On the Number of Crossing-Free Matchings, (Cycles, and Partitions)

Size: px
Start display at page:

Download "On the Number of Crossing-Free Matchings, (Cycles, and Partitions)"

Transcription

1 O the Number of Crossig-Free Matchigs, (Cycles, ad Partitios Micha Sharir Emo Welzl Abstract We show that a set of poits i the plae has at most O(1005 perfect matchigs with crossig-free straight-lie embeddig The expected umber of perfect crossig-free matchigs of a set of poits draw iid from a arbitrary distributio i the plae is at most O(924 Seeral related bouds are deried: (a The umber of all (ot ecessarily perfect crossig-free matchigs is at most O(1043 (b The umber of left-right perfect crossig-free matchigs (where the poits are desigated as left or as right edpoits of the matchig edges is at most O(538 (c The umber of perfect crossig-free matchigs across a lie (where all the matchig edges must cross a fixed halig lie of the set is at most 4 These bouds are employed to ifer that a set of poits i the plae has at most O(8681 crossig-free spaig cycles (simple polygoizatios, ad at most O(1224 crossig-free partitios (partitios of the poit set, so that the coex hulls of the idiidual parts are pairwise disjoit 1 Itroductio Let P be a set of poits i the plae A geometric graph o P is a graph that has P as its ertex set ad its edges are draw as straight segmets coectig the correspodig pairs of poits The graph is crossigfree if o pair of its edges cross each other, ie, ay two edges are ot allowed to share ay poits other tha commo edpoits Therefore, these are plaar graphs with a plae embeddig gie by this specific drawig We are iterested i the umber of crossig-free geometric graphs o P of seeral special types Specifically, we cosider the umbers tr(p, of triagulatios (ie, maximal crossig-free graphs, pm(p, of crossig-free perfect matchigs, sc(p, of crossig-free spaig cycles, ad, cfp(p, of crossig-free partitios 1 (partitios Work by Micha Sharir has bee supported by the US-Israel Biatioal Sci Foudatio, by NSF Grat CCR , by a grat from the Israeli Acad of Sci for a Ceter of Excellece i Geom Comp at Tel Ai Ui, ad by the Herma Mikowski- MINERVA Ceter for Geometry at Tel Ai Ui School of Computer Sciece, Tel Ai Uiersity, Tel Ai 69978, Israel, ad Courat Ist of Math Sci, 251 Mercer Street, NYC, NY 10012, USA Ist Theoretische Iformatik, ETH Zürich, CH-8092 Zürich, Switzerlad 1 Our research was triggered by M a Kreeld askig about the umber of crossig-free partitios, ad, i the same week, by M Hoffma ad Y Okamoto askig about the umber of P, so that the coex hulls of the parts are pairwise disjoit We are cocered with upper bouds for the umbers listed aboe i terms of History This problem goes back to Newbor ad Moser [25] i 1980 who ask for the maximal possible umber of crossig-free spaig cycles i a set of poits 2 ; they gie a upper boud of ! but cojecture that the boud should be of the form c, c a costat This was established i 1982 by Ajtai, Chátal, Newbor, ad Szemerédi [4], who show 3 that there are at most crossig-free graphs 4 Further deelopmets were maily cocered with deriig progressiely better upper bouds for the umber of triagulatios 5 [29, 13, 28], so far culmiatig i a 59 upper boud by Satos ad Seidel [27] i 2003 It compares to Ω(848, the largest kow umber of triagulatios for a set of poits, recetly deried by Aichholzer et al [1]; this improes a earlier lower boud of 8 /poly( gie by García et al [17] (We let poly( deote a polyomial factor i Eery crossig-free graph is cotaied i some triagulatio (with at most 3 6 edges Hece, a c boud for the umber of triagulatios yields a boud of c < (8c for the umber of crossig-free graphs o a set of poits; with c 59, this is at most 472 To the best of our kowledge, all upper bouds so far o the umber of crossig-free graphs of arious types are deried ia a boud o the umber of triagulatios, of crossig-free spaig paths of a poit set (motiated by their quest for good fixed parameter algorithms for the plaar Euclidea Traelig Salesma Problem i the presece of a fixed umber of ier poits [10]; see also [19] 2 Akl s work [5] appeared earlier, but it refers to the mauscript by Newbor ad Moser, ad improes a lower boud (o the maximal umber of crossig-free spaig cycles of theirs 3 This paper is famous for its Crossig Lemma, proed i preparatio of the sigly expoetial boud The lemma gies a upper boud o the umber of edges a geometric graph with a gie umber of crossigs ca hae 4 For motiatio they metio also a questio of D Ais about the maximum umber of triagulatios a set of poits ca hae 5 Iterest was also motiated by the related questio (from geometric modelig [29] of how may bits it takes to ecode a triagulatio

2 Figure 1: 6 poits with 12 crossig-free perfect matchigs, the maximum possible umber; see [3] for the maximum umbers for up to te poits: 3 for 4 poits, 12 for 6, 56 for 8, ad 311 for 10 albeit i more refied ways Oe idea is to exploit the fact that graphs of certai types hae a fixed umber of edges; eg, sice a perfect matchig has 2 edges, we readily obtai pm(p ( 3 6 /2 tr(p < [14] A short historical accout of bouds o sc(p, with refereces icludig [5, 12, 17, 18, 20, 25, 26], ca be foud at the web site [11] (see also [8] The best boud published is 337 tr(p It relies o a 337 boud o the umber of cycles i a plaar graph [6] I the course of our iestigatios, we showed that a graph with m edges ad ertices has at most ( m cycles; hece, a plaar graph has at most 3 cycles The R Seidel proided us with a argumet, based o liear algebra, that a plaar graph has at most 6 < 245 spaig cycles Crossig-free partitios fit ito Figure 2: Graph of a crossig-free partitio the picture, sice eery such partitio ca be uiquely idetified with the graph of edges of the coex hulls of the idiidual parts these edges form a crossig-free geometric graph of at most edges; see Fig 2 The situatio is better uderstood for special cofiguratios, for example for P a set of poits i coex positio (the ertex set of a coex -go, where the Catala umbers C m := m+1( 1 2m m = Θ(m 3/2 4 m, m N 0, play a promiet role I coex positio tr(p = C 2 (the Euler-Seger problem, cf [30, pg 212] for its history, pm(p = C /2 for ee ([16], cf [30], sc(p = 1, ad cfp(p = C ([7] Crossig-free partitios for poit sets i coex positio costitute a well-established otio because of its may coectios to other problems, probably startig with plaar rhyme schemes i Becker s ote [7], cf [30, Solutio to 619pp] The geeral case was cosidered by [9] (uder the ame of pairwise liearly separable partitios for clusterig algorithms They show that that the umber of partitios ito k parts is O( 6k 12 for k costat Uder the assumptio of geeral positio (o three poits o a commo lie it is kow [17] that the umber of crossig-free perfect matchigs o a set of fixed size is miimized whe the set is i coex positio (Recetly, Aichholzer et al [1] showed that ay family of acyclic graphs has the miimal umber of crossig-free embeddigs o a poit set i coex positio With little surprise, the same holds for spaig cycles, but it does ot hold for triagulatios [21, 2, 23] For crossig-free partitios, this is ope Results We show the followig bouds, for a set P of poits i the plae: pm(p = O(1005, sc(p = O(8681, ad cfp(p = O(1224 Also, the expected umber of perfect crossig-free matchigs of a set of poits draw iid from ay distributio i the plae (where two radom poits coicide with probability 0 is O(924 The boud o the umber of crossig-free perfect matchigs is deried by a iductie techique that we hae adapted from the method that Satos ad Seidel [27] used for triagulatios (the adaptio howeer is far from obious We the go o to derie improed bouds o the umber of crossig-free matchigs of arious special types: (a The umber of all (ot ecessarily perfect crossig-free matchigs is at most O(1043 (b The umber of left-right perfect crossig-free matchigs (where the poits are desigated as left or as right edpoits of the matchig edges is at most O(538 (c The umber of perfect crossig-free matchigs across a lie (where all the matchig edges must cross a fixed halig lie of the set is at most 4 Fially, we derie upper bouds for the umbers of crossig-free spaig cycles ad crossig-free partitios of P i terms of the umber of certai types of matchigs of certai poit sets P that are costructed from P This yields the bouds as stated aboe We summarize the state of affairs i Table 1, (icludig lower bouds proofs are omitted here I work i progress, we are curretly refiig a tailored aalysis for spaig cycles ad trees, where the bouds ow stad at O(79 ad O(296, respectiely tr pm sc cfp P : 59 [27] P : 848 [1] 3 [17] 464 [17] 523 ma lrpm alpm rdpm P : P : Table 1: Etries c i the upper boud rows stad for O(c, ad etries c i the lower boud rows for Ω(c /poly(, where := P ma stads for all crossig-free matchigs, lrpm for perfect left-right crossig-free matchigs, alpm for perfect crossig-free matchigs across a lie, ad rdpm for the expected umber of perfect crossig-free matchigs of a set of iid poits

3 2 Matchigs: The Setup ad a Recurrece Let P be a set of poits i the plae i geeral positio, o three o a lie, o two o a ertical lie This is o costrait whe it comes to upper bouds o pm(p A crossig-free matchig M is a collectio of pairwise disjoit segmets whose edpoits belog to P Each poit of P is either matched, if it is a edpoit of a segmet of M, or isolated, otherwise The umber of matched poits is always ee If 2m poits are matched ad s poits are isolated, we call M a crossig-free m- matchig or (m, s-matchig We hae = 2m + s For m R we deote by ma m (P the umber of crossig-free matchigs of P with m segmets (this umber is 0 uless m {0, 1,, 2 }, ad by ma(p the umber of all crossig-free matchigs of P (ie ma(p = m ma m(p Recall pm(p = ma /2 (P Let M be a crossig-free (m, s-matchig o a set P of = 2m + s poits, as aboe The degree d(p of a poit p P i M is defied as follows It is 0 if p is isolated i M Otherwise, if p is a left (resp, right edpoit of a segmet of M, d(p is equal to the umber of isible left (resp, right edpoits of other segmets of M, plus the umber of isible isolated poits; isible meas ertically isible from the relatie iterior of the segmet of M that has p as a edpoit Thus p ad the other edpoit of the segmet are ot couted i d(p See Fig 3 Each left (resp, right ed- PSfrag replacemets poit u i M ca cotribute at u most 2 to the degrees of other poits: 1 to each of the left (resp, right edpoits of the segmets lyig ertically aboe ad below u, if there exist such z w Figure 3: Degrees i a matchig: d(u = 2, d( = 5, d(w = 1, d(z = 2 segmets Similarly, each isolated poit u ca cotribute at most 4 to the degrees of other poits: 1 to each of the edpoits of the segmets lyig ertically aboe ad below u It follows that p P d(p 4m + 4s There are may segmets ready for remoal The idea is to remoe segmets icidet to poits of low degree i a (m, s-matchig (poits of degree at most 3 or at most 4, to be specific We will show that there are may such poits at our disposal The, i the ext step, we show that segmets with a edpoit of low degree ca be reiserted i ot too may ways These two facts will be combied to derie a recurrece for the matchig cout For i N 0, let i = i (M deote the umber of matched poits of P with degree i i M Hece, i 0 i = 2m Lemma 21 Let, m, s N 0, with = 2m + s eery (m, s-matchig of ay set of poits, we hae ( s, ( s Proof Let P be the uderlyig poit set We hae i 0 i i = p P d(p 4s + 4m = 4s + i 0 2 i Therefore, 0 4s + i 0 (2 i i For κ R +, we add κ times = s + i 0 i to both sides to get κ (4 + κs + i 0 (2 + κ i i (4 + κs + 0 i<2+κ (2 + κ i i We set κ = 2 for (21 ad κ = 3 for (22 There are ot too may ways of isertig a segmet Fix some p P ad let M be a crossigfree matchig which leaes p isolated Now we match p with some other isolated poit such that the oerall matchig cotiues to be crossig-free For i N 0, let h i = h i (p, P, M be the umber of ways that ca be doe so that p has degree i after its isertio Lemma 22 (23 (24 4h 0 + 3h 1 + 2h 2 + h 3 24, 5h 0 + 4h 1 + 3h 2 + 2h 3 + h 4 48 Proof Let l i = l i (p, P, M be the umber of ways we ca match the poit p as a left edpoit of degree i First, we claim that l 0 {0, 1} To show this, form the ertical decompositio of M by drawig a ertical segmet up ad dow from each (matched or isolated poit of P \{p}, ad exted these segmets util they meet a edge of M, or else, all the way to ifiity; see Fig 4 We call these ertical segmets walls i order to distiguish them from the segmets i the matchig We obtai a decompositio of the plae itopsfrag ertical replacemets trapezoids Let τ be the trapezoid cotaiig p (assumig geeral u positio, p lies i the iterior of τ See Fig 4 We moe from τ to the right through ertical walls to adjacet trapezoids util we reach a ertical wall that is determied τ p I Figure 4: Isertig a segmet at p; d(p = 1 after isertio by a poit that is either a left edpoit or a isolated poit (if at all we may make our way to ifiity whe p caot be matched as a left edpoit to ay poit, i which case l i = 0 for all i Note that up to that poit there was always a uique choice for the ext trapezoid to eter Eery crossig-free segmet with p as its left edpoit will hae to go through all of these trapezoids It coects either to (which ca happe oly if is isolated,

4 or crosses the ertical wall up or dow from The former case yields a segmet that gies p degree 0 I the latter case, will cotribute 1 to the degree of p So p, if a optio, is the oly possible segmet that lets p hae degree 0 as a left edpoit (p will ot be a optio whe it crosses some segmet, or whe is a left edpoit We will retur to this set-up whe we cosider degrees 1, i which case acts as a bifurcatio poit Before doig so, we first itroduce a fuctio f It maps eery oegatie real ector (λ 0, λ 1,, λ k of arbitrary legth k + 1 N to the maximum possible alue the expressio (25 λ 0 l 0 + λ 1 l λ k l k ca attai (for ay isolated poit i ay matchig of ay fiite poit set of ay size We hae already show that f(λ λ for λ R + 0 We claim that for all (λ 0, λ 1,, λ k (R + 0 k+1, with k 1, we hae { λ0 + f(λ (26 f(λ 0, λ 1,, λ k max 1,, λ k, 2f(λ 1,, λ k Assumig (26 has bee established, we ca coclude that f(1 1, f(2, 1 3, f(3, 2, 1 6, ad f(4, 3, 2, 1 12; that is, 4l 0 + 3l 1 + 2l 2 + l 3 12 ad the first iequality of the lemma follows, sice the same boud clearly holds for the case whe p is a right edpoit The secod iequality follows similarly from f(5, 4, 3, 2, 1 24 It remais to proe (26 Cosider a costellatio with a poit p that realizes the alue of f(λ 0, λ 1,, λ k We retur to the set-up from aboe, where we hae traced a uique sequece of trapezoids from p to the right, till we ecoutered the first bifurcatio poit (if does ot exist the all l i aish Case 1: is isolated We kow that λ 0 l 0 λ 0 If we remoe from the poit set, the eery possible crossig-free segmet emaatig from p to its right has its degree decreased by 1 Therefore, λ 1 l λ k l k f(λ 1,, λ k, so the expressio (25 caot exceed λ 0 + f(λ 1,, λ k i this case Case 2: is a matched left edpoit The λ 0 l 0 = 0 (that is, we caot coect p to Possible crossigfree segmets with p as a left edpoit are discrimiated accordig to whether they pass aboe or below We first cocetrate o the segmets that pass aboe ; we call them releat segmets (emaatig from p Let l i be the umber of releat segmets that gie p degree i We carefully remoe isolated poits from P \ {p} ad segmets with their edpoits from the matchig M (eetually also the segmet of which is a left edpoit, so that i the ed all releat segmets are still aailable ad each oe, if iserted, makes the degree of p exactly 1 uit smaller tha its origial alue (this deletio process may create ew possibilities for segmets from p That will show λ 1 l λ k l k f(λ 1,, λ k The same will apply to segmets that pass below, usig a symmetric argumet, which gies the boud of 2f(λ 1,, λ k for (25 i this secod case The remoal process is performed as follows We defie a relatio o the set whose elemets are the edges of M ad the sigleto sets formed by the isolated poits of P \ {p}: a b if a poit a a is ertically isible from a poit b b, with a below b As is well kow (cf [15, Lemma 114], is acyclic Let + deote the trasitie closure of, ad let deote the trasitie reflexie closure of Let e be the segmet with as its left edpoit, ad cosider a miimal elemet a with a + e Such a elemet exists, uless e itself is a miimal elemet with respect to a is a sigleto: So it cosists of a isolated poit; with abuse of otatio we also deote by a the isolated poit itself a caot be a poit to which p ca coect with a releat edge Ideed, if this were the case, we add that edge e = pa ad modify to iclude e too; more precisely, ay pair i that ioles a is replaced by a correspodig pair iolig e, ad ew pairs iolig e are added (clearly, the relatio remais acyclic ad all pairs related uder + cotiue to be so related after e is icluded ad replaces a See Fig 5(a We hae e e (sice, by assumptio, the left edpoit of e is ertically isible below e, ad e + e (sice the right edpoit a of e satisfies a + e a cotradictio With a similar reasoig we ca rule out the possibility that a cotributes to the degree of p whe matched ia a releat edge pq Ideed, if this were the case, let e be the segmet directly aboe a, which is the first lik i the chai that gies a + e, ie, a e e (e must exist sice a + e After addig pq with a cotributig to its degree, we hae either a pq ad pq e (see Fig 5(b, or we hae pq a (see Fig 5(c I the former case, we hae a pq e e pq cotradictig the acyclicity of I the latter case, we hae pq a + e pq, agai a cotradictio So if we remoe a, the all releat edges from p remai i the game ad the degree of each of them (ie, the degree of p that the edge iduces whe iserted does ot chage p e e a p e e (a (b (c Figure 5: (a The poit a caot be coected to p ia a releat edge (b,c a caot cotribute from below (i (b or from aboe (i (c to the degree of p whe a releat edge pq is iserted a q p e e a q

5 a is a edge: It caot obstruct ay isolated poit or left edpoit below it from cotributig to the degree of a releat edge pq aboe (because a is miimal with respect to If a obstructs a cotributio to a releat edge pq from aboe, the we add pq, thus pq a which, together with PSfrag a replacemets + e ad e pq, cotradicts the acyclicity of (Fig 6 Agai, we ca remoe a without ay chages to releat possible edges from p We keep successiely re- a moig elemets util e is miimal with respect to Note p q e that so far all the releat edges from p are still possible, ad the degree of p that ay of them iduces whe iserted has ot Figure 6: Edge a caot obstruct a poit from cotributig from aboe to the degree of p whe a releat edge pq is iserted chaged Now we remoe e with its edpoits This caot clear the way for ay ew cotributio to the degree of a releat edge I fact, ay such degree decreases by exactly 1 because disappears The claim is show, ad the proof of the lemma is completed Deriig a recurrece Lemma 23 Let, m N 0, such that m 2 ad s := 2m For eery set P of poits, we hae 12(s+2 3s ma m 1 (P if s < 3 ma m (P, ad 16(s+2 7s/3 ma m 1(P if s < 3 7 Let us ote right away that the first iequality supersedes the secod for s < 5 (ie m > 2 5, while the secod oe is superior for s > 5 Proof Fix the set P, ad let X ad Y be the sets of all crossig-free m-matchigs ad (m 1-matchigs, respectiely, i P Let us cocetrate o the first iequality We defie a edge-labeled bipartite graph G o X Y as follows: Gie a m-matchig M, if p is a edpoit of a segmet e M ad d(p 3, the we coect M X to the (m 1-matchig M \ {e} Y with a edge labeled (p, d(p; d(p is the degree label of the edge Note that M ad M \ {e} ca be coected by two (differetly labeled edges, if both edpoits of e hae degree at most 3 For 0 i 3, let x i deote the umber of edges i G with degree label i We hae (2 6s X 4x 0 + 3x 1 + 2x 2 + x 3 24(s + 2 Y }{{} }{{} ma m(p ma m 1(P The first iequality is a cosequece of iequality (21 of Lemma 21 The secod iequality is implied by iequality (23 i Lemma 22, as follows For a fixed (m 1-matchig M i P, cosider a edge of G that is icidet to M ad is labeled by (p, i (if there is such a edge The p must be oe of the s+2 isolated poits of P (with respect to M, ad there is a way to coect p to aother isolated poit i a crossig-free maer, so that p has degree i i the ew matchig Hece, the cotributio by p ad M to the sum 4x 0 +3x 1 +2x 2 +x 3 is at most 24, by iequality (23 i Lemma 22, ad the right iequality follows The combiatio of both iequalities yields the first iequality the lemma By cosiderig edpoits up to degree 4 (istead of 3, we get the secod iequality For m, N 0, let ma m ( be the maximum umber of crossig-free m-matchigs a set of poits ca hae Lemma 24 For s, m, N 0, with = 2m + s, ma 0 (0 = 1, s ma m( 1, for s 1, 12(s+2 ma m ( 3s ma m 1 (, for s < 3, 16(s+2 7s/3 ma m 1(, for s < 3 7 Proof ma 0 (0 = 1 is triial The first of the three iequalities is implied by s ma m (P = p P ma m(p \ {p} ma m ( 1, for ay set P of poits The secod ad third iequalities follow from Lemma 23 3 Solig a Recurrece We derie a upper boud for a fuctio G G λ,µ : N 2 0 R +, for a pair of parameters λ, µ R +, µ 1, which satisfies (with s := 2m G(0, 0 = 1, { s G(m, 1, for s 1, (37 G(m, λ(s+2 µs G(m 1,, for s < µ The recurrece i Lemma 24 implies that a upper boud o G 12,3 (m, seres also as a upper boud for ma m (, ad the same holds for G 16,7/3 (m, We will see how to best combie the two parameter pairs, to obtai ee better bouds for ma m ( Later, we will ecouter other istaces of this recurrece, with other alues of λ ad µ We diide by λ m µ m The (37 becomes G(m, G(m, 1 λ m µ m µs λ m µ, for s 1, 1 m µ(s+2 µs G(m 1, λ m 1 µ m+1, for s < µ G(m, We set H(m, = H µ (m, := λ m µ Therefore, m still with the coetio s := 2m ad the assumptio µ 1, we hae (ote idepedece of λ H(0, 0 = 1, (38 H(m, { µs H(m, 1, for s 1, µ(s+2 µs H(m 1,, for s < µ

6 Lemma 31 Let m, N 0, with m 2 The H(m, ( m Proof H(0, 0 = 1 ( 0 0 forms the basis of a proof by iductio o ad m For all N 0, H(0, µ 1 = ( 0 follows, sice µ 1 Let 1 m m 2 If m µs the s µ < µ Hece, the secod iequality i (38 ca be applied, after which the first iequality ca be applied Hece, H(m, µ(s+2 µs µ(s+2 µs µ(s+2 m H(m 1, ( 1 m 1 = ( m H(m 1, 1 Otherwise, m > µs holds, which esures µs > m 0, ie, s 1 We ca therefore employ the first iequality of (38, ad obtai ( 1 m m ( H(m, µs H(m, 1 < = m By expadig alog the first iequality for a while before employig Lemma 31, we get H(m, µs k+1 µ(s k+1 H(m, k ( 1 k 1 ( k µ k i=0 i s i m = 1 ( k ( k (39 µ k ( k s m = 1 m ( (310 2m, for N0 k s ( 2m µ k ( m k m Whe we stop this uwidig of the recurrece, we could hae alteratiely proceeded oe ( more step, ad upper boud H(m, k by k k 1 µ(s k m, proided k < s As log as this expressio is smaller tha ( k m, we should ideed hae expaded further That is, we expad as log as ( < k k µ(s k ( k 1 m m k < µs+m µ 1 = m ( µ 1 = m ρ, for ρ := µ 1 That is, the best choice of k i (39 is (311 k = m m ρ = ρ I fact, if this suggested alue of k is egatie (or if ρ = 0, we should ot expad at all Istead, we try to expad alog the secod iequality of (38, to get (312 H(m, ( s 2 +k k ( 2µ s 2 k (, m k for N 0 k < 2µ s = m µ 1 2µ + 1; we employ here the usual geeralizatio of biomial coefficiets ( a to a R, amely, ( a k := a(a 1 (a k+1 k! k Rather tha optimizig the alue of k at which we stop the uwidig of the secod recurrece iequality of (38, we approximate it by (313 k = m µ 1 = m ρ, ad ote that it lies i the allowed rage, proided it is positie Whe m = ρ, both alues suggested for k i (311 ad (313 are 0, which idicates that we hae to cotet ourseles with the boud ( m from Lemma 31 Otherwise, it is clear which way to expad, sice m < ρ m ρ 0 ad m > ρ m ρ 0 We are ow ready for a improed boud For that we substitute k i (39 accordig to (311, ad i (312 accordig to (313 Lemma 32 Let m, N 0, where 2m, ad set ρ := µ 1 If m ρ, the 1 ( H µ (m, m/ρ ( m/ρ µ m/ρ ( m/ρ 2m m ad for m > ρ, we hae H µ (m, ( ρ 2 m ρ ( ( m 2 (1 µ 1 m ρ Thus, G λ,µ (m, G λ,µ (m, with ( λ m µ m/ρ m G λ,µ (m, := λ m µ m for m ρ m/ρ ( 2m m/ρ ρ, ad ( 2 ρ m ρ ( m 2 (1 1 µ m ρ for m > ρ ( m/ρ, m (, ρ Next we work out a umber of properties of the upper boud G λ,µ Estimates up to a polyomial factor I the followig deriatios, we sometimes use to deote equality up to a polyomial factor i We will frequetly use the followig estimate (implied by Stirlig s formula, cf [22, Chapter 10, Corollary ( α ( α α β β β (α β, for α, β R, α β α β 0 Big m We ote that for m 1 ρ 9] ( α β G λ,µ (m, = λ(s+2 µs (with s := 2m Sice λ(s+2 µs G λ,µ(m 1, < 1 s < 2λ λ+µ, the fuctio G λ,µ (m, maximizes m > (λ+µ 1+2λ 2(λ+µ for itegers m i the rage ρ m 2 at (314 m := (λ+µ 1+2λ 2(λ+µ = 2 2λ 2(λ+µ, uless this alue is ot i the proided rage Howeer, m 2 uless is ery small ( < 2λ Ad m ρ uless λ < µ 1

7 Small m With the idetity idicated i (310 we hae, for m ρ, that G ca also be writte as( (315 G λ,µ (m, = λ m µ m/ρ m ( 2m m ( m/ρ m m 2m m (4λ(µ 1 m( 2m This boud peaks (up to a additie costat at m := λ(µ 1 Note that m ρ for λ µ λ(µ 1 We summarize, that G λ,µ (m, attais its maximum up to a poly(-factor oer m at { m if λ µ 1, ad (316 m = m otherwise I all applicatios i this paper we hae λ > µ 1, so the peak occurs at m 4 Matchig Bouds 41 Perfect Matchigs For perfect matchigs we cosider the case where is ee, m = 2, ad s = 0 We ote that i this case m/ = 1/2 > ρ, for ay alue of µ Hece, the secod boud of Lemma 32 applies We first calculate 2 2 (1 1 µ = 1 2µ, ad 2 ρ = 2 µ 1 1 = 2( Hece, G λ,µ ( 2, ( (λµ /2 ( = ( 1 = (λµ /2 2µ 1 2( 1 2( 1 2µ 1( µ 1 1 ( µ 1 2( µ 1 2µ( 2µ( 1 2µ ( µ 1 µ 1 ( µ (λ 1 2 (µ 1 µ 1 2µ (2µ 1 2µ µ Substitutig (λ, µ = (12, 3 ad (16, 7 3, as suggested by Lemma 24, we obtai the followig upper bouds for the umber of crossig-free perfect matchigs: ( ( G 12,3 2, = O(105129, ( G 16, 7 3 2, ( = O( While the secod boud is obiously superior, we remember that the recurrece with (λ, µ = (12, 3 is better for m > 2 5 (or s < 5 This obseratio leads to the followig better boud for P a set of poits ad for k = = 10, where we expad as i the first iequality of Lemma 23 ( k 1 pm(p ma /2 k (P i=0 12(2i+2 6i 4 k( /6 1 k G16,7/3 (/2 k, (2 20/21 3 2/7 5 1/ /14 = O( Perfect ersus all matchigs Recall from Lemma 23 that ma m (P 12(s+2 3s ma m 1(P Note that 12(s+2 3s < 1 for m > (ad i this rage is smaller tha the alteratie of- the factor 12(s+2 3s fered i Lemma 23 That is, there are always fewer perfect matchigs tha there are matchigs More specifically, for sets P with := P ee, ad for k = = , we hae k 1 pm(p = ma /2 (P = ( k ( /6 k i=0 12(2i + 2 6i 1 ma/2 k (P ma /2 k (P ( ( 4 /30 1 1/5 ( 4 4/5 /6 5 5 ma (P 5 ( = 2 1/3 5 1/6 ma (P Therefore, pm(p ( 2 1/3 5 1/6 ma(p poly( = O(09635 ma(p, ie i eery poit set there are expoetially (i more crossig-free matchigs tha there are crossig-free perfect matchigs 42 All Matchigs Our cosideratios i the deriatio of the boud for perfect matchigs imply the followig upper boud for matchigs with m segmets G 16,7/3 (m,, m 2 5, ma m (P G 12,3 (m, G 16,7/3( 2 5,, otherwise G 12,3( 2 5 To determie where this expressio maximizes,, we ote that G 16,7/3 does ot peak i its small m -rage (m 4 11 sice 16 > (recall (316 I the big m -rage, it peaks at roughly (see (314, which exceeds 2 5 Therefore, the maximum occurs whe G 12,3 comes ito play, which peaks at roughly 7 15 For that alue the upper boud ealuates to (2 13/21 3 2/7 5 3/ /14 =O( Summig up Theorem 41 For P a set of poits i the plae (1 pm(p ( 2 20/21 3 2/7 5 1/ /14 poly( = O( ( (2 pm(p 2 1/3 5 1/6 ma(p poly( = O(09635 ma(p (3 ma(p ( 2 13/21 3 2/7 5 3/ /14 poly( = O( Radom Poit Sets Let P be ay set of N N poits i the plae, o three o a lie, ad let r N with r N If R is a subset of P chose uiformly at radom from ( P r m µ 1 N = 4 11, the, for λ = 16, µ = 7 3, ad proided N, ad r 2m, we hae, usig (315, E[ma m (R] = ( 1 N ma m (R = r R ( P r ( N 2m r 2m ( N r ma m (P

8 ( N 2m ( N (4λ(µ 1 m 2m r 2m ( N poly(m r m (4λ(µ 1 m( ( r 2m = m ( r 2m We see that if we sample r poits from a large eough set, the the expected umber of crossig-free matchigs obseres for all m the upper boud deried for the rage of small m Suppose ow that, for ee, we sample iid poits from a arbitrary distributio, for which we oly require that two sampled poits coicide with probability 0 The we ca first sample a set P of N > 11 8 poits, ad the choose a subset of size uiformly at radom from the family of all subsets of this size We obtai a set R of iid poits from the gie distributio If P is i geeral positio, by the argumet aboe the expected umber of perfect crossig-free matchigs is at most ( /2 If P exhibits colliearities, we perform a small perturbatio yieldig a set P ad the subset R Now the boud applies to R, ad also to R sice a sufficietly small perturbatio caot decrease the umber of crossigfree perfect matchigs Theorem 42 For ay distributio i the plae for which two sampled poits coicide with probability 0, the expected umber of crossig-free perfect matchigs of iid poits is at most ( /2 poly( = O( Left-Right Perfect Matchigs Here we assume that P is partitioed ito two disjoit subsets L, R ad cosider bipartite matchigs i L R such that, for each edge of the matchig, its left edpoit belogs to L ad its right edpoit to R We modify the defiitio of the degrees of the poits: If p L is a matched to a poit i R, the d(p is equal to the umber of left edpoits plus the umber of right-labeled isolated poits that are ertically isible from (the relatie iterior of e A symmetric defiitio holds for right edpoits (Ituitiely, a rightlabeled isolated poit q has to cotribute oly to the degrees of left-labeled poits, because, whe we isert a right edpoit, it caot coect to q, ad it does ot matter whether its icidet edge passes aboe or below q; that is, q does ot cause ay bifurcatio i the ways i which p ca be coected Sice isolated poits cotribute ow oly 2 to degrees of edpoits, we hae p P d(p 4m + 2s The aalysis further improes, because whe we reisert a poit p L, say, the correspodig umbers h i must be equal to l i, sice p ca oly be the left edpoit of a matchig edge A similar improemet holds for poits q R Hece, we ca boud the sum 4h 0 + 3h 1 + 2h 2 + h 3 by 12, rather tha 24; similarly, we hae 5h 0 + 4h 1 + 3h 2 + 2h 3 + h 4 24 That is, we hae for (λ, µ the pairs (6, 2 ad (8, 5 3 aailable ( k 1 i=0 6(2i+2 4i We ifer a boud of G 8,5/3 ( 2 k,, for k = 6, implyig Theorem 43 Let P be a set of poits i the plae ad assume that the poits are classified as left edpoits or right edpoits The umber of leftright ( perfect crossig-free matchigs i P is at most 2 7/10 3 3/20 7 7/10 poly( = O( Matchigs Across a Lie Cosider ext the special case of crossig-free bipartite perfect matchigs betwee two sets of 2 poits each that are separated by a lie Here we ca obtai a upper boud that is smaller tha the oe i Theorem 43 Theorem 44 Let be a ee iteger The umber of crossig-free perfect bipartite matchigs betwee two separated sets of 2 poits each i the plae is at most C 2 /2 < 4 ; (C m is the mth Catala umber Proof Let L ad R be the gie separated sets Without loss of geerality, take the separatig lie λ to be the y-axis, ad assume that the poits of L lie to the left of λ ad the poits of R lie to its right Let M be a crossig-free perfect bipartite matchig i L R For each edge e of M, let e L (resp, e R deote the portio of e to the left (resp, right of λ, ad refer to them as the left half-edge ad the right half-edge of e, respectiely We will obtai a upper boud for the umber of combiatorially differet ways to draw the left half-edges of a crossig-free perfect matchig i L R The same boud will apply symmetrically to the right half-edges, ad the fial boud will be the square of this boud I more detail, we igore R, ad cosider collectios S of 2 pairwise disjoit segmets, each coectig a poit of L to some poit o λ, so that each poit of L is icidet to exactly oe segmet For each segmet i S, we label its λ-edpoit by the poit of L to which it is coected The icreasig y-order of the λ-edpoits of the segmets thus defies a permutatio of L, ad our goal is to boud the umber of differet permutatios that ca be geerated i this way (I geeral, this is a strict upper boud o the quatity we seek We obtai this boud i the followig recursie maer Write m := L = 2 Sort the poits of L from left to right (we may assume that there are o ties they ca be elimiated by a slight rotatio of λ, ad let p 1, p 2,, p m deote the poits i this order Cosider the half-edge e 1 emaatig from the leftmost poit p 1 Ay other poit p j lies either aboe or below

9 e 1 By rotatig e 1 about p 1, we see that there are at most m (exactly m, if we assume geeral positio ways to split {p 2,, p m } ito a subset L + 1 of poits that lie aboe e 1 ad a complemetary subset L 1 of poits that lie below e 1, where i the i-th split, L + 1 = i 1 ad L 1 = m i Note that, i ay crossig-free perfect bipartite matchig that has e 1 as a left halfedge icidet to p 1, all the poits of L + 1 (resp, of L 1 must be icidet to half-edges that termiate o λ aboe (resp, below the λ-edpoit of e 1 Hece, after haig fixed i, we ca proceed to boud recursiely ad separately the umber of permutatios iduced by L + 1, ad the umber of those iduced by L 1 I other words, deotig by Π(m the maximum possible umber of differet permutatios iduced i this way by a set L of m poits (i geeral positio, we get the recurrece Π(m m i=1 Π(i 1Π(m i, for m 1, where Π(0 = 1 Howeer, this is the recurrece that (with equality defies the Catala umbers, so we coclude that Π(m C m A (probably weak upper boud for the umber of crossig-free perfect bipartite matchigs i L R is thus C 2 m Ideed, for ay permutatio π L of L ad ay permutatio π R of R, there is at most oe crossig-free perfect bipartite matchig i L R that iduces both permutatios Namely, it is the matchig that coects the j-th poit i π L to the j-th poit i π R, for each j = 1,, m The asserted boud of C 2 m =C 2 /2 <4 follows 5 Two Implicatios Spaig Cycles Theorem 51 Let P be a set of poits i the plae The the umber of crossig-free spaig cycles satisfies sc(p (2 7/5 3 7/10 7 7/5 poly( = O( Proof We costruct (from P a ew set P of 2 poits by creatig two copies p +, p of each p P, ad by placig these copies co-ertically close to the origial locatio of p, with p + aboe p Let π be a cycle i P We map π to a perfect matchig i P : For each p P, let q, r be its eighbors i π (i If both q, r lie to the left of p, with the edge qp lyig aboe rp, we coect p + to either q + or q, ad coect p to either r + or r (the actual choices will be determied at q ad r by similar rules (ii The same rule applies whe both q, r lie to the right of p (iii If q lies to the left of p ad r lie to the right of p, the we coect p + to either q + or q, ad coect p to either r + or r Clearly, the resultig graph π is a crossig-free perfect matchig i P, assumig geeral positio of the poits of P, if we draw each pair p +, p sufficietly close to each other We assig to each poit p P a label that depeds o π A poit whose two eighbors i π lie to its left is labeled as a right poit, a poit whose two eighbors i π lie to its right is labeled as a left poit, ad a poit haig oe eighbor i π to its right ad oe to its left is labeled as a middle poit We assig the cycle π to the pair (π, λ, where π is the resultig perfect matchig o P ad λ is the labelig of P, as just defied Each pair (π, λ ca be realized by at most oe cycle π i P, by mergig each pair p +, p back ito the origial poit p (i geeral, the resultig graph is a collectio of pairwise disjoit cycles It therefore suffices to boud the umber of such pairs (π, λ A gie labelig λ of P uiquely classifies each poit of P as beig either a left poit of a edge of the matchig or a right edpoit of such a edge Hece, the umber of crossig-free perfect matchigs π o P that respect this left-right assigmet is at most (2 7/10 3 3/20 7 7/10 2 poly( The umber of labeligs of P is 3 Hece, the umber of crossig-free cycles i P is at most (2 7/5 3 7/10 7 7/5 poly(, as asserted Clearly, it follows from the proof that the boud holds for the umber of crossig-free spaig paths as well, ad also for the umber of cycle coers (or path coers of P Crossig-free Partitios For a boud o cfp(p, we relate crossig-free partitios of a poit set P to matchigs To this ed, eery crossig-free partitio is mapped to a tuple (M, S, I +, I where (see Fig 7 (i M is the matchig i P, whose edges coect the leftmost to the rightmost poit of each set with at least two elemets (such a segmet is called the spie of its set, (ii S is the set of all poits that form sigleto sets i the partitio, ad (iii I + (resp, I is the set of poits i P \ S that are either leftmost or the rightmost i their set, ad which lie aboe (resp, below the spie of their set M is crossig-free, ad the partitio is uiquely determied by (M, S, I +, I Therefore, ay boud o the umber of such tuples will establish a boud o the umber of crossig-free partitios For eery crossig-free matchig M o P there are 3 2 M triples (S, I +, I which form a 4- tuple with M (ot all of them hae to come from a crossig-free partitio Therefore m 3 2m ma m (P is a boud o the umber of crossig-free partitios Igorig the 3 -factor for the time beig, we hae to determie a upper boud o 3 2m ma m (P, for which we employ the boud from (42 We obsere that 3 2m G λ,µ (m, = G λ/9,µ (m,, ad therefore G 16/9,7/3 (m,, m 2 3 2m 5, ma m (P G 4/3,3 (m, G 16,7/3( 2 5,, otherwise G 12,3( 2 5,

10 Sice (see (316 the peak will ot occur i the small m -rage of G 16/9,7/3 I its big m -rage, the maximum occurs at m roughly (see (314 which lies i the iteral [ 4 11, 2 5 ] Also, G 4/3,3 peaks for m 2 5 sice (cosult (316 Therefore, Spies, isolated (, top (, ad bottom ( poits the boud peaks at m roughly 14 Figure 7: Ecodig a crossigfree partitio 37 with the alue 3 G 16/9,7/3 ( 14 37, (2 4/7 3 1/ / /14 Theorem 52 Let P be a set of poits i the plae The the umber of crossig-free partitios satisfies cfp(p ( 2 4/7 3 1/ / /14 poly( = O( Ackowledgmet We thak Adreas Raze for readig a draft ad for seeral helpful commets Refereces [1] O Aichholzer, Th Hackl, C Huemer, F Hurtado, H Krasser, B Vogtehuber, O the umber of plae graphs, Proc 17 th Aual ACM-SIAM Symp o Discrete Algorithms (2006, to appear [2] O Aichholzer, F Hurtado, M Noy, O the umber of triagulatios eery plaar poit set must hae, Proc 13 th Caad Cof Comput Geom (2001, [3] O Aichholzer, H Krasser, The poit-set order-type database: A collectio of applicatios ad results, Proc 13 th Caad Cof Comput Geom (2001, [4] M Ajtai, V Chátal, M M Newbor, E Szemerédi, Crossig-free subgraphs, Aals Discrete Math 12 (1982, 9 12 [5] S G Akl, A lower boud o the maximum umber of crossig-free Hamiltoia cycles i a rectiliear drawig of K, Ars Combiatorica 7 (1979, 7 18 [6] H Alt, U Fuchs, ad K Kriegel, O the umber of simple cycles i plaar graphs, Combiat Probab Comput 8:5 (1999, [7] H W Becker, Plaar rhyme schemes, Math Mag 22 ( , [8] P Brass, W Moser, J Pach, Research Problems i Discrete Geometry, Spriger, New York, 2005 [9] V Capoyleas, G Rote, G Woegiger, Geometric clusterig, Joural of Algorithms 12 (1991, [10] V G Deieko, M Hoffma, Y Okamoto, G J Woegiger, The Traelig Salesma Problem with few ier poits, Proc 10 th Iteratioal Computig ad Combiatorics Coferece, Lecture Notes i Computer Sciece 3106 (2004, [11] E Demaie, Simple polygoizatios, polygoizatio/ (ersio Jauary 9, 2005 [12] L Deee, G Shute, Polygoizatios of poit sets i the plae, Discrete Comput Geom 3 (1988, [13] M O Dey, C A Sohler, Ecodig a triagulatio as a permutatio of its poit set, Proc 9 th Caad Cof Comput Geom (1997, [14] A Dumitrescu, O two lower boud costructios, Proc 11 th Caad Cof Comput Geom (1999 [15] H Edelsbruer, Algorithms i Combiatorial Geometry, EATCS Moographs o Theoretical Computer Sciece 10, Spriger-Verlag, 1987 [16] A Errera, Mém Acad Roy Belgique Coll 8 o (2 11 (1931, 26 pp [17] A García, M Noy, J Tejel, Lower bouds o the umber of crossig-free subgraphs of K N, Comput Geom Theory Appl 16 (2000, [18] A García, J Tejel, A lower boud for the umber of polygoizatios of N poits i the plae, Ars Combiatorica 49 (1998, 3 19 [19] M Gratso, C Borgelt, C Lecopoulos, Miimum weight triagulatio by cuttig out triagles, Proc 16 th A It Symp o Algorithms ad Computatio (2005, to appear [20] R B Hayward, A lower boud for the optimal crossigfree Hamiltoia cycle problem, Discrete Comput Geom 2:4 (1987, [21] F Hurtado, M Noy, Coutig triagulatios of almostcoex polygos, Ars Comb 45 (1997, [22] F J MacWilliams, N J A Sloae, The Theory of Error-Correctig Codes, North-Hollad Mathematical Library 16, 1977 [23] P McCabe, R Seidel, New lower bouds for the umber of straight-edge triagulatios of a plaar poit set, Proc 20 th Europ Workshop Comput Geom (2004 [24] T S Motzki, Relatios betwee hypersurface cross ratios, ad a combiatorial formula for partitios of a polygo, for permaet prepoderace, ad for oassociatie products, Bull Amer Math Soc 54 (1948, [25] M Newbor, W O J Moser, Optimal crossig-free Hamiltoia circuit drawigs of the K, J Combiat Theory, Ser B 29 (1980, [26] J Pach ad G Tóth, Graphs draw with few crossigs per edge, Combiatorica 17:3 (1997, [27] F Satos, R Seidel, A better upper boud o the umber of triagulatios of a plaar poit set, J Combiat Theory, Ser A 102:1 (2003, [28] R Seidel, O the umber of triagulatios of plaar poit sets, Combiatorica 18:2 (1998, [29] WS Smith, Studies i Computatioal Geometry Motiated by Mesh Geeratio, PhDThesis, Priceto Uiersity, 1989 [30] R P Staley, Eumeratie Combiatorics, ol 2, Cambridge Uiersity Press, 1999

Department of Computer Science, University of Otago

Department of Computer Science, University of Otago Departmet of Computer Sciece, Uiversity of Otago Techical Report OUCS-2006-09 Permutatios Cotaiig May Patters Authors: M.H. Albert Departmet of Computer Sciece, Uiversity of Otago Micah Colema, Rya Fly

More information

5 Boolean Decision Trees (February 11)

5 Boolean Decision Trees (February 11) 5 Boolea Decisio Trees (February 11) 5.1 Graph Coectivity Suppose we are give a udirected graph G, represeted as a boolea adjacecy matrix = (a ij ), where a ij = 1 if ad oly if vertices i ad j are coected

More information

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008

In nite Sequences. Dr. Philippe B. Laval Kennesaw State University. October 9, 2008 I ite Sequeces Dr. Philippe B. Laval Keesaw State Uiversity October 9, 2008 Abstract This had out is a itroductio to i ite sequeces. mai de itios ad presets some elemetary results. It gives the I ite Sequeces

More information

Asymptotic Growth of Functions

Asymptotic Growth of Functions CMPS Itroductio to Aalysis of Algorithms Fall 3 Asymptotic Growth of Fuctios We itroduce several types of asymptotic otatio which are used to compare the performace ad efficiecy of algorithms As we ll

More information

I. Chi-squared Distributions

I. Chi-squared Distributions 1 M 358K Supplemet to Chapter 23: CHI-SQUARED DISTRIBUTIONS, T-DISTRIBUTIONS, AND DEGREES OF FREEDOM To uderstad t-distributios, we first eed to look at aother family of distributios, the chi-squared distributios.

More information

A probabilistic proof of a binomial identity

A probabilistic proof of a binomial identity A probabilistic proof of a biomial idetity Joatho Peterso Abstract We give a elemetary probabilistic proof of a biomial idetity. The proof is obtaied by computig the probability of a certai evet i two

More information

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13

Discrete Mathematics and Probability Theory Spring 2014 Anant Sahai Note 13 EECS 70 Discrete Mathematics ad Probability Theory Sprig 2014 Aat Sahai Note 13 Itroductio At this poit, we have see eough examples that it is worth just takig stock of our model of probability ad may

More information

4.1 Sigma Notation and Riemann Sums

4.1 Sigma Notation and Riemann Sums 0 the itegral. Sigma Notatio ad Riema Sums Oe strategy for calculatig the area of a regio is to cut the regio ito simple shapes, calculate the area of each simple shape, ad the add these smaller areas

More information

Convexity, Inequalities, and Norms

Convexity, Inequalities, and Norms Covexity, Iequalities, ad Norms Covex Fuctios You are probably familiar with the otio of cocavity of fuctios. Give a twicedifferetiable fuctio ϕ: R R, We say that ϕ is covex (or cocave up) if ϕ (x) 0 for

More information

Soving Recurrence Relations

Soving Recurrence Relations Sovig Recurrece Relatios Part 1. Homogeeous liear 2d degree relatios with costat coefficiets. Cosider the recurrece relatio ( ) T () + at ( 1) + bt ( 2) = 0 This is called a homogeeous liear 2d degree

More information

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method

Chapter 6: Variance, the law of large numbers and the Monte-Carlo method Chapter 6: Variace, the law of large umbers ad the Mote-Carlo method Expected value, variace, ad Chebyshev iequality. If X is a radom variable recall that the expected value of X, E[X] is the average value

More information

Chapter 7 Methods of Finding Estimators

Chapter 7 Methods of Finding Estimators Chapter 7 for BST 695: Special Topics i Statistical Theory. Kui Zhag, 011 Chapter 7 Methods of Fidig Estimators Sectio 7.1 Itroductio Defiitio 7.1.1 A poit estimator is ay fuctio W( X) W( X1, X,, X ) of

More information

Recursion and Recurrences

Recursion and Recurrences Chapter 5 Recursio ad Recurreces 5.1 Growth Rates of Solutios to Recurreces Divide ad Coquer Algorithms Oe of the most basic ad powerful algorithmic techiques is divide ad coquer. Cosider, for example,

More information

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx

SAMPLE QUESTIONS FOR FINAL EXAM. (1) (2) (3) (4) Find the following using the definition of the Riemann integral: (2x + 1)dx SAMPLE QUESTIONS FOR FINAL EXAM REAL ANALYSIS I FALL 006 3 4 Fid the followig usig the defiitio of the Riema itegral: a 0 x + dx 3 Cosider the partitio P x 0 3, x 3 +, x 3 +,......, x 3 3 + 3 of the iterval

More information

Properties of MLE: consistency, asymptotic normality. Fisher information.

Properties of MLE: consistency, asymptotic normality. Fisher information. Lecture 3 Properties of MLE: cosistecy, asymptotic ormality. Fisher iformatio. I this sectio we will try to uderstad why MLEs are good. Let us recall two facts from probability that we be used ofte throughout

More information

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009)

Lecture 13. Lecturer: Jonathan Kelner Scribe: Jonathan Pines (2009) 18.409 A Algorithmist s Toolkit October 27, 2009 Lecture 13 Lecturer: Joatha Keler Scribe: Joatha Pies (2009) 1 Outlie Last time, we proved the Bru-Mikowski iequality for boxes. Today we ll go over the

More information

A Faster Clause-Shortening Algorithm for SAT with No Restriction on Clause Length

A Faster Clause-Shortening Algorithm for SAT with No Restriction on Clause Length Joural o Satisfiability, Boolea Modelig ad Computatio 1 2005) 49-60 A Faster Clause-Shorteig Algorithm for SAT with No Restrictio o Clause Legth Evgey Datsi Alexader Wolpert Departmet of Computer Sciece

More information

THE ABRACADABRA PROBLEM

THE ABRACADABRA PROBLEM THE ABRACADABRA PROBLEM FRANCESCO CARAVENNA Abstract. We preset a detailed solutio of Exercise E0.6 i [Wil9]: i a radom sequece of letters, draw idepedetly ad uiformly from the Eglish alphabet, the expected

More information

Sequences II. Chapter 3. 3.1 Convergent Sequences

Sequences II. Chapter 3. 3.1 Convergent Sequences Chapter 3 Sequeces II 3. Coverget Sequeces Plot a graph of the sequece a ) = 2, 3 2, 4 3, 5 + 4,...,,... To what limit do you thik this sequece teds? What ca you say about the sequece a )? For ǫ = 0.,

More information

Sequences and Series

Sequences and Series CHAPTER 9 Sequeces ad Series 9.. Covergece: Defiitio ad Examples Sequeces The purpose of this chapter is to itroduce a particular way of geeratig algorithms for fidig the values of fuctios defied by their

More information

1.3. VERTEX DEGREES & COUNTING

1.3. VERTEX DEGREES & COUNTING 35 Chapter 1: Fudametal Cocepts Sectio 1.3: Vertex Degrees ad Coutig 36 its eighbor o P. Note that P has at least three vertices. If G x v is coected, let y = v. Otherwise, a compoet cut off from P x v

More information

1. MATHEMATICAL INDUCTION

1. MATHEMATICAL INDUCTION 1. MATHEMATICAL INDUCTION EXAMPLE 1: Prove that for ay iteger 1. Proof: 1 + 2 + 3 +... + ( + 1 2 (1.1 STEP 1: For 1 (1.1 is true, sice 1 1(1 + 1. 2 STEP 2: Suppose (1.1 is true for some k 1, that is 1

More information

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments

Project Deliverables. CS 361, Lecture 28. Outline. Project Deliverables. Administrative. Project Comments Project Deliverables CS 361, Lecture 28 Jared Saia Uiversity of New Mexico Each Group should tur i oe group project cosistig of: About 6-12 pages of text (ca be loger with appedix) 6-12 figures (please

More information

Irreducible polynomials with consecutive zero coefficients

Irreducible polynomials with consecutive zero coefficients Irreducible polyomials with cosecutive zero coefficiets Theodoulos Garefalakis Departmet of Mathematics, Uiversity of Crete, 71409 Heraklio, Greece Abstract Let q be a prime power. We cosider the problem

More information

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling

Taking DCOP to the Real World: Efficient Complete Solutions for Distributed Multi-Event Scheduling Taig DCOP to the Real World: Efficiet Complete Solutios for Distributed Multi-Evet Schedulig Rajiv T. Maheswara, Milid Tambe, Emma Bowrig, Joatha P. Pearce, ad Pradeep araatham Uiversity of Souther Califoria

More information

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph.

4. Trees. 4.1 Basics. Definition: A graph having no cycles is said to be acyclic. A forest is an acyclic graph. 4. Trees Oe of the importat classes of graphs is the trees. The importace of trees is evidet from their applicatios i various areas, especially theoretical computer sciece ad molecular evolutio. 4.1 Basics

More information

CS103X: Discrete Structures Homework 4 Solutions

CS103X: Discrete Structures Homework 4 Solutions CS103X: Discrete Structures Homewor 4 Solutios Due February 22, 2008 Exercise 1 10 poits. Silico Valley questios: a How may possible six-figure salaries i whole dollar amouts are there that cotai at least

More information

NUMBERS COMMON TO TWO POLYGONAL SEQUENCES

NUMBERS COMMON TO TWO POLYGONAL SEQUENCES NUMBERS COMMON TO TWO POLYGONAL SEQUENCES DIANNE SMITH LUCAS Chia Lake, Califoria a iteger, The polygoal sequece (or sequeces of polygoal umbers) of order r (where r is r > 3) may be defied recursively

More information

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth

.04. This means $1000 is multiplied by 1.02 five times, once for each of the remaining sixmonth Questio 1: What is a ordiary auity? Let s look at a ordiary auity that is certai ad simple. By this, we mea a auity over a fixed term whose paymet period matches the iterest coversio period. Additioally,

More information

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES

SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES SECTION 1.5 : SUMMATION NOTATION + WORK WITH SEQUENCES Read Sectio 1.5 (pages 5 9) Overview I Sectio 1.5 we lear to work with summatio otatio ad formulas. We will also itroduce a brief overview of sequeces,

More information

Incremental calculation of weighted mean and variance

Incremental calculation of weighted mean and variance Icremetal calculatio of weighted mea ad variace Toy Fich faf@cam.ac.uk dot@dotat.at Uiversity of Cambridge Computig Service February 009 Abstract I these otes I eplai how to derive formulae for umerically

More information

Lecture 2: Karger s Min Cut Algorithm

Lecture 2: Karger s Min Cut Algorithm priceto uiv. F 3 cos 5: Advaced Algorithm Desig Lecture : Karger s Mi Cut Algorithm Lecturer: Sajeev Arora Scribe:Sajeev Today s topic is simple but gorgeous: Karger s mi cut algorithm ad its extesio.

More information

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable

Week 3 Conditional probabilities, Bayes formula, WEEK 3 page 1 Expected value of a random variable Week 3 Coditioal probabilities, Bayes formula, WEEK 3 page 1 Expected value of a radom variable We recall our discussio of 5 card poker hads. Example 13 : a) What is the probability of evet A that a 5

More information

THE HEIGHT OF q-binary SEARCH TREES

THE HEIGHT OF q-binary SEARCH TREES THE HEIGHT OF q-binary SEARCH TREES MICHAEL DRMOTA AND HELMUT PRODINGER Abstract. q biary search trees are obtaied from words, equipped with the geometric distributio istead of permutatios. The average

More information

Modified Line Search Method for Global Optimization

Modified Line Search Method for Global Optimization Modified Lie Search Method for Global Optimizatio Cria Grosa ad Ajith Abraham Ceter of Excellece for Quatifiable Quality of Service Norwegia Uiversity of Sciece ad Techology Trodheim, Norway {cria, ajith}@q2s.tu.o

More information

Perfect Packing Theorems and the Average-Case Behavior of Optimal and Online Bin Packing

Perfect Packing Theorems and the Average-Case Behavior of Optimal and Online Bin Packing SIAM REVIEW Vol. 44, No. 1, pp. 95 108 c 2002 Society for Idustrial ad Applied Mathematics Perfect Packig Theorems ad the Average-Case Behavior of Optimal ad Olie Bi Packig E. G. Coffma, Jr. C. Courcoubetis

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Chapter 9 SEQUENCES AND SERIES Natural umbers are the product of huma spirit. DEDEKIND 9.1 Itroductio I mathematics, the word, sequece is used i much the same way as it is i ordiary Eglish. Whe we say

More information

Factors of sums of powers of binomial coefficients

Factors of sums of powers of binomial coefficients ACTA ARITHMETICA LXXXVI.1 (1998) Factors of sums of powers of biomial coefficiets by Neil J. Cali (Clemso, S.C.) Dedicated to the memory of Paul Erdős 1. Itroductio. It is well ow that if ( ) a f,a = the

More information

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER?

WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? WHEN IS THE (CO)SINE OF A RATIONAL ANGLE EQUAL TO A RATIONAL NUMBER? JÖRG JAHNEL 1. My Motivatio Some Sort of a Itroductio Last term I tought Topological Groups at the Göttige Georg August Uiversity. This

More information

Gregory Carey, 1998 Linear Transformations & Composites - 1. Linear Transformations and Linear Composites

Gregory Carey, 1998 Linear Transformations & Composites - 1. Linear Transformations and Linear Composites Gregory Carey, 1998 Liear Trasformatios & Composites - 1 Liear Trasformatios ad Liear Composites I Liear Trasformatios of Variables Meas ad Stadard Deviatios of Liear Trasformatios A liear trasformatio

More information

Linear Algebra II. 4 Determinants. Notes 4 1st November Definition of determinant

Linear Algebra II. 4 Determinants. Notes 4 1st November Definition of determinant MTH6140 Liear Algebra II Notes 4 1st November 2010 4 Determiats The determiat is a fuctio defied o square matrices; its value is a scalar. It has some very importat properties: perhaps most importat is

More information

Chapter 5: Inner Product Spaces

Chapter 5: Inner Product Spaces Chapter 5: Ier Product Spaces Chapter 5: Ier Product Spaces SECION A Itroductio to Ier Product Spaces By the ed of this sectio you will be able to uderstad what is meat by a ier product space give examples

More information

Ramsey-type theorems with forbidden subgraphs

Ramsey-type theorems with forbidden subgraphs Ramsey-type theorems with forbidde subgraphs Noga Alo Jáos Pach József Solymosi Abstract A graph is called H-free if it cotais o iduced copy of H. We discuss the followig questio raised by Erdős ad Hajal.

More information

Basic Elements of Arithmetic Sequences and Series

Basic Elements of Arithmetic Sequences and Series MA40S PRE-CALCULUS UNIT G GEOMETRIC SEQUENCES CLASS NOTES (COMPLETED NO NEED TO COPY NOTES FROM OVERHEAD) Basic Elemets of Arithmetic Sequeces ad Series Objective: To establish basic elemets of arithmetic

More information

7. Sample Covariance and Correlation

7. Sample Covariance and Correlation 1 of 8 7/16/2009 6:06 AM Virtual Laboratories > 6. Radom Samples > 1 2 3 4 5 6 7 7. Sample Covariace ad Correlatio The Bivariate Model Suppose agai that we have a basic radom experimet, ad that X ad Y

More information

3. Greatest Common Divisor - Least Common Multiple

3. Greatest Common Divisor - Least Common Multiple 3 Greatest Commo Divisor - Least Commo Multiple Defiitio 31: The greatest commo divisor of two atural umbers a ad b is the largest atural umber c which divides both a ad b We deote the greatest commo gcd

More information

Theorems About Power Series

Theorems About Power Series Physics 6A Witer 20 Theorems About Power Series Cosider a power series, f(x) = a x, () where the a are real coefficiets ad x is a real variable. There exists a real o-egative umber R, called the radius

More information

Lecture 4: Cheeger s Inequality

Lecture 4: Cheeger s Inequality Spectral Graph Theory ad Applicatios WS 0/0 Lecture 4: Cheeger s Iequality Lecturer: Thomas Sauerwald & He Su Statemet of Cheeger s Iequality I this lecture we assume for simplicity that G is a d-regular

More information

Divide and Conquer. Maximum/minimum. Integer Multiplication. CS125 Lecture 4 Fall 2015

Divide and Conquer. Maximum/minimum. Integer Multiplication. CS125 Lecture 4 Fall 2015 CS125 Lecture 4 Fall 2015 Divide ad Coquer We have see oe geeral paradigm for fidig algorithms: the greedy approach. We ow cosider aother geeral paradigm, kow as divide ad coquer. We have already see a

More information

Chapter 5 O A Cojecture Of Erdíos Proceedigs NCUR VIII è1994è, Vol II, pp 794í798 Jeærey F Gold Departmet of Mathematics, Departmet of Physics Uiversity of Utah Do H Tucker Departmet of Mathematics Uiversity

More information

Permutations, the Parity Theorem, and Determinants

Permutations, the Parity Theorem, and Determinants 1 Permutatios, the Parity Theorem, ad Determiats Joh A. Guber Departmet of Electrical ad Computer Egieerig Uiversity of Wiscosi Madiso Cotets 1 What is a Permutatio 1 2 Cycles 2 2.1 Traspositios 4 3 Orbits

More information

Class Meeting # 16: The Fourier Transform on R n

Class Meeting # 16: The Fourier Transform on R n MATH 18.152 COUSE NOTES - CLASS MEETING # 16 18.152 Itroductio to PDEs, Fall 2011 Professor: Jared Speck Class Meetig # 16: The Fourier Trasform o 1. Itroductio to the Fourier Trasform Earlier i the course,

More information

Infinite Sequences and Series

Infinite Sequences and Series CHAPTER 4 Ifiite Sequeces ad Series 4.1. Sequeces A sequece is a ifiite ordered list of umbers, for example the sequece of odd positive itegers: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29...

More information

Notes on exponential generating functions and structures.

Notes on exponential generating functions and structures. Notes o expoetial geeratig fuctios ad structures. 1. The cocept of a structure. Cosider the followig coutig problems: (1) to fid for each the umber of partitios of a -elemet set, (2) to fid for each the

More information

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find

Approximating Area under a curve with rectangles. To find the area under a curve we approximate the area using rectangles and then use limits to find 1.8 Approximatig Area uder a curve with rectagles 1.6 To fid the area uder a curve we approximate the area usig rectagles ad the use limits to fid 1.4 the area. Example 1 Suppose we wat to estimate 1.

More information

MARTINGALES AND A BASIC APPLICATION

MARTINGALES AND A BASIC APPLICATION MARTINGALES AND A BASIC APPLICATION TURNER SMITH Abstract. This paper will develop the measure-theoretic approach to probability i order to preset the defiitio of martigales. From there we will apply this

More information

The Stable Marriage Problem

The Stable Marriage Problem The Stable Marriage Problem William Hut Lae Departmet of Computer Sciece ad Electrical Egieerig, West Virgiia Uiversity, Morgatow, WV William.Hut@mail.wvu.edu 1 Itroductio Imagie you are a matchmaker,

More information

Overview of some probability distributions.

Overview of some probability distributions. Lecture Overview of some probability distributios. I this lecture we will review several commo distributios that will be used ofte throughtout the class. Each distributio is usually described by its probability

More information

Lesson 15 ANOVA (analysis of variance)

Lesson 15 ANOVA (analysis of variance) Outlie Variability -betwee group variability -withi group variability -total variability -F-ratio Computatio -sums of squares (betwee/withi/total -degrees of freedom (betwee/withi/total -mea square (betwee/withi

More information

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return

where: T = number of years of cash flow in investment's life n = the year in which the cash flow X n i = IRR = the internal rate of return EVALUATING ALTERNATIVE CAPITAL INVESTMENT PROGRAMS By Ke D. Duft, Extesio Ecoomist I the March 98 issue of this publicatio we reviewed the procedure by which a capital ivestmet project was assessed. The

More information

The second difference is the sequence of differences of the first difference sequence, 2

The second difference is the sequence of differences of the first difference sequence, 2 Differece Equatios I differetial equatios, you look for a fuctio that satisfies ad equatio ivolvig derivatives. I differece equatios, istead of a fuctio of a cotiuous variable (such as time), we look for

More information

A Constant-Factor Approximation Algorithm for the Link Building Problem

A Constant-Factor Approximation Algorithm for the Link Building Problem A Costat-Factor Approximatio Algorithm for the Lik Buildig Problem Marti Olse 1, Aastasios Viglas 2, ad Ilia Zvedeiouk 2 1 Ceter for Iovatio ad Busiess Developmet, Istitute of Busiess ad Techology, Aarhus

More information

Solutions to Exercises Chapter 4: Recurrence relations and generating functions

Solutions to Exercises Chapter 4: Recurrence relations and generating functions Solutios to Exercises Chapter 4: Recurrece relatios ad geeratig fuctios 1 (a) There are seatig positios arraged i a lie. Prove that the umber of ways of choosig a subset of these positios, with o two chose

More information

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem

Lecture 4: Cauchy sequences, Bolzano-Weierstrass, and the Squeeze theorem Lecture 4: Cauchy sequeces, Bolzao-Weierstrass, ad the Squeeze theorem The purpose of this lecture is more modest tha the previous oes. It is to state certai coditios uder which we are guarateed that limits

More information

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection

The analysis of the Cournot oligopoly model considering the subjective motive in the strategy selection The aalysis of the Courot oligopoly model cosiderig the subjective motive i the strategy selectio Shigehito Furuyama Teruhisa Nakai Departmet of Systems Maagemet Egieerig Faculty of Egieerig Kasai Uiversity

More information

1.3 Binomial Coefficients

1.3 Binomial Coefficients 18 CHAPTER 1. COUNTING 1. Biomial Coefficiets I this sectio, we will explore various properties of biomial coefficiets. Pascal s Triagle Table 1 cotais the values of the biomial coefficiets ( ) for 0to

More information

Section 1.6: Proof by Mathematical Induction

Section 1.6: Proof by Mathematical Induction Sectio.6 Proof by Iductio Sectio.6: Proof by Mathematical Iductio Purpose of Sectio: To itroduce the Priciple of Mathematical Iductio, both weak ad the strog versios, ad show how certai types of theorems

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies ( 3.1.1) Limitations of Experiments. Pseudocode ( 3.1.2) Theoretical Analysis Ruig Time ( 3.) Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Most algorithms trasform iput objects ito output objects.

More information

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets

BENEFIT-COST ANALYSIS Financial and Economic Appraisal using Spreadsheets BENEIT-CST ANALYSIS iacial ad Ecoomic Appraisal usig Spreadsheets Ch. 2: Ivestmet Appraisal - Priciples Harry Campbell & Richard Brow School of Ecoomics The Uiversity of Queeslad Review of basic cocepts

More information

Algebra Vocabulary List (Definitions for Middle School Teachers)

Algebra Vocabulary List (Definitions for Middle School Teachers) Algebra Vocabulary List (Defiitios for Middle School Teachers) A Absolute Value Fuctio The absolute value of a real umber x, x is xifx 0 x = xifx < 0 http://www.math.tamu.edu/~stecher/171/f02/absolutevaluefuctio.pdf

More information

2. Degree Sequences. 2.1 Degree Sequences

2. Degree Sequences. 2.1 Degree Sequences 2. Degree Sequeces The cocept of degrees i graphs has provided a framewor for the study of various structural properties of graphs ad has therefore attracted the attetio of may graph theorists. Here we

More information

Figure 40.1. Figure 40.2

Figure 40.1. Figure 40.2 40 Regular Polygos Covex ad Cocave Shapes A plae figure is said to be covex if every lie segmet draw betwee ay two poits iside the figure lies etirely iside the figure. A figure that is ot covex is called

More information

A note on the boundary behavior for a modified Green function in the upper-half space

A note on the boundary behavior for a modified Green function in the upper-half space Zhag ad Pisarev Boudary Value Problems (015) 015:114 DOI 10.1186/s13661-015-0363-z RESEARCH Ope Access A ote o the boudary behavior for a modified Gree fuctio i the upper-half space Yulia Zhag1 ad Valery

More information

A Recursive Formula for Moments of a Binomial Distribution

A Recursive Formula for Moments of a Binomial Distribution A Recursive Formula for Momets of a Biomial Distributio Árpád Béyi beyi@mathumassedu, Uiversity of Massachusetts, Amherst, MA 01003 ad Saverio M Maago smmaago@psavymil Naval Postgraduate School, Moterey,

More information

An example of non-quenched convergence in the conditional central limit theorem for partial sums of a linear process

An example of non-quenched convergence in the conditional central limit theorem for partial sums of a linear process A example of o-queched covergece i the coditioal cetral limit theorem for partial sums of a liear process Dalibor Volý ad Michael Woodroofe Abstract A causal liear processes X,X 0,X is costructed for which

More information

3 Basic Definitions of Probability Theory

3 Basic Definitions of Probability Theory 3 Basic Defiitios of Probability Theory 3defprob.tex: Feb 10, 2003 Classical probability Frequecy probability axiomatic probability Historical developemet: Classical Frequecy Axiomatic The Axiomatic defiitio

More information

THE UNLIKELY UNION OF PARTITIONS AND DIVISORS

THE UNLIKELY UNION OF PARTITIONS AND DIVISORS THE UNLIKELY UNION OF PARTITIONS AND DIVISORS Abdulkadir Hasse, Thomas J. Osler, Mathematics Departmet ad Tirupathi R. Chadrupatla, Mechaical Egieerig Rowa Uiversity Glassboro, NJ 828 I the multiplicative

More information

Engineering 323 Beautiful Homework Set 3 1 of 7 Kuszmar Problem 2.51

Engineering 323 Beautiful Homework Set 3 1 of 7 Kuszmar Problem 2.51 Egieerig 33 eautiful Homewor et 3 of 7 Kuszmar roblem.5.5 large departmet store sells sport shirts i three sizes small, medium, ad large, three patters plaid, prit, ad stripe, ad two sleeve legths log

More information

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM

PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM PROCEEDINGS OF THE YEREVAN STATE UNIVERSITY Physical ad Mathematical Scieces 2015, 1, p. 15 19 M a t h e m a t i c s AN ALTERNATIVE MODEL FOR BONUS-MALUS SYSTEM A. G. GULYAN Chair of Actuarial Mathematics

More information

Lecture 5: Span, linear independence, bases, and dimension

Lecture 5: Span, linear independence, bases, and dimension Lecture 5: Spa, liear idepedece, bases, ad dimesio Travis Schedler Thurs, Sep 23, 2010 (versio: 9/21 9:55 PM) 1 Motivatio Motivatio To uderstad what it meas that R has dimesio oe, R 2 dimesio 2, etc.;

More information

3. Covariance and Correlation

3. Covariance and Correlation Virtual Laboratories > 3. Expected Value > 1 2 3 4 5 6 3. Covariace ad Correlatio Recall that by takig the expected value of various trasformatios of a radom variable, we ca measure may iterestig characteristics

More information

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations

CS103A Handout 23 Winter 2002 February 22, 2002 Solving Recurrence Relations CS3A Hadout 3 Witer 00 February, 00 Solvig Recurrece Relatios Itroductio A wide variety of recurrece problems occur i models. Some of these recurrece relatios ca be solved usig iteratio or some other ad

More information

Trading the randomness - Designing an optimal trading strategy under a drifted random walk price model

Trading the randomness - Designing an optimal trading strategy under a drifted random walk price model Tradig the radomess - Desigig a optimal tradig strategy uder a drifted radom walk price model Yuao Wu Math 20 Project Paper Professor Zachary Hamaker Abstract: I this paper the author iteds to explore

More information

Lesson 17 Pearson s Correlation Coefficient

Lesson 17 Pearson s Correlation Coefficient Outlie Measures of Relatioships Pearso s Correlatio Coefficiet (r) -types of data -scatter plots -measure of directio -measure of stregth Computatio -covariatio of X ad Y -uique variatio i X ad Y -measurig

More information

Sum and Product Rules. Combinatorics. Some Subtler Examples

Sum and Product Rules. Combinatorics. Some Subtler Examples Combiatorics Sum ad Product Rules Problem: How to cout without coutig. How do you figure out how may thigs there are with a certai property without actually eumeratig all of them. Sometimes this requires

More information

Swaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps

Swaps: Constant maturity swaps (CMS) and constant maturity. Treasury (CMT) swaps Swaps: Costat maturity swaps (CMS) ad costat maturity reasury (CM) swaps A Costat Maturity Swap (CMS) swap is a swap where oe of the legs pays (respectively receives) a swap rate of a fixed maturity, while

More information

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design

A Combined Continuous/Binary Genetic Algorithm for Microstrip Antenna Design A Combied Cotiuous/Biary Geetic Algorithm for Microstrip Atea Desig Rady L. Haupt The Pesylvaia State Uiversity Applied Research Laboratory P. O. Box 30 State College, PA 16804-0030 haupt@ieee.org Abstract:

More information

4 n. n 1. You shold think of the Ratio Test as a generalization of the Geometric Series Test. For example, if a n ar n is a geometric sequence then

4 n. n 1. You shold think of the Ratio Test as a generalization of the Geometric Series Test. For example, if a n ar n is a geometric sequence then SECTION 2.6 THE RATIO TEST 79 2.6. THE RATIO TEST We ow kow how to hadle series which we ca itegrate (the Itegral Test), ad series which are similar to geometric or p-series (the Compariso Test), but of

More information

2-3 The Remainder and Factor Theorems

2-3 The Remainder and Factor Theorems - The Remaider ad Factor Theorems Factor each polyomial completely usig the give factor ad log divisio 1 x + x x 60; x + So, x + x x 60 = (x + )(x x 15) Factorig the quadratic expressio yields x + x x

More information

On the L p -conjecture for locally compact groups

On the L p -conjecture for locally compact groups Arch. Math. 89 (2007), 237 242 c 2007 Birkhäuser Verlag Basel/Switzerlad 0003/889X/030237-6, ublished olie 2007-08-0 DOI 0.007/s0003-007-993-x Archiv der Mathematik O the L -cojecture for locally comact

More information

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT

Vladimir N. Burkov, Dmitri A. Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT Keywords: project maagemet, resource allocatio, etwork plaig Vladimir N Burkov, Dmitri A Novikov MODELS AND METHODS OF MULTIPROJECTS MANAGEMENT The paper deals with the problems of resource allocatio betwee

More information

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients

BINOMIAL EXPANSIONS 12.5. In this section. Some Examples. Obtaining the Coefficients 652 (12-26) Chapter 12 Sequeces ad Series 12.5 BINOMIAL EXPANSIONS I this sectio Some Examples Otaiig the Coefficiets The Biomial Theorem I Chapter 5 you leared how to square a iomial. I this sectio you

More information

arxiv:1012.1336v2 [cs.cc] 8 Dec 2010

arxiv:1012.1336v2 [cs.cc] 8 Dec 2010 Uary Subset-Sum is i Logspace arxiv:1012.1336v2 [cs.cc] 8 Dec 2010 1 Itroductio Daiel M. Kae December 9, 2010 I this paper we cosider the Uary Subset-Sum problem which is defied as follows: Give itegers

More information

On the Capacity of Hybrid Wireless Networks

On the Capacity of Hybrid Wireless Networks O the Capacity of Hybrid ireless Networks Beyua Liu,ZheLiu +,DoTowsley Departmet of Computer Sciece Uiversity of Massachusetts Amherst, MA 0002 + IBM T.J. atso Research Ceter P.O. Box 704 Yorktow Heights,

More information

The Gompertz Makeham coupling as a Dynamic Life Table. Abraham Zaks. Technion I.I.T. Haifa ISRAEL. Abstract

The Gompertz Makeham coupling as a Dynamic Life Table. Abraham Zaks. Technion I.I.T. Haifa ISRAEL. Abstract The Gompertz Makeham couplig as a Dyamic Life Table By Abraham Zaks Techio I.I.T. Haifa ISRAEL Departmet of Mathematics, Techio - Israel Istitute of Techology, 32000, Haifa, Israel Abstract A very famous

More information

Measures of Spread and Boxplots Discrete Math, Section 9.4

Measures of Spread and Boxplots Discrete Math, Section 9.4 Measures of Spread ad Boxplots Discrete Math, Sectio 9.4 We start with a example: Example 1: Comparig Mea ad Media Compute the mea ad media of each data set: S 1 = {4, 6, 8, 10, 1, 14, 16} S = {4, 7, 9,

More information

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8

CME 302: NUMERICAL LINEAR ALGEBRA FALL 2005/06 LECTURE 8 CME 30: NUMERICAL LINEAR ALGEBRA FALL 005/06 LECTURE 8 GENE H GOLUB 1 Positive Defiite Matrices A matrix A is positive defiite if x Ax > 0 for all ozero x A positive defiite matrix has real ad positive

More information

Confidence Intervals for One Mean

Confidence Intervals for One Mean Chapter 420 Cofidece Itervals for Oe Mea Itroductio This routie calculates the sample size ecessary to achieve a specified distace from the mea to the cofidece limit(s) at a stated cofidece level for a

More information

CS85: You Can t Do That (Lower Bounds in Computer Science) Lecture Notes, Spring 2008. Amit Chakrabarti Dartmouth College

CS85: You Can t Do That (Lower Bounds in Computer Science) Lecture Notes, Spring 2008. Amit Chakrabarti Dartmouth College CS85: You Ca t Do That () Lecture Notes, Sprig 2008 Amit Chakrabarti Dartmouth College Latest Update: May 9, 2008 Lecture 1 Compariso Trees: Sortig ad Selectio Scribe: William Che 1.1 Sortig Defiitio 1.1.1

More information

Journal of Combinatorial Theory, Series A

Journal of Combinatorial Theory, Series A Joural of Combiatorial Theory, Series A 118 011 319 345 Cotets lists available at ScieceDirect Joural of Combiatorial Theory, Series A www.elsevier.com/locate/jcta Geeratig all subsets of a fiite set with

More information