The kbinomial Transforms and the Hankel Transform


 Bernard Bates
 2 years ago
 Views:
Transcription
1 Journal of Integer Sequences, Vol. 9 (2006, Artcle The kbnomal Transforms and the Hankel Transform Mchael Z. Spvey Department of Mathematcs and Computer Scence Unversty of Puget Sound Tacoma, Washngton USA Laura L. Stel Department of Mathematcs and Computer Scence Samford Unversty Brmngham, Alabama USA Abstract We gve a new proof of the nvarance of the Hankel transform under the bnomal transform of a sequence. Our method of proof leads to three varatons of the bnomal transform; we call these the kbnomal transforms. We gve a smple means of constructng these transforms va a trangle of numbers. We show how the exponental generatng functon of a sequence changes after our transforms are appled, and we use ths to prove that several sequences n the OnLne Encyclopeda of Integer Sequences are related va our transforms. In the process, we prove three conectures n the OEIS. Addressng a queston of Layman, we then show that the Hankel transform of a sequence s nvarant under one of our transforms, and we show how the Hankel transform changes after the other two transforms are appled. Fnally, we use these results to determne the Hankel transforms of several nteger sequences. 1
2 1 Introducton Gven a sequence A = {a 0, a 1,...}, defne the bnomal transform B of a sequence A to be the sequence B(A = {b n }, where b n s gven by b n = =0 ( n a. Defne the Hankel matrx of order n of A to be the (n + 1 (n + 1 upper left submatrx of a 0 a 1 a 2 a 3 a 1 a 2 a 3 a 4 a 2 a 3 a 4 a 5. a 3 a 4 a 5 a Let h n denote the determnant of the Hankel matrx of order n. Then defne the Hankel transform H of A to be the sequence H(A = {h 0, h 1, h 2,..., }. For example, the Hankel matrx of order 3 of the derangement numbers, {D n } = {1, 0, 1, 2, 9, 44, 265,...}, s The determnant of ths matrx s 144, whch s (0! 2 (1! 2 (2! 2 (3! 2. Flaolet [4] and Radoux [9] have shown that the Hankel transform of the derangement numbers s { n =0 (n!2 }. Although the determnants of Hankel matrces had been studed before, the term Hankel transform was ntroduced n 2001 by Layman [7]. Other recent papers nvolvng Hankel determnants nclude those by Ehrenborg [3], Peart [8], and Woan and Peart [15]. Layman [7] proves that the Hankel transform s nvarant under the bnomal transform, n the sense that H(B(A = H(A. He also proves that the Hankel transform s nvarant under the nvert transform (see the OnLne Encyclopeda of Integer Sequences [11] for the defnton, and he asks f there are other transforms for whch the Hankel transform s nvarant. Ths artcle partly addresses Layman s queston. We provde a new proof of the nvarance of the Hankel transform under the bnomal transform. Our method of proof generalzes to three varatons of the bnomal transform that we call the kbnomal transform, the rsng kbnomal transform, and the fallng kbnomal transform. Collectvely, we refer to these as the kbnomal transforms. (There should be no confuson n context. We gve a smple means of constructng these transforms usng a trangle of numbers, and we provde combnatoral nterpretatons of the transforms as well. We show how the exponental generatng functon of a sequence changes after applyng our transforms, and we use these results to prove that several sequences n the OnLne Encyclopeda of Integer Sequences (OEIS [11] are related by the transforms. In the process, we prove three conectures lsted n the OEIS concernng the bnomal mean transform. Then, gvng an answer to Layman s queston, we. 2
3 show that the Hankel transform s nvarant under the fallng kbnomal transform. The Hankel transform s not nvarant under the kbnomal transform and the rsng kbnomal transform, but we gve a formula showng how the Hankel transform changes under these two transforms. These results, together wth our proofs of relatonshps between sequences n the OEIS, determne the Hankel transforms of several sequences n the OEIS. (Unfortunately, ths s some dscrepancy n the lterature n the defntons of the Hankel determnant and the bnomal transform of an nteger sequence. The defntons of the Hankel determnant n Layman [7] and n Ehrenborg [3] are slghtly dfferent from each other, and ours s slghtly dfferent from both of these. As many sequences of nterest begn ndexng wth 0, we defne the Hankel determnant and the Hankel transform so that the frst elements n the sequences A and H(A are ndexed by 0. Layman s defnton begns ndexng A and H(A by 1, whereas Ehrenborg s defnton results n ndexng A begnnng wth 0 and H(A begnnng wth 1. Our defnton of the bnomal transform s that used by Layman [7] and the OEIS [11]. Somewhat dfferent defntons are gven n Knuth [6] (p. 136 and by MathWorld [13]. 2 Invarance of the Hankel transform under the bnomal transform Layman [7] proves that H(B(A = H(A for any sequence A. He does ths by showng that the Hankel matrx of order n of B(A can be obtaned by multplyng the Hankel matrx of order n of A by certan upper and lower trangular matrces, each of whch have determnant 1. We present a new proof of ths result. Our proof technque suggests generalzatons of the bnomal transform, whch we dscuss n subsequent sectons. We requre the followng lemma. Lemma 2.1. Gven a sequence A = {a 0, a 1, a 2,...}, create a trangle of numbers T usng the followng rule: 1. The left dagonal of the trangle conssts of the elements of A. 2. Any number off the left dagonal s the sum of the number to ts left and the number dagonally above t to the left. Then the sequence on the rght dagonal s the bnomal transform of A. For example, the bnomal transform of the derangement numbers s the factoral numbers [7]. Fgure 1 llustrates how the factoral numbers can be generated from the derangement numbers usng the trangle descrbed n Lemma 2.1. Although they do not use the term bnomal transform, Lemma 2.1 s essentally proven by Graham, Knuth, and Patashnk [5] (pp We present a dfferent proof, one that allows us to prove smlar results for the kbnomal transforms we dscuss n subsequent sectons. 3
4 Fgure 1: Derangement trangle Proof. Let t n be the n th element on the rght dagonal of the trangle. By constructon of the trangle, we can see from Fgure 2 that the number of tmes element a contrbutes to the value of t n s the number of paths from a to t n. To move from a to t n requres n path segments, of whch move drectly to the rght. Thus there are ways to choose whch of the n ordered segments are the rghtwardmovng segments, and the down segments are completely determned by ths choce. Therefore the contrbuton of a to t n s a, and the value of t n, n terms of the elements on the left dagonal, s n =0 a. But ths s the defnton of the bnomal transform, makng the rght dagonal of the trangle the bnomal transform of A. a 0 t 0 a 1 t 1 a 2 t 2 a 3 t 3 a 4 t 4 Fgure 2: Drected graph underlyng the bnomal transform The bnomal transform has the followng combnatoral nterpretaton: If a n represents the number of arrangements of n labeled obects wth some property P, then b n represents the number of ways of dvdng n labeled obects nto two groups such that the frst group has property P. In terms of the derangement and factoral numbers, then, as D n s the number of permutatons of n ordered obects n whch no obect remans n ts orgnal poston, n! s the number of ways that one can dvde n labeled obects nto two groups, order the obects n the frst group, and then permute the frst group obects so that none remans n ts orgnal poston. The numbers n the trangle descrbed n Lemma 2.1, not ust the rght and left dagonals, can also have combnatoral nterpretatons. For nstance, the trangle of numbers n Fgure 1 4
5 s dscussed as a combnatoral entty n ts own rght n another artcle by the frst author [12]. The number n row, poston, n the trangle s the number of permutatons of ordered obects such that every obect after does not reman n ts orgnal poston. We now gve our proof of Layman s result. Theorem 2.1. (Layman The Hankel transform s nvarant under the bnomal transform. Proof. We defne a procedure for transformng the Hankel matrx of order n of a sequence A to the Hankel matrx of order n of B(A usng only matrx row and column addton. Whle perhaps more complcated than Layman s proof, ours has the vrtue of beng easly modfed to gve proofs for the Hankel transforms of the kbnomal transforms that we dscuss subsequently. The procedure s as follows: 1. Gven a sequence A = {a 0, a 1,...}, create the trangle of numbers descrbed n Lemma 2.1, where T, s the (, th entry n the trangle. 2. Let T n be the followng matrx consstng of numbers from the left dagonal of T : T 0,0 T 1,0 T 2,0 T n,0 T 1,0 T 2,0 T 3,0 T n+1,0 T 2,0 T 3,0 T 4,0 T n+2, T n,0 T n+1,0 T n+2,0 T 2n,0 Snce a = T,0, T n s the Hankel matrx of order n of A. 3. Then apply the followng transformatons to T n, where rows and columns of the matrx are ndexed begnnng wth 0. (a Let range from 1 to n. Durng stage, for each row, add row 1 to row and replace row wth the result. (b Then let agan range from 1 to n. Durng stage, for each column, add column 1 to column and replace column wth the result. Clam 1: After stage n 3(a, row m of the matrx s of the followng form: [ Tm,m T m+1,m T n+m,m ], f m ; [ Tm, T m+1, T n+m, ], f m >. The clam s clearly true ntally, when = 0. Now, assume the clam s true for all values of from 0 to k 1. Then, n stage = k, the only rows that change are rows k, k + 1,..., n. Row m, for m k, s the sum of rows m and m 1 from the prevous teraton: [ Tm,k 1 + T m 1,k 1 T m+1,k 1 + T m,k 1 T n+m,k 1 + T n+m 1,k 1 ]. But, by the defnton of T, T, + T 1, = T,+1. Thus ths row s equal to [ Tm,k T m+1,k T n+m,k ], 5
6 provng the clam. After the transformatons n 3(a are appled, then, we have the matrx T 0,0 T 1,0 T 2,0 T n,0 T 1,1 T 2,1 T 3,1 T n+1,1 T 2,2 T 3,2 T 4,2 T n+2, T n,n T n+1,n T n+2,n T 2n,n Clam 2: After stage n 3(b, column m of the matrx s of the followng form: [ ] T Tm,m T m+1,m+1 T n+m,n+m, f m ; [ Tm, T m+1,+1 T n+m,n+ ] T, f m >. The proof s almost the same as that for Clam 1. The clam s clearly true for = 0. Assume the clam s true for all values of from 0 to k 1. In stage = k, the only columns that change are columns k, k + 1,..., n. Column m, for m k, s the sum of columns m and m 1 from the prevous teraton: [ Tm,k 1 + T m 1,k 1 T m+1,k + T m,k T n+m,n+k 1 + T n+m 1,n+k 1 ] T. Agan, by the defnton of T, T, + T 1, = T,+1. Thus ths column s equal to [ Tm,k T m+1,k+1 T n+m,n+k ] T, whch proves the clam. After applyng the transformatons n 3(b, then, we have the matrx T 0,0 T 1,1 T 2,2 T n,n T 1,1 T 2,2 T 3,3 T n+1,n+1 T 2,2 T 3,3 T 4,4 T n+2,n T n,n T n+1,n+1 T n+2,n+2 T 2n,2n But ths s the Hankel matrx of order n of B(A, as B(A s the rght dagonal of trangle T. Snce the only matrx manpulatons we used were addng a row to another row and addng a column to another column, and the determnant of a matrx s nvarant under these operatons [2] (p. 262, the determnant of the Hankel matrx of order n of A s equal to the determnant of the Hankel matrx of order n of B(A. The operatons descrbed n the proof of Theorem 2.1, when done n the order prescrbed by the procedure, have a smple nterpretaton n terms of the trangle. Addng a row to another row shfts a left dagonal n the trangle one place to the rght, and addng a column to another column shfts a rght dagonal one place to the rght. We can see ths n the case 6
7 of the Hankel matrx of order 2 of the derangement numbers by a comparson of Fgure 1 and the sequence of matrces arsng from the procedure descrbed n the proof of Theorem The kbnomal transforms We now consder three varatons of the bnomal transform. All three transforms take two parameters: the nput sequence A and a scalar k. The kbnomal transform W of a sequence A s the sequence W (A, k = {w n }, where w n s gven by { n w n = =0 k n a = k n n =0 a, f k 0 or n 0; a 0, f k = 0, n = 0. The rsng kbnomal transform R of a sequence A s the sequence R(A, k = {r n }, where r n s gven by { n r n = =0 k a, f k 0; a 0, f k = 0. The fallng kbnomal transform F of a sequence A s the sequence F (A, k = {f n }, where f n s gven by { n f n = =0 k n a, f k 0; a n, f k = 0. The case k = 0 must be dealt wth separately because 0 0 would occur n the formulas otherwse. Our defntons effectvely take 0 0 to be 1. These turn out to be good defntons, n the sense that all the results dscussed subsequently hold under our defntons for the k = 0 case. When k = 0, the kbnomal transform of A s the sequence {a 0, 0, 0, 0,...}, the rsng kbnomal transform of A s {a 0, a 0, a 0,...}, and the fallng kbnomal transform s the dentty transform. The kbnomal transform when k = 1/2 s of specal nterest; ths s the bnomal mean transform, defned n the OEIS [11], sequence A When k s a postve nteger, these varatons of the bnomal transform all have combnatoral nterpretatons smlar to that of the bnomal transform, although, unlke the bnomal transform, they have a twodmensonal component. If a n represents the number of arrangements of n labeled obects wth some property P, then w n represents the number of ways of dvdng n obects such that In one dmenson, the n obects are dvded nto two groups so that the frst group has property P. In a second dmenson, the n obects are dvded nto k labeled groups. The nterpretaton of the second dmenson could be somethng as smple as a colorng of each obect from a choce of k colors, ndependent of the dvson of the obects n the 7
8 frst dmenson. For example, f the nput sequence s the derangement numbers, w n s the number of ways of dvdng n labeled obects nto two groups such that the obects n the frst group are deranged and each of the n obects has been colored one of k colors, ndependently of the ntal dvson nto two groups. Wth ths nterpretaton of w n n mnd, r n represents the number of ways of dvdng n labeled obects nto two groups such that the frst group has property P and each obect n the frst group s further placed nto one of k labeled groups (e.g., colored usng one of k colors. Smlarly, f n represents the number of ways of dvdng n labeled obects nto two groups such that the frst group has property P and the obects n the second group are further placed nto k labeled groups (e.g., colored usng one of k colors. Each of the kbnomal transforms can also be replcated va a trangle lke that descrbed n Lemma 2.1. Theorem 3.1. Gven a sequence A = {a 0, a 1, a 2,...}, 1. Create a trangle of numbers so that the left dagonal conssts of the elements of A, and any number off the left dagonal s k tmes the sum of the numbers to ts left and dagonally above t to the left. Then the rght dagonal s the kbnomal transform W (A, k of A. 2. Create a trangle of numbers so that the left dagonal conssts of the elements of A, and any number off the left dagonal s the sum of the number dagonally above t to the left and k tmes the number to ts left. Then the rght dagonal s the rsng kbnomal transform R(A, k of A. 3. Create a trangle of numbers so that the left dagonal conssts of the elements of A, and any number off the left dagonal s the sum of the number to ts left and k tmes the number dagonally above t to the left. Then the rght dagonal s the fallng kbnomal transform F (A, k of A. For example, applyng the procedure n Part 1 wth k = 2 to the derangement numbers (sequence A yelds the trangle of numbers n Fgure ,200 46,080 Fgure 3: Derangement trangle wth 2bnomal transform As we shall see, the sequence on the rght dagonal s the double factoral numbers (sequence A000165, the 2bnomal transform of the derangement numbers. We now prove Theorem
9 Proof. Suppose k 0. (Part 1. The proof s smlar to that of Lemma 2.1. Let t n be the n th element on the rght dagonal of the trangle. There are stll n path segments from a to t n ; however, traversng a path segment now ncurs a multplcatve factor of k rather than the understood factor of 1 ncurred wth the bnomal transform. Thus each path from a to t n contrbutes k n a to the value of t n. Wth moves to the rght, there are stll dfferent paths from a to t n ; thus the total contrbuton from a to the value of t n s ( n k n a. Therefore, t n = n =0 k n a, the n th element of W (A, k. (Part 2. The proof s smlar to that of Part 1. The dfference s that the multplcatve factor of k only appears on moves to the rght, not moves down. Wth rghtwardmovng path segments, the contrbuton from a along one path to t n s thus k a. The total contrbuton from a over all paths s therefore ( n k a, and we have t n = n =0 k a, the n th element of R(A, k. (Part 3. The proof s dentcal to that of Part 2, except that the multplcatve factor of k occurs on down path segments rather than on rghtmovng segments. As there are n down path segments from a to t n, we have t n = n =0 k n a, the n th element of F (A, k. Now, suppose k = 0. For the trangle descrbed n Part 1, every path from the left dagonal to t n s a zero path, except for the empty path from a 0 to t 0. Thus t 0 = a 0, and t n = 0 for n 0. For the trangle n Part 2, the only nonzero path to t n from the left dagonal s the one straght down from a 0 ; thus t n = a 0 for all n. For Part 3, the only nonzero path to t n from the left dagonal s the one straght rght from a n ; therefore, t n = a n for each n. There are also some nce relatonshps between our transforms, the bnomal transform, and the nverse bnomal transform. Gven a sequence A = {a 0, a 1,...}, the nverse bnomal transform of A s defned to be the sequence B 1 (A = {c n }, where c n s gven by c n = =0 ( n ( 1 n a. It s easy to show that B 1 (B(A = B(B 1 (A = A. For k Z +, let B k (A denote k successve applcatons of the bnomal transform;.e., B k (A = B(B( B(A, where B occurs k tmes. For k Z, let B k (A denote k successve applcatons of the nverse bnomal transform. Fnally, let B 0 (A denote the dentty transform. Then we have Theorem 3.2. If k Z, B k (A = F (A, k. In other words, k successve applcatons of the bnomal transform (or the nverse bnomal transform, f k Z s equvalent to the fallng kbnomal transform. Proof. The theorem s clearly true when k = 1. Assume the theorem s true for values of k from 1 to m. Let b k n denote the n th element n the sequence B k (A, and let f k n denote the 9
10 n th element n the sequence F (A, k. For k = m + 1, we have b m+1 n = = = =0 ( n b m = = =0 ( n f m = =0 m a = a (m + 1 n = f m+1 n. ( n a ( m a = = = ( n m = ( m a n a l=0 The second equalty holds by the nducton hypothess, the ffth by trnomal revson [10] (p. 104, and the secondtolast by the Bnomal Theorem. Ths proves the relatonshp for postve values of k. The proof for k Z s an almost dentcal nducton wth k = 1 as the base case and movng down. When k = 0 we have the dentty transform n both cases. We also have the followng relatonshp between the W, R, and F transforms. Theorem 3.3. W (A, k = F (R(A, k, k 1. In other words, the kbnomal transform s equvalent to the composton of the fallng (k 1bnomal transform wth the rsng kbnomal transform. Proof. For k = 1, we have W (A, 1 = B(A, and F (R(A, 1, 0 = F (B(A, 0 = B(A. For k = 0, R(A, 0 = {a 0, a 0, a 0,...}. Then the n th term of F (R(A, 0, 1 s gven by =0 ( n ( 1 n a 0 = a 0 =0 ( n ( 1 n. But the summaton on the rghthand sde s 1 f n = 0 and 0 otherwse [10] (p Thus F (R(A, 0, 1 = {a 0, 0, 0,...}, whch s the sequence W (A, 0. Assumng k 0 and k 1, the n th term of F (R(A, k, k 1 s gven by =0 = = ( n (k 1 n = n k a l=0 ( k a = (k 1 n k a = The last term s the n th term of W (A, k. l ( = (k 1 n l = (k 1 n k a k a = ( n k a k n = ( n (k 1 n ( n k n a. l m l 10
11 4 Integer sequences related by the kbnomal transforms The kbnomal transforms relate many sequences lsted n the OnLne Encyclopeda of Integer Sequences [11]. Several of these relatonshps are gven n Layman [7]. He lsts a number of tables of nteger sequences related by repeated applcatons of the bnomal transform and thus (va Theorem 3.2 by the fallng kbnomal transform. The sequences n Tables 1, 2, and 3 are related or appear to be related by the kbnomal transform, the rsng kbnomal transform, and the fallng kbnomal transform, respectvely. (Table 3 s short because we do not duplcate Layman s lsts [7]. The tables were mostly obtaned by an nvestgaton of several of the entres n the OEIS; undoubtedly there are more sequences related by these transforms. Under the status column, K ( known means that we were able to fnd a reference for the transform relatonshp, P ( proved means that we could not fnd a reference for the transform relatonshp but that t can be proved va the technques n the followng secton, and C ( conecture means that we were not able to fnd a reference for the transform relatonshp and were not able to prove t. In addton, the expresson (E n front of a sequence means that the sequence has been shfted; ts frst term has been deleted. Status A Name (f common k W (A, k Name (f common P A Factoral Numbers 2 A P A Factoral Numbers 3 A P A Factoral Numbers 4 A P A Factoral Numbers 5 A P A Derangement Numbers 2 A Double Factoral Numbers P A Derangement Numbers 3 A Trple Factoral Numbers P A Derangement Numbers 4 A Quadruple Factoral Numbers P A Derangement Numbers 5 A Quntuple Factoral Numbers K A /2 A Factoral Numbers P (EA /2 A P A A Quadruple Factoral Numbers K A Euler or Secant Numbers 1/2 A C A /2 A P A Motzkn Numbers 2 A C A /2 A P A /2 A Double Factoral Numbers P A /2 A NSW Numbers P A Central Trnomal Coeffcents 2 A C A /2 A K A /2 A Table 1: kbnomal transforms 11
12 Status A Name (f common k R(A, k Name (f common K A Lucas Numbers 2 A Even Lucas Numbers, or L 3n K A Fbonacc Numbers 2 A Even Fbonacc Numbers, or F 3n C (EA Catalan Numbers 2 A P A Factoral Numbers 2 A P A Factoral Numbers 3 A P A Factoral Numbers 4 A P A Factoral Numbers 5 A P A Central Bnomal Coeffcents 2 A Table 2: Rsng kbnomal transforms Status A Name (f common k F (A, k Name (f common P A A Table 3: Fallng kbnomal transforms A close examnaton of Table 1 ndcates that a couple of new bnomal transform relatonshps should exst as well; these are gven n Table 4. Status A Name (f common B(A Name (f common P A A Double Factoral Numbers P A A Quadruple Factoral Numbers Table 4: New bnomal transforms The relatonshps n the tables were found prmarly by determnng that the frst several terms of a sequence correspond to a transform of another sequence. Ths, of course, does not consttute proof, and so the relatonshps that we were unable to prove and for whch we could not fnd references reman conectures. The relatonshps nvolvng the Lucas numbers and the Fbonacc numbers are gven n Benamn and Qunn [1] (p. 135, and the other known relatonshps are mentoned n ther respectve entres n the OEIS [11]. The relatonshps ndcated by a P n the status column we were able to prove va the generatng functon method we dscuss n the next secton. In partcular, the entres n Table 1 nvolvng the bnomal mean transform W (A, 1/2 and the nput sequences (EA000354, A001907, and A are lsted as conectures n the OEIS, and we prove these conectures n the next secton. Fnally, we note that the Hankel transforms of many of these sequences, such as the derangement numbers, the factoral numbers, and the Catalan numbers, are known. Thus Tables 1, 2, 3, and 4, together wth Theorems 6.1, 6.2, and 6.3 (proved n Secton 6, contan several results and conectures concernng the Hankel transforms of varous nteger sequences. 12
13 5 Exponental generatng functons of the kbnomal transforms The method we use to prove the relatonshps ndcated wth a P n Tables 1, 2, 3, and 4 s that of generatng functons. (See Wlf s text [14] for an extensve dscusson of generatng functons and ther uses. The exponental generatng functon (egf f of a sequence A = {a 0, a 1,...} s the formal power seres f(x = a 0 + a 1 x + a 2 x 2 /2! + = =0 a x /!. We have the followng: Theorem 5.1. If g(x s the exponental generatng functon of a sequence A = {a 0, a 1,...}, then: 1. The exponental generatng functon of the sequence F (A, k s e kx g(x. 2. The exponental generatng functon of the sequence W (A, k s e kx g(kx. 3. The exponental generatng functon of the sequence R(A, k s e x g(kx. Proof. For the specal case k = 0, the theorem states that the egf of F (A, k s g(x, whch s the egf of A. The theorem gves the egf of W (A, k to be g(0, whch generates the sequence {a 0, 0, 0, 0,...}. The theorem gves the egf of R(A, k to be e x g(0, whch generates the sequence {a 0, a 0, a 0,...} [14] (p. 52. These are all consstent wth the defntons of the kbnomal transforms when k = 0. Now, suppose k 0. (Part 1. It s known [14] (p. 42 that f f s the egf of some sequence {a n } and h s the egf of some sequence {b n }, then fh s the egf of the sequence { =0 ( } b a b n. (1 (Ths s known as the bnomal convoluton of {a n } and {b n }. F (A, k s, agan, the sequence { =0 ( } b k n a. Wth (1 n mnd, then, b n = k n for each n. The egf of the sequence {1, k, k 2,...} s e kx [5] (p Thus the egf of F (A, k s e kx g(x. (Part 2. By defnton, W (A, k = {k n b n }, where {b n } = B(A. From the proof of Part 1, we know that the egf of B(A s e x g(x. By defnton, t s also =0 b x /!. The egf of W (A, k s thus =0 k b x /! = =0 b (kx /! = e kx g(kx. (Part 3. By Theorem 3.3, W (A, k = F (R(A, k, k 1. Thus the egf of the sequence on the left must equal that of the sequence on the rght. Lettng G(x denote the egf of R(A, k, we have, by Parts 1 and 2, e kx g(kx = e (k 1x G(x. Thus G(x = e x g(kx. We now use Theorem 5.1 to prove three relatonshps lsted n Table 1 that are gven as conectures n the OEIS. They all nvolve the bnomal mean transform W (A, 1/2. The 13
14 proofs of the other relatonshps ndcated wth a P n Tables 1, 2, 3, and 4 are smlar n flavor to these, and so we do not prove them explctly here. In fact, most can be proved smply by applyng Theorem 5.1 to a drect comparson of the egf s gven n the OEIS [11]. Corollary 5.1. The bnomal mean transform of sequence (EA s sequence A Proof. The egf of sequence A s gven by the OEIS as e x /(1 2x. The egf of sequence (EA s thus d/dx[e x /(1 2x] [14] (p. 40, whch s e x (1 + 2x (1 2x 2. By Theorem 5.1, the egf of the bnomal mean transform of (EA s therefore ( e e x/2 x/2 (1 + x = 1 + x (1 x 2 (1 x, 2 whch s the egf of sequence A as lsted n the OEIS. Corollary 5.2. The bnomal mean transform of sequence A s sequence A (the double factoral numbers. Proof. The egf of sequence A s gven by the OEIS as e x /(1 4x. Thus the egf of the bnomal mean transform of A s ( e e x/2 x/2 = 1 1 2x 1 2x, whch, accordng to the OEIS, s the egf of sequence A Corollary 5.3. The bnomal mean transform of sequence A s sequence A Proof. The OEIS does not lst egf s for these sequences. It does, however, lst twoterm recurrence relatonshps for the sequences, and we can use these relatonshps to set up dfferental equatons for the egf s. Sequence A s generated by the recursve relatonshp a n = 6a n 1 a n 2, for n 2, wth ntal values a 0 = 1, a 1 = 7. Ths means that the egf f of sequence A satsfes the ntalvalue problem f 6f + f = 0, f(0 = 1, f (0 = 7 [14] (p. 40. The characterstc equaton for the dfferental equaton s r 2 6r +1 = 0, whch has soluton r = 3 ± 2 2. Thus the soluton to the dfferental equaton, and therefore the egf of A002315, s f(x = c 1 e (3+2 2x +c 2 e (3 2 2x. Usng the ntal condtons, the constants are found to be c 1 = 1/2 + 1/ 2, c 2 = 1/2 1/ 2. The recurrence relaton for sequence A s gven by a n = 4a n 1 2a n 2, for n 2, wth a 0 = 1, a 1 = 4. Usng the same method as before, ts egf s found to be c 1 e (2+ 2x + c 2 e (2 2x, for the same values of c 1 and c 2 found prevously. Ths s also the egf of the bnomal mean transform of sequence A002315: e x/2[ c 1 e (3/2+ 2x + c 2 e (3/2 2x ] = c 1 e (2+ 2x + c 2 e (2 2x. 14
15 Theorem 5.1 can also be used to prove many addtonal relatonshps between sequences n the OEIS nvolvng compostons of the kbnomal transforms. For example, let D(a, b denote the sequence of central coeffcents of (1 + ax + bx 2 n, and let C denote the sequence of central bnomal coeffcents (A Then we have Corollary 5.4. D(a, b = F (R(C, b, a 2 b 1. Proof. The central bnomal coeffcents have e 2x BesselI(0, 2x as ther exponental generatng functon. Thus the egf of F (R(C, b, a 2 b 1 s e (a 2 b 1x e x e 2 bx BesselI(0, 2 bx, whch s e ax BesselI(0, 2 bx. Ths last expresson, accordng to the comments under sequence A n the OEIS, s the egf of the central coeffcents of (1 + ax + bx 2 n. Another example nvolves the Motzkn numbers (sequence A and the super Catalan numbers wth the frst element deleted (sequence (EA Denotng the sequence of Motzkn numbers by M and the sequence of supercatalan numbers by S, we have Corollary 5.5. (ES = F (R(M, 2, 2 2. Proof. The Motzkn numbers have egf e x BesselI(1, 2x/x. The egf of F (R(M, 2, 2 2 s therefore e (2 2x e x e 2x BesselI(1, 2 2x/( 2x. Smplfed, ths s e 3x BesselI(1, 2 2x/( 2x, whch s the egf of the supercatalan numbers wth the frst element deleted. 6 Hankel transforms of the kbnomal transforms At the end of hs artcle [7], Layman asks f there are transforms besdes the bnomal and nvert under whch the Hankel transform s nvarant. We now address ths queston for the three kbnomal transforms. Theorem 6.1. The Hankel transform s nvarant under the fallng kbnomal transform. In other words, for a gven sequence A = {a 0, a 1,...}, H(F (A, k = H(A. For k Z, ths follows from Theorems 2.1 and 3.2. However, the theorem s true for k Z as well. Our proof of ths entals a slght modfcaton of the proof of Theorem 2.1. Proof. Create a trangle of numbers T by lettng the left dagonal consst of A and every number off of the left dagonal be the sum of the number to ts left and k tmes the number dagonally above t to the left. Then, va Theorem 3.1, the rght dagonal conssts of the sequence F (A, k. Construct the matrx T n as n the proof of Theorem 2.1. Change the transformaton rules n part 3 of the procedure descrbed n that proof so that one adds k tmes row (column 1 to row (column and replaces row (column wth the result. By thus mmckng the rule for the creaton of the numbers off the left dagonal of T, Clams 1 and 2 n the proof of Theorem 2.1 stll hold. Therefore, the fnal matrx resultng from the transformaton procedure s the Hankel matrx of order n of F (A, k. Snce the transformaton rules only nvolve addng a multple of a row to another row and addng a multple of a column to another column, and the determnant s nvarant under these 15
16 operatons, the determnant of the Hankel matrx of order n of F (A, k s equal to the determnant of the Hankel matrx of order n of A. Ths proof technque works for determnng how the Hankel transforms of A, W (A, k, and R(A, k relate as well. However, the relatonshps are more complcated, as the Hankel transform s not nvarant under W (A, k and R(A, k. We begn wth the transform for R(A, k. Theorem 6.2. Gven a sequence A = {a 0, a 1,...}, let H(A = {h n }. Then H(R(A, 0 = {a 0, 0, 0,...}. If k 0, H(R(A, k = {k n(n+1 h n }. Proof. If k = 0, then H n s the (n + 1 (n + 1 matrx whose entres are all a 0. Thus det(h 0 = a 0, and for n > 0, det(h n = 0. Now, assume k 0. Create a trangle of numbers T by lettng the left dagonal consst of A and lettng each number off the left dagonal be the sum of the number dagonally above t to the left and k tmes the number to ts left. By Theorem 3.1, the rght dagonal of T s R(A, k. Construct the matrx T n as n the proofs of Theorems 2.1 and 6.1, but change the transformaton rule so that one adds row (column 1 to k tmes row (column and replaces row (column wth that result. Agan, Clams 1 and 2 n the proof of Theorem 2.1 hold, and so the fnal matrx resultng from the transformaton procedure s the Hankel matrx of order n of R(A, k. The transformaton rule can be broken nto two parts: Frst multply a row (column by k. Then replace that row (column wth the sum of tself and the prevous row (column. Multplyng a row of a matrx by a factor of k changes the determnant by a factor of k, whle replacng a row by the sum of tself and another row does not affect the determnant. The same s true for columns [2] (p In order to determne how the Hankel transform changes under the rsng kbnomal transform, then, we need to determne the number of tmes a row or column s multpled by a factor of k. Accordng to the transformaton procedure, row s multpled by a factor of k n stages 1, 2,...,. Thus row s multpled by a factor of k a total of tmes. Therefore the total number of tmes a row s multpled by a factor of k s n =0 = n(n + 1/2. Smlarly, the number of tmes a column s multpled by a factor of k s n(n + 1/2. Thus the determnant of the Hankel matrx of order n of R(A, k s k n(n+1 tmes that of the Hankel matrx of order n of A. Fnally, we have Theorem 6.3. Gven a sequence A = {a 0, a 1,...}, let H(A = {h n }. Then H(W (A, 0 = {a 0, 0, 0,...}. If k 0, H(W (A, k = {k n(n+1 h n }. Proof. By Theorems 3.3 and 6.1, H(W (A, k = H(F (R(A, k, k 1 = H(R(A, k. Alternatvely, one can prove ths result wth a modfcaton of the proofs of Theorems 6.1 and 6.2. The transformaton rule would be addng row (column 1 to row (column and replacng row (column wth k tmes that sum. Ths s equvalent to frst multplyng row (column by a factor of k and then addng k tmes row (column 1 to that and replacng row (column wth ths sum. As we ust noted, multplyng a row or column by a factor of 16
17 k changes the determnant by a factor of k, whle addng a multple of a row to another row or a multple of a column to another column does not change the determnant. The analyss of the number of tmes a row or column s multpled by a factor of k s exactly the same as that n the proof of Theorem 6.2. Theorems 6.1, 6.2, and 6.3, together the relatonshps gven n Tables 1, 2, 3, and 4, allow us to determne the Hankel transforms of several nteger sequences. Gven that the factoral numbers (A and the derangement numbers (A have Hankel transform { n =0 (!2 } (sequence A055209, the Motzkn numbers (A have Hankel transform {1, 1, 1,...} (sequence A000012, and the central bnomal coeffcents (A and trnomal coeffcents (A have Hankel transform {2 n } (sequence A [7], we have the Hankel transforms of several nteger sequences gven n Table 5. Hankel Transform Some sequences wth ths Hankel transform {2 n(n+1 } A {2 n(n+2 } A059304, A {2 n(n+1 n =0 (!2 } A000165, A000354, A010844, A {3 n(n+1 n =0 (!2 } A010845, A032031, A {4 n(n+1 n =0 (!2 } A001907, A047053, A056545, A {5 n(n+1 n =0 (!2 } A052562, A056546, A Table 5: Hankel transforms of some nteger sequences In addton, Theorems 6.1, 6.2, and 6.3 can be used to relate the Hankel transforms of sequences that are themselves related by compostons of the kbnomal transforms. For example, Theorems 6.1 and 6.2 appled to Corollary 5.4 tell us that the Hankel transform of the central coeffcents of (1 + ax + bx 2 n s {2 n b n(n+1/2 }. Appled to Corollary 5.5, they tell us that the Hankel transform of the supercatalan numbers wth the frst element deleted s {2 n(n+1/2 }. 7 Conclusons We have ntroduced three generalzatons of the bnomal transform: the kbnomal transform, the rsng kbnomal transform, and the fallng kbnomal transform. We have gven a smple method for constructng these transforms, and we have gven combnatoral nterpretatons of each of them. We have also shown how the generatng functon of a sequence changes after applyng one of the transforms. Ths allows us to prove that several sequences n the OnLne Encyclopeda of Integer Sequences are related by one of these transforms, as well as prove three specfc conectures n the OEIS. In addton, we have shown how the Hankel transform of a sequence changes after applyng one of the transforms. These results determne the Hankel transforms of several sequences lsted n the OEIS. We see several areas of further study. One nvolves contnung to answer Layman s queston [7]. We have proved that the Hankel transform s nvarant under the fallng kbnomal 17
18 transform, and he proves that the Hankel transform s nvarant under the bnomal and nvert transforms. Are there other nterestng transforms under whch the Hankel transform s nvarant? Second, are there varatons of our trangle method that would prove for other transforms T how H(A and H(T (A relate? Thrd, we conecture four relatonshps n Tables 1 and 2: Sequences A002078, A007052, and A are the bnomal mean transforms of A000609, A001653, and A007696, respectvely, and sequence A s the rsng 2bnomal transform of (EA (The frst three are also conectured n the OEIS. Can these conectures be proved? Fnally, what sequences, other than those lsted n Layman [7] and n Tables 1, 2, 3, and 4, are related by the kbnomal transforms? References [1] Arthur T. Benamn and Jennfer J. Qunn, Proofs That Really Count, MAA, Washngton, D. C., [2] Otto M. Bretscher, Lnear Algebra wth Applcatons, Pearson Prentce Hall, Upper Saddle Rver, NJ, thrd ed., [3] Rchard Ehrenborg, The Hankel determnant of exponental polynomals, Amer. Math. Monthly, 107 (2000, [4] Phlppe Flaolet, On congruences and contnued fractons for some classcal combnatoral quanttes, Dscrete Math., 41 (1982, [5] R. L. Graham, D. E. Knuth, and O. Patashnk, Concrete Mathematcs, AddsonWesley, Readng, MA, second ed., [6] Donald E. Knuth, The Art of Computer Programmng, Vol. III: Sortng and Searchng, AddsonWesley, Readng, MA, second ed., [7] John W. Layman, The Hankel transform and some of ts propertes, J. Integer Seq., 4 (2001, Artcle [8] Paul Peart, Hankel determnants va Steltes matrces, Congr. Numer., 144 (2000, [9] Chrstan Radoux, Détermnant de Hankel construt sur des polynômes lés aux nombres de dérangements, European J. Combn., 12 ( [10] Kenneth H. Rosen, ed., Handbook of Dscrete and Combnatoral Mathematcs, CRC Press, Boca Raton, FL, [11] Nel J. A. Sloane, The OnLne Encyclopeda of Integer Sequences, 2005, publshed electroncally at nas/sequences/. 18
19 [12] Mchael Z. Spvey, Exams wth deranged questons, n preparaton. [13] Erc W. Wessten, Bnomal transform, from MathWorld A Wolfram Web Resource, [14] Herbert S. Wlf, Generatngfunctonology, Academc Press, Boston, second ed., [15] WnJn Woan and Paul Peart, Determnants of Hankel matrces of aerated sequences, Congressus Numerantum, 157 (2002, Mathematcs Subect Classfcaton: Prmary 11B65; Secondary 11B75. Keywords: bnomal transform, Hankel transform. (Concerned wth sequences A000012, A000032, A000045, A000079, A000108, A000142, A000165, A000166, A000354, A000364, A000609, A000984, A001003, A001006, A001653, A001907, A002078, A002315, A002426, A002801, A003645, A005799, A007052, A007070, A007680, A007696, A010844, A010845, A014445, A014448, A032031, A047053, A052562, A055209, A056545, A056546, A059231, A059304, A075271, A075272, A082032, A084770, A084771, A097814, A097815, and A Receved June ; revsed verson receved November Publshed n Journal of Integer Sequences, November Return to Journal of Integer Sequences home page. 19
Graph Theory and Cayley s Formula
Graph Theory and Cayley s Formula Chad Casarotto August 10, 2006 Contents 1 Introducton 1 2 Bascs and Defntons 1 Cayley s Formula 4 4 Prüfer Encodng A Forest of Trees 7 1 Introducton In ths paper, I wll
More information8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by
6 CHAPTER 8 COMPLEX VECTOR SPACES 5. Fnd the kernel of the lnear transformaton gven n Exercse 5. In Exercses 55 and 56, fnd the mage of v, for the ndcated composton, where and are gven by the followng
More informationgreatest common divisor
4. GCD 1 The greatest common dvsor of two ntegers a and b (not both zero) s the largest nteger whch s a common factor of both a and b. We denote ths number by gcd(a, b), or smply (a, b) when there s no
More informationv a 1 b 1 i, a 2 b 2 i,..., a n b n i.
SECTION 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS 455 8.4 COMPLEX VECTOR SPACES AND INNER PRODUCTS All the vector spaces we have studed thus far n the text are real vector spaces snce the scalars are
More informationBERNSTEIN POLYNOMIALS
OnLne Geometrc Modelng Notes BERNSTEIN POLYNOMIALS Kenneth I. Joy Vsualzaton and Graphcs Research Group Department of Computer Scence Unversty of Calforna, Davs Overvew Polynomals are ncredbly useful
More informationRecurrence. 1 Definitions and main statements
Recurrence 1 Defntons and man statements Let X n, n = 0, 1, 2,... be a MC wth the state space S = (1, 2,...), transton probabltes p j = P {X n+1 = j X n = }, and the transton matrx P = (p j ),j S def.
More information6. EIGENVALUES AND EIGENVECTORS 3 = 3 2
EIGENVALUES AND EIGENVECTORS The Characterstc Polynomal If A s a square matrx and v s a nonzero vector such that Av v we say that v s an egenvector of A and s the correspondng egenvalue Av v Example :
More informationWe are now ready to answer the question: What are the possible cardinalities for finite fields?
Chapter 3 Fnte felds We have seen, n the prevous chapters, some examples of fnte felds. For example, the resdue class rng Z/pZ (when p s a prme) forms a feld wth p elements whch may be dentfed wth the
More information8 Algorithm for Binary Searching in Trees
8 Algorthm for Bnary Searchng n Trees In ths secton we present our algorthm for bnary searchng n trees. A crucal observaton employed by the algorthm s that ths problem can be effcently solved when the
More informationIMPROVEMENT OF CONVERGENCE CONDITION OF THE SQUAREROOT INTERVAL METHOD FOR MULTIPLE ZEROS 1
Nov Sad J. Math. Vol. 36, No. 2, 2006, 009 IMPROVEMENT OF CONVERGENCE CONDITION OF THE SQUAREROOT INTERVAL METHOD FOR MULTIPLE ZEROS Modrag S. Petkovć 2, Dušan M. Mloševć 3 Abstract. A new theorem concerned
More informationLuby s Alg. for Maximal Independent Sets using Pairwise Independence
Lecture Notes for Randomzed Algorthms Luby s Alg. for Maxmal Independent Sets usng Parwse Independence Last Updated by Erc Vgoda on February, 006 8. Maxmal Independent Sets For a graph G = (V, E), an ndependent
More informationn + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)
MATH 16T Exam 1 : Part I (InClass) Solutons 1. (0 pts) A pggy bank contans 4 cons, all of whch are nckels (5 ), dmes (10 ) or quarters (5 ). The pggy bank also contans a con of each denomnaton. The total
More informationNew bounds in BalogSzemerédiGowers theorem
New bounds n BalogSzemerédGowers theorem By Tomasz Schoen Abstract We prove, n partcular, that every fnte subset A of an abelan group wth the addtve energy κ A 3 contans a set A such that A κ A and A
More informationLinear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits
Lnear Crcuts Analyss. Superposton, Theenn /Norton Equalent crcuts So far we hae explored tmendependent (resste) elements that are also lnear. A tmendependent elements s one for whch we can plot an / cure.
More informationSection 5.4 Annuities, Present Value, and Amortization
Secton 5.4 Annutes, Present Value, and Amortzaton Present Value In Secton 5.2, we saw that the present value of A dollars at nterest rate per perod for n perods s the amount that must be deposted today
More informationwhere the coordinates are related to those in the old frame as follows.
Chapter 2  Cartesan Vectors and Tensors: Ther Algebra Defnton of a vector Examples of vectors Scalar multplcaton Addton of vectors coplanar vectors Unt vectors A bass of noncoplanar vectors Scalar product
More informationRing structure of splines on triangulations
www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAMReport 201448 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon
More informationFormula of Total Probability, Bayes Rule, and Applications
1 Formula of Total Probablty, Bayes Rule, and Applcatons Recall that for any event A, the par of events A and A has an ntersecton that s empty, whereas the unon A A represents the total populaton of nterest.
More informationA Note on the Decomposition of a Random Sample Size
A Note on the Decomposton of a Random Sample Sze Klaus Th. Hess Insttut für Mathematsche Stochastk Technsche Unverstät Dresden Abstract Ths note addresses some results of Hess 2000) on the decomposton
More information1 Example 1: Axisaligned rectangles
COS 511: Theoretcal Machne Learnng Lecturer: Rob Schapre Lecture # 6 Scrbe: Aaron Schld February 21, 2013 Last class, we dscussed an analogue for Occam s Razor for nfnte hypothess spaces that, n conjuncton
More informationPERRON FROBENIUS THEOREM
PERRON FROBENIUS THEOREM R. CLARK ROBINSON Defnton. A n n matrx M wth real entres m, s called a stochastc matrx provded () all the entres m satsfy 0 m, () each of the columns sum to one, m = for all, ()
More informationModule 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur
Module LOSSLESS IMAGE COMPRESSION SYSTEMS Lesson 3 Lossless Compresson: Huffman Codng Instructonal Objectves At the end of ths lesson, the students should be able to:. Defne and measure source entropy..
More information+ + +   This circuit than can be reduced to a planar circuit
MeshCurrent Method The meshcurrent s analog of the nodeoltage method. We sole for a new set of arables, mesh currents, that automatcally satsfy KCLs. As such, meshcurrent method reduces crcut soluton to
More informationAryabhata s Root Extraction Methods. Abhishek Parakh Louisiana State University Aug 31 st 2006
Aryabhata s Root Extracton Methods Abhshek Parakh Lousana State Unversty Aug 1 st 1 Introducton Ths artcle presents an analyss of the root extracton algorthms of Aryabhata gven n hs book Āryabhatīya [1,
More informationWhat is Candidate Sampling
What s Canddate Samplng Say we have a multclass or mult label problem where each tranng example ( x, T ) conssts of a context x a small (mult)set of target classes T out of a large unverse L of possble
More informationEE201 Circuit Theory I 2015 Spring. Dr. Yılmaz KALKAN
EE201 Crcut Theory I 2015 Sprng Dr. Yılmaz KALKAN 1. Basc Concepts (Chapter 1 of Nlsson  3 Hrs.) Introducton, Current and Voltage, Power and Energy 2. Basc Laws (Chapter 2&3 of Nlsson  6 Hrs.) Voltage
More information2.4 Bivariate distributions
page 28 2.4 Bvarate dstrbutons 2.4.1 Defntons Let X and Y be dscrete r.v.s defned on the same probablty space (S, F, P). Instead of treatng them separately, t s often necessary to thnk of them actng together
More informationAn Alternative Way to Measure Private Equity Performance
An Alternatve Way to Measure Prvate Equty Performance Peter Todd Parlux Investment Technology LLC Summary Internal Rate of Return (IRR) s probably the most common way to measure the performance of prvate
More informationA Computer Technique for Solving LP Problems with Bounded Variables
Dhaka Unv. J. Sc. 60(2): 163168, 2012 (July) A Computer Technque for Solvng LP Problems wth Bounded Varables S. M. Atqur Rahman Chowdhury * and Sanwar Uddn Ahmad Department of Mathematcs; Unversty of
More informationPLANAR GRAPHS. Plane graph (or embedded graph) A graph that is drawn on the plane without edge crossing, is called a Plane graph
PLANAR GRAPHS Basc defntons Isomorphc graphs Two graphs G(V,E) and G2(V2,E2) are somorphc f there s a onetoone correspondence F of ther vertces such that the followng holds:  u,v V, uv E, => F(u)F(v)
More information1. Fundamentals of probability theory 2. Emergence of communication traffic 3. Stochastic & Markovian Processes (SP & MP)
6.3 /  Communcaton Networks II (Görg) SS20  www.comnets.unbremen.de Communcaton Networks II Contents. Fundamentals of probablty theory 2. Emergence of communcaton traffc 3. Stochastc & Markovan Processes
More informationCommunication Networks II Contents
8 / 1  Communcaton Networs II (Görg)  www.comnets.unbremen.de Communcaton Networs II Contents 1 Fundamentals of probablty theory 2 Traffc n communcaton networs 3 Stochastc & Marovan Processes (SP
More informationSupport Vector Machines
Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada wellng@cs.toronto.edu Abstract Ths s a note to explan support vector machnes.
More informationConversion between the vector and raster data structures using Fuzzy Geographical Entities
Converson between the vector and raster data structures usng Fuzzy Geographcal Enttes Cdála Fonte Department of Mathematcs Faculty of Scences and Technology Unversty of Combra, Apartado 38, 3 454 Combra,
More informationQuestions that we may have about the variables
Antono Olmos, 01 Multple Regresson Problem: we want to determne the effect of Desre for control, Famly support, Number of frends, and Score on the BDI test on Perceved Support of Latno women. Dependent
More information1 Approximation Algorithms
CME 305: Dscrete Mathematcs and Algorthms 1 Approxmaton Algorthms In lght of the apparent ntractablty of the problems we beleve not to le n P, t makes sense to pursue deas other than complete solutons
More informationA CHARACTERIZATION FOR THE LENGTH OF CYCLES OF THE N  NUMBER DUCCI GAME
A CHARACTERIZATION FOR THE LENGTH OF CYCLES OF THE N  NUMBER DUCCI GAME NEIL J. CALKIN DEPARTMENT OF MATHEMATICAL SCIENCES CLEMSON UNIVERSITY CLEMSON, SC 296341907 AND JOHN G. STEVENS AND DIANA M. THOMAS
More informationSimple Interest Loans (Section 5.1) :
Chapter 5 Fnance The frst part of ths revew wll explan the dfferent nterest and nvestment equatons you learned n secton 5.1 through 5.4 of your textbook and go through several examples. The second part
More informationDEFINING %COMPLETE IN MICROSOFT PROJECT
CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMISP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,
More informationExtending Probabilistic Dynamic Epistemic Logic
Extendng Probablstc Dynamc Epstemc Logc Joshua Sack May 29, 2008 Probablty Space Defnton A probablty space s a tuple (S, A, µ), where 1 S s a set called the sample space. 2 A P(S) s a σalgebra: a set
More informationThe Magnetic Field. Concepts and Principles. Moving Charges. Permanent Magnets
. The Magnetc Feld Concepts and Prncples Movng Charges All charged partcles create electrc felds, and these felds can be detected by other charged partcles resultng n electrc force. However, a completely
More informationbenefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).
REVIEW OF RISK MANAGEMENT CONCEPTS LOSS DISTRIBUTIONS AND INSURANCE Loss and nsurance: When someone s subject to the rsk of ncurrng a fnancal loss, the loss s generally modeled usng a random varable or
More informationSection B9: Zener Diodes
Secton B9: Zener Dodes When we frst talked about practcal dodes, t was mentoned that a parameter assocated wth the dode n the reverse bas regon was the breakdown voltage, BR, also known as the peaknverse
More informationHYPOTHESIS TESTING OF PARAMETERS FOR ORDINARY LINEAR CIRCULAR REGRESSION
HYPOTHESIS TESTING OF PARAMETERS FOR ORDINARY LINEAR CIRCULAR REGRESSION Abdul Ghapor Hussn Centre for Foundaton Studes n Scence Unversty of Malaya 563 KUALA LUMPUR Emal: ghapor@umedumy Abstract Ths paper
More informationLecture 2: Single Layer Perceptrons Kevin Swingler
Lecture 2: Sngle Layer Perceptrons Kevn Sngler kms@cs.str.ac.uk Recap: McCullochPtts Neuron Ths vastly smplfed model of real neurons s also knon as a Threshold Logc Unt: W 2 A Y 3 n W n. A set of synapses
More informationMultivariate EWMA Control Chart
Multvarate EWMA Control Chart Summary The Multvarate EWMA Control Chart procedure creates control charts for two or more numerc varables. Examnng the varables n a multvarate sense s extremely mportant
More informationThe Mathematical Derivation of Least Squares
Pscholog 885 Prof. Federco The Mathematcal Dervaton of Least Squares Back when the powers that e forced ou to learn matr algera and calculus, I et ou all asked ourself the ageold queston: When the hell
More informationThe eigenvalue derivatives of linear damped systems
Control and Cybernetcs vol. 32 (2003) No. 4 The egenvalue dervatves of lnear damped systems by YeongJeu Sun Department of Electrcal Engneerng IShou Unversty Kaohsung, Tawan 840, R.O.C emal: yjsun@su.edu.tw
More informationImplementation of Deutsch's Algorithm Using Mathcad
Implementaton of Deutsch's Algorthm Usng Mathcad Frank Roux The followng s a Mathcad mplementaton of Davd Deutsch's quantum computer prototype as presented on pages  n "Machnes, Logc and Quantum Physcs"
More informationSection 5.3 Annuities, Future Value, and Sinking Funds
Secton 5.3 Annutes, Future Value, and Snkng Funds Ordnary Annutes A sequence of equal payments made at equal perods of tme s called an annuty. The tme between payments s the payment perod, and the tme
More informationPSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 12
14 The Chsquared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304C) Lecture 1 If a normal varable X, havng mean µ and varance σ, s standardsed, the new varable Z has a mean 0 and varance 1. When ths standardsed
More informationState function: eigenfunctions of hermitian operators> normalization, orthogonality completeness
Schroednger equaton Basc postulates of quantum mechancs. Operators: Hermtan operators, commutators State functon: egenfunctons of hermtan operators> normalzaton, orthogonalty completeness egenvalues and
More informationIntroduction: Analysis of Electronic Circuits
/30/008 ntroducton / ntroducton: Analyss of Electronc Crcuts Readng Assgnment: KVL and KCL text from EECS Just lke EECS, the majorty of problems (hw and exam) n EECS 3 wll be crcut analyss problems. Thus,
More informationSPEE Recommended Evaluation Practice #6 Definition of Decline Curve Parameters Background:
SPEE Recommended Evaluaton Practce #6 efnton of eclne Curve Parameters Background: The producton hstores of ol and gas wells can be analyzed to estmate reserves and future ol and gas producton rates and
More informationA hybrid global optimization algorithm based on parallel chaos optimization and outlook algorithm
Avalable onlne www.ocpr.com Journal of Chemcal and Pharmaceutcal Research, 2014, 6(7):18841889 Research Artcle ISSN : 09757384 CODEN(USA) : JCPRC5 A hybrd global optmzaton algorthm based on parallel
More informationChapter 7. RandomVariate Generation 7.1. Prof. Dr. Mesut Güneş Ch. 7 RandomVariate Generation
Chapter 7 RandomVarate Generaton 7. Contents Inversetransform Technque AcceptanceRejecton Technque Specal Propertes 7. Purpose & Overvew Develop understandng of generatng samples from a specfed dstrbuton
More informationThe OC Curve of Attribute Acceptance Plans
The OC Curve of Attrbute Acceptance Plans The Operatng Characterstc (OC) curve descrbes the probablty of acceptng a lot as a functon of the lot s qualty. Fgure 1 shows a typcal OC Curve. 10 8 6 4 1 3 4
More informationComplex Number Representation in RCBNS Form for Arithmetic Operations and Conversion of the Result into Standard Binary Form
Complex Number epresentaton n CBNS Form for Arthmetc Operatons and Converson of the esult nto Standard Bnary Form Hatm Zan and. G. Deshmukh Florda Insttute of Technology rgd@ee.ft.edu ABSTACT Ths paper
More informationMath 131: Homework 4 Solutions
Math 3: Homework 4 Solutons Greg Parker, Wyatt Mackey, Chrstan Carrck October 6, 05 Problem (Munkres 3.) Let {A n } be a sequence of connected subspaces of X such that A n \ A n+ 6= ; for all n. Then S
More information2. Linear Algebraic Equations
2. Lnear Algebrac Equatons Many physcal systems yeld smultaneous algebrac equatons when mathematcal functons are requred to satsfy several condtons smultaneously. Each condton results n an equaton that
More informationResearch on Transformation Engineering BOM into Manufacturing BOM Based on BOP
Appled Mechancs and Materals Vols 1012 (2008) pp 99103 Onlne avalable snce 2007/Dec/06 at wwwscentfcnet (2008) Trans Tech Publcatons, Swtzerland do:104028/wwwscentfcnet/amm101299 Research on Transformaton
More information9.1 The Cumulative Sum Control Chart
Learnng Objectves 9.1 The Cumulatve Sum Control Chart 9.1.1 Basc Prncples: Cusum Control Chart for Montorng the Process Mean If s the target for the process mean, then the cumulatve sum control chart s
More informationQUANTUM MECHANICS, BRAS AND KETS
PH575 SPRING QUANTUM MECHANICS, BRAS AND KETS The followng summares the man relatons and defntons from quantum mechancs that we wll be usng. State of a phscal sstem: The state of a phscal sstem s represented
More informationA Probabilistic Theory of Coherence
A Probablstc Theory of Coherence BRANDEN FITELSON. The Coherence Measure C Let E be a set of n propostons E,..., E n. We seek a probablstc measure C(E) of the degree of coherence of E. Intutvely, we want
More informationFisher Markets and Convex Programs
Fsher Markets and Convex Programs Nkhl R. Devanur 1 Introducton Convex programmng dualty s usually stated n ts most general form, wth convex objectve functons and convex constrants. (The book by Boyd and
More informationNondegenerate Hilbert Cubes in Random Sets
Journal de Théore des Nombres de Bordeaux 00 (XXXX), 000 000 Nondegenerate Hlbert Cubes n Random Sets par Csaba Sándor Résumé. Une légère modfcaton de la démonstraton du lemme des cubes de Szemeréd donne
More informationGeneralizing the degree sequence problem
Mddlebury College March 2009 Arzona State Unversty Dscrete Mathematcs Semnar The degree sequence problem Problem: Gven an nteger sequence d = (d 1,...,d n ) determne f there exsts a graph G wth d as ts
More informationLossless Data Compression
Lossless Data Compresson Lecture : Unquely Decodable and Instantaneous Codes Sam Rowes September 5, 005 Let s focus on the lossless data compresson problem for now, and not worry about nosy channel codng
More informationJoe Pimbley, unpublished, 2005. Yield Curve Calculations
Joe Pmbley, unpublshed, 005. Yeld Curve Calculatons Background: Everythng s dscount factors Yeld curve calculatons nclude valuaton of forward rate agreements (FRAs), swaps, nterest rate optons, and forward
More informationCHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol
CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK Sample Stablty Protocol Background The Cholesterol Reference Method Laboratory Network (CRMLN) developed certfcaton protocols for total cholesterol, HDL
More informationHow Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence
1 st Internatonal Symposum on Imprecse Probabltes and Ther Applcatons, Ghent, Belgum, 29 June 2 July 1999 How Sets of Coherent Probabltes May Serve as Models for Degrees of Incoherence Mar J. Schervsh
More informationMath 31 Lesson Plan. Day 27: Fundamental Theorem of Finite Abelian Groups. Elizabeth Gillaspy. November 11, 2011
Math 31 Lesson Plan Day 27: Fundamental Theorem of Fnte Abelan Groups Elzabeth Gllaspy November 11, 2011 Supples needed: Colored chal Quzzes Homewor 4 envelopes: evals, HW, presentaton rubrcs, * probs
More informationA FASTER EXTERNAL SORTING ALGORITHM USING NO ADDITIONAL DISK SPACE
47 A FASTER EXTERAL SORTIG ALGORITHM USIG O ADDITIOAL DISK SPACE Md. Rafqul Islam +, Mohd. oor Md. Sap ++, Md. Sumon Sarker +, Sk. Razbul Islam + + Computer Scence and Engneerng Dscplne, Khulna Unversty,
More informationTHE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
The goal: to measure (determne) an unknown quantty x (the value of a RV X) Realsaton: n results: y 1, y 2,..., y j,..., y n, (the measured values of Y 1, Y 2,..., Y j,..., Y n ) every result s encumbered
More informationQuality Adjustment of Secondhand Motor Vehicle Application of Hedonic Approach in Hong Kong s Consumer Price Index
Qualty Adustment of Secondhand Motor Vehcle Applcaton of Hedonc Approach n Hong Kong s Consumer Prce Index Prepared for the 14 th Meetng of the Ottawa Group on Prce Indces 20 22 May 2015, Tokyo, Japan
More informationOnline Learning from Experts: Minimax Regret
E0 370 tatstcal Learnng Theory Lecture 2 Nov 24, 20) Onlne Learnng from Experts: Mn Regret Lecturer: hvan garwal crbe: Nkhl Vdhan Introducton In the last three lectures we have been dscussng the onlne
More informationFORCED CONVECTION HEAT TRANSFER IN A DOUBLE PIPE HEAT EXCHANGER
FORCED CONVECION HEA RANSFER IN A DOUBLE PIPE HEA EXCHANGER Dr. J. Mchael Doster Department of Nuclear Engneerng Box 7909 North Carolna State Unversty Ralegh, NC 276957909 Introducton he convectve heat
More informationFINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals
FINANCIAL MATHEMATICS A Practcal Gude for Actuares and other Busness Professonals Second Edton CHRIS RUCKMAN, FSA, MAAA JOE FRANCIS, FSA, MAAA, CFA Study Notes Prepared by Kevn Shand, FSA, FCIA Assstant
More informationHÜCKEL MOLECULAR ORBITAL THEORY
1 HÜCKEL MOLECULAR ORBITAL THEORY In general, the vast maorty polyatomc molecules can be thought of as consstng of a collecton of two electron bonds between pars of atoms. So the qualtatve pcture of σ
More information7.5. Present Value of an Annuity. Investigate
7.5 Present Value of an Annuty Owen and Anna are approachng retrement and are puttng ther fnances n order. They have worked hard and nvested ther earnngs so that they now have a large amount of money on
More informationDescribing Communities. Species Diversity Concepts. Species Richness. Species Richness. SpeciesArea Curve. SpeciesArea Curve
peces versty Concepts peces Rchness pecesarea Curves versty Indces  mpson's Index  hannonwener Index  rlloun Index peces Abundance Models escrbng Communtes There are two mportant descrptors of a communty:
More informationJournal of Computational and Applied Mathematics
Journal of Computatonal and Appled Mathematcs 37 (03) 6 35 Contents lsts avalable at ScVerse ScenceDrect Journal of Computatonal and Appled Mathematcs journal homepage: wwwelsevercom/locate/cam The nverse
More informationNasdaq Iceland Bond Indices 01 April 2015
Nasdaq Iceland Bond Indces 01 Aprl 2015 Fxed duraton Indces Introducton Nasdaq Iceland (the Exchange) began calculatng ts current bond ndces n the begnnng of 2005. They were a response to recent changes
More informationTime Value of Money Module
Tme Value of Money Module O BJECTIVES After readng ths Module, you wll be able to: Understand smple nterest and compound nterest. 2 Compute and use the future value of a sngle sum. 3 Compute and use the
More informationTexas Instruments 30X IIS Calculator
Texas Instruments 30X IIS Calculator Keystrokes for the TI30X IIS are shown for a few topcs n whch keystrokes are unque. Start by readng the Quk Start secton. Then, before begnnng a specfc unt of the
More informationNordea G10 Alpha Carry Index
Nordea G10 Alpha Carry Index Index Rules v1.1 Verson as of 10/10/2013 1 (6) Page 1 Index Descrpton The G10 Alpha Carry Index, the Index, follows the development of a rule based strategy whch nvests and
More informationCalculation of Sampling Weights
Perre Foy Statstcs Canada 4 Calculaton of Samplng Weghts 4.1 OVERVIEW The basc sample desgn used n TIMSS Populatons 1 and 2 was a twostage stratfed cluster desgn. 1 The frst stage conssted of a sample
More informationSection C2: BJT Structure and Operational Modes
Secton 2: JT Structure and Operatonal Modes Recall that the semconductor dode s smply a pn juncton. Dependng on how the juncton s based, current may easly flow between the dode termnals (forward bas, v
More informationDiVA Digitala Vetenskapliga Arkivet
DVA Dgtala Vetenskaplga Arkvet http://umudvaportalorg Ths s a book chapter publshed n Hghperformance scentfc computng: algorthms and applcatons (ed Berry, MW; Gallvan, KA; Gallopoulos, E; Grama, A; Phlppe,
More informationA Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy Scurve Regression
Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy Scurve Regresson ChengWu Chen, Morrs H. L. Wang and TngYa Hseh Department of Cvl Engneerng, Natonal Central Unversty,
More informationUsing Series to Analyze Financial Situations: Present Value
2.8 Usng Seres to Analyze Fnancal Stuatons: Present Value In the prevous secton, you learned how to calculate the amount, or future value, of an ordnary smple annuty. The amount s the sum of the accumulated
More informationDescriptive Statistics (60 points)
Economcs 30330: Statstcs for Economcs Problem Set 2 Unversty of otre Dame Instructor: Julo Garín Sprng 2012 Descrptve Statstcs (60 ponts) 1. Followng a recent government shutdown, Mnnesota Governor Mark
More informationThe covariance is the two variable analog to the variance. The formula for the covariance between two variables is
Regresson Lectures So far we have talked only about statstcs that descrbe one varable. What we are gong to be dscussng for much of the remander of the course s relatonshps between two or more varables.
More informationSearching Algorithms. Searching and Sorting Algorithms, Complexity Analysis. Searching Algorithms. Searching Algorithms.
Searchng Algorthms Searchng and Sortng Algorthms, General defnton Locate an element x n a lst of dstnct elements a 1, or determne that t s not n the lst Lnear search Algorthm : Lnear search Input: unsorted
More informationLecture 2: Absorbing states in Markov chains. Mean time to absorption. WrightFisher Model. Moran Model.
Lecture 2: Absorbng states n Markov chans. Mean tme to absorpton. WrghtFsher Model. Moran Model. Antonna Mtrofanova, NYU, department of Computer Scence December 8, 2007 Hgher Order Transton Probabltes
More informationLoop Parallelization
  Loop Parallelzaton C52 Complaton steps: nested loops operatng on arrays, sequentell executon of teraton space DECLARE B[..,..+] FOR I :=.. FOR J :=.. I B[I,J] := B[I,J]+B[I,J] ED FOR ED FOR analyze
More informationProject Networks With MixedTime Constraints
Project Networs Wth MxedTme Constrants L Caccetta and B Wattananon Western Australan Centre of Excellence n Industral Optmsaton (WACEIO) Curtn Unversty of Technology GPO Box U1987 Perth Western Australa
More informationInequality and The Accounting Period. Quentin Wodon and Shlomo Yitzhaki. World Bank and Hebrew University. September 2001.
Inequalty and The Accountng Perod Quentn Wodon and Shlomo Ytzha World Ban and Hebrew Unversty September Abstract Income nequalty typcally declnes wth the length of tme taen nto account for measurement.
More informationGenetic algorithm for searching for critical slip surface in gravity dams based on stress fields CHEN Jianyun 1, WANG Shu 2, XU Qiang 3, LI Jing 4
Advanced Materals Research Onlne: 2030904 ISSN: 6628985, Vol. 790, pp 4649 do:0.4028/www.scentfc.net/amr.790.46 203 Trans Tech Publcatons, Swtzerland Genetc algorthm for searchng for crtcal slp surface
More information) of the Cell class is created containing information about events associated with the cell. Events are added to the Cell instance
Calbraton Method Instances of the Cell class (one nstance for each FMS cell) contan ADC raw data and methods assocated wth each partcular FMS cell. The calbraton method ncludes event selecton (Class Cell
More information