Cluster algebras were introduced by Fomin and Zelevinsky (1)

Size: px
Start display at page:

Download "Cluster algebras were introduced by Fomin and Zelevinsky (1)"

Transcription

1 Greedy bases n rank quantum cluster algebras Kyungyong Lee a,ll b, Dylan Rupel c,, and Andre Zelensky c, a Department of Mathematcs, Wayne State Unersty, Detrot, MI 480; b Department of Mathematcs and Statstcs, Oakland Unersty, Rochester, MI 48309; and c Department of Mathematcs, Northeastern Unersty, Boston, MA 05 Edted by Bernard Leclerc, Unersty of Caen, France, and accepted by the Edtoral Board December 5, 03 (receed for reew August 6, 03) We dentfy a quantum lft of the greedy bass for rank coeffcentfree cluster algebras. Our man result s that our constructon does not depend on the choce of ntal cluster, that t bulds all cluster monomals, and that t produces bar-narant elements. We also present seeral conjectures related to ths quantum greedy bass and the trangular bass of Berensten and Zelensky. standard monomal bass trangular bass Cluster algebras were ntroduced by Fomn and Zelensky () as a combnatoral tool for understandng the canoncal bass and postty phenomena n the coordnate rng of an algebrac group. Ths was followed by the defnton of quantum cluster algebras by Berensten and Zelensky () as a smlar combnatoral tool for understandng canoncal bases of quantum groups. In connecton wth these foundatonal goals, t s mportant to understand the arous bases of cluster algebras and quantum cluster algebras. In ths paper, we confrm the exstence of a quantum greedy bass n rank quantum cluster algebras. It s ndependent of the choce of an ntal cluster, contans all cluster monomals, and specalzes at = to the greedy bass of a classcal rank cluster algebra ntroduced by Lee et al. (3). Another mportant bass n an acyclc quantum cluster algebra s the trangular bass of Berensten and Zelensky (4). Lke trangular bases, quantum greedy bases can be easly computed. Ths enables us to study and compare these bases computatonally. The structure of the paper s as follows. Secton begns wth a recollecton of rank commutate cluster algebras. We recall the constructon of greedy bases here and n the process we prode an axomatc descrpton. In Sec. we reew the defnton of rank quantum cluster algebras. In Sec. 3 we present our man result: the exstence of a quantum lft of the greedy bass usng the axomatc descrpton. Secton 4 recalls the defnton of the trangular bass and Sec. 5 prodes an oerew of results and open problems on the comparson between arous bases of rank quantum cluster algebras.. Rank Cluster Algebras and Ther Greedy Bases Fx poste ntegers b; c > 0. The commutate cluster algebra Aðb; cþ s the Z subalgebra of Qðx ; x Þ generated by the cluster arables fx m g m Z, where the x m are ratonal functons n x and x defned recursely by the exchange relatons x b x m x m = m f m s odd; x c m f m s een: It s a fundamental result of Fomn and Zelensky () that, although the exchange relatons appear to produce ratonal functons, one always obtans a Laurent polynomal whose denomnator s smply a monomal n x and x. They actually showed the followng slghtly stronger result. Theorem. (ref., theorem 3., Laurent phenomenon) For any m Z we hae Aðb; cþ Z½x m ± ; x ± m Š. The cluster algebra Aðb; cþ s of fnte type f the collecton of all cluster arables s a fnte set. Fomn and Zelensky (5) went on to classfy cluster algebras of fnte type. Theorem. (ref. 5, theorem.4) The cluster algebra Aðb; cþ s of fnte type f and only f bc 3. The proof of ths theorem n partcular establshes a connecton between denomnator ectors of cluster arables and almost poste roots n a root system Φ Z. Thus, we say Aðb; cþ s of affne (respectely, wld) type f bc = 4 (respectely, bc 5). An element x T m Z Z x m ± ; x m ± s called unersally Laurent because the expanson of x n eery cluster s Laurent. If for a nonzero element x the coeffcents of the Laurent expanson n each cluster are poste ntegers, then x s called unersally poste. A unersally poste element n Aðb; cþ s sad to be ndecomposable f t cannot be expressed as a sum of two unersally poste elements. Sherman and Zelensky (6) studed n great detal the collecton of ndecomposable unersally poste elements. One of ther man results s the followng: f the commutate cluster algebra Aðb; cþ s of fnte or affne type, then the ndecomposable unersally poste elements form a Z bass n Aðb; cþ, moreoer ths bass contans the set of all cluster monomals fx a m xa m : a ; a Z 0 ; m Zg. Howeer, the stuaton becomes much more complcated n wld types. In partcular, t was shown by Lee et al. (7) that for bc 5 the ndecomposable unersally poste elements of Aðb; cþ are not lnearly ndependent. The greedy bass of Aðb; cþ ntroduced by Lee et al. (3) s a subset of the ndecomposable unersally poste elements whch admts a beautful combnatoral descrpton. In ths secton we wll descrbe an axomatc characterzaton of the greedy bass. The elements of the greedy bass take on a partcular form whch s motated by a well-known pattern n the ntal cluster expanson of cluster monomals. An element x Aðb; cþ s ponted at ða ; a Þ Z f t can be wrtten n the form x = x a x a p;q 0 eð p; qþx bp xcq wth eð0; 0Þ = and eðp; qþ = eða ; a ; p; qþ Z for all p; q 0. It s well-known that x a xa s the denomnator of a cluster monomal f and only f ða ; a Þ Z Φ m (cf. 8, 9), where Φm s the set of poste magnary roots,.e., Sgnfcance The quantum cluster algebras are a famly of noncommutate rngs ntroduced by Berensten and Zelensky as the quantum deformaton of the commutate cluster algebras. At the heart of ther defnton s a desre to understand bases of quantum algebras arsng from the representaton theory of nonassocate algebras. Thus a natural and mportant problem n the study of quantum cluster algebras s to study ther bases wth good propertes. In ths paper, we lay out a framework for understandng the nterrelatonshps between arous bases of rank two quantum cluster algebras. Author contrbutons: K.L., L.L., D.R., and A.Z. desgned research; K.L., L.L., D.R., and A.Z. performed research; K.L., L.L., D.R., and A.Z. analyzed data; and K.L., L.L., and D.R. wrote the paper. The authors declare no conflct of nterest. Ths artcle s a PNAS Drect Submsson. B.L. s a guest edtor nted by the Edtoral Board. To whom correspondence should be addressed. Emal: [email protected]. Deceased Aprl 0, PNAS July 8, 04 ol. no. 7

2 Φ m d ða ; a Þ Z >0 : ca bca a ba 0 : For ða ; a Þ Φ m, defne the regon R greedy = ðp; qþ R 0 j q b ba p < a or ca p c ca Sfð0; q < a a Þ; ða ; 0Þg: ba In other words, R greedy s the regon bounded by the broken lne ð0; 0Þ; ða ; 0Þ; ða =b; a =cþ; ð0; a Þ; ð0; 0Þ; wth the conenton that ths regon ncludes the closed segments ½ð0; 0Þ; ða ; 0ÞŠ and ½ð0; a Þ; ð0; 0ÞŠ but excludes the rest of the boundary (Fg. ). We can defne greedy elements n two dfferent ways, ether by axoms or by recurrence relatons. Here we choose to defne them usng recurrence relatons as follows. Theorem 3. (ref. 3, proposton.6) For each ða ; a Þ Z, there exsts a unque element n Aðb; cþ ponted at ða ; a Þ whose coeffcents eð p; qþ satsfy the followng recurrence relaton: eð0; 0Þ =, eð p; qþ = 8 p ð Þ >< k ½a cqš eðp k; qþ k f ca k q ba p; >: k= q l= ð Þ l ½a bpš eðp; q lþ l l f ca q ba p; where we use the standard notaton ½aŠ = maxða; 0Þ. We defne the greedy element ponted at ða ; a Þ, denoted x½a ; a Š, to be the unque element determned by Theorem 3. For ða ; a Þ Φ m, we ge a drect characterzaton for the greedy element. Theorem 4. For each ða ; a Þ Φ m, the greedy element x½a ; a Š s the unque element n Aðb; cþ ponted at ða ; a Þ whose coeffcents satsfy the followng two axoms: (Support) eðp; qþ = 0 for ð p; qþ R greedy ; (Dsblty) f a > cq; then ð tþ a cq eð; qþt ; f a > bp; then ð tþ a bp eðp; Þt : The key step n the proof of Theorem 4 s based on an obseraton made n ref. 3, sec. : the frst (respectely, second) recurrence relaton n Theorem 3 s equalent to the anshng of the (p, q)th coeffcent of the Laurent expanson of x½a ; a Š wth respect to the cluster fx 0 ; x g (respectely, fx ; x 3 g). It s straghtforward to check that such anshng mples the Support and Dsblty axoms n Theorem 4. The followng theorem summarzes the results from ref. 3. Theorem 5. (ref. 3, theorem.7) (a) The greedy elements x½a ; a Š for ða ; a Þ Z form a Z bass n Aðb; cþ, whch we refer to as the greedy bass. (b) The greedy bass s ndependent of the choce of an ntal cluster. (c) The greedy bass contans all cluster monomals. (d) Greedy elements are unersally poste and ndecomposable. Our goal n ths work s to generalze the aboe theorem to the settng of rank quantum cluster algebras. The proof of Theorem 5 gen n ref. 3 uses combnatoral objects called compatble pars n an essental way (cf. ref. 3, theorem.). Unfortunately ths method has dffcultes lmtatons n generalzng to the study of quantum cluster algebras. More precsely, f one could smply assgn a power of to each compatble par then the quantum greedy elements would hae agan been unersally poste, whch s unfortunately false n general (see the example at the end of Sec. 3). Ths justfes our approach to Theorem 5, wth the excepton of (d), whch can easly be generalzed to the quantum settng.. Rank Quantum Cluster Algebras In ths secton we defne our man objects of study, namely quantum cluster algebras, and recall mportant fundamental facts related to these algebras. We restrct attenton to rank quantum cluster algebras where we can descrbe the setup n ery concrete terms. We follow (as much as possble) the notaton and conentons of refs. 3, 4. Consder the followng quantum torus: T = Z ± D E ± ; ± : = (ths setup s related to the one n ref. 0, whch uses the formal arable q nstead of by settng q = ). There are many choces for quantzng cluster algebras; to rgdfy the stuaton we requre the quantum cluster algebra to be narant under a certan noluton. The bar noluton s the Z-lnear antautomorphsm of T determned by f ðþ = f ð Þ for f Z½ ± Š and f a a = fa a = a a f a a ða ; a ZÞ: An element whch s narant under the bar noluton s sad to be bar-narant. Let F be the skew feld of fractons of T. The quantum cluster algebra A ðb; cþ s the Z½ ± Š subalgebra of F generated by the quantum cluster arables f m g m Z defned recursely from the quantum exchange relatons MATHEMATICS SPECIAL FEATURE m m = b m b c m c f m s odd; f m s een: Fg.. Support regon of (quantum) greedy elements. By a smple nducton one can easly check the followng (quas-) commutaton relatons between neghborng cluster arables m ; m n A ðb; cþ: m m = m m ðm ZÞ: [] It then follows that all cluster arables are bar-narant, therefore A ðb; cþ s also stable under the bar noluton. Moreoer, Eq. mples that each cluster f m ; m g generates a quantum torus Lee et al. PNAS July 8, 04 ol. no

3 T m = Z ± ± m ; ± m : m m = m m : It s easy to see that the bar noluton does not depend on the choce of an ntal quantum torus T m. The approprate quantum analogs of cluster monomals for A ðb; cþ are the (bar-narant) quantum cluster monomals whch are certan elements of a quantum torus T m,moreprecsely they are ða;aþ m = aa a m a m ða ; a Z 0 ; m ZÞ: The followng quantum analog of the Laurent phenomenon was proen by Berensten and Zelensky (). Theorem 6 (). For any m Z we hae A ðb; cþ T m. Moreoer, A ðb; cþ = T T m : m Z A nonzero element of A ðb; cþ s called unersally poste f t les n T m Z Z 0 h ± m ± ; ± m. A unersally poste element n A ðb; cþ s ndecomposable f t cannot be expressed as a sum of two unersally poste elements. It s known as a ery specal case of the results of refs. 5 that cluster monomals are unersally poste Laurent polynomals. Explct combnatoral expressons for these poste coeffcents can be obtaned from the results of refs Quantum Greedy Bases Ths secton contans the man result of the paper. Here we ntroduce the quantum greedy bass and present ts nce propertes. Analogous to the constructon of greedy elements, the elements of the quantum greedy bass take on the followng partcular form. An element A ðb; cþ s sad to be ponted at ða ; a Þ Z f t has the form = eð p; qþ ðbp a ;cq a Þ p;q 0 wth eð0; 0Þ = and eð p; qþ Z½ ± Š for all p and q. The next theorem s an analog of Theorem 3; to state our result precsely we need more notaton. Let w denote a formal nertble arable. For n Z, k Z 0 defne the bar-narant quantum numbers and bar-narant quantum bnomal coeffcents by ½nŠ w = wn w n w w = wn w n 3 w n ; n = ½nŠ w ½n Š w ½n k Š w: k ½kŠ w w ½k Š w ½Š w h n Recall that wll always be a Laurent polynomal n w. Hence k w takng w = b or c we obtan Laurent polynomals n as well. Theorem 7. For each ða ; a Þ Z, there exsts a unque element n A ðb; cþ ponted at ða ; a Þ whose coeffcents eðp; qþ satsfy the followng recurrence relaton: eð0; 0Þ =, eðp; 8 qþ = p ½a ð Þ k cqš k eðp k; qþ f ca q ba p; >< k= k b q ½a ð Þ l bpš l >: eðp; q lþ f ca q ba p: l= l c We defne the quantum greedy element ponted at ða ; a Þ, denoted ½a ; a Š, to be the unque element determned by Theorem 7. The followng theorem s analogous to Theorem 4. Theorem 8. For each ða ; a Þ Φ m, the quantum greedy element ½a ; a Š s the unque element n A ðb; cþ ponted at ða ; a Þ whose coeffcents satsfy the followng two axoms: (Support) eðp; qþ = 0 for ðp; qþ R greedy ; (Dsblty) Let t denote a formal arable whch commutes wth. Then a cq bða cq jþ t eð; qþt ; j= a bp cða bp jþ t eðp; Þt : j= Theorem 8 follows from an argument smlar to the proof of Theorem 4. Our man theorem below states that quantum greedy elements possess all of the desred propertes descrbed n Theorem 5 except the postty (d). Theorem 9. (Man Theorem) (a) The quantum greedy elements ½a ; a Š for ða ; a Þ Z form a Z½ ± Š bass n A ðb; cþ, whch we refer to as the quantum greedy bass. (b) The quantum greedy bass s bar-narant and ndependent of the choce of an ntal cluster. (c) The quantum greedy bass contans all cluster monomals. (d) If ½a ; a Š s unersally poste, then t s ndecomposable. (e) The quantum greedy bass specalzes to the commutate greedy bass by the substtuton =. Full proofs of Theorems 7, 8, and 9 wll appear elsewhere. The hardest part s to show the exstence of quantum greedy elements,.e., that the recursons n Theorem 7 termnate. The man dea n the proof s to realze ½a ; a Š as a degeneraton of certan elements n A ðb; cþ whose supports are contaned n the closure of R greedy. We end ths secton wth an example where postty of quantum greedy elements fals. Let ðb; cþ = ð; 3Þ and consder the ponted coeffcent eð; Þ n the Laurent expanson of ½3; 4Š. Usng Theorem 7, we see that eð; Þ s not n Z 0 ½ ± Š: eð; Þ = eð; Þ eð0; Þ = eð; Þ eð0; Þ 4 3 = eð; 0Þ eð0; Þ = eð0; 0Þ eð0; 0Þ 3 3 = = : Further computaton shows that the greedy elements ½3; 5Š; ½5; 4Š; ½5; 7Š; ½5; 8Š; ½7; 5Š; ½7; 0Š; ½7; Š; etc: are not poste. Smlarly, postty can be seen to fal for a large class of quantum greedy elements when ðb; cþ = ð; 5Þ; ð3; 4Þ; ð4; 6Þ: 4. Trangular Bases The constructon of the trangular bass begns wth the standard monomal bass. For eery ða ; a Þ Z, we defne the standard monomal M½a ; a Š (whch s denoted E ð a ; a Þ n ref. 4) by settng M½a ; a Š = aa ½a Š 3 ½ a Š ½ aš ½aŠ 0 : [] It s known () that the elements M½a ; a Š form a Z½ ± Š bass of A ðb; cþ Lee et al.

4 The mportance of ths bass comes from ts computablty; howeer, ths bass wll not sere n our goals of understandng quantum cluster algebras. Indeed, t s easy to see that the standard monomals are not bar-narant and do not contan all of the cluster monomals, moreoer they are nherently dependent on the choce of an ntal cluster. These drawbacks prode a motaton to consder the trangular bass (as defned below) constructed from the standard monomal bass wth a bult-n bar-narance property. As the name suggests, the trangular bass s defned by a trangularty property relatng t to the standard monomal bass. To descrbe ths trangular relatonshp we ntroduce the -poste lattce L = Z½ŠM½a ; a Š, where the summaton runs oer all ða ; a Þ Z. We now defne a bass fc½a ; a Š : ða ; a Þ Z g by specfyng how t relates to the standard monomal bass: (P) Each C½a ; a Š s bar-narant. (P) For each ða ; a Þ Z, we hae C½a ; a Š M½a ; a Š L: Usng the unersal acyclcty of rank quantum cluster algebras, we apply the followng theorem. Theorem 0. (ref. 4, theorem.6]) The trangular bass does not depend on the choce of ntal cluster and t contans all cluster monomals. Smlar to the support condton for (quantum) greedy elements, we make the followng conjecture about the supports of trangular elements. Conjecture. Let ða ; a Þ Φ m. For 0 p a,0 q a, the ponted coeffcent eðp; qþ of ðbp a ;cq a Þ n C½a ; a Š s nonzero f and only f (Fg. ). bp bcpq cq ca q ba p; 5. Open Problems In ths secton we present open problems and conjectures relatng to the trangular bass and quantum greedy bass of a rank quantum cluster algebra. Our am s to fnd Z½ ± Š bases of A ðb; cþ satsfyng the propertes lad out n the followng defnton. Fg.. Conjectured support regon of trangular bass elements s the closed regon OAC wth a cured edge AC, whereo = ð0,0þ, A = ða,0þ, and C = ð0,a Þ. The support regon of the (quantum or nonquantum) greedy element ponted at ða,a Þ s the polygon OABC, whereb = ða =b,a =cþ. Note that the lne BA (respectely, BC) s tangent to the cured edge AC at pont A (respectely, C). Fg. 3. Propertes of the quantum greedy bass for bjc or cjb. A bass B for A ðb; cþ s sad to be strongly poste f the followng hold: () each element of B s bar-narant; () B s ndependent of the choce of an ntal cluster; (3) B contans all cluster monomals; (4) any product of elements from B can be expanded as a lnear combnaton of elements of B wth coeffcents n Z 0 ½ ± Š. Note that all elements n a strongly poste bass are unersally poste. Indeed, consder multplyng a bass element B B by a cluster monomal ða;aþ wth a ; a suffcently large to clear the denomnator n the ntal cluster expanson of B. By propertes (3) and (4) of a strongly poste bass the product B ða ;a Þ s n Z 0 ½ ± н ; Š and thus we see that a strongly poste bass element has nonnegate coeffcents n ts ntal cluster expanson. It then follows that B s unersally poste because t s ndependent of the choce of an ntal cluster. Kmura and Qn (5) showed the exstence of bases for acyclc skew-symmetrc quantum cluster algebras [hence for A ðb; cþ wth b = c] whch satsfy (), (3), and (4). In the rank case, ther bases are exactly the trangular bases, and hence satsfy () as well (see ref. 4, remark.8]). Conjecture. Gen any strongly poste bass B, there exsts a unque bass element ponted at ða ; a Þ for each ða ; a Þ Z. It s easy to see that property (3) of strongly poste bases together wth Conjecture mples that eery element of B s ponted. We note that the concluson of Conjecture holds for both the quantum greedy bass and the trangular bass. In what follows, we wrte Y Z for Y; Z A ðb; cþ f Z Y s a Laurent polynomal wth coeffcents n Z 0 ½ ± Š. Conjecture 3. (a) There exsts a unque strongly poste bass B upper satsfyng the bass element n the bass element n B ponted at ða ; a Þ B upper ponted at ða ; a Þ for eery strongly poste bass B and eery ða ; a Þ Z. (b) The trangular bass s B upper. Conjecture 4. Suppose that bjcorcjb. (a) There exsts a unque strongly poste bass B lower satsfyng the bass element n the bass element n B lower ponted at ða ; a Þ B ponted at ða ; a Þ for eery strongly poste bass B and eery ða ; a Þ Z. (b) The quantum greedy bass s B lower. In contrast wth the example at the end of Sec. 3, we do not obsere a falure of postty when ðb; cþ = ð; 5Þ; ð; 6Þ; ð; 4Þ; MATHEMATICS SPECIAL FEATURE Lee et al. PNAS July 8, 04 ol. no

5 Proposton 5. h r a ;a a ;a = 0 ða ;a Þ>ða ;a Þ h r a ;a a ;a q a ;a a ;a 0 : [5] Fg. 4. ð; 6Þ; ð3; 3Þ; ð3; 6Þ. Ths prodes the motaton for assumng the condton bjc orcjb n Conjecture 4. Conjectures, 3, and 4 are trally true for fnte types ðbc < 4Þ, and are also known for affne types ðbc = 4Þ (9, 0). Next we study the expanson coeffcents relatng the arous bases. Let us begn by ntroducng notaton. For ða ; a Þ; ða ; a Þ Z we defne expanson coeffcents q a;a a ;a ; r a;a a ;a Z½ ± Š as follows: ½a ; a Š = M½a ; a Š C½a ; a Š = ½a ; a Š Propertes of the trangular bass. ða ;a Þ<ða ;a Þ ða ;a Þ<ða ;a Þ q a ;a a ;a M½a ; a Š; [3] r a ;a a ;a ½a ; a Š; [4] where we wrte ða ; a Þ < ða ; a Þ when a < a and a < a. We now dere a recurson for the expanson coeffcents n Eq. 4 relatng the trangular bass to the quantum greedy bass. Our man ngredents wll be the defnng propertes (P) and (P) of the trangular bass. Note that because each ½a ; a Š s bar-narant, we hae r a ;a a ;a = r a ;a a ;a by (P). For f Z½ ± Š let ½f Š 0 denote the nonposte part of f,.e., n f we ealuate = 0 n all terms for whch ths makes sense. Wth ths notaton, we note that because r a ;a a ;a s bar-narant, t s determned by ½r a ;a a ;a Š 0. Ths says that we hae an easy recurse way to compute the decomposton n Eq. 4 f we know the decomposton n Eq. 3. Ths proposton can be proed by reducng Eq. 3 mod L to get r a ;a a ;a ½a ; a Š h r a ;a a ;a 0 M½a ; a Š P ða ;a Þ<ða ;a Þ h r a;a a ;a q a ;a a ;a M½a ; a [6] Š mod L: 0 Keepng n mnd (P), we reduce the equalty 4 mod L and apply Eq. 6 to arre at the desred recurson. Based on extense computatons usng Proposton 5 we make the followng postty conjecture. Conjecture 6. For any ða ; a Þ Z the expanson coeffcents of the trangular bass element C½a ; a Š n terms of the quantum greedy bass are poste; more precsely, we hae r a ;a a ;a Z 0 ± for all ða ; a Þ Z : More generally, the expanson coeffcents of any strongly poste bass n terms of the quantum greedy bass are poste. We end ths artcle by summarzng the aforementoned proen or conjectured propertes of the quantum greedy bass (Fg. 3) and the trangular bass (Fg. 4). ACKNOWLEDGMENTS. Most of the deas toward ths work from D.R. and A.Z. were had durng ther stay at the Mathematcal Scences Research Insttute (MSRI) as part of the Cluster Algebras Program. They thank the MSRI for ther hosptalty and support. Sadly Andre Zelensky passed away n the early stages of wrtng. The authors offer the sncerest grattude to Sergey Fomn for hs careful readng of seeral drafts of ths note. We hope to hae acheed the clarty of exposton that Andre s artful eye would hae proded. The authors thank F. Qn for aluable dscussons. Research supported n part by Natonal Scence Foundaton Grants DMS (to K.L.) and DMS-0383 (to A.Z.), and by Oakland Unersty s Unersty Research Commttee Faculty Research Fellowshp Award (to L.L.).. Fomn S, Zelensky A (00) Cluster algebras I: Foundatons. J Am Math Soc 5(): Berensten A, Zelensky A (005) Quantum cluster algebras. Ad Math 95(): Lee K, L L, Zelensky A (0) Greedy elements n rank cluster algebras. Sel Math, 0.007/s Berensten A, Zelensky A (0) Trangular bases n quantum cluster algebras. Int Math Res Notces, 0.093/mrn/rns Fomn S, Zelensky A (003) Cluster algebras II: Fnte type classfcaton. Inent Math 54(): Sherman P, Zelensky A (004) Postty and canoncal bases n rank cluster algebras of fnte and affne types. Mosc Math J 4(4): Lee K, L L, Zelensky A (03) Postty and tameness n rank cluster algebras. ar: Caldero P, Chapoton F (006) Cluster algebras as Hall algebras of quer representatons. Comment Math Hel 8(3): Caldero P, Keller B (006) From trangulated categores to cluster algebras. II. Ann Sc École Norm Sup 39(6): Rupel D (0) On a quantum analog of the Caldero-Chapoton formula. Int Math Res Notces 0(4): Qn F, Keller B (0) Quantum cluster arables a Serre polynomals. J Rene Angew Math 0(668): Efmo A (0) Quantum cluster arables a anshng cycles. ar: Dason B, Maulk D, Schürmann J, Szendr}o B (03) Purty for graded potentals and quantum cluster postty. ar: Nakajma H (0) Quer aretes and cluster algebras. Kyoto J Math 5(): Kmura Y, Qn F (0) Graded quer aretes, quantum cluster algebras and dual canoncal bass. ar: Lee K, Schffler R (0) Proof of a postty conjecture of M. Kontsech on noncommutate cluster arables. Compos Math 48(6): Rupel D (0) Proof of the Kontsech non-commutate cluster postty conjecture. C R Math Acad Sc Pars 350(-): Lee K, L L (03) On natural maps from strata of quer Grassmannans to ordnary Grassmannans. AMS Contemp Math 59: Chen, Dng M, Sheng J (0) Bar-narant bases of the quantum cluster algebra of type A ðþ. Czech Math J 6(4): Dng M, u F (0) Bases of the quantum cluster algebra of the Kronecker quer. Acta Math Sn (Engl Ser) 8(6): Lee et al.

BERNSTEIN POLYNOMIALS

BERNSTEIN POLYNOMIALS On-Lne 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 information

+ + + - - This circuit than can be reduced to a planar circuit

+ + + - - 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 information

Recurrence. 1 Definitions and main statements

Recurrence. 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 information

8.5 UNITARY AND HERMITIAN MATRICES. The conjugate transpose of a complex matrix A, denoted by A*, is given by

8.5 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 information

On Lockett pairs and Lockett conjecture for π-soluble Fitting classes

On Lockett pairs and Lockett conjecture for π-soluble Fitting classes On Lockett pars and Lockett conjecture for π-soluble Fttng classes Lujn Zhu Department of Mathematcs, Yangzhou Unversty, Yangzhou 225002, P.R. Chna E-mal: [email protected] Nanyng Yang School of Mathematcs

More information

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2)

n + d + q = 24 and.05n +.1d +.25q = 2 { n + d + q = 24 (3) n + 2d + 5q = 40 (2) MATH 16T Exam 1 : Part I (In-Class) 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 information

Ring structure of splines on triangulations

Ring structure of splines on triangulations www.oeaw.ac.at Rng structure of splnes on trangulatons N. Vllamzar RICAM-Report 2014-48 www.rcam.oeaw.ac.at RING STRUCTURE OF SPLINES ON TRIANGULATIONS NELLY VILLAMIZAR Introducton For a trangulated regon

More information

Luby s Alg. for Maximal Independent Sets using Pairwise Independence

Luby 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 information

Linear Circuits Analysis. Superposition, Thevenin /Norton Equivalent circuits

Linear 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 information

benefit is 2, paid if the policyholder dies within the year, and probability of death within the year is ).

benefit 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 information

Embedding lattices in the Kleene degrees

Embedding lattices in the Kleene degrees F U N D A M E N T A MATHEMATICAE 62 (999) Embeddng lattces n the Kleene degrees by Hsato M u r a k (Nagoya) Abstract. Under ZFC+CH, we prove that some lattces whose cardnaltes do not exceed ℵ can be embedded

More information

v a 1 b 1 i, a 2 b 2 i,..., a n b n i.

v 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 information

1 Example 1: Axis-aligned rectangles

1 Example 1: Axis-aligned 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 information

Generalizing the degree sequence problem

Generalizing 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 information

Using Series to Analyze Financial Situations: Present Value

Using 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 information

Calculation of Sampling Weights

Calculation 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 two-stage stratfed cluster desgn. 1 The frst stage conssted of a sample

More information

Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks

Product-Form Stationary Distributions for Deficiency Zero Chemical Reaction Networks Bulletn of Mathematcal Bology (21 DOI 1.17/s11538-1-9517-4 ORIGINAL ARTICLE Product-Form Statonary Dstrbutons for Defcency Zero Chemcal Reacton Networks Davd F. Anderson, Gheorghe Cracun, Thomas G. Kurtz

More information

FINITE HILBERT STABILITY OF (BI)CANONICAL CURVES

FINITE HILBERT STABILITY OF (BI)CANONICAL CURVES FINITE HILBERT STABILITY OF (BICANONICAL CURVES JAROD ALPER, MAKSYM FEDORCHUK, AND DAVID ISHII SMYTH* To Joe Harrs on hs sxteth brthday Abstract. We prove that a generc canoncally or bcanoncally embedded

More information

Minimal Coding Network With Combinatorial Structure For Instantaneous Recovery From Edge Failures

Minimal Coding Network With Combinatorial Structure For Instantaneous Recovery From Edge Failures Mnmal Codng Network Wth Combnatoral Structure For Instantaneous Recovery From Edge Falures Ashly Joseph 1, Mr.M.Sadsh Sendl 2, Dr.S.Karthk 3 1 Fnal Year ME CSE Student Department of Computer Scence Engneerng

More information

1. Math 210 Finite Mathematics

1. Math 210 Finite Mathematics 1. ath 210 Fnte athematcs Chapter 5.2 and 5.3 Annutes ortgages Amortzaton Professor Rchard Blecksmth Dept. of athematcal Scences Northern Illnos Unversty ath 210 Webste: http://math.nu.edu/courses/math210

More information

A Probabilistic Theory of Coherence

A 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 information

Extending Probabilistic Dynamic Epistemic Logic

Extending 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 σ-algebra: a set

More information

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12

PSYCHOLOGICAL RESEARCH (PYC 304-C) Lecture 12 14 The Ch-squared dstrbuton PSYCHOLOGICAL RESEARCH (PYC 304-C) 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 information

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis

The Development of Web Log Mining Based on Improve-K-Means Clustering Analysis The Development of Web Log Mnng Based on Improve-K-Means Clusterng Analyss TngZhong Wang * College of Informaton Technology, Luoyang Normal Unversty, Luoyang, 471022, Chna [email protected] Abstract.

More information

Support Vector Machines

Support Vector Machines Support Vector Machnes Max Wellng Department of Computer Scence Unversty of Toronto 10 Kng s College Road Toronto, M5S 3G5 Canada [email protected] Abstract Ths s a note to explan support vector machnes.

More information

PERRON FROBENIUS THEOREM

PERRON 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 information

Lecture 3: Force of Interest, Real Interest Rate, Annuity

Lecture 3: Force of Interest, Real Interest Rate, Annuity Lecture 3: Force of Interest, Real Interest Rate, Annuty Goals: Study contnuous compoundng and force of nterest Dscuss real nterest rate Learn annuty-mmedate, and ts present value Study annuty-due, and

More information

Project Networks With Mixed-Time Constraints

Project Networks With Mixed-Time Constraints Project Networs Wth Mxed-Tme 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 information

Section 5.3 Annuities, Future Value, and Sinking Funds

Section 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 information

Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3

Series Solutions of ODEs 2 the Frobenius method. The basic idea of the Frobenius method is to look for solutions of the form 3 Royal Holloway Unversty of London Department of Physs Seres Solutons of ODEs the Frobenus method Introduton to the Methodology The smple seres expanson method works for dfferental equatons whose solutons

More information

where the coordinates are related to those in the old frame as follows.

where 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 non-coplanar vectors Scalar product

More information

On Leonid Gurvits s proof for permanents

On Leonid Gurvits s proof for permanents On Leond Gurvts s proof for permanents Monque Laurent and Alexander Schrver Abstract We gve a concse exposton of the elegant proof gven recently by Leond Gurvts for several lower bounds on permanents.

More information

REGULAR MULTILINEAR OPERATORS ON C(K) SPACES

REGULAR MULTILINEAR OPERATORS ON C(K) SPACES REGULAR MULTILINEAR OPERATORS ON C(K) SPACES FERNANDO BOMBAL AND IGNACIO VILLANUEVA Abstract. The purpose of ths paper s to characterze the class of regular contnuous multlnear operators on a product of

More information

8 Algorithm for Binary Searching in Trees

8 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 information

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services

An Evaluation of the Extended Logistic, Simple Logistic, and Gompertz Models for Forecasting Short Lifecycle Products and Services An Evaluaton of the Extended Logstc, Smple Logstc, and Gompertz Models for Forecastng Short Lfecycle Products and Servces Charles V. Trappey a,1, Hsn-yng Wu b a Professor (Management Scence), Natonal Chao

More information

An Alternative Way to Measure Private Equity Performance

An 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 information

Section 5.4 Annuities, Present Value, and Amortization

Section 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 information

Module 2 LOSSLESS IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 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

"Research Note" APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES *

Research Note APPLICATION OF CHARGE SIMULATION METHOD TO ELECTRIC FIELD CALCULATION IN THE POWER CABLES * Iranan Journal of Scence & Technology, Transacton B, Engneerng, ol. 30, No. B6, 789-794 rnted n The Islamc Republc of Iran, 006 Shraz Unversty "Research Note" ALICATION OF CHARGE SIMULATION METHOD TO ELECTRIC

More information

THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES

THE 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 information

Implied (risk neutral) probabilities, betting odds and prediction markets

Implied (risk neutral) probabilities, betting odds and prediction markets Impled (rsk neutral) probabltes, bettng odds and predcton markets Fabrzo Caccafesta (Unversty of Rome "Tor Vergata") ABSTRACT - We show that the well known euvalence between the "fundamental theorem of

More information

Texas Instruments 30X IIS Calculator

Texas Instruments 30X IIS Calculator Texas Instruments 30X IIS Calculator Keystrokes for the TI-30X 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 information

The OC Curve of Attribute Acceptance Plans

The 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 information

How To Understand The Results Of The German Meris Cloud And Water Vapour Product

How To Understand The Results Of The German Meris Cloud And Water Vapour Product Ttel: Project: Doc. No.: MERIS level 3 cloud and water vapour products MAPP MAPP-ATBD-ClWVL3 Issue: 1 Revson: 0 Date: 9.12.1998 Functon Name Organsaton Sgnature Date Author: Bennartz FUB Preusker FUB Schüller

More information

IT09 - Identity Management Policy

IT09 - Identity Management Policy IT09 - Identty Management Polcy Introducton 1 The Unersty needs to manage dentty accounts for all users of the Unersty s electronc systems and ensure that users hae an approprate leel of access to these

More information

1. Measuring association using correlation and regression

1. Measuring association using correlation and regression How to measure assocaton I: Correlaton. 1. Measurng assocaton usng correlaton and regresson We often would lke to know how one varable, such as a mother's weght, s related to another varable, such as a

More information

POLYSA: A Polynomial Algorithm for Non-binary Constraint Satisfaction Problems with and

POLYSA: A Polynomial Algorithm for Non-binary Constraint Satisfaction Problems with and POLYSA: A Polynomal Algorthm for Non-bnary Constrant Satsfacton Problems wth and Mguel A. Saldo, Federco Barber Dpto. Sstemas Informátcos y Computacón Unversdad Poltécnca de Valenca, Camno de Vera s/n

More information

Financial Mathemetics

Financial Mathemetics Fnancal Mathemetcs 15 Mathematcs Grade 12 Teacher Gude Fnancal Maths Seres Overvew In ths seres we am to show how Mathematcs can be used to support personal fnancal decsons. In ths seres we jon Tebogo,

More information

What is Candidate Sampling

What 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 information

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification

Logistic Regression. Lecture 4: More classifiers and classes. Logistic regression. Adaboost. Optimization. Multiple class classification Lecture 4: More classfers and classes C4B Machne Learnng Hlary 20 A. Zsserman Logstc regresson Loss functons revsted Adaboost Loss functons revsted Optmzaton Multple class classfcaton Logstc Regresson

More information

Implementation of Deutsch's Algorithm Using Mathcad

Implementation 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 information

DEFINING %COMPLETE IN MICROSOFT PROJECT

DEFINING %COMPLETE IN MICROSOFT PROJECT CelersSystems DEFINING %COMPLETE IN MICROSOFT PROJECT PREPARED BY James E Aksel, PMP, PMI-SP, MVP For Addtonal Informaton about Earned Value Management Systems and reportng, please contact: CelersSystems,

More information

Addendum to: Importing Skill-Biased Technology

Addendum to: Importing Skill-Biased Technology Addendum to: Importng Skll-Based Technology Arel Bursten UCLA and NBER Javer Cravno UCLA August 202 Jonathan Vogel Columba and NBER Abstract Ths Addendum derves the results dscussed n secton 3.3 of our

More information

Stability, observer design and control of networks using Lyapunov methods

Stability, observer design and control of networks using Lyapunov methods Stablty, observer desgn and control of networks usng Lyapunov methods von Lars Naujok Dssertaton zur Erlangung des Grades enes Doktors der Naturwssenschaften - Dr. rer. nat. - Vorgelegt m Fachberech 3

More information

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression

A Novel Methodology of Working Capital Management for Large. Public Constructions by Using Fuzzy S-curve Regression Novel Methodology of Workng Captal Management for Large Publc Constructons by Usng Fuzzy S-curve Regresson Cheng-Wu Chen, Morrs H. L. Wang and Tng-Ya Hseh Department of Cvl Engneerng, Natonal Central Unversty,

More information

Do Hidden Variables. Improve Quantum Mechanics?

Do Hidden Variables. Improve Quantum Mechanics? Radboud Unverstet Njmegen Do Hdden Varables Improve Quantum Mechancs? Bachelor Thess Author: Denns Hendrkx Begeleder: Prof. dr. Klaas Landsman Abstract Snce the dawn of quantum mechancs physcst have contemplated

More information

Section C2: BJT Structure and Operational Modes

Section 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 information

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting

Causal, Explanatory Forecasting. Analysis. Regression Analysis. Simple Linear Regression. Which is Independent? Forecasting Causal, Explanatory Forecastng Assumes cause-and-effect relatonshp between system nputs and ts output Forecastng wth Regresson Analyss Rchard S. Barr Inputs System Cause + Effect Relatonshp The job of

More information

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently.

To manage leave, meeting institutional requirements and treating individual staff members fairly and consistently. Corporate Polces & Procedures Human Resources - Document CPP216 Leave Management Frst Produced: Current Verson: Past Revsons: Revew Cycle: Apples From: 09/09/09 26/10/12 09/09/09 3 years Immedately Authorsaton:

More information

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network

Forecasting the Demand of Emergency Supplies: Based on the CBR Theory and BP Neural Network 700 Proceedngs of the 8th Internatonal Conference on Innovaton & Management Forecastng the Demand of Emergency Supples: Based on the CBR Theory and BP Neural Network Fu Deqang, Lu Yun, L Changbng School

More information

How To Assemble The Tangent Spaces Of A Manfold Nto A Coherent Whole

How To Assemble The Tangent Spaces Of A Manfold Nto A Coherent Whole CHAPTER 7 VECTOR BUNDLES We next begn addressng the queston: how do we assemble the tangent spaces at varous ponts of a manfold nto a coherent whole? In order to gude the decson, consder the case of U

More information

Efficient Project Portfolio as a tool for Enterprise Risk Management

Efficient Project Portfolio as a tool for Enterprise Risk Management Effcent Proect Portfolo as a tool for Enterprse Rsk Management Valentn O. Nkonov Ural State Techncal Unversty Growth Traectory Consultng Company January 5, 27 Effcent Proect Portfolo as a tool for Enterprse

More information

Characterization of Assembly. Variation Analysis Methods. A Thesis. Presented to the. Department of Mechanical Engineering. Brigham Young University

Characterization of Assembly. Variation Analysis Methods. A Thesis. Presented to the. Department of Mechanical Engineering. Brigham Young University Characterzaton of Assembly Varaton Analyss Methods A Thess Presented to the Department of Mechancal Engneerng Brgham Young Unversty In Partal Fulfllment of the Requrements for the Degree Master of Scence

More information

Damage detection in composite laminates using coin-tap method

Damage detection in composite laminates using coin-tap method Damage detecton n composte lamnates usng con-tap method S.J. Km Korea Aerospace Research Insttute, 45 Eoeun-Dong, Youseong-Gu, 35-333 Daejeon, Republc of Korea [email protected] 45 The con-tap test has the

More information

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process

Rate Monotonic (RM) Disadvantages of cyclic. TDDB47 Real Time Systems. Lecture 2: RM & EDF. Priority-based scheduling. States of a process Dsadvantages of cyclc TDDB47 Real Tme Systems Manual scheduler constructon Cannot deal wth any runtme changes What happens f we add a task to the set? Real-Tme Systems Laboratory Department of Computer

More information

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia

NON-CONSTANT SUM RED-AND-BLACK GAMES WITH BET-DEPENDENT WIN PROBABILITY FUNCTION LAURA PONTIGGIA, University of the Sciences in Philadelphia To appear n Journal o Appled Probablty June 2007 O-COSTAT SUM RED-AD-BLACK GAMES WITH BET-DEPEDET WI PROBABILITY FUCTIO LAURA POTIGGIA, Unversty o the Scences n Phladelpha Abstract In ths paper we nvestgate

More information

An Overview of Financial Mathematics

An Overview of Financial Mathematics An Overvew of Fnancal Mathematcs Wllam Benedct McCartney July 2012 Abstract Ths document s meant to be a quck ntroducton to nterest theory. It s wrtten specfcally for actuaral students preparng to take

More information

Conversion between the vector and raster data structures using Fuzzy Geographical Entities

Conversion 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 information

How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence

How 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 information

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt.

Solution: Let i = 10% and d = 5%. By definition, the respective forces of interest on funds A and B are. i 1 + it. S A (t) = d (1 dt) 2 1. = d 1 dt. Chapter 9 Revew problems 9.1 Interest rate measurement Example 9.1. Fund A accumulates at a smple nterest rate of 10%. Fund B accumulates at a smple dscount rate of 5%. Fnd the pont n tme at whch the forces

More information

Sketching Sampled Data Streams

Sketching Sampled Data Streams Sketchng Sampled Data Streams Florn Rusu, Aln Dobra CISE Department Unversty of Florda Ganesvlle, FL, USA [email protected] [email protected] Abstract Samplng s used as a unversal method to reduce the

More information

Forecasting the Direction and Strength of Stock Market Movement

Forecasting the Direction and Strength of Stock Market Movement Forecastng the Drecton and Strength of Stock Market Movement Jngwe Chen Mng Chen Nan Ye [email protected] [email protected] [email protected] Abstract - Stock market s one of the most complcated systems

More information

On the Optimal Control of a Cascade of Hydro-Electric Power Stations

On the Optimal Control of a Cascade of Hydro-Electric Power Stations On the Optmal Control of a Cascade of Hydro-Electrc Power Statons M.C.M. Guedes a, A.F. Rbero a, G.V. Smrnov b and S. Vlela c a Department of Mathematcs, School of Scences, Unversty of Porto, Portugal;

More information

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money

Time Value of Money. Types of Interest. Compounding and Discounting Single Sums. Page 1. Ch. 6 - The Time Value of Money. The Time Value of Money Ch. 6 - The Tme Value of Money Tme Value of Money The Interest Rate Smple Interest Compound Interest Amortzng a Loan FIN21- Ahmed Y, Dasht TIME VALUE OF MONEY OR DISCOUNTED CASH FLOW ANALYSIS Very Important

More information

Traffic-light a stress test for life insurance provisions

Traffic-light a stress test for life insurance provisions MEMORANDUM Date 006-09-7 Authors Bengt von Bahr, Göran Ronge Traffc-lght a stress test for lfe nsurance provsons Fnansnspetonen P.O. Box 6750 SE-113 85 Stocholm [Sveavägen 167] Tel +46 8 787 80 00 Fax

More information

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1.

1.1 The University may award Higher Doctorate degrees as specified from time-to-time in UPR AS11 1. HIGHER DOCTORATE DEGREES SUMMARY OF PRINCIPAL CHANGES General changes None Secton 3.2 Refer to text (Amendments to verson 03.0, UPR AS02 are shown n talcs.) 1 INTRODUCTION 1.1 The Unversty may award Hgher

More information

CHOLESTEROL REFERENCE METHOD LABORATORY NETWORK. Sample Stability Protocol

CHOLESTEROL 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 information

The Distribution of Eigenvalues of Covariance Matrices of Residuals in Analysis of Variance

The Distribution of Eigenvalues of Covariance Matrices of Residuals in Analysis of Variance JOURNAL OF RESEARCH of the Natonal Bureau of Standards - B. Mathem atca l Scence s Vol. 74B, No.3, July-September 1970 The Dstrbuton of Egenvalues of Covarance Matrces of Resduals n Analyss of Varance

More information

Level Annuities with Payments Less Frequent than Each Interest Period

Level Annuities with Payments Less Frequent than Each Interest Period Level Annutes wth Payments Less Frequent than Each Interest Perod 1 Annuty-mmedate 2 Annuty-due Level Annutes wth Payments Less Frequent than Each Interest Perod 1 Annuty-mmedate 2 Annuty-due Symoblc approach

More information

Multiplication Algorithms for Radix-2 RN-Codings and Two s Complement Numbers

Multiplication Algorithms for Radix-2 RN-Codings and Two s Complement Numbers Multplcaton Algorthms for Radx- RN-Codngs and Two s Complement Numbers Jean-Luc Beuchat Projet Arénare, LIP, ENS Lyon 46, Allée d Itale F 69364 Lyon Cedex 07 [email protected] Jean-Mchel Muller

More information

How To Calculate The Accountng Perod Of Nequalty

How To Calculate The Accountng Perod Of Nequalty 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 information

Integer Programming Formulations for the Uncapacitated Vehicle Routing p-hub Center Problem

Integer Programming Formulations for the Uncapacitated Vehicle Routing p-hub Center Problem 21st Internatonal Congress on Modellng and Smulaton, Gold Coast, Australa, 29 No to 4 Dec 2015 www.mssanz.org.au/modsm2015 Integer Programmng Formulatons for the Uncapactated Vehcle Routng p-hub Center

More information

SIMPLE LINEAR CORRELATION

SIMPLE LINEAR CORRELATION SIMPLE LINEAR CORRELATION Smple lnear correlaton s a measure of the degree to whch two varables vary together, or a measure of the ntensty of the assocaton between two varables. Correlaton often s abused.

More information

FINANCIAL MATHEMATICS. A Practical Guide for Actuaries. and other Business Professionals

FINANCIAL 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 information

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA )

Hollinger Canadian Publishing Holdings Co. ( HCPH ) proceeding under the Companies Creditors Arrangement Act ( CCAA ) February 17, 2011 Andrew J. Hatnay [email protected] Dear Sr/Madam: Re: Re: Hollnger Canadan Publshng Holdngs Co. ( HCPH ) proceedng under the Companes Credtors Arrangement Act ( CCAA ) Update on CCAA Proceedngs

More information

Fixed income risk attribution

Fixed income risk attribution 5 Fxed ncome rsk attrbuton Chthra Krshnamurth RskMetrcs Group [email protected] We compare the rsk of the actve portfolo wth that of the benchmark and segment the dfference between the two

More information

FREQUENCY OF OCCURRENCE OF CERTAIN CHEMICAL CLASSES OF GSR FROM VARIOUS AMMUNITION TYPES

FREQUENCY OF OCCURRENCE OF CERTAIN CHEMICAL CLASSES OF GSR FROM VARIOUS AMMUNITION TYPES FREQUENCY OF OCCURRENCE OF CERTAIN CHEMICAL CLASSES OF GSR FROM VARIOUS AMMUNITION TYPES Zuzanna BRO EK-MUCHA, Grzegorz ZADORA, 2 Insttute of Forensc Research, Cracow, Poland 2 Faculty of Chemstry, Jagellonan

More information

CHAPTER 14 MORE ABOUT REGRESSION

CHAPTER 14 MORE ABOUT REGRESSION CHAPTER 14 MORE ABOUT REGRESSION We learned n Chapter 5 that often a straght lne descrbes the pattern of a relatonshp between two quanttatve varables. For nstance, n Example 5.1 we explored the relatonshp

More information

ON CYCLOTOMIC POLYNOMIALS WITH ±1 COEFFICIENTS

ON CYCLOTOMIC POLYNOMIALS WITH ±1 COEFFICIENTS ON CYCLOTOMIC POLYNOMIALS WITH ±1 COEFFICIENTS PETER BORWEIN AND KWOK-KWONG STEPHEN CHOI Abstract. We characterze all cyclotomc polynomals of even egree wth coeffcents restrcte to the set {+1, 1}. In ths

More information

4 Cosmological Perturbation Theory

4 Cosmological Perturbation Theory 4 Cosmologcal Perturbaton Theory So far, we have treated the unverse as perfectly homogeneous. To understand the formaton and evoluton of large-scale structures, we have to ntroduce nhomogenetes. As long

More information

Small pots lump sum payment instruction

Small pots lump sum payment instruction For customers Small pots lump sum payment nstructon Please read these notes before completng ths nstructon About ths nstructon Use ths nstructon f you re an ndvdual wth Aegon Retrement Choces Self Invested

More information

Implementation of Boolean Functions through Multiplexers with the Help of Shannon Expansion Theorem

Implementation of Boolean Functions through Multiplexers with the Help of Shannon Expansion Theorem Internatonal Journal o Computer pplcatons (975 8887) Volume 62 No.6, January 23 Implementaton o Boolean Functons through Multplexers wth the Help o Shannon Expanson Theorem Saurabh Rawat Graphc Era Unversty.

More information