The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices CAUCHY –Jurnal Matematika Murni dan Aplikasi Volume 6(3) (2020), Pages 140-148 p-ISSN: 2086-0382; e-ISSN: 2477-3344 Submitted: July 14, 2020 Reviewed: October 13, 2020 Accepted: November 10, 2020 DOI: http://dx.doi.org/10.18860/ca.v6i3.9939 The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah1, Agustin Absari Wahyu Kuntarini 2 1,2Department of Mathematics, Faculty of Science and Technology UIN Walisongo Semarang Email: khasanah.nur@walisongo.ac.id ABSTRACT The application of centrosymmetric matrix on engineering takes their part, particularly about determinant rule. This basic rule needs a computational process for determining the appropriate algorithm. Therefore, the algorithm of the determinant kind of Hessenberg matrix is used for computing the determinant of the centrosymmetric matrix more efficiently. This paper shows the algorithm of lower Hessenberg and sparse Hessenberg matrix to construct the efficient algorithm of the determinant of a centrosymmetric matrix. Using the special structure of a centrosymmetric matrix, the algorithm of this determinant is useful for their characteristics. Key Words : Hessenberg; Determinant; Centrosymmetric INTRODUCTION One of the widely used studies in the use of centrosymmetric matrices is how to get determinant from centrosymmetric matrices. Besides this special matrix has some applications [1], it also has some properties used for determinant purpose [2]. Special characteristic centrosymmetric at this entry is evaluated at [3] resulting in the algorithm of centrosymmetric matrix at determinant. Due to sparse structure of this entry, the evaluation of the determinant matrix has simpler operations than full matrix entries. One special sparse matrix having rules on numerical analysis and arise at centrosymmetric the determinant matrix is the Hessenberg matrix. The role of Hessenberg matrix decomposition is the important role of computing the eigenvalue matrix. In the discussion, the recursive algorithm is explained to compute the n-per-n determinant of the Hessenberg matrix [4]. This study is the evaluation of some researches before about the determinant Hessenberg matrix [5,6]. The rule of the Hessenberg matrix for computing the determinant of general centrosymmetric matrix based on block matrix has been done by [7]. Moreover, this study continued by [8] on evaluating the previous work and shows only lower Hessenbeg matrix as block centrosymmetric matrix can use this algorithm. Then necessary and sufficient condition for this algorithm is constructed also that can be evaluated for the determinant process [9]. A Numerical example also shows for more understanding at a special centrosymmetric matrix on its block. Furthermore, the determinant of sparse Hessenberg matrix is also constructed for resulting in the new algorithm for a pentadiagonal matrix[10]. Some studies are also http://dx.doi.org/10.18860/ca.v6i3.9939 The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah 141 focussing on the determinant algorithm for computational process [11-17]. Based on the previous study, we propose a new form of pentadiagonal matrix with construction entries based on a centrosymmetric matrix called pentadiagonal centrosymmetric matrix. We then continue applying the previous algorithm to compute our new form of pentadiagonal centrosymmetric matrix for determinant purpose. Finally, this paper shows two algorithms of centrosymmetric matrix determinant by using the algorithm of the determinant of the Hessenberg matrix. PRELIMINARIES Before we discuss the main result, let introduce some definitions and properties used to explain the determinant of centrosymmetric matrix using Hessenberg’s determinant. Definition 1 [3] The form of n -by- n lower Hessenberg matrix is written as the matrix with the entries as follow                    nnnnnn nnnnnn hhhh hhhh hhh hh ,1,2,1, ,11,12,11,1 3,22,221 2,111 0 00 000   H . (1) Definition 2 [10] The sparse Hessenberg matrix with order n is the matrix with construction as                           nnnn nnnnnn nn hh hhh h hhhh hhhh hhh ,1, ,11,12,1 ,2 5,34,33,32,3 4,23,22,221 3,12,111  S . (2) Definition 3 [2] Let   nn nnij Ra   A be a centrosymmetric matrix, if the entries of the matrix satisfy 1,1   jninij aa for ni 1 , nj 1 or                  1112,1 2122,2 ,22221 ,11211 aaa aaa aaa aaa n n n n      A . (3) The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah 142 Definition 4 [7] The centrosymmetric matrix has a special structure that can be constructed as AAJJ nn  , where  11nnn eeeJ ,,,  and ie is the unit vector with the i-th element 1 and others 0. Definition 5 [8] The centrosymmetric matrix with n order where mn 2 is even number order can be partitioned with the form        mm mm BJJC CJJB A . (4) RESULTS AND DISCUSSION This part shows the rule of the determinant of lower Hessenberg and sparse Hessenberg matrix for determinant concept of the centrosymmetric matrix. Further discussion is also given for explaining the result and deeper understanding. Determinant Lower Hessenberg Matrix for Centrosymmetric Matrix’s Determinant The algorithm of the determinant of lower Hessenberg is presented based on the definition and properties of this matrix. The following is the Lemma of the determinant of lower Hessenberg, which is constructed as a block matrix. Lemma 6 [7] Let                                 1 0 0 00 0000 0001 0~ ,1,2,1, ,11,12,11,1 3,22,221 2,111 nnnnnn nnnnnn hhhh hhhh hhh hh      n T 1 eH e H is the partition matrix and                                 1 0 0 00 0000 0001 0~ ,1,2,1, ,11,12,11,1 3,22,221 2,111 nnnnnn nnnnnn hhhh hhhh hhh hh      n T 1 eH e H as the inverse matrix of its matrix, then the determinant of this matrix is written as          1 1 1, 1det n i ii n hhH . This algorithm is used for evaluating the algorithm of the determinant of centrosymmetric matrix with lower Hessenberg as a block matrix. The algorithm of the determinant of the centrosymmetric matrix with lower Hessenberg as block matrix can be proposed as follow. Input : Matrix        mm mm BJJC CJJB A Output :  Adet 1) Operate                N M CJB CJB APP m mT 0 0 0 0 The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah 143 2) Construct NM, are Hessenberg matrices become          m T 1 eM e M 0~ ,          m T 1 eN e N 0~ 3) Let inverse matrices        T M MM1 β Lα M M h ~ ,         T N NN1 β Lα N N h ~ 4) Compute          NMP N M PH T detdetdet 0 0 detdetdet        5)              1 1 1 1 1,1, 11det m i m i iiN m iiM m qhghH . Or we can write this algorithm by the following theorem for computing the determinant of centrosymmetric matrix with applying the algorithm of the determinant of lower Hessenberg matrix. This algorithm is only applied on a specific centrosymmetric matrix based on the special block matrix. Theorem 7 [9] Let centrosymmetric matrix with its block matrix, lower Hessenberg matrix, and its notation, such 11 NMNMPH  ~ , ~ ,,,, , then the determinant of the centrosymmetric matrix is written as         1 1 1,1, det m i iiiiMN qghhH . (5) Proof. From the definition of the centrosymmetric matrix, this matrix can be formed as a block matrix        mm mm BJJC CJJB A . By the orthogonal matrix         mm mm JJ II P 2 2 , this block matrix can be constructed as                N M CJB CJB HPP m mT 0 0 0 0 . Based on this matrix, the determinant of centrosymmetric matrix only calculate on block matrices the determinant. These block matrices NM, are Hessenberg form, therefore the algorithm of determinant lower Hessenberg can be applied for this determinant. This step becomes the main necessary for the next algorithm. By the algorithm of lower Hessenberg matrix, let’s form          m T 1 eM e M 0~ ,          m T 1 eN e N 0~ , and assume         T M MM1 β Lα M M h ~ ,          T N NN1 β Lα N N h ~ , where NM, α , NM, L , NM, β are matrices with the size of 1n , nn  , 1n respectively and NM h , is scalar. By analytical process at lower Hessenberg algorithm, then we find 0 M h and 0Nh . Finally, the determinant of the centrosymmetric matrix based on lower Hessenberg form is          NMP N M PH T detdetdet 0 0 detdetdet        .∎ (6) The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah 144 Determinant Sparse Hessenberg Matrix for Centrosymmetric Matrix’s Determinant Based on the previous study, [10] shows the efficient algorithm general pentadiagonal matrix. Then, this paper gives a specific discussion on the determinant of pentadiagonal centrosymmetric matrix by applying a pentadiagonal determinant matrix. There are some basic definitions for further study on the determinant of pentadiagonal centrosymmetric matrix. Definition [17,18] Let   njiij d   ,1 D is called nn  general pentadiagonal matrix which the entry 0 ij d for 2 ji or it can be constructed as                             nnnnnn nnnnnnnn nn ddd dddd d ddddd dddd ddd ,1,2, ,11,12,13,1 ,2 3534333231 24232221 131211  D . (7) This performance of the general pentadiagonal matrix has been described, and moreover, the algorithm of determinant general pentadiagonal is also evaluated [10]. This paper will give a different point of view from a general structure pentadigonal matrix, which can be constructed as a centrosymmetric matrix. This work combines the general pentadiagonal matrix’s definition, which has a centrosymmetric matrix structure, then we call the pentadiagonal centrosymmetric matrix. Therefore, we construct the algorithm of the pentadiagonal centrosymmetric matrix based on the algorithm of the determinant of the general pentadiagonal matrix. The algorithm has the following steps. 1. Construct pentadiagonal centrosymmetric matrix Based on the definition of pentadiagonal and centrosymmetric matrix, then the pentadiagonal centrosymmetric matrix is written as                             nnnnnn nnnnnnnn nn ddd dddd d ddddd dddd ddd ,1,2, ,11,12,13,1 ,2 3534333231 24232221 131211  D where the entries have a centrosymmetric structure 1,1   jninij aa for ni 1 , nj 1 . 2. Transform into sperse Hessenberg matrix According to the form of sparse Hessenberg matrix, then the construction of sparse Hessenberg matrix   njiij d   ,1 D is written as The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah 145                             nnnn nnnnnn nn dd ddd d dddd dddd ddd ,1, ,11,12,1 ,2 35343332 24232221 131211  H . (8) The form of (8) matrix is resulted by choosing i1,i λ  from the matrix of                    1 ,1 1 1 1 1 in ii i i I I  λ . (9) For instance, let pentadiagonal centrosymmetric matrix with the size 88 and choose 1,2 1,3 2,3 d d  for transforming matrix as follows.                                                          888786 78777675 6867666564 5756555453 5645444342 3534333231 24232221 131211 1,2 1,3 1 1 1 1 1 1 1 1 ddd dddd ddddd ddddd ddddd ddddd dddd ddd d d Dλ 3                            888786 78777675 6867666564 5756555453 5645444342 35343332 24232221 131211 ~ ddd dddd ddddd ddddd ddddd dddd dddd ddd . By the same way, taking 76 86 87 32 42 3,4 ,, d d d d    and the process will have the following Hessenberg matrix as follow. The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah 146                            8887 787776 68676665 57565554 56454443 35343332 24232221 131211 ~~ ~~~ ~~~ ~~~ ~~~ ~~~ dd ddd dddd dddd dddd dddd dddd ddd Dλλλλ 3678  . (10) Generally, the formula of the value of ii ,1  is defined as 1, 1,1 ,1 ~     ii ii ii d d  with 2121 ~ dd  . Remembering that, 31     nn V is the matrix which formed by λ matrix , having the form of HVD  . Consequently,          HVDDVD detdetdetdetdet  , where   1det V (11) By the entries of D which integer numbers or Zd i j , then it can use the following matrix                     1 1,1,1 1 1 ~ 1 in iiii i i I dd I λ . (12) Furthermore, to compute the determinant of the pentadiagonal centrosymmetric matrix is only on computing the determinant of the Hessenberg matrix. Then, the next step is how to compute the Hessenberg matrix. 3. Compute the Hessenberg Matrix By applying the previous algorithm, this determinant is constructed by using the two-term recurrence. This step takes the following explanation. Let nn  matrix          n1n T 1n1n n sr qP Z , where the size of block matrices is 1n Z  has    11  nn , n1n s,q  are the scalar and 1n T r  has  11  n . Then the determinant of n Z recursively is written as : 1 0 f 1iii fαf   , where 11 dα  , 1i 1 1i T 1iii qZrsα     Then, the determinant of i Z matrix is   ii fZ det , where ni ,,2,1  . Now, we apply the previous algorithm for our sparse Hessenberg matrix, and we have the following recursive.   1nn1,n2nn2,n1n 1 1n T 1nn ,nn , ededHehdα     n The Rule of Hessenberg Matrix for Computing the Determinant of Centrosymmetric Matrices Nur Khasanah 147   1n1n 1 1n T n1,n2n1n 1 1n T n2,n1nn ,nn , eHedeHedddα       n , where e is a vector unit. By substituting the equations 2n1,n 1n 3n 2n1n 1 1n T d f f eHe        and 1n 2n 1n1n 1 1n T f f eHe       then become                 n1,n 1n 2n n2,n2n1,n 1n 3n 1nn ,nn , d f f dd f f ddα n . Then, by multiply the equation with 1n f we have   n1,n2nn2,n2n1,n3n1nn,nn,1n1nnn dfddfddffαf   . 4. Construct the Algorithm of Determinant Pentadiagonal Centrosymmetric Matrix 111 df  1221221 dddff  2   2311321323323 dfddddff  for ni ,,5,4    iiiiiiiiiiiii dfddfddf ,12,22,131,,1  f end   n fD det . This algorithm shows the rule of the determinant of Hessenberg matrix can be contructed for the general determinant of the pentadiagonal centrosymmetric matrix. Based on two different algorithms above, the rule of determinant Hesseberg matrix can evaluate the determinant of the centrosymmetric matrix. For efficient algorithm, the first algorithm evaluates the determinant of centrosymmetric matrix applying the algorithm of the determinant of the lower Hessenberg matrix. It happens caused by the lower Hessenberg matrix appear as its block matrix, then the algorithm of centrosymmetric matrix only a half working. On the next algorithm, the algorithm of the sparse Hessenberg matrix is applied to construct the algorithm of determinant general pentadiagonal centrosymmetric matrix. This algorithm is used caused by the same structure of the main matrix is a sparse Hessenberg matrix, therefore is applicable. CONCLUSIONS The different cases of the application of determinant Hesseberg matrix are applied to a different form of a centrosymmetric matrix . It is necessary to identify the form of the main general centrosymmetric from the beginning. This part determines the best algorithm for the computational process more efficient. 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