THE NUMBER OF STABLE MATCHINGS IN MODELS OF THE GALE SHAPLEY TYPE WITH PREFERENCES GIVEN BY PARTIAL ORDERS

Size: px
Start display at page:

Download "THE NUMBER OF STABLE MATCHINGS IN MODELS OF THE GALE SHAPLEY TYPE WITH PREFERENCES GIVEN BY PARTIAL ORDERS"

Transcription

1 OPERATIONS RESEARCH AND DECISIONS No DOI: /ord Ewa DRGAS-BURCHARDT 1 THE NUMBER OF STABLE MATCHINGS IN MODELS OF THE GALE SHAPLEY TYPE WITH PREFERENCES GIVEN BY PARTIAL ORDERS From the famous Gale Shapley theorem we know that each classcal marrage problem admts at least one stable matchng. Ths fact has nspred researchers to search for the maxmum number of possble stable matchngs, whch s equvalent to fndng the mnmum number of unstable matchngs among all such problems of sze n. In ths paper, we deal wth ths ssue for the Gale Shapley model wth preferences represented by arbtrary partal orders. Also, we dscuss ths model n the context of the classcal Gale Shapley model. Key words: combnatoral problems, stable matchng, Gale Shapley model 1. Prelmnares The problem of determnng the maxmum number of possble stable matchngs among all the classcal marrage problems of sze n was posed by Knuth [6] and stll remans an open queston. Knuth establshed that ths number exceeds 2 n/2 for n 2 and Gusfeld and Irvng showed [3] that when n s a power of 2 t s at least 2 n 1, whch can be mproved to (2.28) n /(1 + 3) based on the constructon gven by Irvng and Leather [4]. Some propertes of ths number analyzed as a functon of the problem sze were consdered n [10]. The frst upper bound on ths number was derved n [1]. It approxmately equals three quarters of all n! possble matchngs and s stll far from the expected number of stable matchngs, whch s asymptotc to e 1 nlnn for n [8]. In many practcal applcatons of the marrage problem, the lsts of preferences for prospectve partners allow ncomparable elements. In ths context, consder the fol- 1 Faculty of Mathematcs, Computer Scence and Econometrcs, Unversty of Zelona Góra, ul. Prof. Z. Szafrana 4a, Zelona Góra, Poland, e-mal: E.Drgas-Burchardt@wme.uz.zgora.pl

2 6 E. DRGAS-BURCHARDT lowng example. Let us assume that a company begns work on n dfferent computer programmng projects smultaneously, employng at the same tme n people as project managers. These people possess such sklls as: competence n human resource management, moblty, knowledge of spoken and programmng languages, knowledge of the topc sets connected to the projects, access to dfferent data bases. The task of the company management s to assgn managers to the projects, so that each of them s responsble for exactly one project. Such an assgnment s consdered stable f there exsts no unassocated par (manager and project j) such that manager would prefer managng project j rather than the one assgned to hm and, moreover, manager would be better for project j than the manager who s presently assgned. Note that t may be hard to decde who s a better canddate for beng the manager of a gven project, because some of the managers attrbutes are not comparable. It can happen that project needs both the knowledge of a partcular programmng language and the ablty to negotate n Fnnsh. Assume that no manager can do both thngs, but there are two, let us say m l, m k who each possesses one of these sklls. Ths fact shows the ncomparablty of m l and m k wth respect to project. Smlarly, specfc features of the projects can make two of them ncomparable for a gven manager. Usually, many attrbutes are needed to manage a gven project and people can possess some of them. Ths means that the preferences of each manager are descrbed by a partal order over the set of projects. Smlarly, the approprateness of a manager for a gven project s gven by a partal order over the set of all managers. In ths stuaton, a problem of sze n nvolves fndng a matchng between n managers and n projects wth 2n ndexed partally ordered sets, where n are partal orders descrbed over the set of projects and n over the set of managers. In ths paper, we nvestgate the mnmum number of unstable matchngs n models of the Gale Shapley type wth preferences that are not necessarly lnear. Among other thngs, we explan the dfference between the classcal model and the model consdered n ths paper. We show that f preferences are gven by arbtrary partal orders, then, unlke the classcal model, there s no upper bound on the number of stable matchngs concernng all the problems of sze n. On the other hand, f all 2n Hasse dagrams representng the preferences (for managers and for projects) have parwse the same shape for two dfferent problems of sze n (they are parwse somorphc), then the number of stable matchngs for both these problems can be bounded from above by the same expresson (Theorem 1). In partcular, we gve a smpler form of ths expresson n the case when all 4n Hasse dagrams for two dfferent problems of order n are somorphc (Corollary 3). Note that the classcal Gale Shapley model s of ths last knd, because all 4n Hasse dagrams for any two problems of sze n are somorphc (n fact they are represented by lnear orders over an n-element set).

3 Stable matchngs n models of the Gale Shapley type 7 Snce people have dfferent and ncomparable attrbutes, the example mentoned above can be wrtten n the language of a marrage problem. We use ths classcal descrpton n the remanng part of the paper. 2. Man results In general, we follow the notaton and termnology of [9]. Let W be a set of n women w 1,..., w n and M be a set of n men m 1,..., m n and for a natural number n, let the notaton [n] denote the set {1,..., n}. Next, suppose that for a fxed j [n], the partal order p(m j ) represents the preferences of the man m j over the set of women. Ths means that for gven s, k, j [n] we have (w s, w k ) P(m j ) f and only f ether s = k or m j prefers w s to w k. It follows that (w s, w k ) P(m j ) f and only f k s and ether (w k, w s ) P(m j ) or w s and w k are ncomparable for m j. Smlarly, for fxed [n], a partal order P(w ) represents the preferences of the woman w over the set of men. Thus preferences for prospectve partners are represented by the set P = {P(m 1 ),..., P(m n ), P(w 1 ),..., P(w n )}. Next, a trple (M, W; P) denotes a specfc marrage market (of sze n). It should be mentoned here that, accordng to ths model, we do not allow any ndvdual to reman sngle and assume that all prospectve partners are preferred to the opton of beng sngle. Moreover, all preferences are transtve, but two alternatves can be ncomparable. Ths means that ndvduals are not necessarly ratonal. A matchng s an arbtrary bjecton of W onto M. For smplcty, we wrte () = j nstead of (w ) = m j. By ( 1, j 1, 2, j 2 ) we denote the set of all matchngs satsfyng ( 1 ) = j 1 and ( 2 ) = j 2. In ths descrpton, we mplctly assume that 1 2 and j 1 j 2. A par w, m j s sad to block a matchng (, j 1, 2, j) n(m, W; P) f (m j, m j1 ) P(w ) and(w, w 2 ) P(m j ). A matchng s unstable for (M, W; P) f there exsts at least one par blockng n (M, W; P), otherwse s stable for (M, W; P). For a gven marrage market (M, W; P) of sze n and for, j [n], let a, j = {s: (m j, m s ) P(w ) and s j} and b, j = {s: (w j, w s ) P(m ) and s j}. The number a, j gves us nformaton on how many of the men are worse than m j accordng to w, and b, j tells how many of the women are worse than w j accordng to m. Next, for fxed [n] and P(m ), by M we denote the set of all lnear orders over W, say ( w,..., w ), such that b... b. Smlarly, for fxed [n] and P(w ), by W we denote the set of all s, 1 s, n lnear orders over M, say ( m,..., m ), such that a s1 s n... a. s, 1 s, n Proposton 1. Let (M, W; P) be a marrage market of sze n and let P * be an arbtrary set {P * (m 1 ),..., P * (m n ), P * (w 1 ),..., P * (w n )} such that for all [n] the condtons s1 s n

4 8 E. DRGAS-BURCHARDT P * (m ) M and P * (w ) W are satsfed. If a matchng s stable for (M, W; P), then s stable for (M, W; P). Proof. Suppose that Proposton 1 does not hold,.e. s stable for (M, W; P * ) and unstable for (M, W; P). It follows that there exsts a par w s, m k blockng when preferences are gven by P, whch consequently means that there exsts a par (j 1, j 2 ) such that (s, j 1, 2, k) and ( mk, mj ) P(m 1 s ) and ( ws, w ) P(w 2 k ) and also k j 1 and s. From the defnton of the numbers a, j, b, j, we obtan a s, k > a s, j1 and b k, s > b k,2. Ths yelds ( mk, mj ) P * (w 1 s ) and ( ws, w ) P * (m 2 k ). Usng the defnton of a blockng par once agan, we obtan that w s, m k blocks when the preferences are gven by P *, contrary to the ntal assumpton. Let (M, W; P) denote the set of all stable matchngs for the marrage market (M, W; P). Takng nto account Proposton 1 we mmedately obtan the followng result: Corollary 1. If (M, W; P) s a marrage market of sze n, then (M, W; P * ) (M, W; P), where the unon s taken over all P * = {P * (m 1 ),..., P * (m n ), P * (w 1 ),..., P * (w n )} such that P * (m ) and P * (w ) W for each [n]. From Gale and Shapley [2] we know that f all the partal orders n P are lnear, then there exsts at least one stable matchng for (M, W; P). In the lght of the above corollary, ths fact mples the next result. Corollary 2. If (M, W; P) s a marrage market of sze n, then there exsts at least one stable matchng for (M, W; P). Unfortunately, the converson to Proposton 1 s not true. For nstance n Fg. 1 we present a set P = {P(m 1 ), P(m 2 ), P(m 3 ), P(w 1 ), P(w 2 ), P(w 3 )} of partal orders, such that the matchng defned by () = for each permssble, s stable for (M, W; P). Moreover, s unstable for any marrage market (M, W; P * ) such that P * = {P * (m 1 ), P * (m 2 ), P * (m 3 ), P * (w 1 ), P * (w 2 ), P * (w 3 )}, where P * (m ) M, P * (w ) W wth {1, 2, 3}. Indeed, for each P * (m 1 ) M 1 we have (w 2, w 1 ) P * (m 1 ) and for each P * (w 2 ) w 2 we have (m 1, m 2 ) P * (w 2 ). Thus the par w 2, m 1 blocks n the marrage market (M, W; P * ). Fg. 1. An llustraton of the fact that the contanment n Proposton 1 can be strct

5 Stable matchngs n models of the Gale Shapley type 9 Of course there are many examples for whch the ncluson n Corollary 1 can be substtuted by an equalty. Analyss of partcular marrage markets havng preferences wth tes gves us nfntely many such examples. Markets of ths type have been consdered n many papers, especally from the aspect of the exstence of stable matchngs [5, 7]. A partal order over [n] s called smple f there exsts a partton of [n] nto k parts X 1,..., X k, such that (a, b) f and only f a = b or a X, b X j and < j. Proposton 2. Let (M, W; P) be a marrage market of order n such that all the partal orders from P are smple. If s a stable matchng for (M, W; P), then there exsts a set P * consstng of lnear orders P * (m 1 ),..., P * (m n ), P * (w 1 ),..., P * (w n ) such that P * (m ) M and P * (w ) W for [n] and s stable for (M, W; P * ). Proof. Let, p [n] satsfy () = p. Because P(m p ), P(w ) are smple, there exst parttons X 1,..., X k and Y 1,..., Y q of W and M, respectvely, and ndces s [k], j [q] such that w X s and m p Y j. In addton, we know that all the elements of X s are ncomparable based on P(m p ) and all the elements of Y j are ncomparable based on P(w ). We construct P * (m p ) M p and P * (w ) W n an arbtrary way that satsfes (w, w l ) P * (m p ) for each w l X s and (m p, m l ) P * (w ) for each m l Y q. For every other par of ndces 1, p 1 [n] satsfyng the equalty ( 1 ) = p 1, we use a smlar argument to produce a set P * = {P * (m 1 ),..., P * (m n ),..., P * (w 1 ),..., P * (w n )} that satsfes the theorem. It should be mentoned that marrage markets wth all preferences gven by smple orders are sometmes consdered n the lterature as markets wth lnear preferences and tes. Indvduals n such markets are ndfferent between some elements. Above we ndcated a connecton between any marrage market wth preferences gven by partal orders and some set of markets wth preferences gven by lnear orders. It was shown n [1] that the lnearty of preferences guarantees the exstence of a non-trval lower bound on the number of unstable matchngs. The rato of ths bound to the number n! of all possble matchngs tends to 1/4 when n tends to nfnty. We have no such concluson f for at least one man or at least one woman the preferences for potental spouses are gven by a non-lnear order. One specfc stuaton occurs when for each woman any two men are ncomparable and for each man any two women are ncomparable. Namely, n ths case, any matchng s stable. Thus we cannot guarantee the exstence of unstable matchngs at all but f they exst, then ther number can be bounded from below by parameters defned here. The correspondng result wll be preceded by some helpful defntons. For a marrage market (M, W; P) of sze n and for, j [n], let A, j (M, W; P) denote the set of all tuples (, j 1, 1, j) such that w, m j s a blockng par for each matchng (, j 1, 1, j). Evdently, A, j (M, W; P) = {(, j 1, 1, j): (m j, m j1 ) P(w ) and (w, w 1 )

6 10 E. DRGAS-BURCHARDT P(m j ) wth j j 1 and 1 }, whch means that A, j (M, W; P) = a, j b j,. Snce each tuple (, j 1, 1, j) belongng to A, j (M, W; P) corresponds to (n 2)! unstable matchngs from (, j 1, 1, j) and because for any two tuples from A, j (M, W; P) these sets of unstable matchngs are dsjont (each of cardnalty (n 2)!) we obtan the followng fact. Remark 1. Let (M, W; P) be a marrage market of sze n. If, j [n], then (M, W; P) n! (n 2)!a, j b j,. Recall that two partal orders 1, 2 over the sets X, Y, respectvely, are somorphc f there exsts a bjecton : X Y such that for any, w X we have (v, w) 1 f and only f ((), (w)) 2. In the next theorem, we use Remark 1 to obtan an upper bound on the number max(m, W; P), where the maxmum s taken over all marrage markets (M, W; P) of sze n, n whch the partal orders that represent preferences for correspondng women and men are parwse somorphc. Frst, we focus on the general case, next we turn our attenton to a specal case, thus mprovng the upper bound obtaned usng the frst approach. Theorem 1. (M, W; P) be a marrage market of sze n wth n 2 and let for [n] the orderngs ( s1,..., s ), ( k1,..., k ) of [n] satsfy a... a and b... b. If n n s, 1 s, n s, n/21 k, n/2 1, d1 mn : [ ] when s odd a n n s, k, n1/2 n1/2 c k, 1 k, n mn a : [ n] when n s even mn b : n when [ n] s even mn b : n when [ n] s odd and c a n, n s k, n/2 n mn : [ ] when s even mn b : [ n] when n s even, /2 1 d c when n s odd d when n s odd then (M, W; P) n! (n 2)! max{c 1 d 1, c 2 d 2 }. Moreover, n! (n 2)! max{c 1 d 1, c 2 d 2 } s an upper bound on (M, W; P ) for any other marrage market (M, W; P ) of sze n such that for each [n] the partal orders P(m ) and P (m ) are somorphc and the partal orders P(w ) and P (w ) are somorphc. Proof. Frst we shall show that the theorem holds for (M, W; P). It s suffcent to prove that there exst ndces 1, j 1, 2, j 2 [n] such that a 1, j 1 c 1 and b j1, 1 d 1 whle

7 Stable matchngs n models of the Gale Shapley type 11 a 2, j 2 c 2 and b j2, 2 d 2, before usng Remark 1. By symmetry, t s enough to show the exstence of ndces 1, j 1. To obtan a contradcton, assume that such ndces 1, j 1 do not exst. Ths means that for any par of ndces (, j) ether a, j c 1 1 or b j, d 1 1. We defne two sets A = {(, j, a,j ):, j [n]} and B = {(, j, b, j ):, j [n]} and a mappng that assgns the element (j,, b j, ) from B to the element (, j, a, j ) from A. Evdently, A = B = n 2 and s a bjecton. In the remanng part of the proof we shall construct two sets E A and F B such that maps E to a subset of F and the cardnalty of F s less than the cardnalty of E. Obvously, ths contradcts the fact that s a bjecton. Frst consder the case of even n. From the defnton of c 1 t follows that for each [n], the nequalty a c holds. Moreover, a... a. Hence for each [n], the numbers b,..., b s1, j sn/2 1, j b s, ( n/2) 1,..., b 1 s1, j sn/2 1, j s, 1 s, n/21 do not exceed d 1 1. Thus the values j n l satsfy j D, where D { k p : p[ n], l{ n/2 1,..., n}}. Now the m- age under of the set E {(, j, a, j) : { n], j { s1,..., s( n/2) 1}} of cardnalty (n 2 /2) + n s a subset of F {(, j, b, j) : [ n], j D}. Note that the cardnalty of F s equal to n 2 /2, whch proves the theorem when n s even. We use the same reasonng n the case of an odd n by constructng the same mappng, sets A, B and ther subsets E {(, j, a, j) : [ n], j{ s1,..., s( n 1)/2}} and F {(, j, b, j) : [ n], jd}, where D { k p s : p[ n], s{( n3) / 2,..., n}}. By the defntons of c 1, d 1, we know that (E) F. Smlarly to the prevous case, the cardnalty of E equals n(n + 1)/2 and the cardnalty of F equals n(n 1)/2, whch contradcts the fact that s a bjecton. Thus n! (n 2)!max{c 1 d 1, c 2 d 2 } s an upper bound on (M, W; P). The last statement of the theorem follows snce all the numbers c 1, c 2, d 1, d 2 depend only on the shape of the Hasse dagrams and are ndependent of the assgnments of women and men to them (somorphc partal orders produce the same numbers c 1, c 2, d 1, d 2 ). We llustrate Theorem 1 usng a marrage market (M, W; P) of sze 5, where P = {P(m 1 ),..., P(m 5 ), P(w 1 ),..., P(w 5 )} (Fg. 2). In ths case, the orderngs whose exstence s assumed n Theorem 1 could be ( s1,..., s = (1, 2, 3, 4,, ( s1,..., s = (5, , 1, 2, 3), ( s1,..., s = (2, 3, 4, 5, 1), ( s1,..., s = (1, 2, 3, 4,, ( s1,..., s = (4, 3, , 5, 1), ( k1,..., k = (3, 1, 5, 2, 4), ( k1,..., k = (1, 5, 4, 3, 2), ( k1,..., k = (5, 1, 4, 2, 3), ( k 4,..., k 4 ) = (2, 4, 3, 5, 1), ( k 5,..., k 5 ) = (1, 5, 2, 3, 4). Next, we have , 4, s a 1 a 2 a 3 a 5 2 and a whch gves c 1, s3 2, s3 3, s3 5, s3 4 1 = 1 (5 s an odd number). 3 Moreover, b 1 b 2 b 3 b 4 b 5 whch mples that d 1 = 2. Thus, from 2, 1, k3 2, k3 3, k3 4, k3 5, k3

8 12 E. DRGAS-BURCHARDT Theorem 1, we can fnd at least 12 unstable matchngs for (m, W; P) and t does not depend on the assgnment of the elements from the sets W, M to the vertces of Hasse dagrams presented n Fg. 2. Fg. 2. The Hasse dagrams that represent preferences For example, the same number of 12 unstable matchngs s guaranteed n the marrage market (M, W; P) of sze 5 whose set of preferences P = {P (m 1 ),..., P (m n ), P (w 1 ),..., P (w n )} s gven n Fg. 3. Corollary 3. Let (M, W; P) be a marrage market of sze n such that P = {P(m 1 ),..., P(m n ), P(w 1 ),..., P(w n )} and let all the partal orders from P be somorphc to. Next, let be defned over [n] such that for e = {j [n]: j and (, j) }. It follows that e 1... e n. If e,when n s even n/2 1 en /2,when n s even c and d e 1/2, when n s odd e 1/2, when n s odd n n then (M, W; P) n! cd(n 2)!.

9 Stable matchngs n models of the Gale Shapley type 13 Fg. 3. The Hasse dagrams that represent preferences Fg. 4. Hasse dagrams

10 14 E. DRGAS-BURCHARDT Note that Corollary 3 can be appled f all the partal orders from P are somorphc. One such possblty occurs when the preferences for all women and all men are lnear. In ths case, Corollary 3 mples one of the man results gven n [1]. To llustrate ths corollary usng another example, consder an arbtrary marrage market (M, W; P) of sze n for whch all P(m ) and all P(w ) are somorphc to the one gven n Fg. 4a. Note that the numbers e defned n Corollary 3 (the ndexes correspond to the vertces of Hasse dagram from Fg. 4a satsfy e 1 = 14, e 2 = 10, e 3 = e 4 = 9, e 5 = e 6 = e 7 = 8, e 8 = 7, e 9 = 6, e 10 = 5, e 11 = 4, e 12 = 3, e 13 = 2, e 14 = 1, e 15 = 0. Ths means that c = e (15+1)/2 = e 8 = 7. Hence, there exst at least 13! 49 unstable matchngs for the market (M, W; P) and ths does not depend on the correspondence between the ndvduals and the elements from the set [15]. Concludng remarks It s worth notng that the upper bound on the number of stable matchngs derved n ths paper depends on the number of ndvduals that are worse than gven partcpants of the marrage market. A detaled analyss of the numbers c 1, c 2, d 1, d 2 or c, d, respectvely, leads to the concluson that the value of ths bound s very dependent on the partcpants who occupy central postons n the preference orderngs. For a better understandng of ths fact, let us consder four classes of marrage markets of sze 15. The frst, where all of the preferences are represented by lnear orders, the second, where all the partal orders descrbng preferences are somorphc to the one from Fg. 4a, the thrd and the fourth where all the partal orders representng preferences are somorphc to those from Fgs. 4b and 4c, respectvely. In the frst and second cases, Corollary 3 demonstrates that a randomly selected matchng s unstable wth the probablty of at least 49/(14 1 = 7/30, n the thrd one wth the probablty of at least 25/(14 1 = 5/42, and n the last one wth the probablty of at least 4/(14 1 = 2/105. Thus, the best possble upper bounds correspond to marrage markets n whch the worst elements are ordered lnearly and any ncomparable elements are relatvely good. In [1] the well-known prncple of ncluson and excluson was used to derve an exact expresson for the number of stable matchngs n a marrage market wth preferences gven by lnear orders. A smlar expresson can be derved for marrage markets wth preferences gven by arbtrary partal orders. Indeed, n both cases, we count all the matchngs whch do not le n any of the sets A, j. Unfortunately, n the case of arbtrary partal orders, t s harder to determne the sets A, j, whose cardnaltes determne the number of stable matchngs. For completeness, we gve the desred ex-

11 Stable matchngs n models of the Gale Shapley type 15 presson, together wth the requred notons but we omt the proof whch s exactly analogous to the one presented for the smpler (lnear) case. Let (M, W; P) be a marrage market of sze n and A, jnn A, j (M, W; P). Next, let G A be a bpartte graph (X, Y; E A ) such that X = {x 1,..., x n }, Y = {y 1,..., y n } and E A ={x p y q : ( p, q, 2, j 2 ) A or ( 1, j 1, p, q) A}. By F(M, W; P) we denote a set { A A ( M, W; P): AA ( M, W; P) 1 and (G A ) = 1}, where, jnn j, jnn,, j k (G) s the maxmum degree of a vertex n the graph G. Fnally, let Fs ( M, W, P ) = {A F(M, W; P): A = s and E A = k}. Theorem 2. Let n 2. If (M, W; P) s a marrage market of sze n, then nn 1 s k, W; P) = n! 1 nk! F, W; P ) n s1 k2 s Acknowledgement The author would lke to thank the referees for many valuable comments. Research fnancally supported by the grant DEC-2011/01/B/HS4/ References [1] DRGAS-BURCHARDT E., ŚWITALSKI Z., A number of stable matchngs n models of the Gale Shapley type, Dscrete Appled Mathematcs, 2013, 162 (18), [2] GALE D., SHAPLEY L.S., College Admssons and the Stablty of Marrage, Amercan Mathematcal Monthly, 1962, 69, [3] GUSFIELD D., IRVING R.W., The Stable Marrage Problem: Structure and Algorthms, MIT Press, Cambrdge, MA, [4] IRVING R.W., LEATHER P., The complexty of countng stable marrages, SIAM Journal on Computng, 1986, 15, [5] IRVING R.W., Stable marrage and ndfference, Dscrete Appled Mathematcs, 1994, 48, [6] KNUTH D.E., Marages Stables, Les Presses de l Unversté de Montréal, Montréal [7] MANLOVE D.F., IRVING R.W., IWAMA K., MIYAZAKI S., MORITA Y., Hard varants of stable marrage, Theoretcal Computer Scence, 2002, 276, [8] PITTEL B., The Average Number of Stable Matchngs, SIAM Journal on Dscrete Mathematcs, 1989, 2 4, [9] ROTH A.E., SOTOMAYOR M.A.O., Two-sded matchngs, A study n game-theoretc modelng and analyss, Econometrc Socety Monographs, 18, [10] THURBER E.G., Concernng the maxmum number of stable matchngs n the stable marrage problem, Dscrete Mathematcs, 2002, 248, Receved 12 July 2014 Accepted 3 February 2015

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Week 2. This week, we covered operations on sets and cardinality.

Week 2. This week, we covered operations on sets and cardinality. Week 2 Ths week, we covered operatons on sets and cardnalty. Defnton 0.1 (Correspondence). A correspondence between two sets A and B s a set S contaned n A B = {(a, b) a A, b B}. A correspondence from

More information

UNIQUE FACTORIZATION OF COMPOSITIVE HEREDITARY GRAPH PROPERTIES

UNIQUE FACTORIZATION OF COMPOSITIVE HEREDITARY GRAPH PROPERTIES UNIQUE FACTORIZATION OF COMPOSITIVE HEREDITARY GRAPH PROPERTIES IZAK BROERE AND EWA DRGAS-BURCHARDT Abstract. A graph property s any class of graphs that s closed under somorphsms. A graph property P s

More information

Graph Reconstruction by Permutations

Graph Reconstruction by Permutations Graph Reconstructon by Permutatons Perre Ille and Wllam Kocay* Insttut de Mathémathques de Lumny CNRS UMR 6206 163 avenue de Lumny, Case 907 13288 Marselle Cedex 9, France e-mal: lle@ml.unv-mrs.fr Computer

More information

Maximizing the number of nonnegative subsets

Maximizing the number of nonnegative subsets Maxmzng the number of nonnegatve subsets Noga Alon Hao Huang December 1, 213 Abstract Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what s the maxmum

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

Module 9. Lecture 6. Duality in Assignment Problems

Module 9. Lecture 6. Duality in Assignment Problems Module 9 1 Lecture 6 Dualty n Assgnment Problems In ths lecture we attempt to answer few other mportant questons posed n earler lecture for (AP) and see how some of them can be explaned through the concept

More information

The Order Relation and Trace Inequalities for. Hermitian Operators

The Order Relation and Trace Inequalities for. Hermitian Operators Internatonal Mathematcal Forum, Vol 3, 08, no, 507-57 HIKARI Ltd, wwwm-hkarcom https://doorg/0988/mf088055 The Order Relaton and Trace Inequaltes for Hermtan Operators Y Huang School of Informaton Scence

More information

Complete subgraphs in multipartite graphs

Complete subgraphs in multipartite graphs Complete subgraphs n multpartte graphs FLORIAN PFENDER Unverstät Rostock, Insttut für Mathematk D-18057 Rostock, Germany Floran.Pfender@un-rostock.de Abstract Turán s Theorem states that every graph G

More information

A new construction of 3-separable matrices via an improved decoding of Macula s construction

A new construction of 3-separable matrices via an improved decoding of Macula s construction Dscrete Optmzaton 5 008 700 704 Contents lsts avalable at ScenceDrect Dscrete Optmzaton journal homepage: wwwelsevercom/locate/dsopt A new constructon of 3-separable matrces va an mproved decodng of Macula

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

NP-Completeness : Proofs

NP-Completeness : Proofs NP-Completeness : Proofs Proof Methods A method to show a decson problem Π NP-complete s as follows. (1) Show Π NP. (2) Choose an NP-complete problem Π. (3) Show Π Π. A method to show an optmzaton problem

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

Affine transformations and convexity

Affine transformations and convexity Affne transformatons and convexty The purpose of ths document s to prove some basc propertes of affne transformatons nvolvng convex sets. Here are a few onlne references for background nformaton: http://math.ucr.edu/

More information

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of

More information

arxiv: v1 [cs.gt] 14 Mar 2019

arxiv: v1 [cs.gt] 14 Mar 2019 Stable Roommates wth Narcssstc, Sngle-Peaked, and Sngle-Crossng Preferences Robert Bredereck 1, Jehua Chen 2, Ugo Paavo Fnnendahl 1, and Rolf Nedermeer 1 arxv:1903.05975v1 [cs.gt] 14 Mar 2019 1 TU Berln,

More information

arxiv: v1 [math.co] 1 Mar 2014

arxiv: v1 [math.co] 1 Mar 2014 Unon-ntersectng set systems Gyula O.H. Katona and Dánel T. Nagy March 4, 014 arxv:1403.0088v1 [math.co] 1 Mar 014 Abstract Three ntersecton theorems are proved. Frst, we determne the sze of the largest

More information

Perron Vectors of an Irreducible Nonnegative Interval Matrix

Perron Vectors of an Irreducible Nonnegative Interval Matrix Perron Vectors of an Irreducble Nonnegatve Interval Matrx Jr Rohn August 4 2005 Abstract As s well known an rreducble nonnegatve matrx possesses a unquely determned Perron vector. As the man result of

More information

Self-complementing permutations of k-uniform hypergraphs

Self-complementing permutations of k-uniform hypergraphs Dscrete Mathematcs Theoretcal Computer Scence DMTCS vol. 11:1, 2009, 117 124 Self-complementng permutatons of k-unform hypergraphs Artur Szymańsk A. Paweł Wojda Faculty of Appled Mathematcs, AGH Unversty

More information

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP C O L L O Q U I U M M A T H E M A T I C U M VOL. 80 1999 NO. 1 FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP BY FLORIAN K A I N R A T H (GRAZ) Abstract. Let H be a Krull monod wth nfnte class

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Games of Threats. Elon Kohlberg Abraham Neyman. Working Paper

Games of Threats. Elon Kohlberg Abraham Neyman. Working Paper Games of Threats Elon Kohlberg Abraham Neyman Workng Paper 18-023 Games of Threats Elon Kohlberg Harvard Busness School Abraham Neyman The Hebrew Unversty of Jerusalem Workng Paper 18-023 Copyrght 2017

More information

Volume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2].

Volume 18 Figure 1. Notation 1. Notation 2. Observation 1. Remark 1. Remark 2. Remark 3. Remark 4. Remark 5. Remark 6. Theorem A [2]. Theorem B [2]. Bulletn of Mathematcal Scences and Applcatons Submtted: 016-04-07 ISSN: 78-9634, Vol. 18, pp 1-10 Revsed: 016-09-08 do:10.1805/www.scpress.com/bmsa.18.1 Accepted: 016-10-13 017 ScPress Ltd., Swtzerland

More information

The L(2, 1)-Labeling on -Product of Graphs

The L(2, 1)-Labeling on -Product of Graphs Annals of Pure and Appled Mathematcs Vol 0, No, 05, 9-39 ISSN: 79-087X (P, 79-0888(onlne Publshed on 7 Aprl 05 wwwresearchmathscorg Annals of The L(, -Labelng on -Product of Graphs P Pradhan and Kamesh

More information

Min Cut, Fast Cut, Polynomial Identities

Min Cut, Fast Cut, Polynomial Identities Randomzed Algorthms, Summer 016 Mn Cut, Fast Cut, Polynomal Identtes Instructor: Thomas Kesselhem and Kurt Mehlhorn 1 Mn Cuts n Graphs Lecture (5 pages) Throughout ths secton, G = (V, E) s a mult-graph.

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

5 The Rational Canonical Form

5 The Rational Canonical Form 5 The Ratonal Canoncal Form Here p s a monc rreducble factor of the mnmum polynomal m T and s not necessarly of degree one Let F p denote the feld constructed earler n the course, consstng of all matrces

More information

Errors for Linear Systems

Errors for Linear Systems Errors for Lnear Systems When we solve a lnear system Ax b we often do not know A and b exactly, but have only approxmatons  and ˆb avalable. Then the best thng we can do s to solve ˆx ˆb exactly whch

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

THE CHVÁTAL-ERDŐS CONDITION AND 2-FACTORS WITH A SPECIFIED NUMBER OF COMPONENTS

THE CHVÁTAL-ERDŐS CONDITION AND 2-FACTORS WITH A SPECIFIED NUMBER OF COMPONENTS Dscussones Mathematcae Graph Theory 27 (2007) 401 407 THE CHVÁTAL-ERDŐS CONDITION AND 2-FACTORS WITH A SPECIFIED NUMBER OF COMPONENTS Guantao Chen Department of Mathematcs and Statstcs Georga State Unversty,

More information

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA

A CLASS OF RECURSIVE SETS. Florentin Smarandache University of New Mexico 200 College Road Gallup, NM 87301, USA A CLASS OF RECURSIVE SETS Florentn Smarandache Unversty of New Mexco 200 College Road Gallup, NM 87301, USA E-mal: smarand@unmedu In ths artcle one bulds a class of recursve sets, one establshes propertes

More information

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016

U.C. Berkeley CS294: Spectral Methods and Expanders Handout 8 Luca Trevisan February 17, 2016 U.C. Berkeley CS94: Spectral Methods and Expanders Handout 8 Luca Trevsan February 7, 06 Lecture 8: Spectral Algorthms Wrap-up In whch we talk about even more generalzatons of Cheeger s nequaltes, and

More information

n ). This is tight for all admissible values of t, k and n. k t + + n t

n ). This is tight for all admissible values of t, k and n. k t + + n t MAXIMIZING THE NUMBER OF NONNEGATIVE SUBSETS NOGA ALON, HAROUT AYDINIAN, AND HAO HUANG Abstract. Gven a set of n real numbers, f the sum of elements of every subset of sze larger than k s negatve, what

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Discussion 11 Summary 11/20/2018

Discussion 11 Summary 11/20/2018 Dscusson 11 Summary 11/20/2018 1 Quz 8 1. Prove for any sets A, B that A = A B ff B A. Soluton: There are two drectons we need to prove: (a) A = A B B A, (b) B A A = A B. (a) Frst, we prove A = A B B A.

More information

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced, FREQUENCY DISTRIBUTIONS Page 1 of 6 I. Introducton 1. The dea of a frequency dstrbuton for sets of observatons wll be ntroduced, together wth some of the mechancs for constructng dstrbutons of data. Then

More information

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg

princeton univ. F 17 cos 521: Advanced Algorithm Design Lecture 7: LP Duality Lecturer: Matt Weinberg prnceton unv. F 17 cos 521: Advanced Algorthm Desgn Lecture 7: LP Dualty Lecturer: Matt Wenberg Scrbe: LP Dualty s an extremely useful tool for analyzng structural propertes of lnear programs. Whle there

More information

FINITELY-GENERATED MODULES OVER A PRINCIPAL IDEAL DOMAIN

FINITELY-GENERATED MODULES OVER A PRINCIPAL IDEAL DOMAIN FINITELY-GENERTED MODULES OVER PRINCIPL IDEL DOMIN EMMNUEL KOWLSKI Throughout ths note, s a prncpal deal doman. We recall the classfcaton theorem: Theorem 1. Let M be a fntely-generated -module. (1) There

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 4: November 17, Part 1 Single Buffer Management Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input

More information

The internal structure of natural numbers and one method for the definition of large prime numbers

The internal structure of natural numbers and one method for the definition of large prime numbers The nternal structure of natural numbers and one method for the defnton of large prme numbers Emmanul Manousos APM Insttute for the Advancement of Physcs and Mathematcs 3 Poulou str. 53 Athens Greece Abstract

More information

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

20. Mon, Oct. 13 What we have done so far corresponds roughly to Chapters 2 & 3 of Lee. Now we turn to Chapter 4. The first idea is connectedness.

20. Mon, Oct. 13 What we have done so far corresponds roughly to Chapters 2 & 3 of Lee. Now we turn to Chapter 4. The first idea is connectedness. 20. Mon, Oct. 13 What we have done so far corresponds roughly to Chapters 2 & 3 of Lee. Now we turn to Chapter 4. The frst dea s connectedness. Essentally, we want to say that a space cannot be decomposed

More information

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space.

Linear, affine, and convex sets and hulls In the sequel, unless otherwise specified, X will denote a real vector space. Lnear, affne, and convex sets and hulls In the sequel, unless otherwse specfed, X wll denote a real vector space. Lnes and segments. Gven two ponts x, y X, we defne xy = {x + t(y x) : t R} = {(1 t)x +

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

Foundations of Arithmetic

Foundations of Arithmetic Foundatons of Arthmetc Notaton We shall denote the sum and product of numbers n the usual notaton as a 2 + a 2 + a 3 + + a = a, a 1 a 2 a 3 a = a The notaton a b means a dvdes b,.e. ac = b where c s an

More information

Introductory Cardinality Theory Alan Kaylor Cline

Introductory Cardinality Theory Alan Kaylor Cline Introductory Cardnalty Theory lan Kaylor Clne lthough by name the theory of set cardnalty may seem to be an offshoot of combnatorcs, the central nterest s actually nfnte sets. Combnatorcs deals wth fnte

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

Lecture 17 : Stochastic Processes II

Lecture 17 : Stochastic Processes II : Stochastc Processes II 1 Contnuous-tme stochastc process So far we have studed dscrete-tme stochastc processes. We studed the concept of Makov chans and martngales, tme seres analyss, and regresson analyss

More information

Simultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals

Simultaneous Optimization of Berth Allocation, Quay Crane Assignment and Quay Crane Scheduling Problems in Container Terminals Smultaneous Optmzaton of Berth Allocaton, Quay Crane Assgnment and Quay Crane Schedulng Problems n Contaner Termnals Necat Aras, Yavuz Türkoğulları, Z. Caner Taşkın, Kuban Altınel Abstract In ths work,

More information

The Second Anti-Mathima on Game Theory

The Second Anti-Mathima on Game Theory The Second Ant-Mathma on Game Theory Ath. Kehagas December 1 2006 1 Introducton In ths note we wll examne the noton of game equlbrum for three types of games 1. 2-player 2-acton zero-sum games 2. 2-player

More information

2.3 Nilpotent endomorphisms

2.3 Nilpotent endomorphisms s a block dagonal matrx, wth A Mat dm U (C) In fact, we can assume that B = B 1 B k, wth B an ordered bass of U, and that A = [f U ] B, where f U : U U s the restrcton of f to U 40 23 Nlpotent endomorphsms

More information

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product

12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA. 4. Tensor product 12 MATH 101A: ALGEBRA I, PART C: MULTILINEAR ALGEBRA Here s an outlne of what I dd: (1) categorcal defnton (2) constructon (3) lst of basc propertes (4) dstrbutve property (5) rght exactness (6) localzaton

More information

A Simple Research of Divisor Graphs

A Simple Research of Divisor Graphs The 29th Workshop on Combnatoral Mathematcs and Computaton Theory A Smple Research o Dvsor Graphs Yu-png Tsao General Educaton Center Chna Unversty o Technology Tape Tawan yp-tsao@cuteedutw Tape Tawan

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

Appendix B. Criterion of Riemann-Stieltjes Integrability

Appendix B. Criterion of Riemann-Stieltjes Integrability Appendx B. Crteron of Remann-Steltes Integrablty Ths note s complementary to [R, Ch. 6] and [T, Sec. 3.5]. The man result of ths note s Theorem B.3, whch provdes the necessary and suffcent condtons for

More information

On the Multicriteria Integer Network Flow Problem

On the Multicriteria Integer Network Flow Problem BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 5, No 2 Sofa 2005 On the Multcrtera Integer Network Flow Problem Vassl Vasslev, Marana Nkolova, Maryana Vassleva Insttute of

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

Theoretical Computer Science

Theoretical Computer Science Theoretcal Computer Scence 412 (2011) 1263 1274 Contents lsts avalable at ScenceDrect Theoretcal Computer Scence journal homepage: www.elsever.com/locate/tcs Popular matchngs wth varable tem copes Telkepall

More information

On quasiperfect numbers

On quasiperfect numbers Notes on Number Theory and Dscrete Mathematcs Prnt ISSN 1310 5132, Onlne ISSN 2367 8275 Vol. 23, 2017, No. 3, 73 78 On quasperfect numbers V. Sva Rama Prasad 1 and C. Suntha 2 1 Nalla Malla Reddy Engneerng

More information

Convexity preserving interpolation by splines of arbitrary degree

Convexity preserving interpolation by splines of arbitrary degree Computer Scence Journal of Moldova, vol.18, no.1(52), 2010 Convexty preservng nterpolaton by splnes of arbtrary degree Igor Verlan Abstract In the present paper an algorthm of C 2 nterpolaton of dscrete

More information

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

First day August 1, Problems and Solutions

First day August 1, Problems and Solutions FOURTH INTERNATIONAL COMPETITION FOR UNIVERSITY STUDENTS IN MATHEMATICS July 30 August 4, 997, Plovdv, BULGARIA Frst day August, 997 Problems and Solutons Problem. Let {ε n } n= be a sequence of postve

More information

k(k 1)(k 2)(p 2) 6(p d.

k(k 1)(k 2)(p 2) 6(p d. BLOCK-TRANSITIVE 3-DESIGNS WITH AFFINE AUTOMORPHISM GROUP Greg Gamble Let X = (Z p d where p s an odd prme and d N, and let B X, B = k. Then t was shown by Praeger that the set B = {B g g AGL d (p} s the

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

Distribution of subgraphs of random regular graphs

Distribution of subgraphs of random regular graphs Dstrbuton of subgraphs of random regular graphs Zhcheng Gao Faculty of Busness Admnstraton Unversty of Macau Macau Chna zcgao@umac.mo N. C. Wormald Department of Combnatorcs and Optmzaton Unversty of Waterloo

More information

Bernoulli Numbers and Polynomials

Bernoulli Numbers and Polynomials Bernoull Numbers and Polynomals T. Muthukumar tmk@tk.ac.n 17 Jun 2014 The sum of frst n natural numbers 1, 2, 3,..., n s n n(n + 1 S 1 (n := m = = n2 2 2 + n 2. Ths formula can be derved by notng that

More information

Edge Isoperimetric Inequalities

Edge Isoperimetric Inequalities November 7, 2005 Ross M. Rchardson Edge Isopermetrc Inequaltes 1 Four Questons Recall that n the last lecture we looked at the problem of sopermetrc nequaltes n the hypercube, Q n. Our noton of boundary

More information

COS 521: Advanced Algorithms Game Theory and Linear Programming

COS 521: Advanced Algorithms Game Theory and Linear Programming COS 521: Advanced Algorthms Game Theory and Lnear Programmng Moses Charkar February 27, 2013 In these notes, we ntroduce some basc concepts n game theory and lnear programmng (LP). We show a connecton

More information

SL n (F ) Equals its Own Derived Group

SL n (F ) Equals its Own Derived Group Internatonal Journal of Algebra, Vol. 2, 2008, no. 12, 585-594 SL n (F ) Equals ts Own Derved Group Jorge Macel BMCC-The Cty Unversty of New York, CUNY 199 Chambers street, New York, NY 10007, USA macel@cms.nyu.edu

More information

Notes on Frequency Estimation in Data Streams

Notes on Frequency Estimation in Data Streams Notes on Frequency Estmaton n Data Streams In (one of) the data streamng model(s), the data s a sequence of arrvals a 1, a 2,..., a m of the form a j = (, v) where s the dentty of the tem and belongs to

More information

Assortment Optimization under MNL

Assortment Optimization under MNL Assortment Optmzaton under MNL Haotan Song Aprl 30, 2017 1 Introducton The assortment optmzaton problem ams to fnd the revenue-maxmzng assortment of products to offer when the prces of products are fxed.

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

Beyond Zudilin s Conjectured q-analog of Schmidt s problem

Beyond Zudilin s Conjectured q-analog of Schmidt s problem Beyond Zudln s Conectured q-analog of Schmdt s problem Thotsaporn Ae Thanatpanonda thotsaporn@gmalcom Mathematcs Subect Classfcaton: 11B65 33B99 Abstract Usng the methodology of (rgorous expermental mathematcs

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results.

For now, let us focus on a specific model of neurons. These are simplified from reality but can achieve remarkable results. Neural Networks : Dervaton compled by Alvn Wan from Professor Jtendra Malk s lecture Ths type of computaton s called deep learnng and s the most popular method for many problems, such as computer vson

More information

The lower and upper bounds on Perron root of nonnegative irreducible matrices

The lower and upper bounds on Perron root of nonnegative irreducible matrices Journal of Computatonal Appled Mathematcs 217 (2008) 259 267 wwwelsevercom/locate/cam The lower upper bounds on Perron root of nonnegatve rreducble matrces Guang-Xn Huang a,, Feng Yn b,keguo a a College

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

Exercises of Chapter 2

Exercises of Chapter 2 Exercses of Chapter Chuang-Cheh Ln Department of Computer Scence and Informaton Engneerng, Natonal Chung Cheng Unversty, Mng-Hsung, Chay 61, Tawan. Exercse.6. Suppose that we ndependently roll two standard

More information

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013

ISSN: ISO 9001:2008 Certified International Journal of Engineering and Innovative Technology (IJEIT) Volume 3, Issue 1, July 2013 ISSN: 2277-375 Constructon of Trend Free Run Orders for Orthogonal rrays Usng Codes bstract: Sometmes when the expermental runs are carred out n a tme order sequence, the response can depend on the run

More information

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION

ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EQUATION Advanced Mathematcal Models & Applcatons Vol.3, No.3, 2018, pp.215-222 ON A DETERMINATION OF THE INITIAL FUNCTIONS FROM THE OBSERVED VALUES OF THE BOUNDARY FUNCTIONS FOR THE SECOND-ORDER HYPERBOLIC EUATION

More information

Power law and dimension of the maximum value for belief distribution with the max Deng entropy

Power law and dimension of the maximum value for belief distribution with the max Deng entropy Power law and dmenson of the maxmum value for belef dstrbuton wth the max Deng entropy Bngy Kang a, a College of Informaton Engneerng, Northwest A&F Unversty, Yanglng, Shaanx, 712100, Chna. Abstract Deng

More information

( 1) i [ d i ]. The claim is that this defines a chain complex. The signs have been inserted into the definition to make this work out.

( 1) i [ d i ]. The claim is that this defines a chain complex. The signs have been inserted into the definition to make this work out. Mon, Apr. 2 We wsh to specfy a homomorphsm @ n : C n ()! C n (). Snce C n () s a free abelan group, the homomorphsm @ n s completely specfed by ts value on each generator, namely each n-smplex. There are

More information

Stanford University CS254: Computational Complexity Notes 7 Luca Trevisan January 29, Notes for Lecture 7

Stanford University CS254: Computational Complexity Notes 7 Luca Trevisan January 29, Notes for Lecture 7 Stanford Unversty CS54: Computatonal Complexty Notes 7 Luca Trevsan January 9, 014 Notes for Lecture 7 1 Approxmate Countng wt an N oracle We complete te proof of te followng result: Teorem 1 For every

More information

Société de Calcul Mathématique SA

Société de Calcul Mathématique SA Socété de Calcul Mathématque SA Outls d'ade à la décson Tools for decson help Probablstc Studes: Normalzng the Hstograms Bernard Beauzamy December, 202 I. General constructon of the hstogram Any probablstc

More information

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis

Appendix for Causal Interaction in Factorial Experiments: Application to Conjoint Analysis A Appendx for Causal Interacton n Factoral Experments: Applcaton to Conjont Analyss Mathematcal Appendx: Proofs of Theorems A. Lemmas Below, we descrbe all the lemmas, whch are used to prove the man theorems

More information

Lecture 4: Universal Hash Functions/Streaming Cont d

Lecture 4: Universal Hash Functions/Streaming Cont d CSE 5: Desgn and Analyss of Algorthms I Sprng 06 Lecture 4: Unversal Hash Functons/Streamng Cont d Lecturer: Shayan Oves Gharan Aprl 6th Scrbe: Jacob Schreber Dsclamer: These notes have not been subjected

More information

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions

Math 426: Probability MWF 1pm, Gasson 310 Homework 4 Selected Solutions Exercses from Ross, 3, : Math 26: Probablty MWF pm, Gasson 30 Homework Selected Solutons 3, p. 05 Problems 76, 86 3, p. 06 Theoretcal exercses 3, 6, p. 63 Problems 5, 0, 20, p. 69 Theoretcal exercses 2,

More information

APPENDIX A Some Linear Algebra

APPENDIX A Some Linear Algebra APPENDIX A Some Lnear Algebra The collecton of m, n matrces A.1 Matrces a 1,1,..., a 1,n A = a m,1,..., a m,n wth real elements a,j s denoted by R m,n. If n = 1 then A s called a column vector. Smlarly,

More information

Case A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k.

Case A. P k = Ni ( 2L i k 1 ) + (# big cells) 10d 2 P k. THE CELLULAR METHOD In ths lecture, we ntroduce the cellular method as an approach to ncdence geometry theorems lke the Szemeréd-Trotter theorem. The method was ntroduced n the paper Combnatoral complexty

More information

CS : Algorithms and Uncertainty Lecture 17 Date: October 26, 2016

CS : Algorithms and Uncertainty Lecture 17 Date: October 26, 2016 CS 29-128: Algorthms and Uncertanty Lecture 17 Date: October 26, 2016 Instructor: Nkhl Bansal Scrbe: Mchael Denns 1 Introducton In ths lecture we wll be lookng nto the secretary problem, and an nterestng

More information

Some basic inequalities. Definition. Let V be a vector space over the complex numbers. An inner product is given by a function, V V C

Some basic inequalities. Definition. Let V be a vector space over the complex numbers. An inner product is given by a function, V V C Some basc nequaltes Defnton. Let V be a vector space over the complex numbers. An nner product s gven by a functon, V V C (x, y) x, y satsfyng the followng propertes (for all x V, y V and c C) (1) x +

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

Perfect Competition and the Nash Bargaining Solution

Perfect Competition and the Nash Bargaining Solution Perfect Competton and the Nash Barganng Soluton Renhard John Department of Economcs Unversty of Bonn Adenauerallee 24-42 53113 Bonn, Germany emal: rohn@un-bonn.de May 2005 Abstract For a lnear exchange

More information

THERE ARE INFINITELY MANY FIBONACCI COMPOSITES WITH PRIME SUBSCRIPTS

THERE ARE INFINITELY MANY FIBONACCI COMPOSITES WITH PRIME SUBSCRIPTS Research and Communcatons n Mathematcs and Mathematcal Scences Vol 10, Issue 2, 2018, Pages 123-140 ISSN 2319-6939 Publshed Onlne on November 19, 2018 2018 Jyot Academc Press http://jyotacademcpressorg

More information

An (almost) unbiased estimator for the S-Gini index

An (almost) unbiased estimator for the S-Gini index An (almost unbased estmator for the S-Gn ndex Thomas Demuynck February 25, 2009 Abstract Ths note provdes an unbased estmator for the absolute S-Gn and an almost unbased estmator for the relatve S-Gn for

More information

7. Products and matrix elements

7. Products and matrix elements 7. Products and matrx elements 1 7. Products and matrx elements Based on the propertes of group representatons, a number of useful results can be derved. Consder a vector space V wth an nner product ψ

More information