INTRODUCTION Let us begin by recalling two well-known conjectures regarding orthogonal matrices, which are known respectively as the Hadamard matrix [8] and complex Hadamard matrix [12] conjectures. CONJECTURE 1. There is an Hadamard matrix of order n > 2 if and only if n-~ 0 rood 4.
    [Show full text]
  • Introduction to Graph Signal Processing
    Noname manuscript No. (will be inserted by the editor) Introduction to Graph Signal Processing LjubiˇsaStankovi´c,MiloˇsDakovi´c,and Ervin Sejdi´c Received: date / Accepted: date Abstract Graph signal processing deals with signals whose domain, defined by a graph, is irregular. An overview of basic graph forms and definitions is presented first. Spectral analysis of graphs is discussed next. Some simple forms of processing signal on graphs, like filtering in the vertex and spectral do- main, subsampling and interpolation, are given. Graph topologies are reviewed and analyzed as well. Theory is illustrated through examples, including few applications at the end of the chapter. 1 Introduction Graph signal processing is an active research area in recent years resulting in many advanced solutions in various applications. In numerous practical cases the signal domain is not a set of equidistant instants in time or a set of points in space on a regular grid. The data sensing domain could be irregular and, in some cases, not related to the time or space. The data sensing domain is then related to other properties of the considered system/network. For example, in many social or web related networks, the sensing points and their connectiv- ity are related to specific objects and their links. In some physical processes other properties than the space or time coordinates define the relation between points where the signal is sensed. Even for the data sensed in the well defined time and space domain, the introduction of new relations between the sensing points may produce new insights in the analysis and result in more advanced data processing techniques.
    [Show full text]