Enumeration of unrooted odd-valent regular planar maps

Size: px
Start display at page:

Download "Enumeration of unrooted odd-valent regular planar maps"

Transcription

1 Enumeration of unroote o-valent regular planar maps Zhicheng Gao School of Mathematics an Statistics Carleton University Ottawa, Ontario Canaa K1S 5B6 Valery A. Lisovets Institute of Mathematics National Acaemy of Sciences Mins, Belarus Nicholas Wormal Department of Combinatorics an Optimization University of Waterloo Waterloo ON Canaa N2L 3G1 Abstract We erive close formulae for the numbers of roote maps with a fixe number of vertices of the same o egree except for the root vertex an one other exceptional vertex of egree 1. The same applies to the generating functions for these numbers. Similar results, but without the vertex of egree 1, were obtaine by the first author an Rahman. We also show, by manipulating a recursion of Bouttier, Di Francesco an Guitter, that there are close formulae when the exceptional vertex has arbitrary egree. We combine these formulae with results of the secon author to count unroote regular maps of o egree. In this way we obtain, for each even n, a close Supporte by University of Macau an NSERC. Supporte by the Belarusian RFFR (grant No. F Supporte by the Canaa Research Chairs program, NSERC an the University of Macau. 1

2 formula for the function f n whose value at o positive integers r is the number of unroote maps (up to orientation-preserving homeomorphisms with n vertices an egree r. The formula for f n becomes more cumbersome as n increases, but for n > 2 each has a boune number of terms inepenent of r Mathematics Subject Classification. Primary: 05C30, Seconary: 05A15. Key wors. o egree, unroote map, roote planar map, regular map, rotation, quotient map, close formula 1 Introuction 1.1 Motivation In the late 1970 s the secon-name author evelope a metho of counting unroote planar maps [Lis81] (see also [Lis85, Lis98]. It relies on the concept of the quotient map of a (geometrically implemente map with respect to a rotational automorphism. The iea is to reuce the problem to the enumeration of roote maps of the same type an the arising quotient maps. The metho turne out to be effective for iverse classes of maps (see [Lis04]. In particular it is quite applicable for counting unroote vertex egree specifie maps, although the resulting reuctive formulae are necessarily fairly cumbersome. But in general we encounter a more serious obstacle in applying this approach: the enumeration of the arising roote egree specifie maps. If the maps uner consieration are eulerian, that is even-valent, then all the quotient maps are eulerian or unicursal (the latter term means that only two vertices are of o egree, an there are remarable sum-free formulae iscovere by Tutte [Tut62] for counting roote eulerian or unicursal maps with a given vertex egree istribution. In particular, this enable the secon author to count unroote regular maps of even egree [Lis85]. Sum-free close formulae are appealing goals for research in enumeration. However, in the general case for maps with o-egree vertices such simple formulae are not nown (see [BenC94], where a certain general but inconvenient formula is given. So the corresponing reuctive formulae are impractical for maps having many o-egree vertices. At present, the only nown notable exception is a simple close formula for the number of roote 3-regular maps [Mul66]. Accoringly, there is a simple formula for the number of unroote 3-regular maps. This was publishe (with a minor error in [LisW87]. A technique evelope by the first author [Gao93] (cf. [GaoR97] shows that there is another class of effectively enumerable roote maps with many o-egree vertices, namely, r-regular maps with a fixe number of vertices. One special case of this has alreay been treate: the case of two vertices, by Bousquet, Labelle an Leroux [BouLL02]. However, in orer to be applicable for counting unroote maps of the same classes, the roote enumeration shoul be extene to the maps of a similar specification but possessing one or two aitional o-egree vertices. One of the main aims of the present paper is to o this for the o-valent regular maps (i.e. maps with all vertices of o egree. We use two methos for this. The first achieves simple close formulae, if we permit one extra vertex of egree 1 an another of arbitrary egree. Even this requires consierable effort an a nontrivial extension of the technique in [GaoR97] to 2

3 be able to eal with truncations of power series which somewhat mysteriously cancel with other nice functions (see (3.28. We obtain simple formulae for the generating functions in this way. Further extensions along these lines loo ifficult. The other main aim is to translate these results to the enumeration of unroote regular maps with n vertices all of o egree r. The results on roote maps mentione above o not cover the cases when n > 2 an gc(r, n 2 has a factor other than 1 an r. To cover these cases, we use a secon metho for the roote maps. We show that recursions erive by Bouttier, Di Francesco an Guitter [BoutDG02] can be manipulate an expane in such a way to obtain close formulae for the misse cases. These cases involve a fixe number of vertices of arbitrary egree r, together with up to two other vertices of egree that is a fixe fraction of r. However, this metho oes not reveal a simple formula for the generating function, as obtaine using the first metho. In fact, the expansion technique we use can hanle any fixe number of vertices of arbitrary egrees in this manner. As a result of all this, we show that there exist close formulae for the numbers of unroote regular maps with a given number n of vertices as a function of the o egree r. In Section 6 we give computational results: formulae for the functions of r (for small n, an some actual numbers for small n an r. 1.2 Main efinitions an notation A planar map is a 2-cell embeing of a planar connecte graph (loops an multiple eges allowe in an oriente sphere. We consier only planar maps, therefore for brevity we will merely call them maps hereafter. A map is roote if one of its ege-ens (nown also as ege-vertex incience pairs, arts, semi-eges, or brins in French is istinguishe as the root. The corresponing vertex is calle the root-vertex. It is well nown an important that no non-trivial automorphism of a map leaves the root fixe. The egree of a vertex is the number of ege-ens incient with it. This is often calle valency in the map enumeration literature. We retain this terminology in the following form: an o-valent map is one with all vertices of o egree. In this paper, counting unroote maps means counting up to orientation-preserving homeomorphisms. There is also a metho of counting up to all homeomorphisms given by the thir author [Wor81], but this is in general very recursive in nature an not so convenient for obtaining close formulae. If K(m is a class of maps with m eges, then K + (m enotes the number of unroote maps in K(m an K(m enotes the number of roote maps. Hence K + (m = K(m = Γ K 1, an it is clear that K(m = Γ K 2m Aut(Γ, where Aut(Γ is the orer of the automorphism group of the map Γ. We efine A(1 a 1 2 a 2 3 a3 ; m to be the class of planar maps of the inicate vertex egree specification, that is, with a i vertices of egree i; the implie number of eges, m = 3

4 (a 1 + 2a 2 + 3a 3 + /2, is also recore in the notation. These maps have vertices an (by Euler s formula v = a i f = m v + 2 (1.1 faces. We call maps of such a specification r-regular if a i = 0 for i r. Moreover, we nee to consier maps in which one or two vertices of egree 1 are istinguishe as singular; a map cannot be roote at a singular vertex. The presence of singular vertices is inicate by the corresponing number of asteriss ( in the subscript. Singular vertices play the role of axial elements in a rotational automorphism of a covering of the roote map (see below. 2 Reuctive enumeration of unroote regular maps In this section we relate the number A + (r n = A + (r n ; m of unroote r-regular maps with n vertices an m = nr/2 eges to the numbers A(r n ; m of the corresponing roote maps, an roote maps of certain relate types. The technique evelope in [Lis81, Lis85] will be use; these references shoul be consulte for further etails of the general iscussion in this section. 2.1 Quotient maps an liftings Consier a map Γ an its rotational automorphism α of orer ρ > 1. Then α is etermine by two axial cells (poles an the rotation angle 2πl/ρ, where l, 1 l < ρ, is prime to ρ. Now there is a unique quotient map = Γ/α. It is efine topologically an can be constructe geometrically by cutting the unerlying sphere into ρ ientical sectors, taing one of them an glueing it into a new sphere. Conversely, given ρ an two arbitrary axial cells, neither of which may be an ege, is lifte into a unique map Γ. In orer to use this approach for the enumeration of unroote maps Γ K(m, one nees first to properly ifferentiate all possible rotation axes by types to ensure a certain uniformity of the quotient maps an of their liftings. Namely, a classification W = {ω} of the axes is proper if the number of ways to lift a quotient map bac into K(m (in other wors, this is the multiplicity of the appearances of as a quotient map epens only on ρ an the type ω of the axis with respect to which has been obtaine. We enote this number by ψ ω (ρ. Further the corresponing classes of quotient maps shoul be escribe thoroughly an enumerate as the roote ones: K + (m is easily expresse in terms of these numbers multiplie by ψ ω (ρ an summe over all possible ρ an ω. Typically the classification of axes into types accoring to the nature of the two axial cells (face face, face vertex, etc. suffices. This is inee true for the present application. Constraints on ρ, together with the numbers of the corresponing quotient maps, are escribe for each type in Table 1 (where, an thereafter, for brevity, (, enotes the greatest common ivisor. Recall that m = nr/2, an note that n is always even since r is o. 4

5 Table 1: Quotient maps for A(r n ; m Type ω Constraint := P ω (ρ #(roote quotient maps := A[ω](ρ face face ρ n/2 A ( r n/ρ ; m ρ face vertex ρ (r, n 1 A ( ( r ρ 1 r (n 1/ρ ; m ρ ( face ege ρ=2 (n/2 + 1 A 1 1 r n/2 ; m+1 2 vertex vertex ρ (r, n 2 A ( ( r ρ 2 r (n 2/ρ ; m ρ ege ege ρ=2 n/2 A ( 1 2 r n/2 ; m In orer to explain these ata we note that the egrees of the axial cells are iminishe ρ times in the quotient maps, while the non-axial cells preserve their egrees. On the other han, ρ non-axial cells turn into one cell, whereas every axial cell of the original map turns into one axial cell of the quotient map. If the axial cell of Γ is an ege (in which case ρ = 2, it turns into an ege ening in a special aitional vertex of egree 1. This vertex of the quotient map is calle singular. A singular vertex is necessarily axial, an the quotient map cannot be roote at a singular vertex. Finally, the quotient map contains m/ρ eges if it has no singular vertices, an apart from this, every singular vertex contributes an aitional term of 1/2 to the number of eges of the quotient. The constraints in the secon column of Table 1 arise from the conition that all parameters of the quotient maps are integers. When ω is face face, we have ρ (n, nr/2 an so ρ n/2 since r is o. When ω is face ege, ρ = 2 (n, nr/2 + 1/2 an so n/2 must be o. The other cases shoul be obvious. Note that ω cannot be of the form vertex ege because ρ woul then be 2 (because of the axial ege but woul also have to ivie the egree of the axial vertex, which is o. So this sixth type ω oes not occur for these maps. Table 2 contains the values of number ψ ω (ρ of amissible choices of the axial cells in the quotient maps. The type is efine as in the lifte map. Here f is the number of faces, a function of other parameters as etermine by (1.1. The number of faces of the quotient maps (the secon column is significant for calculating ψ only if at least one axial cell is a face. 2.2 Reuction Now we can establish the esire uniform reuctive formula for the number of unroote regular maps. Theorem 1 A + (r n = 1 [ A(r n ; m + 2m 2 ρ mφ(ρ ω ] δ Pω(ρ ψ ω (ρ A[ω](ρ, (2.1 5

6 Table 2: Choice of axial cells in quotient maps for lifting Type ω #(faces #(axes choices := ψ ω (ρ face face face vertex face ege f 2 ρ + 2 f 1 ρ + 1 f+1 2 vertex vertex 1 ege ege 1 ( f 2 ρ f 1 ρ + 1 f ( f 2 ρ + 1 /2 where m = rn/2, φ(ρ = {i : 1 i ρ, gc(i, ρ = 1} is the Euler totient function, the secon sum is taen over the five axis types represente in the first column of Table 1, P ω (ρ an A[ω](ρ are the corresponing constraint an roote enumerator represente, respectively, in the secon an thir columns of the same table, the values of ψ ω (ρ are taen from the last column of Table 2, where the parameter f is etermine by formula (1.1 an δ Pω(ρ is the characteristic function: δ P = 1 if the conition P hols an δ P = 0 if P is false. Proof: Immeiately from the foregoing tables an formulae accoring to the general enumerative scheme for unroote planar maps [Lis81, Lis85]. Since the term A(r n ; m is the number of r-regular roote maps for which a formula was erive in [GaoR97], it remains to obtain close enumerative formulae for the functions A[ω](ρ appearing in the right-han sie of (2.1. This will be one in the rest of this paper. We can state the form of the answer as follows. Theorem 2 Let n > 2 be fixe an let enote (r 1/2. Then A + (r n is expressible as a polynomial in ( ( 2 an the quantities 2 /i δi r /i (for all o i > 2 iviing n 1 or n 2 with coefficients being polynomials in. Some cases of this theorem are treate in Sections 3 an 4 (for rational functions of rather than polynomials; see Section 3.2. The theorem is prove in full in Section 5.2. For n = 2, formulae (83 (the o half an (38 of [BouLL02] yiel A + (r 2 = 1 2 ( ( ρ 2+1 ( 2 2 /ρ φ(ρ. /ρ Along the way, we will reerive this formula. A list of explicit formulae for A + (r n for all even n 10 is given in Section 6. 3 Near-regular maps with a egree 1 vertex This section eals with roote maps which have all but the root vertex of egree r, an one other vertex of egree 1. These are important for evaluating formula (2.1. 6

7 We immeiately switch to the uals of the maps to be more in line with the arguments in [GaoR97]. Let M {r},m enote the number of roote maps with all internal faces of egree r, with faces altogether (incluing the unboune face, an m eges. Similarly, M {r}[h],m enotes the number of roote maps with all internal faces of egree r except for one istinguishe face of egree h, with faces altogether (incluing the unboune face, an m eges. From all these notations, when r is unerstoo it can be omitte; thus M [h],m = M {r}[h],m ; but if r has ifferent values in two parts of the same formula it will be inclue. We efine the generating functions M (x = m 0 M,m x m, M [h] (x = M [h],m xm. m Reuction to M,m an M [1],m We nee to express the entries A[ω](r in Table 1 in terms of M,j an M [1],j. In the following we use = 2r + 1 as before. For all but the fourth type (vertex vertex, we have (see justification below A(r n/ρ ; m/ρ = M n/ρ,m/ρ = M,+/2 ( = n/ρ (3.1 A((r/ρ 1 r (n 1/ρ ; m/ρ = nm 1+(n 1/ρ,m/ρ = nm, +(+r/ρ 1/2 ( = 1 + (n 1/ρ (3.2 A (1 1 r n/2 ; (m + 1/2 = M [1] 1+n/2,(m+1/2 = M [1], +/2 ( = 1 + n/2, (3.3 A (1 2 r n/2 ; m/2 + 1 = (rn/4m [1] 2+n/2,(m+2/2 For the vertex vertex type, if ρ = r n 2 an n > 2 an if n = 2 = (rn/4m [1], 2+/2 ( = 2 + n/2. (3.4 A((r/ρ 2 r (n 2/ρ ; m/ρ = (n/2m [1] 2+(n 2/r,n/2 = (n/2m [1], 2+/2 ( = 2 + (n 2/r (3.5 A((r/ρ 2 r (n 2/ρ ; m/ρ = M {r/ρ} 2,m/ρ, (3.6 whilst in any other case for the vertex vertex type (where n > 2 an ρ < r there is no reuction to these generating functions. Thus, nowing M an M [1] is not sufficient to obtain a final result if n > 2 an gc(r, n 2 has any factors (which are potential values of ρ other than 1 an r. The numbers n for which this can happen are those for which n 2 is not a power of 2. Since r is o, the require conition for this metho to wor can be restate as r is an o prime or gc(r, n 2 = 1 or n = 2. (3.7 To justify equations (3.1 (3.6, we consier the uals of the maps counte by the M s. Then faces of egree r become vertices of egree r. The equation for the first type 7

8 is immeiate. For the secon, the quotient map has nr/ρ rootings, of which r/ρ are on the singular vertex, maing a n-to-1 corresponence between the maps require an the ones counte by M 1+(n 1/ρ,n/ρ (where the vertex of egree 1 must be the root vertex. For the thir type, we coul have use M with a similar ajustment, but by using M [1] no ajustment is require. For the fourth type, M [1] counts maps with the root at a vertex of egree 1, so the ajustment factor is the number of possible rootings of the quotient map, ivie by 2 (as there are two vertices of egree 1. As the quotient map has nr/2ρ = n/2 eges, the factor is n/2. For the fifth case, the argument is similar but the special vertices isallow two possible rootings in the maps counte by A. 3.2 The form of M,m an M [1],m Close formulae for the numbers M,m were compute in [GaoR97], so for evaluating (3.1 (3.6 the only new results require are for M [1],m. We first escribe what in of functions will appear in the argument. In particular, the generating function for roote plane trees is well nown: M 1 (x = M {r} 1 (x = 1 1 4x. (3.8 2x We will wor with the set S[X] of polynomials in X an X 1 whose coefficients are rational functions of. It follows easily from the equations in [GaoR97] that where For M [1] (x we will prove a similar result. Lemma 1 For 2, M (xx 1/2 x ( 1 ( 2 1 S[X] (3.9 X = (1 4x 1. (3.10 M [1] (xx 1/2 x ( 2 ( 2 2 S[X]. This lemma is prove at the en of this section, along with the specific calculation of M [1] (x. The following result is obtaine irectly from (3.9 an Lemma 1 by consiering the extraction of the appropriate coefficient of x. Note that the coefficient of x i in any half-integer power of X is a polynomial in ( 2 with coefficients being rational functions of. Details of this in of calculation appear in Section 3.7. Corollary 1 For each fixe, M,+/2, M [1] [1], +/2 an M, 2+/2 are all polynomials in ( 2 with coefficients being rational functions of, an M, +t is a polynomial in ( ( 2 an 2t t with coefficients being rational functions of an t. Note that most cases of Theorem 2 (for rational functions rather than polynomials follow immeiately from Corollary 1, (3.1 (3.6 an Theorem 1. 8

9 3.3 A recursion for M [h] (x Our point of access is a recursive type relation analogous to [GaoR97, Lemma 1]. Notation For i 0 an a generating function A(x = m 0 c mx m, let R i (A(x enote the part of A(x consisting of terms with exponents at least i, that is R i (A(x = m i c m x m. Lemma 2 For 3 we have M [h] (x = x (2 1 4x 1 j=2 M j (xm [h] +1 j (x + R a/2(m [h] 1 (x + R (a+δ/2(m 1 (x where a = ( 2r + h 1 an δ = 1 if h = 1 an 0 otherwise. Proof: The common egree r is constant in the following argument. Using the type of ecomposition in [GaoR97], let M be a map of the type counte by M [h] (x, with m eges. There are two cases if the root ege of M is elete. (As usual for such arguments, the new map(s resulte are roote in canonical ways. In the first case, two maps result. If the one containing the istinguishe face has j faces then the other has + 1 j faces. The generating function for these is 2xM j (xm [h] +1 j (x. In the secon case, eleting the root ege merges the ajacent face with the root face, an there are two subcases. In the first subcase, the ajacent face has egree r, in which case the number of possibilities for the map prouce is M [h] 1,m 1, an in the secon, the ajacent face is the istinguishe one of egree h, so the new map is counte by M 1,m 1. However, in the first of these cases the egree of the root face of M must be at least 1, implying (summing the face egrees that 2m ( 2r + h + 1, an so m 1 (( 2r + h 1/2. Conversely, for m 1 in this range, the reverse operation can be carrie out in a unique way. On the other han, in the secon case, we obtain the same constraint if h 2, but the stronger constraint m 1 (( 2r + h/2 in the case h = 1 since the egree of M must be at least 2 when the root ege encompasses a face of egree 1. (For = 2, if it ha been permitte, the secon case woul have the same restriction as the first one. Thus M [h] (x = j=1 2xM j (xm [h] +1 j (x + x an the lemma follows upon solving for M [h] factor. m (( 2r+h 1/2 (M [h] 1,m xm + M 1,m x m, an using (3.8 to simplify the leaing In further wor we consier only the case h = 1. Since the number of eges in maps counte by M 1 (x is at least ( ( 2( /2 = (a + 1/2, the following is immeiate. 9

10 Corollary 2 M [1] (x = x (2 1 4x 1 j=2 M j (xm [1] +1 j (x + R a/2(m [1] 1 (x + M 1(x Now for some computations for small values of. As a founation in aition to (3.8 we use the following formula of Tutte [Tut62] (see also equation (1 in [GaoR97] for just two faces, the interior one of egree r (even or o: M {r} 2,m = m r/2 2m ( 2m 2 r/2 m r/2 ( 2 r/2 r/2. (3.11 We also mae use, in one of our alternative erivations of M [1] 3, of Tutte s formula in the case that the non-root face egree is even: M {2},m (m 1!(m ( 1 = ( 1!(m + 2! ( 2m 2( 1 m ( 1 ( (3.12 For later use, note that (3.11 implies for the generating function (see [GaoR97, Equation 13] ( 2 ( m 2m 2 M 2 (x = x m (3.13 2m m m +1 an hence From [GaoR97] we also have M 2(x = M 3 (x = ( 2 x. (3.14 (1 4x 3/2 ( 2 2 x 2+2 (1 4x 3/2. (3.15 We henceforth assume that r is an o integer, an write = (r 1/2 so that r = 2+1. The value of a/2 in Lemma 2 is then, in the case h = 1, given by a/2 = ( 2 + /2 1. (3.16 Formulae such as (3.14 an (3.15 we will call close since they are simple explicit formulae for the generating functions. In the present section, more specifically, a close expression means that it is a sum of terms involving the power of x an (1 4x where the number of terms epens only on the number of vertices, not on. In general, our aim woul be to fin such close expressions for M [h] (x in general, though we only succee in the case h = 1. For the next two calculations, we explore a irect combinatorial argument rather than the recursion in Lemma 2. (However, note that we will later evelop a general recursion that also covers the case of M [1]

11 3.4 M [1] 2 Start with a roote plane tree with m 1 eges an insert a loop into any of its 2m 2 corners, or insert the loop into the root corner an transfer the rooting onto the loop. This shows that M [1] 2,m = (2m 1M 1,m 1 as M 1,m is the number of roote planar trees with m eges. Thus, using (3.8, we have regarless of r. 3.5 M [1] 3 M [1] 2 (x = x 1 2, (3.17 A map Γ of this sort can be obtaine by inserting a loop into another map, Γ. For the loop to be inserte into the unboune face, there are 2m 2 r corners available, an we also permit the possibility of shifting the rooting onto the loop if the root corner is use. In this case Γ is counte by M 2,m 1 = M 2,m 1. {r} For the loop to be inserte in the interior face, there are r 1 corners as Γ is counte by M 2,m 1. {r 1} Hence M {r}[1] 3,m = (2m r 1M {r} 2,m 1 + (r 1M {r 1} 2,m 1. Recalling that r = we may apply (3.11 an (3.12 to obtain ( ( ( M [1] 2 2m 2 2 (m 1 2 3,m = + m 1 m 1 ( ( 2 2m 2 2 = (m 1 m 1 Note that, comparing with (3.11, we have (m 1 m 1. (3.18 M [1] 3 (x = 2x 2 M 2(x, (3.19 a relation which it woul be nice to erive via a irect bijection between the maps counte by M [1] 3 (x an those counte by M 2(x (with one less ege an with a istinguishe corner. Unfortunately, simply saying that the loop is inserte into the istinguishe corner oes not apply, since the loop cannot be inserte into a corner in the interior face without estroying the egree conition, an also the M 3,n maps with a root on the loop must be prouce somehow. 3.6 M [1] 4 The general metho in the next Section is quite complicate so we introuce it by ealing with the case of M [1] 4 separately using a very relate approach. This permits the reaer to see the central iea in a simpler context. From Corollary 2 an using (3.16, ( 3 M [1] x 4 (x = 2M j (xm [1] 1 4x 5 j (x + R 2+1(M [1] 3 (x + M 3 (x. (3.20 j=2 11

12 Of the terms in the summation, by (3.19. Also by (3.15 an (3.17 ( 2M 3 (xm [1] 2 2 (x = 2 For the next term, note that, using (3.19, 2M 2 (xm [1] 3 (x = 2x 2 ((M 2 (x 2 ( x 2+2 (1 4x 3/2 ( R 2+1 (M [1] 3 (x = R 2+1 (2x 2 M 2(x = 2x 2 R 2 1 (M 2(x Aing (3.21 an (3.23 using (3.11 gives x 1. 2 = 2x 2 (R 2 (M 2 (x (3.22 = 2x 2 (R 2+1 (M 2 (x + x 2 M 2,2. (3.23 2x 2 ((M 2 (x 2 + R 2+1 (M 2 (x + 4x 2+1 ( 2 It is shown in [GaoR97] just after (18 that (M 2 (x 2 + R 2+1 (M 2 (x = an so putting this all together we have from (3.15 an (3.20 which is ( ( (2 x 2x 2 1 4x ( 2 2 x 2+1 (1 4x 1, ( ( 2 2 x 2+1 (1 4x 1 + 4x ( 2 ( 2 +2 x 2+2 (1 4x 3/ x x 1 4x ( 2 ( 2 2 x 2+2 (1 4x 3/2 2 ( 2x 2 ( (2 + 1x 2 (1 4x 1 + 4x 2+1 (1 4x 2 + 4x i.e. ( +2x 2+2 (1 4x 3/ x 1 + x 2+2 (1 4x 3/2 2 x x ( 2 2 ( 2x ( (2 + 1(1 4x 1 + 4x(1 4x 2 + ( +2x(1 4x 3/ x 1 + x(1 4x 3/2 2 12

13 i.e. i.e. x x ( 2 2 ( 2x ( (2 + 1(1 4x 1 + 4x(1 4x 2 + +x(1 4x 2 M [1] 4 (x = x x ( 2 2 ( + 2x(2 + 1(1 4x 1 + x(8x + 1(1 4x 2. ( Higher M [1] In the formula for M [1] 5 from Lemma 2 we have close expressions plus 2M 2 (xm [1] 4 (x + 2M 3 (xm [1] 3 (x + R 3+3/2 (M [1] 4 (x. (3.26 The mile term is close. Now in general 2M 2 f(x + R (f(x (3.27 is close when f(x = (1 4x α for α a half-integer. To be precise, we will calculate H j (x for integers j 1 such that ( j 3/2 F((1 4x j 1/2 = ( 4x H j (x ( where F is the linear operator efine as First, ifferentiating (3.24 we have F(f(x = 2M 2 (xf(x + R f(x. (3.29 2M 2 (xm 2(x + R 2 (M 2(x = = ( 2 2 (x 2+1 (1 4x 1 ( 2 2 ( (2 + 1x 2 (1 4x 1 + 4x 2+1 (1 4x 2. So using (3.14 an iviing by ( 2 x, an then using ( 5/2 1 ( 4 = 2(2+1 3 etermines H 1 in (3.28: H 1 (x = ( 2, this 3( x 2(2 + 1(1 4x. ( Next, note that ( j 3/2 R x(1 4x j 3/2 = xr 1 (1 4x j 3/2 = xr (1 4x j 3/2 +x ( 4x

14 an hence F(x(1 4x j 3/2 = x2m 2 (x(1 4x j 3/2 + R x(1 4x j 3/2 ( j 3/2 = xf((1 4x j 3/2 + x ( 4x 1. 1 So, using linearity of F, for all integers j, ( j 3/2 ( 4x H j (x = F((1 4x j 1/2 1 Writing = F((1 4x(1 4x j 3/2 = F((1 4x j 3/2 + 4F(x(1 4x j 3/2 ( j 3/2 = (1 4xF((1 4x j 3/2 + 4x ( 4x 1 1 [ ( ( ] j 5/2 j 3/2 = (1 4x H j+1 (x ( 4x. 1 1 B(x, y = we may rewrite the equation above as ( x 1/2 + y 1 B( j 2, 1 H j (x = X B( j 1, 1 H j+1(x 1 with X as in (3.10. Now it is easy to chec that for all integers p an q an thus B(p 1, q B(p, q = 2p 1 2 2q 2p 1 H j+1 (x = X 2j + 3 2j (H j(x + 1. Starting with (3.30, which may be rewritten as H 1 = X(X + 2 2(2 + 1, (3.31 (3.32 we may use this recurrence to compute H j recursively for all fixe integers j, both positive an negative. Moreover, we may write H j (x = H j (X (3.33 where H j is a polynomial in X an 1/X whose coefficients are rational functions of with a boune number of terms for fixe j. The resulting recurrence for H j can be solve using stanar techniques to give H j = (3/2 1 (j + 3/2 1 X j H0 + j 1 1 (i + 3/2 1 X j i, (3.34 (j + 3/ i=0

15 where H 0 = X/(2, an (a b = a(a + 1 (a + b 1 is the rising factorial. We may chec that (3.34 is also vali for negative integers j, in which case the summation symbol is interprete as 1 i=j. We also nee to truncate at some other places. For b 0, efine F b (f(x = 2M 2 (xf(x + R b f(x (3.35 (so in particular, F = F. To simplify things a little, let us efine { M [1] (x = M [1] (x for > 2 M [1] 2 (x + 1 = for = x (3.36 Then the lone term M 1 (x in Corollary 2 is amalgamate into the term in the summation for j = 1, an we have for 3 M [1] (x = x 1 (2 M j (x 1 4x = j=2 x 1 (2 M j (x 1 4x j=3 by (3.16. From (3.34 we have for fixe j that [1] M +1 j (x + R [1] a/2( M 1 (x [1] M +1 j (x + F [1] ( 2+/2 1( M 1 (x (3.37 H j (x S[X], where S is efine as in Section 3.2. It is easy to chec that ( 2 B(0, 0 = ( 4, (3.38 an thus from (3.32, an a similar formula for B(p, q 1/B(p, q, for any fixe integers p an q ( 2 ( 4 B(p, q/ is a rational function of (3.39 an hence by (3.28 for each fixe j. F((1 4x j 1/2 ( 2 x S[X] (3.40 Proof of Lemma 1 We claim that (3.37 implies by inuction that for 2 L (X := [1] M (xx 1/2 x ( 2 ( 2 2 S[X], (3.41 which immeiately implies the lemma. 15

16 This is true for = 2 by (3.17. Assume L 1 (X ( 2 3S[X]. Then F ( 2+/2 1 ( M [1] 1 (x = x( 3 F +/2 1 (L 1 (XX 1/2. This can be evaluate by noticing that F +/2 1 is linear an also F +/2 1 (X j+1/2 = F (X j+1/2 + /2 3/2 i= x i [x i ]X j+1/2 where square bracets enote the extraction of a coefficient. Thus by (3.28 F ( 2+/2 1 ( M [1] 1 (x = x( 3 j Q(j, [X j ]L 1 (X (3.42 where Q(j, = ( j 3/2 1 ( 4x H j (x + = ( 4x B( j 1, 1 H j (X + x ( 2 S[X] + /2 3/2 i= /2 3/2 i=0 x i [x i ]X j+1/2 ( 4x +i B( j, i (3.43 by (3.39 an inverting (3.10 to x = (1 1/X/4. We now obtain (3.41 recursively from (3.37, (3.9 an (3.42. Computational note The coefficients of L (X can be obtaine from the equations referre to in the last line of the above proof together with (3.38, (3.32, (3.34 an (3.33. This gives the value of M [1] for 3 by the efinition of L in ( Computations using M an M [1] Computations in this section were basically all one using Maple. We are aiming for evaluation of the right han sies of equations ( , an for this we fin the generating functions M (x an M [1] (x for small. Note that the coefficients of M 2(x are given in (3.11 an we o not have a close formula for the generating function. For 3, we give the formulae for M (x compute using the equations in [GaoR97], which clearly emonstrate the general form of these functions. The first three cases are as follows; we compute these up to = 20 in orer to obtain our later results for maps with up to 20 vertices. M 3 (x = 1 ( 2 2 (X 1 2 x 2 16 X M 4 (x = 1 ( 3 2 (X 1 2 (2 + X 1 x 3 64 X 16

17 M 5 (x = ( 4 2 (X 1 3 ( X 18 X X 2 x 4. X 3/2 Similarly, M [1] (x can be compute for small as escribe at the en of Section 3. The functions L (X efine in (3.41 are easily erive from these. Recall that M an M are the same except when = 2. For up to 5 we have the following; more cases were compute for obtaining the numbers given later. M [1] 5 (x = M [1] 3 (x = 1 8 M [1] 4 (x = 1 ( 2 64 ( 2 M [1] 2 (x = 1 2 X ( 2 (X 1 2 x X 2 (X 1 2 (4 + 3 X 3 x 2 X 3 (X 1 3 (5 X 2 6 X + 12 X x 3 X 3/2. Writing U, for M,+/2 as in (3.1, V, for M [1], +/2 as in (3.3, an W, for M [1], 2+/2 as in (3.4 an (3.5, we can compute the small values from (3.11 an the equations above; the first few are as follows: U 2, = ( U 4, = 2 3 (2 + 1 ( 2 U 6, = (2 + 1 (36 1 ( 6 2 U 8, = 1 ( (2 + 1 ( U 10, = (2 + 1 ( V 2, = ( V 4, = 3 2 (2 + 1 ( 2 3 V 6, = (25 1 (2 + 1 ( 5 2 V 8, = 7 ( (2 + 1 ( (

18 W 2, = 1 ( 2 W 4, = 2 W 6, = (16 1 ( 4 2 W 8, = ( This leaves only equations (3.2 an (3.6 unresolve. For (3.2 we require M, +t ( 6 2. which arises for t = ( + (2 + 1/ρ 1/2. This only occurs when ρ n 1 as shown in Table 1 (face vertex case an can easily ( be compute in each case using the formula for t M (x. The result when = 2 is 2t ( 2 2+t t, where t = ((2+1/ρ+1/2 = /ρ since ( 4s+2 t is an integer. Expresse in terms of s = /ρ = t 1, this becomes 2s ( 2 2+s+1 s. Note that s = (2+1 ρ/(2ρ, which maes the first factor 4(2+1/(4ρ+2+ρ+1, an this gives simple expressions for particular ρ. For = 4 we compute M 4,3+ /ρ +1 by expaning the formula given above in powers of X an replacing the coefficient of X i 1/2 by ( 4 s+2 B(i, 2, where B is efine in (3.31. This simplifies to M 4,3+s+2 = 2s s 4s 2 ( 2s s ( 2 In the case ρ = 3 the above expression for s shows that the first factor is ( /(4 10. We o not compute this for arbitrary. It is easy to compute using Maple for the particular values of require in this paper (an much larger values as well. Regaring (3.6, this metho only suffices for n = 2 when there is a special term arising from the vertex vertex type, when ψ = 1 an P ω (ρ is ρ r. This is φ(ρm {r/ρ} 2,r/ρ. The binomial is evaluate as ( (r/ρ 1/2 using (3.11 (as for U2 above but with r replace by r/ρ, which is then clearly equal to ( 2 /ρ /ρ. 5 Results from Bouttier-Di Francesco-Guitter Recursions In this section we use the recursions erive in [BoutDG02] to compute the missing A s neee in the formula for A + (r n. We note that these recursions were also erive by Bousquet-Mélou an Jehanne [BousJ06]. 5.1 Recursions for two vertex egrees Specialising the treatment in [BoutDG02] to two vertex egrees, we efine the following generating functions for roote maps with u maring the number of vertices of egree 3. 18

19 2 + 1 an v maring the number of vertices of egree 2h + 1, where an h are both at least 1. (Warning: for convenience, here we use 2h + 1 where we use h in earlier sections. We have no nee of the variable t in [BoutDG02] an set it equal to 1. In that paper, t was use to mar the eges; the number of eges is etermine by the egrees of the vertices. We now efine three generating functions. E(u, v is the generating function for roote maps all of whose vertices have egrees in {2 + 1, 2h + 1}. Γ 1 (u, v is the generating function for roote maps all of whose non-root vertices have egrees in {2 + 1, 2h + 1}, with root vertex egree 1. Γ 2 (u, v is the generating function for roote maps with root vertex egree 1 an another vertex of egree 1 in the root face, an all other vertices of egrees in {2 + 1, 2h + 1}. We now efine S an R to be power series in u an v efine implicitly from the equations where [z i ] enotes coefficient extraction, S = [z 0 ]W R = 1 + [z 1 ]W W = uq 2 + vq 2h, Q = z + S + R/z an z is a ummy variable. Then the equations [BoutDG02, (2.6,(2.1] state that Γ 1 = S [z 2 ]W Γ 2 = R [z 3 ]W ([z 2 ]W 2 E = Γ Γ 2 1. Hence we obtain Γ 1 = S u ( ( 2 2 2i S 2i R +1 i 2i + 1 i i 0 v ( ( 2h 2h 2i S 2i R h+1 i (5.1 2i h + 1 i i 0 Γ 2 = R (S Γ 1 2 u ( ( 2 2 2i 1 S 2i+1 R +1 i 2i i i 0 v ( ( 2h 2h 2i 1 S 2i+1 R h+1 i (5.2 2i + 1 h + 1 i i 0 E = Γ Γ 2 1 (5.3 19

20 where S = u ( ( 2 2 2i 2i i i 0 R = 1 + u ( ( 2 2 2i 1 2i + 1 i 1 i 0 +v ( ( 2h 2h 2i 1 2i + 1 h i 1 i 0 S 2i R i + v i 0 ( 2h 2i S 2i+1 R i ( 2h 2i h i S 2i R h i (5.4 S 2i+1 R h i. (5.5 To use these results in the context of the present paper, note that A(r n/ρ ; m/ρ = [u n/ρ w 0 ]E(= M,+/2 (5.6 A((r/ρ 1 r (n 1/ρ ; m/ρ = [u (n 1/ρ v]e with 2h + 1 = r/ρ, (5.7 A (1 1 r n/2 ; (m + 1/2 = (nr/2[u n/2 v 0 ]Γ 1, (5.8 A (1 2 r n/2 ; m/2 + 1 = (rn/4[u n/2 v 1 ]Γ 1 with h = 0, (5.9 A((r/ρ 2 r (n 2/ρ ; m/ρ = [u (n 2/ρ v 2 ]E with 2h + 1 = r/ρ. ( Close formulae for arbitrary egrees In [BoutDG02] it is shown that the general relations they obtain simplify in the case of cubic (3-regular maps, an eventually prouce the well-nown formula for the number of roote cubic maps. The expansion technique we use in this section emonstrates that the relations also yiel in principle explicit general formula for the numbers of roote maps whose vertices have arbitary egrees, as long as the number of vertices is fixe (in the same spirit as the results in Section 4. We only nee results for two istinct vertex egrees (hence our specialisation in Section 5.1, an at most two vertices of the secon egree, which maes explicit computations easier. Equations (5.4 an (5.5 are use to calculate ([u v 0 ]S, [u v 0 ]R, ([u v 1 ]S, [u v 1 ]R, an ([u v 2 ]S, [u v 2 ]R recursively. In the following we use (x j to enote the rising factorial x(x + 1 (x + j 1 when j 0. We also efine (x 1 = 1/(x 1. We note ( ( ( 2x 2x 2i 2x = ((x + 1 i i 2 /(2i! 2i x i x ( ( ( 2x 2x 2i 1 2x = (x + 1 i i (x i i+1 /(2i + 1! 2i + 1 x i 1 x ( ( ( 2x 2x 2i 2x = (x + 2 i i 1 (x i i+1 /(2i! ( 2x 2i + 1 2i x + 1 i ( 2x 2i 1 x + 1 i = x ( 2x x (x + 2 i i 1 (x i 1 i+2 /(2i + 1!. We also note that to compute the coefficient of u, we only nee to use S j for j 1 on the right han sie of the recursions, an we use the first terms in the expansion 1 ( x (R0 R x = (u + R 1 (uv + R 2 (uv 2 j +. j j=0 20

21 For computational purposes, we set ( 2 z = u, w = ( 2h v, h S = S 0 (z + S 1 (zw + S 2 (zw 2, R = 1 + R 0 (z + R 1 (zw + R 2 (zw 2. It is not ifficult to see from recursions (5.4 an (5.5 that S 0 (z, R 1 (z an S 2 (z contain only o powers of z, an R 0 (z, S 1 (z, R 2 (z contain only even powers of z. Also [z 2+1 ]S 0 (z an [z 2 ]R 0 (z are polynomials of of egree 2 an 2 1, respectively; [z 2 ]S 1 (z an [z 2+1 ]R 1 (z are polynomials of an h with egrees 2 an 2 + 1, respectively; [z 2+1 ]S 2 (z an [z 2 ]R 2 (z are polynomials of an h of egree an 2 + 1, respectively. Proof of Theorem 2: It follows from equations ( that [w 0 ]Γ 1 an [w 1 ]E contain only o powers of z, an [w 1 ]Γ 1, [w 0 ]E, [w 2 ]E contain only even powers of z. Also for 0, [w 0 z 2+1 ]Γ 1 an [w 0 z 2+2 ]E are polynomials of with egree 2 1 an 2, respectively; [w 1 z 2 ]Γ 1, [w 1 z 2+1 ]E an [w 2 z 2 ]E are polynomials of an h with egree 2 1, 2, an 2, respectively. The theorem now follows from Theorem 1 by using ( for the entries in Table 1, an noting that we require 2h an thus h = /i for an integer i. The first few terms of these functions are given below, obtaine using Maple. [w 0 ]Γ 1 = z/( (1/2z 3 + (1/24 2 (25 1z 5 +(1/720 2 ( z 7 +(1/ ( z 9 + [w 1 ]Γ 1 = 1/(h (1/2(2 + hz 2 + (1/24( h h + 12h 2 h 2 z 4 +(1/720( h h h 60h h h + 30h 2 4h h h h h 2 + h 5 z 6 + [w 0 ]E = z 2 + (2/3(2 + 1z 4 + (1/20 2 (2 + 1(36 1z 6 +(1/315 2 (2 + 1( z 8 +(1/ (2 + 1( z 10 +(1/ (2 + 1( z 12 + [w 1 ]E = 2z + (1/3(3 + h + 2(h + 3z 3 +(1/60(h ( h h 2 + h 3 h 2 z 5 + [w 2 ]E = 1 + 2( + h + 1( + hz 2 +(1/12(2h ( h + 24h 2 + 4h 3 h 2 z 4 +(1/90(3 + h + 2( h h h h h h + 120h 4 90h h 2 + 8h 5 12h 4 h 2 + 5h 3 z

22 These expansions can be use in conjunction with ( an Theorem 1 to calculate expressions for A + (r n for all small n. The ones we will nee in particular because they are not obtaine using the other metho are, from (5.10, ( 2 ( /3 A((r/3 2 r 2 ; 4r/3 = (4/9(2 + 1(4 1, (5.11 /3 ( 4 ( 2 2 2h A((r/3 2 r 4 = (1/12(2h ( h + 24h 2 + 4h 3 h 2, h (5.12 an A((r/3 2 r 6 = ( 6 ( 2 2 2h (1/90(3 + h + 2P (, h, h (5.13 P (, h = h h h h h h + 120h 4 90h h 2 + 8h 5 12h 4 h 2 + 5h 3. 6 Final computational results In this section we give general formulae for the number of unroote regular r-valent maps for all o r when n 10. We also compute some actual numbers for n 20. All results are foun counte using (2.1, Tables 1 an 2, an equations (3.1 (3.6 using the appropriate coefficients of M (x an M [1] (x as escribe in Section 4, together with the three particular expressions (5.11, (5.12 an (5.13 for A((r/3 2 r 2 ; 4r/3 an so on. A((r/3 2 r 2 is neee when n = 8 an r > 3 is a multiple of 3, A((r/3 2 r 4 is neee with n = 14 when r = 9 (an hence = 4 an h = 1, an A((r/3 2 r 6 with n = 20 when r = 9 ( = 4, h = 1. The general formulae are the following, for which one shoul recall that δ a b is 1 if 22

23 a b an 0 otherwise, an that = (r 1/2. A + (r 2 = 1 ( 2 1 ( 2 2 /ρ + φ(ρ 2 2(2 + 1 /ρ 1 ρ 2+1 A + (r 4 = 1 ( ( 2 ( ( /3 2 + δ 3 r 3 /3 A + (r 6 = 36 1 ( ( ( 2 ( ( /5 2 + δ 5 r 5 /5 ( 8 A + (r = 2520 ( ( 4 2 ( ( 2 ( 2 ( /3 + δ 3 r (2 + 1(4 1 9r /3 ( ( 6 2 /7 2 +δ 7 r 7 /7 ( A + (r = ( ( ( 2 48 ( ( 2 /3 /3 +δ 3 r ( ( δ 9 r 2 3 ( 2 /9 /9 ( 2 Computing for specific small values of r gives the following table for the number A + (r n of unroote r-regular maps with n vertices.. 10 Table 3: Numbers of unroote regular n-vertex maps, 2 n 20 r A + (r

24 r A + (r r A + (r r A + (r

25 r A + (r r A + (r r A + (r r A + (r r A + (r

26 r A + (r For the reaer s convenience, in Table 4 we represent the same numerical ata for r = 3, 5 an 7 groupe by vertex egrees. Table 4: Numbers of unroote r-regular maps, r = 3, 5, 7 n A + (3 n A + (5 n A + (7 n Acnowlegement We wish to than a referee of an earlier version of this paper, for a suggestion which stimulate further examination of [BoutDG02] an le, though somewhat inirectly, to the use of the recursions therein. References [BenC94] E. A. Bener an E. R. Canfiel, The number of egree restricte roote maps on the sphere, SIAM J. Discrete Math. 7:1 (1994, MR (94:05093 [BouLL02] M. Bousquet, G. Labelle an P. Leroux, Enumeration of planar two-face maps, Discrete Math., 222 (2000, MR (2001c:05073 [BousJ06] M. Bousquet-Mélou an A. Jehanne, Polynomial equations with one catalytic variable, algebraic series an map enumeration, J. Combin. Theory, Ser. B, 96:5 (2006, MR [BoutDG02] J. Bouttier, P. Di Francesco, an E. Guitter, Census of planar maps: from the one-matrix moel solution to a combinatorial proof, Nuclear Physics B 645:3 (2002, MR (2004f:05004 [Gao93] Z. C. Gao, The number of egree restricte maps on general surfaces, Discrete Math. 123 (1993, MR (94j:05008 [GaoR97] Z. C. Gao an M. Rahman, Enumeration of -poles, Annals of Combin. 1:1 (1997, MR (98:

27 [Lis81] V. A. Lisovets, A census of non-isomorphic planar maps, Colloq. Math. Soc. J. Bolyai 25 (1981, Proc. Intern. Conf. Algebr. Methos in Graph Th., Szege, Hungary, 1978, MR (83a:05073 [Lis85] V. A. Lisovets, Enumeration of nonisomorphic planar maps, Selecta Math. Sov. 4:4 (1985, (Translation from two-part Russian original article publishe in: Geometric methos in problems in analysis an algebra, , 129, 132 an Problems in group theory an homological algebra, , Yaroslav. Gos. Univ., Yaroslavl, MR (84j:05057, MR (88c:05064 [Lis98] V. Lisovets, Reuctive enumeration uner mutually orthogonal group actions, Acta Applic. Math. 52 (1998, MR (2000b:05012 [Lis04] V. A. Lisovets, Enumerative formulae for unroote planar maps: a pattern, Electron. J. Combin. 11:1 (2004, RP 88, 14 pp. (electronic. MR (2005h:05101 [LisW87] V. A. Lisovets an T. R. S. Walsh, Ten steps to counting planar graphs, Congr. Numer. 60 (1987, MR (89g:05057 [Mul66] R. C. Mullin, On the average number of trees in certain maps, Cana. J. Math. 18:1 (1966, MR (32 #4040 [Tut62] W. T. Tutte, A census of slicings, Cana. J. Math. 14:4 (1962, MR (26 #39 [Wor81] N. C. Wormal, On the number of planar maps, Cana. J. Math. 33:1 (1981, MR (83a:

NOTES ON EULER-BOOLE SUMMATION (1) f (l 1) (n) f (l 1) (m) + ( 1)k 1 k! B k (y) f (k) (y) dy,

NOTES ON EULER-BOOLE SUMMATION (1) f (l 1) (n) f (l 1) (m) + ( 1)k 1 k! B k (y) f (k) (y) dy, NOTES ON EULER-BOOLE SUMMATION JONATHAN M BORWEIN, NEIL J CALKIN, AND DANTE MANNA Abstract We stuy a connection between Euler-MacLaurin Summation an Boole Summation suggeste in an AMM note from 196, which

More information

Linear First-Order Equations

Linear First-Order Equations 5 Linear First-Orer Equations Linear first-orer ifferential equations make up another important class of ifferential equations that commonly arise in applications an are relatively easy to solve (in theory)

More information

Math 1B, lecture 8: Integration by parts

Math 1B, lecture 8: Integration by parts Math B, lecture 8: Integration by parts Nathan Pflueger 23 September 2 Introuction Integration by parts, similarly to integration by substitution, reverses a well-known technique of ifferentiation an explores

More information

Implicit Differentiation

Implicit Differentiation Implicit Differentiation Thus far, the functions we have been concerne with have been efine explicitly. A function is efine explicitly if the output is given irectly in terms of the input. For instance,

More information

The Exact Form and General Integrating Factors

The Exact Form and General Integrating Factors 7 The Exact Form an General Integrating Factors In the previous chapters, we ve seen how separable an linear ifferential equations can be solve using methos for converting them to forms that can be easily

More information

ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS

ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS MARK SCHACHNER Abstract. When consiere as an algebraic space, the set of arithmetic functions equippe with the operations of pointwise aition an

More information

Discrete Mathematics

Discrete Mathematics Discrete Mathematics 309 (009) 86 869 Contents lists available at ScienceDirect Discrete Mathematics journal homepage: wwwelseviercom/locate/isc Profile vectors in the lattice of subspaces Dániel Gerbner

More information

Two formulas for the Euler ϕ-function

Two formulas for the Euler ϕ-function Two formulas for the Euler ϕ-function Robert Frieman A multiplication formula for ϕ(n) The first formula we want to prove is the following: Theorem 1. If n 1 an n 2 are relatively prime positive integers,

More information

On the enumeration of partitions with summands in arithmetic progression

On the enumeration of partitions with summands in arithmetic progression AUSTRALASIAN JOURNAL OF COMBINATORICS Volume 8 (003), Pages 149 159 On the enumeration of partitions with summans in arithmetic progression M. A. Nyblom C. Evans Department of Mathematics an Statistics

More information

TOEPLITZ AND POSITIVE SEMIDEFINITE COMPLETION PROBLEM FOR CYCLE GRAPH

TOEPLITZ AND POSITIVE SEMIDEFINITE COMPLETION PROBLEM FOR CYCLE GRAPH English NUMERICAL MATHEMATICS Vol14, No1 Series A Journal of Chinese Universities Feb 2005 TOEPLITZ AND POSITIVE SEMIDEFINITE COMPLETION PROBLEM FOR CYCLE GRAPH He Ming( Λ) Michael K Ng(Ξ ) Abstract We

More information

2Algebraic ONLINE PAGE PROOFS. foundations

2Algebraic ONLINE PAGE PROOFS. foundations Algebraic founations. Kick off with CAS. Algebraic skills.3 Pascal s triangle an binomial expansions.4 The binomial theorem.5 Sets of real numbers.6 Surs.7 Review . Kick off with CAS Playing lotto Using

More information

6 General properties of an autonomous system of two first order ODE

6 General properties of an autonomous system of two first order ODE 6 General properties of an autonomous system of two first orer ODE Here we embark on stuying the autonomous system of two first orer ifferential equations of the form ẋ 1 = f 1 (, x 2 ), ẋ 2 = f 2 (, x

More information

Witt#5: Around the integrality criterion 9.93 [version 1.1 (21 April 2013), not completed, not proofread]

Witt#5: Around the integrality criterion 9.93 [version 1.1 (21 April 2013), not completed, not proofread] Witt vectors. Part 1 Michiel Hazewinkel Sienotes by Darij Grinberg Witt#5: Aroun the integrality criterion 9.93 [version 1.1 21 April 2013, not complete, not proofrea In [1, section 9.93, Hazewinkel states

More information

Permanent vs. Determinant

Permanent vs. Determinant Permanent vs. Determinant Frank Ban Introuction A major problem in theoretical computer science is the Permanent vs. Determinant problem. It asks: given an n by n matrix of ineterminates A = (a i,j ) an

More information

Applications of the Wronskian to ordinary linear differential equations

Applications of the Wronskian to ordinary linear differential equations Physics 116C Fall 2011 Applications of the Wronskian to orinary linear ifferential equations Consier a of n continuous functions y i (x) [i = 1,2,3,...,n], each of which is ifferentiable at least n times.

More information

Lecture XII. where Φ is called the potential function. Let us introduce spherical coordinates defined through the relations

Lecture XII. where Φ is called the potential function. Let us introduce spherical coordinates defined through the relations Lecture XII Abstract We introuce the Laplace equation in spherical coorinates an apply the metho of separation of variables to solve it. This will generate three linear orinary secon orer ifferential equations:

More information

Iterated Point-Line Configurations Grow Doubly-Exponentially

Iterated Point-Line Configurations Grow Doubly-Exponentially Iterate Point-Line Configurations Grow Doubly-Exponentially Joshua Cooper an Mark Walters July 9, 008 Abstract Begin with a set of four points in the real plane in general position. A to this collection

More information

Lower Bounds for the Smoothed Number of Pareto optimal Solutions

Lower Bounds for the Smoothed Number of Pareto optimal Solutions Lower Bouns for the Smoothe Number of Pareto optimal Solutions Tobias Brunsch an Heiko Röglin Department of Computer Science, University of Bonn, Germany brunsch@cs.uni-bonn.e, heiko@roeglin.org Abstract.

More information

Zachary Scherr Math 503 HW 3 Due Friday, Feb 12

Zachary Scherr Math 503 HW 3 Due Friday, Feb 12 Zachary Scherr Math 503 HW 3 Due Friay, Feb 1 1 Reaing 1. Rea sections 7.5, 7.6, 8.1 of Dummit an Foote Problems 1. DF 7.5. Solution: This problem is trivial knowing how to work with universal properties.

More information

4.2 First Differentiation Rules; Leibniz Notation

4.2 First Differentiation Rules; Leibniz Notation .. FIRST DIFFERENTIATION RULES; LEIBNIZ NOTATION 307. First Differentiation Rules; Leibniz Notation In this section we erive rules which let us quickly compute the erivative function f (x) for any polynomial

More information

Survey Sampling. 1 Design-based Inference. Kosuke Imai Department of Politics, Princeton University. February 19, 2013

Survey Sampling. 1 Design-based Inference. Kosuke Imai Department of Politics, Princeton University. February 19, 2013 Survey Sampling Kosuke Imai Department of Politics, Princeton University February 19, 2013 Survey sampling is one of the most commonly use ata collection methos for social scientists. We begin by escribing

More information

Lecture Introduction. 2 Examples of Measure Concentration. 3 The Johnson-Lindenstrauss Lemma. CS-621 Theory Gems November 28, 2012

Lecture Introduction. 2 Examples of Measure Concentration. 3 The Johnson-Lindenstrauss Lemma. CS-621 Theory Gems November 28, 2012 CS-6 Theory Gems November 8, 0 Lecture Lecturer: Alesaner Mąry Scribes: Alhussein Fawzi, Dorina Thanou Introuction Toay, we will briefly iscuss an important technique in probability theory measure concentration

More information

The Three-dimensional Schödinger Equation

The Three-dimensional Schödinger Equation The Three-imensional Schöinger Equation R. L. Herman November 7, 016 Schröinger Equation in Spherical Coorinates We seek to solve the Schröinger equation with spherical symmetry using the metho of separation

More information

Euler equations for multiple integrals

Euler equations for multiple integrals Euler equations for multiple integrals January 22, 2013 Contents 1 Reminer of multivariable calculus 2 1.1 Vector ifferentiation......................... 2 1.2 Matrix ifferentiation........................

More information

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors Math 18.02 Notes on ifferentials, the Chain Rule, graients, irectional erivative, an normal vectors Tangent plane an linear approximation We efine the partial erivatives of f( xy, ) as follows: f f( x+

More information

Separation of Variables

Separation of Variables Physics 342 Lecture 1 Separation of Variables Lecture 1 Physics 342 Quantum Mechanics I Monay, January 25th, 2010 There are three basic mathematical tools we nee, an then we can begin working on the physical

More information

Math 1271 Solutions for Fall 2005 Final Exam

Math 1271 Solutions for Fall 2005 Final Exam Math 7 Solutions for Fall 5 Final Eam ) Since the equation + y = e y cannot be rearrange algebraically in orer to write y as an eplicit function of, we must instea ifferentiate this relation implicitly

More information

JUST THE MATHS UNIT NUMBER DIFFERENTIATION 2 (Rates of change) A.J.Hobson

JUST THE MATHS UNIT NUMBER DIFFERENTIATION 2 (Rates of change) A.J.Hobson JUST THE MATHS UNIT NUMBER 10.2 DIFFERENTIATION 2 (Rates of change) by A.J.Hobson 10.2.1 Introuction 10.2.2 Average rates of change 10.2.3 Instantaneous rates of change 10.2.4 Derivatives 10.2.5 Exercises

More information

Robustness and Perturbations of Minimal Bases

Robustness and Perturbations of Minimal Bases Robustness an Perturbations of Minimal Bases Paul Van Dooren an Froilán M Dopico December 9, 2016 Abstract Polynomial minimal bases of rational vector subspaces are a classical concept that plays an important

More information

Schrödinger s equation.

Schrödinger s equation. Physics 342 Lecture 5 Schröinger s Equation Lecture 5 Physics 342 Quantum Mechanics I Wenesay, February 3r, 2010 Toay we iscuss Schröinger s equation an show that it supports the basic interpretation of

More information

On colour-blind distinguishing colour pallets in regular graphs

On colour-blind distinguishing colour pallets in regular graphs J Comb Optim (2014 28:348 357 DOI 10.1007/s10878-012-9556-x On colour-blin istinguishing colour pallets in regular graphs Jakub Przybyło Publishe online: 25 October 2012 The Author(s 2012. This article

More information

Calculus of Variations

Calculus of Variations 16.323 Lecture 5 Calculus of Variations Calculus of Variations Most books cover this material well, but Kirk Chapter 4 oes a particularly nice job. x(t) x* x*+ αδx (1) x*- αδx (1) αδx (1) αδx (1) t f t

More information

Lecture 5. Symmetric Shearer s Lemma

Lecture 5. Symmetric Shearer s Lemma Stanfor University Spring 208 Math 233: Non-constructive methos in combinatorics Instructor: Jan Vonrák Lecture ate: January 23, 208 Original scribe: Erik Bates Lecture 5 Symmetric Shearer s Lemma Here

More information

u!i = a T u = 0. Then S satisfies

u!i = a T u = 0. Then S satisfies Deterministic Conitions for Subspace Ientifiability from Incomplete Sampling Daniel L Pimentel-Alarcón, Nigel Boston, Robert D Nowak University of Wisconsin-Maison Abstract Consier an r-imensional subspace

More information

MA 2232 Lecture 08 - Review of Log and Exponential Functions and Exponential Growth

MA 2232 Lecture 08 - Review of Log and Exponential Functions and Exponential Growth MA 2232 Lecture 08 - Review of Log an Exponential Functions an Exponential Growth Friay, February 2, 2018. Objectives: Review log an exponential functions, their erivative an integration formulas. Exponential

More information

Math 300 Winter 2011 Advanced Boundary Value Problems I. Bessel s Equation and Bessel Functions

Math 300 Winter 2011 Advanced Boundary Value Problems I. Bessel s Equation and Bessel Functions Math 3 Winter 2 Avance Bounary Value Problems I Bessel s Equation an Bessel Functions Department of Mathematical an Statistical Sciences University of Alberta Bessel s Equation an Bessel Functions We use

More information

Zachary Scherr Math 503 HW 5 Due Friday, Feb 26

Zachary Scherr Math 503 HW 5 Due Friday, Feb 26 Zachary Scherr Math 503 HW 5 Due Friay, Feb 26 1 Reaing 1. Rea Chapter 9 of Dummit an Foote 2 Problems 1. 9.1.13 Solution: We alreay know that if R is any commutative ring, then R[x]/(x r = R for any r

More information

The Non-abelian Hodge Correspondence for Non-Compact Curves

The Non-abelian Hodge Correspondence for Non-Compact Curves 1 Section 1 Setup The Non-abelian Hoge Corresponence for Non-Compact Curves Chris Elliott May 8, 2011 1 Setup In this talk I will escribe the non-abelian Hoge theory of a non-compact curve. This was worke

More information

7.1 Support Vector Machine

7.1 Support Vector Machine 67577 Intro. to Machine Learning Fall semester, 006/7 Lecture 7: Support Vector Machines an Kernel Functions II Lecturer: Amnon Shashua Scribe: Amnon Shashua 7. Support Vector Machine We return now to

More information

A FURTHER REFINEMENT OF MORDELL S BOUND ON EXPONENTIAL SUMS

A FURTHER REFINEMENT OF MORDELL S BOUND ON EXPONENTIAL SUMS A FURTHER REFINEMENT OF MORDELL S BOUND ON EXPONENTIAL SUMS TODD COCHRANE, JEREMY COFFELT, AND CHRISTOPHER PINNER 1. Introuction For a prime p, integer Laurent polynomial (1.1) f(x) = a 1 x k 1 + + a r

More information

Diagonalization of Matrices Dr. E. Jacobs

Diagonalization of Matrices Dr. E. Jacobs Diagonalization of Matrices Dr. E. Jacobs One of the very interesting lessons in this course is how certain algebraic techniques can be use to solve ifferential equations. The purpose of these notes is

More information

The total derivative. Chapter Lagrangian and Eulerian approaches

The total derivative. Chapter Lagrangian and Eulerian approaches Chapter 5 The total erivative 51 Lagrangian an Eulerian approaches The representation of a flui through scalar or vector fiels means that each physical quantity uner consieration is escribe as a function

More information

The Press-Schechter mass function

The Press-Schechter mass function The Press-Schechter mass function To state the obvious: It is important to relate our theories to what we can observe. We have looke at linear perturbation theory, an we have consiere a simple moel for

More information

2.1 Derivatives and Rates of Change

2.1 Derivatives and Rates of Change 1a 1b 2.1 Derivatives an Rates of Change Tangent Lines Example. Consier y f x x 2 0 2 x-, 0 4 y-, f(x) axes, curve C Consier a smooth curve C. A line tangent to C at a point P both intersects C at P an

More information

Laplace s Equation in Cylindrical Coordinates and Bessel s Equation (II)

Laplace s Equation in Cylindrical Coordinates and Bessel s Equation (II) Laplace s Equation in Cylinrical Coorinates an Bessel s Equation (II Qualitative properties of Bessel functions of first an secon kin In the last lecture we foun the expression for the general solution

More information

19 Eigenvalues, Eigenvectors, Ordinary Differential Equations, and Control

19 Eigenvalues, Eigenvectors, Ordinary Differential Equations, and Control 19 Eigenvalues, Eigenvectors, Orinary Differential Equations, an Control This section introuces eigenvalues an eigenvectors of a matrix, an iscusses the role of the eigenvalues in etermining the behavior

More information

Diophantine Approximations: Examining the Farey Process and its Method on Producing Best Approximations

Diophantine Approximations: Examining the Farey Process and its Method on Producing Best Approximations Diophantine Approximations: Examining the Farey Process an its Metho on Proucing Best Approximations Kelly Bowen Introuction When a person hears the phrase irrational number, one oes not think of anything

More information

Lectures - Week 10 Introduction to Ordinary Differential Equations (ODES) First Order Linear ODEs

Lectures - Week 10 Introduction to Ordinary Differential Equations (ODES) First Order Linear ODEs Lectures - Week 10 Introuction to Orinary Differential Equations (ODES) First Orer Linear ODEs When stuying ODEs we are consiering functions of one inepenent variable, e.g., f(x), where x is the inepenent

More information

Perfect Matchings in Õ(n1.5 ) Time in Regular Bipartite Graphs

Perfect Matchings in Õ(n1.5 ) Time in Regular Bipartite Graphs Perfect Matchings in Õ(n1.5 ) Time in Regular Bipartite Graphs Ashish Goel Michael Kapralov Sanjeev Khanna Abstract We consier the well-stuie problem of fining a perfect matching in -regular bipartite

More information

Lower bounds on Locality Sensitive Hashing

Lower bounds on Locality Sensitive Hashing Lower bouns on Locality Sensitive Hashing Rajeev Motwani Assaf Naor Rina Panigrahy Abstract Given a metric space (X, X ), c 1, r > 0, an p, q [0, 1], a istribution over mappings H : X N is calle a (r,

More information

Three-Variable Bracket Polynomial for Two-Bridge Knots

Three-Variable Bracket Polynomial for Two-Bridge Knots Three-Variable Bracket Polynomial for Two-Brige Knots Matthew Overuin Research Experience for Unergrauates California State University, San Bernarino San Bernarino, CA 92407 August 23, 2013 Abstract In

More information

d dx But have you ever seen a derivation of these results? We ll prove the first result below. cos h 1

d dx But have you ever seen a derivation of these results? We ll prove the first result below. cos h 1 Lecture 5 Some ifferentiation rules Trigonometric functions (Relevant section from Stewart, Seventh Eition: Section 3.3) You all know that sin = cos cos = sin. () But have you ever seen a erivation of

More information

Computing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions

Computing Exact Confidence Coefficients of Simultaneous Confidence Intervals for Multinomial Proportions and their Functions Working Paper 2013:5 Department of Statistics Computing Exact Confience Coefficients of Simultaneous Confience Intervals for Multinomial Proportions an their Functions Shaobo Jin Working Paper 2013:5

More information

Acute sets in Euclidean spaces

Acute sets in Euclidean spaces Acute sets in Eucliean spaces Viktor Harangi April, 011 Abstract A finite set H in R is calle an acute set if any angle etermine by three points of H is acute. We examine the maximal carinality α() of

More information

Table of Common Derivatives By David Abraham

Table of Common Derivatives By David Abraham Prouct an Quotient Rules: Table of Common Derivatives By Davi Abraham [ f ( g( ] = [ f ( ] g( + f ( [ g( ] f ( = g( [ f ( ] g( g( f ( [ g( ] Trigonometric Functions: sin( = cos( cos( = sin( tan( = sec

More information

A Sketch of Menshikov s Theorem

A Sketch of Menshikov s Theorem A Sketch of Menshikov s Theorem Thomas Bao March 14, 2010 Abstract Let Λ be an infinite, locally finite oriente multi-graph with C Λ finite an strongly connecte, an let p

More information

Mathcad Lecture #5 In-class Worksheet Plotting and Calculus

Mathcad Lecture #5 In-class Worksheet Plotting and Calculus Mathca Lecture #5 In-class Worksheet Plotting an Calculus At the en of this lecture, you shoul be able to: graph expressions, functions, an matrices of ata format graphs with titles, legens, log scales,

More information

LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION

LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION The Annals of Statistics 1997, Vol. 25, No. 6, 2313 2327 LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION By Eva Riccomagno, 1 Rainer Schwabe 2 an Henry P. Wynn 1 University of Warwick, Technische

More information

APPPHYS 217 Thursday 8 April 2010

APPPHYS 217 Thursday 8 April 2010 APPPHYS 7 Thursay 8 April A&M example 6: The ouble integrator Consier the motion of a point particle in D with the applie force as a control input This is simply Newton s equation F ma with F u : t q q

More information

Math 342 Partial Differential Equations «Viktor Grigoryan

Math 342 Partial Differential Equations «Viktor Grigoryan Math 342 Partial Differential Equations «Viktor Grigoryan 6 Wave equation: solution In this lecture we will solve the wave equation on the entire real line x R. This correspons to a string of infinite

More information

A Note on Modular Partitions and Necklaces

A Note on Modular Partitions and Necklaces A Note on Moular Partitions an Neclaces N. J. A. Sloane, Rutgers University an The OEIS Founation Inc. South Aelaie Avenue, Highlan Par, NJ 08904, USA. Email: njasloane@gmail.com May 6, 204 Abstract Following

More information

Final Exam Study Guide and Practice Problems Solutions

Final Exam Study Guide and Practice Problems Solutions Final Exam Stuy Guie an Practice Problems Solutions Note: These problems are just some of the types of problems that might appear on the exam. However, to fully prepare for the exam, in aition to making

More information

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x)

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x) Y. D. Chong (2016) MH2801: Complex Methos for the Sciences 1. Derivatives The erivative of a function f(x) is another function, efine in terms of a limiting expression: f (x) f (x) lim x δx 0 f(x + δx)

More information

Similar Operators and a Functional Calculus for the First-Order Linear Differential Operator

Similar Operators and a Functional Calculus for the First-Order Linear Differential Operator Avances in Applie Mathematics, 9 47 999 Article ID aama.998.067, available online at http: www.iealibrary.com on Similar Operators an a Functional Calculus for the First-Orer Linear Differential Operator

More information

The Principle of Least Action

The Principle of Least Action Chapter 7. The Principle of Least Action 7.1 Force Methos vs. Energy Methos We have so far stuie two istinct ways of analyzing physics problems: force methos, basically consisting of the application of

More information

Sturm-Liouville Theory

Sturm-Liouville Theory LECTURE 5 Sturm-Liouville Theory In the three preceing lectures I emonstrate the utility of Fourier series in solving PDE/BVPs. As we ll now see, Fourier series are just the tip of the iceberg of the theory

More information

Stable Polynomials over Finite Fields

Stable Polynomials over Finite Fields Rev. Mat. Iberoam., 1 14 c European Mathematical Society Stable Polynomials over Finite Fiels Domingo Gómez-Pérez, Alejanro P. Nicolás, Alina Ostafe an Daniel Saornil Abstract. We use the theory of resultants

More information

Chapter 6: Integration: partial fractions and improper integrals

Chapter 6: Integration: partial fractions and improper integrals Chapter 6: Integration: partial fractions an improper integrals Course S3, 006 07 April 5, 007 These are just summaries of the lecture notes, an few etails are inclue. Most of what we inclue here is to

More information

TAYLOR S POLYNOMIAL APPROXIMATION FOR FUNCTIONS

TAYLOR S POLYNOMIAL APPROXIMATION FOR FUNCTIONS MISN-0-4 TAYLOR S POLYNOMIAL APPROXIMATION FOR FUNCTIONS f(x ± ) = f(x) ± f ' (x) + f '' (x) 2 ±... 1! 2! = 1.000 ± 0.100 + 0.005 ±... TAYLOR S POLYNOMIAL APPROXIMATION FOR FUNCTIONS by Peter Signell 1.

More information

11.7. Implicit Differentiation. Introduction. Prerequisites. Learning Outcomes

11.7. Implicit Differentiation. Introduction. Prerequisites. Learning Outcomes Implicit Differentiation 11.7 Introuction This Section introuces implicit ifferentiation which is use to ifferentiate functions expresse in implicit form (where the variables are foun together). Examples

More information

3.7 Implicit Differentiation -- A Brief Introduction -- Student Notes

3.7 Implicit Differentiation -- A Brief Introduction -- Student Notes Fin these erivatives of these functions: y.7 Implicit Differentiation -- A Brief Introuction -- Stuent Notes tan y sin tan = sin y e = e = Write the inverses of these functions: y tan y sin How woul we

More information

Construction of the Electronic Radial Wave Functions and Probability Distributions of Hydrogen-like Systems

Construction of the Electronic Radial Wave Functions and Probability Distributions of Hydrogen-like Systems Construction of the Electronic Raial Wave Functions an Probability Distributions of Hyrogen-like Systems Thomas S. Kuntzleman, Department of Chemistry Spring Arbor University, Spring Arbor MI 498 tkuntzle@arbor.eu

More information

6 Wave equation in spherical polar coordinates

6 Wave equation in spherical polar coordinates 6 Wave equation in spherical polar coorinates We now look at solving problems involving the Laplacian in spherical polar coorinates. The angular epenence of the solutions will be escribe by spherical harmonics.

More information

Agmon Kolmogorov Inequalities on l 2 (Z d )

Agmon Kolmogorov Inequalities on l 2 (Z d ) Journal of Mathematics Research; Vol. 6, No. ; 04 ISSN 96-9795 E-ISSN 96-9809 Publishe by Canaian Center of Science an Eucation Agmon Kolmogorov Inequalities on l (Z ) Arman Sahovic Mathematics Department,

More information

Some vector algebra and the generalized chain rule Ross Bannister Data Assimilation Research Centre, University of Reading, UK Last updated 10/06/10

Some vector algebra and the generalized chain rule Ross Bannister Data Assimilation Research Centre, University of Reading, UK Last updated 10/06/10 Some vector algebra an the generalize chain rule Ross Bannister Data Assimilation Research Centre University of Reaing UK Last upate 10/06/10 1. Introuction an notation As we shall see in these notes the

More information

Calculus in the AP Physics C Course The Derivative

Calculus in the AP Physics C Course The Derivative Limits an Derivatives Calculus in the AP Physics C Course The Derivative In physics, the ieas of the rate change of a quantity (along with the slope of a tangent line) an the area uner a curve are essential.

More information

Generalized Tractability for Multivariate Problems

Generalized Tractability for Multivariate Problems Generalize Tractability for Multivariate Problems Part II: Linear Tensor Prouct Problems, Linear Information, an Unrestricte Tractability Michael Gnewuch Department of Computer Science, University of Kiel,

More information

'HVLJQ &RQVLGHUDWLRQ LQ 0DWHULDO 6HOHFWLRQ 'HVLJQ 6HQVLWLYLW\,1752'8&7,21

'HVLJQ &RQVLGHUDWLRQ LQ 0DWHULDO 6HOHFWLRQ 'HVLJQ 6HQVLWLYLW\,1752'8&7,21 Large amping in a structural material may be either esirable or unesirable, epening on the engineering application at han. For example, amping is a esirable property to the esigner concerne with limiting

More information

1 dx. where is a large constant, i.e., 1, (7.6) and Px is of the order of unity. Indeed, if px is given by (7.5), the inequality (7.

1 dx. where is a large constant, i.e., 1, (7.6) and Px is of the order of unity. Indeed, if px is given by (7.5), the inequality (7. Lectures Nine an Ten The WKB Approximation The WKB metho is a powerful tool to obtain solutions for many physical problems It is generally applicable to problems of wave propagation in which the frequency

More information

Integration Review. May 11, 2013

Integration Review. May 11, 2013 Integration Review May 11, 2013 Goals: Review the funamental theorem of calculus. Review u-substitution. Review integration by parts. Do lots of integration eamples. 1 Funamental Theorem of Calculus In

More information

Quantum mechanical approaches to the virial

Quantum mechanical approaches to the virial Quantum mechanical approaches to the virial S.LeBohec Department of Physics an Astronomy, University of Utah, Salt Lae City, UT 84112, USA Date: June 30 th 2015 In this note, we approach the virial from

More information

On the minimum distance of elliptic curve codes

On the minimum distance of elliptic curve codes On the minimum istance of elliptic curve coes Jiyou Li Department of Mathematics Shanghai Jiao Tong University Shanghai PRChina Email: lijiyou@sjtueucn Daqing Wan Department of Mathematics University of

More information

Calculus of Variations

Calculus of Variations Calculus of Variations Lagrangian formalism is the main tool of theoretical classical mechanics. Calculus of Variations is a part of Mathematics which Lagrangian formalism is base on. In this section,

More information

0.1 Differentiation Rules

0.1 Differentiation Rules 0.1 Differentiation Rules From our previous work we ve seen tat it can be quite a task to calculate te erivative of an arbitrary function. Just working wit a secon-orer polynomial tings get pretty complicate

More information

Counting Lattice Points in Polytopes: The Ehrhart Theory

Counting Lattice Points in Polytopes: The Ehrhart Theory 3 Counting Lattice Points in Polytopes: The Ehrhart Theory Ubi materia, ibi geometria. Johannes Kepler (1571 1630) Given the profusion of examples that gave rise to the polynomial behavior of the integer-point

More information

Qubit channels that achieve capacity with two states

Qubit channels that achieve capacity with two states Qubit channels that achieve capacity with two states Dominic W. Berry Department of Physics, The University of Queenslan, Brisbane, Queenslan 4072, Australia Receive 22 December 2004; publishe 22 March

More information

Math 210 Midterm #1 Review

Math 210 Midterm #1 Review Math 20 Miterm # Review This ocument is intene to be a rough outline of what you are expecte to have learne an retaine from this course to be prepare for the first miterm. : Functions Definition: A function

More information

Antiderivatives. Definition (Antiderivative) If F (x) = f (x) we call F an antiderivative of f. Alan H. SteinUniversity of Connecticut

Antiderivatives. Definition (Antiderivative) If F (x) = f (x) we call F an antiderivative of f. Alan H. SteinUniversity of Connecticut Antierivatives Definition (Antierivative) If F (x) = f (x) we call F an antierivative of f. Antierivatives Definition (Antierivative) If F (x) = f (x) we call F an antierivative of f. Definition (Inefinite

More information

θ x = f ( x,t) could be written as

θ x = f ( x,t) could be written as 9. Higher orer PDEs as systems of first-orer PDEs. Hyperbolic systems. For PDEs, as for ODEs, we may reuce the orer by efining new epenent variables. For example, in the case of the wave equation, (1)

More information

PDE Notes, Lecture #11

PDE Notes, Lecture #11 PDE Notes, Lecture # from Professor Jalal Shatah s Lectures Febuary 9th, 2009 Sobolev Spaces Recall that for u L loc we can efine the weak erivative Du by Du, φ := udφ φ C0 If v L loc such that Du, φ =

More information

Make graph of g by adding c to the y-values. on the graph of f by c. multiplying the y-values. even-degree polynomial. graph goes up on both sides

Make graph of g by adding c to the y-values. on the graph of f by c. multiplying the y-values. even-degree polynomial. graph goes up on both sides Reference 1: Transformations of Graphs an En Behavior of Polynomial Graphs Transformations of graphs aitive constant constant on the outsie g(x) = + c Make graph of g by aing c to the y-values on the graph

More information

Chapter 5. Factorization of Integers

Chapter 5. Factorization of Integers Chapter 5 Factorization of Integers 51 Definition: For a, b Z we say that a ivies b (or that a is a factor of b, or that b is a multiple of a, an we write a b, when b = ak for some k Z 52 Theorem: (Basic

More information

Consider for simplicity a 3rd-order IIR filter with a transfer function. where

Consider for simplicity a 3rd-order IIR filter with a transfer function. where Basic IIR Digital Filter The causal IIR igital filters we are concerne with in this course are characterie by a real rational transfer function of or, equivalently by a constant coefficient ifference equation

More information

arxiv: v2 [math.pr] 27 Nov 2018

arxiv: v2 [math.pr] 27 Nov 2018 Range an spee of rotor wals on trees arxiv:15.57v [math.pr] 7 Nov 1 Wilfrie Huss an Ecaterina Sava-Huss November, 1 Abstract We prove a law of large numbers for the range of rotor wals with ranom initial

More information

Differentiation ( , 9.5)

Differentiation ( , 9.5) Chapter 2 Differentiation (8.1 8.3, 9.5) 2.1 Rate of Change (8.2.1 5) Recall that the equation of a straight line can be written as y = mx + c, where m is the slope or graient of the line, an c is the

More information

THE MONIC INTEGER TRANSFINITE DIAMETER

THE MONIC INTEGER TRANSFINITE DIAMETER MATHEMATICS OF COMPUTATION Volume 00, Number 0, Pages 000 000 S 005-578(XX)0000-0 THE MONIC INTEGER TRANSFINITE DIAMETER K. G. HARE AND C. J. SMYTH ABSTRACT. We stuy the problem of fining nonconstant monic

More information

The Subtree Size Profile of Plane-oriented Recursive Trees

The Subtree Size Profile of Plane-oriented Recursive Trees The Subtree Size Profile of Plane-oriente Recursive Trees Michael FUCHS Department of Applie Mathematics National Chiao Tung University Hsinchu, 3, Taiwan Email: mfuchs@math.nctu.eu.tw Abstract In this

More information

Hyperbolic Moment Equations Using Quadrature-Based Projection Methods

Hyperbolic Moment Equations Using Quadrature-Based Projection Methods Hyperbolic Moment Equations Using Quarature-Base Projection Methos J. Koellermeier an M. Torrilhon Department of Mathematics, RWTH Aachen University, Aachen, Germany Abstract. Kinetic equations like the

More information

. Using a multinomial model gives us the following equation for P d. , with respect to same length term sequences.

. Using a multinomial model gives us the following equation for P d. , with respect to same length term sequences. S 63 Lecture 8 2/2/26 Lecturer Lillian Lee Scribes Peter Babinski, Davi Lin Basic Language Moeling Approach I. Special ase of LM-base Approach a. Recap of Formulas an Terms b. Fixing θ? c. About that Multinomial

More information

Math 115 Section 018 Course Note

Math 115 Section 018 Course Note Course Note 1 General Functions Definition 1.1. A function is a rule that takes certain numbers as inputs an assigns to each a efinite output number. The set of all input numbers is calle the omain of

More information