Edge Cover Time for Regular Graphs

Similar documents
Dorin Andrica Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania

SUFFICIENT CONDITIONS FOR MAXIMALLY EDGE-CONNECTED AND SUPER-EDGE-CONNECTED GRAPHS DEPENDING ON THE CLIQUE NUMBER

BEST CONSTANTS FOR UNCENTERED MAXIMAL FUNCTIONS. Loukas Grafakos and Stephen Montgomery-Smith University of Missouri, Columbia

Online-routing on the butterfly network: probabilistic analysis

A Short Combinatorial Proof of Derangement Identity arxiv: v1 [math.co] 13 Nov Introduction

556: MATHEMATICAL STATISTICS I

Approximating the minimum independent dominating set in perturbed graphs

Polar Coordinates. a) (2; 30 ) b) (5; 120 ) c) (6; 270 ) d) (9; 330 ) e) (4; 45 )

The Congestion of n-cube Layout on a Rectangular Grid S.L. Bezrukov J.D. Chavez y L.H. Harper z M. Rottger U.-P. Schroeder Abstract We consider the pr

KOEBE DOMAINS FOR THE CLASSES OF FUNCTIONS WITH RANGES INCLUDED IN GIVEN SETS

The Chromatic Villainy of Complete Multipartite Graphs

k. s k=1 Part of the significance of the Riemann zeta-function stems from Theorem 9.2. If s > 1 then 1 p s

Kepler s problem gravitational attraction

On the ratio of maximum and minimum degree in maximal intersecting families

Multiple Criteria Secretary Problem: A New Approach

Probabilistic number theory : A report on work done. What is the probability that a randomly chosen integer has no square factors?

(n 1)n(n + 1)(n + 2) + 1 = (n 1)(n + 2)n(n + 1) + 1 = ( (n 2 + n 1) 1 )( (n 2 + n 1) + 1 ) + 1 = (n 2 + n 1) 2.

H5 Gas meter calibration

arxiv: v1 [math.co] 4 May 2017

A Bijective Approach to the Permutational Power of a Priority Queue

6 PROBABILITY GENERATING FUNCTIONS

Quasi-Randomness and the Distribution of Copies of a Fixed Graph

Journal of Inequalities in Pure and Applied Mathematics

On the integration of the equations of hydrodynamics

CMSC 425: Lecture 5 More on Geometry and Geometric Programming

arxiv: v1 [math.nt] 28 Oct 2017

Product Rule and Chain Rule Estimates for Hajlasz Gradients on Doubling Metric Measure Spaces

6 Matrix Concentration Bounds

Auchmuty High School Mathematics Department Advanced Higher Notes Teacher Version

Numerical approximation to ζ(2n+1)

Chromatic number and spectral radius

5. Properties of Abstract Voronoi Diagrams

Chapter 3: Theory of Modular Arithmetic 38

Cross section dependence on ski pole sti ness

Problem Set #10 Math 471 Real Analysis Assignment: Chapter 8 #2, 3, 6, 8

So, if we are finding the amount of work done over a non-conservative vector field F r, we do that long ur r b ur =

ON INDEPENDENT SETS IN PURELY ATOMIC PROBABILITY SPACES WITH GEOMETRIC DISTRIBUTION. 1. Introduction. 1 r r. r k for every set E A, E \ {0},

A Multivariate Normal Law for Turing s Formulae

New problems in universal algebraic geometry illustrated by boolean equations

of the contestants play as Falco, and 1 6

CONGRUENCES INVOLVING ( )

Solution to HW 3, Ma 1a Fall 2016

On the ratio of maximum and minimum degree in maximal intersecting families

COLLAPSING WALLS THEOREM

arxiv: v1 [math.co] 6 Mar 2008

Random Variables and Probability Distribution Random Variable

Bounds for the Density of Abundant Integers

On decompositions of complete multipartite graphs into the union of two even cycles

Then the number of elements of S of weight n is exactly the number of compositions of n into k parts.

Permutations and Combinations

H.W.GOULD West Virginia University, Morgan town, West Virginia 26506

Miskolc Mathematical Notes HU e-issn Tribonacci numbers with indices in arithmetic progression and their sums. Nurettin Irmak and Murat Alp

Fall 2014 Randomized Algorithms Oct 8, Lecture 3

Chapter 2: Basic Physics and Math Supplements

On the Quasi-inverse of a Non-square Matrix: An Infinite Solution

Recognizable Infinite Triangular Array Languages

JANOWSKI STARLIKE LOG-HARMONIC UNIVALENT FUNCTIONS

Do Managers Do Good With Other People s Money? Online Appendix

The least common multiple of a quadratic sequence

THE NUMBER OF TWO CONSECUTIVE SUCCESSES IN A HOPPE-PÓLYA URN

Numerical solution of the first order linear fuzzy differential equations using He0s variational iteration method

Math 301: The Erdős-Stone-Simonovitz Theorem and Extremal Numbers for Bipartite Graphs

ON THE INVERSE SIGNED TOTAL DOMINATION NUMBER IN GRAPHS. D.A. Mojdeh and B. Samadi

SOME SOLVABILITY THEOREMS FOR NONLINEAR EQUATIONS

9.1 The multiplicative group of a finite field. Theorem 9.1. The multiplicative group F of a finite field is cyclic.

Lot-sizing for inventory systems with product recovery

Analysis of Arithmetic. Analysis of Arithmetic. Analysis of Arithmetic Round-Off Errors. Analysis of Arithmetic. Analysis of Arithmetic

1. Review of Probability.

GROWTH ESTIMATES THROUGH SCALING FOR QUASILINEAR PARTIAL DIFFERENTIAL EQUATIONS

arxiv:math/ v2 [math.ag] 21 Sep 2005

Data Structures and Algorithm Analysis (CSC317) Randomized algorithms (part 2)

Measure Estimates of Nodal Sets of Polyharmonic Functions

Graphs of Sine and Cosine Functions

Brief summary of functional analysis APPM 5440 Fall 2014 Applied Analysis

Maximal Inequalities for the Ornstein-Uhlenbeck Process

Boundedness for Marcinkiewicz integrals associated with Schrödinger operators

10/04/18. P [P(x)] 1 negl(n).

Introduction Common Divisors. Discrete Mathematics Andrei Bulatov

Stanford University CS259Q: Quantum Computing Handout 8 Luca Trevisan October 18, 2012

Information Retrieval Advanced IR models. Luca Bondi

Localization of Eigenvalues in Small Specified Regions of Complex Plane by State Feedback Matrix

What Form of Gravitation Ensures Weakened Kepler s Third Law?

arxiv: v1 [math.nt] 12 May 2017

Semicanonical basis generators of the cluster algebra of type A (1)

Solutions to Problem Set 8

An upper bound on the number of high-dimensional permutations

A generalization of the Bernstein polynomials

MATH 220: SECOND ORDER CONSTANT COEFFICIENT PDE. We consider second order constant coefficient scalar linear PDEs on R n. These have the form

Physics 107 TUTORIAL ASSIGNMENT #8

SUPPLEMENTARY MATERIAL CHAPTER 7 A (2 ) B. a x + bx + c dx

Lecture 28: Convergence of Random Variables and Related Theorems

Unobserved Correlation in Ascending Auctions: Example And Extensions

3.1 Random variables

Sincere Voting and Information Aggregation with Voting Costs

Conditional Convergence of Infinite Products

Journal of Number Theory

Errors in Nobel Prize for Physics (3) Conservation of Energy Leads to Probability Conservation of Parity, Momentum and so on

Physics 161 Fall 2011 Extra Credit 2 Investigating Black Holes - Solutions The Following is Worth 50 Points!!!

An Application of Fuzzy Linear System of Equations in Economic Sciences

Deterministic vs Non-deterministic Graph Property Testing

Transcription:

1 2 3 47 6 23 11 Jounal of Intege Sequences, Vol. 11 (28, Aticle 8.4.4 Edge Cove Time fo Regula Gahs Robeto Tauaso Diatimento di Matematica Univesità di Roma To Vegata via della Riceca Scientifica 133 Roma Italy tauaso@mat.unioma2.it Abstact Conside the following stochastic ocess on a gah: initially all vetices ae uncoveed and at each ste cove the two vetices of a andom edge. What is the exected numbe of stes equied to cove all vetices of the gah? In this note we show that the mean cove time fo a egula gah of N vetices is asymtotically (N log N/2. Moeove, we comute the exact mean cove time fo some egula gahs via geneating functions. 1 Intoduction The classical couon collecto s oblem can be extended in many ways. In some vaiants that can be found in the liteatue (see, fo examle, [1, 2, 3, 4] the objects to be collected ae the vetices of a gah. Thee ae vaious inteesting collection ocesses (e.g., a andom walk though the gah, but the following one does not seem to have been consideed much. Let G be a connected gah with N 2 vetices and M 1 edges (no loos. An edge coveing of G is a set of edges so that evey vetex of G is adjacent to (o coveed by at least one edge in this set. Initially all vetices of the gah ae uncoveed and at each ste we ick a andom edge among all edges and we cove its two vetices. Let τ G be the edge cove time, i.e., the andom vaiable that counts the numbe of stes equied to cove all vetices of G. What is its exected value E[τ G ]? Let C(G,k be the numbe of edge coveings of G with exactly k edges. Then the obability that at the n-th ste the whole gah is coveed is given by M { } n k! (n = C(G, k k M n. k=1 1

Since the following identity holds { } n k! = k then whee (n = Ĉ(G, = The obability geneating function is P(x = (nx n = n=1 k ( k ( 1 k n, M k= M Ĉ(G, ( n Ĉ(G, M M ( k ( 1 k C(G,k. n=1 ( x M M 1 n = x Ĉ(G, M x + because Ĉ(G,M = C(G,M = 1. In ode to comute E[τ G] we define Then Q(x = ((n (n 1x n + (1x = P(x(1 x. n=2 x 1 x Q (x = P (x(1 x P(x ( M 1 M 1 M = Ĉ(G, (M x + 1 x (1 x Ĉ(G, 2 (1 x 2 M x x 1 x ( M 1 M 1 M x = Ĉ(G, (1 x Ĉ(G, (M x 2 M x + 1. Finally we ae able to exess the answe in finite tems (see [6] and we obtain E[τ G ] = Q (1 = 1 M 1 Ĉ(G, M. (1 In the next sections we will aly the above fomula to seveal kind of gahs, afte the geneating function whose coefficients give C(G, k has been detemined. We decided to conside only egula gahs, so that no vetex is ivileged with esect to the othes. Befoe we stat, we would like to establish some bounds fo E[τ G ] when G is a geneic d-egula gah with N vetices (and dn/2 edges. Since the gah is egula, at each ste evey vetex has the same obability to be coveed. Hence if we assume that only one vetex of the chosen edge is coveed, then the modified ocess is just the classical couon collecto s oblem, and theefoe its mean cove time NH N is geate than E[τ G ]. A moe ecise asymtotic bound is given by the following theoem, which uses the obabilistic method (see, fo examle, [2]. 2

Theoem 1.1. Let G be a d-egula gah with N vetices and let τ G its cove time. Then fo any α > ( τ G (N log N/2 P N α 1 2e 2α + o(1. (2 Moeove, E[τ G ] (N log N/2. (3 Poof. Let A(v be the event such that the vetex v is not coveed afte f(n = N(log N + a/2 stes with a R. Since the obability that the vetex v is coveed at any ste is = d/(nd/2 = 2/N, it follows that P(A(v = (1 k 1 = (1 f(n = e a N + o(1/n. k>f(n If v w and v w is not an edge, then the obability that v o w ae coveed at any ste is = 2d/(Nd/2 = 4/N. Hence P(A(v A(w = (1 f(n = e 2a N 2 + o(1/n2. On the othe hand, if v w and v w is an edge then the obability that v o w ae coveed at any ste is = (2d 1/(Nd/2 = 4/N 2/(Nd then P(A(v A(w = (1 f(n = e a(2 1/d + o(1/n 2 1/d. N 2 1/d Let X(v be the indicato fo the event A(v and let X = v X(v be the numbe of vetices v such that A(v occus. We have the following estimates: E[X(v] = 1 P(A(v = e a N + o(1/n, Va[X(v] = E[(X(v] 2 E[X(v] 2 = e a N + o(1/n, Cov[X(v,X(w] = E[X(v X(w] E[X(v] E[X(w] = P(A(v A(w e 2a N + 2 o(1/n2 o(1/n 2, if v w is not an edge; = e a(2 1/d + o(1/n 2 1/d, if v w is not an edge. N 2 1/d 3

Theefoe E[X] = v E[X(v] = e a + o(1, Va[X] = Va[X(v] + Cov[X(v,X(w] v v w ( e = e a a(2 1/d + o(1 + Nd + o(1/n 2 1/d N 2 1/d +N(N 1 do(1/n 2 = e a + o(1. So we can find an exlicit ue and lowe bound fo P(X =, that is, the the obability that the cove time τ G is less than N(log N + a/2. By Chebyshev s inequality, P(X = P( X E[X] E[X] Va[X] (E[X] 2 = e a + o(1 e 2a + o(1 = ea + o(1. (4 On the othe hand P(X = = 1 P(X > 1 E[X] = 1 e a + o(1. (5 Let α >. By (4, if a = 2α then By (5, if a = 2M then Finally P(τ G (N log N/2 < αn e 2α + o(1. P(τ G (N log N/2 > αn = 1 P(τ G (N log N/2 < αn e 2α + o(1. P( τ G (N log N/2 < αn = 1 P(τ G (N log N/2 < αn P(τ G (N log N/2 > αn 1 2e 2α + o(1 and Eq. (2 has been oved. Eq. (3 follows diectly fom (2. 2 The cycle gah C n The cycle C n is a 2-egula gah with N = n vetices laced aound a cicle and M = n edges. In ode to comute the numbe of ways C(C n,k,v such that k edges cove v vetices of C n, we choose one of the n vetices and, fom thee, we lace clockwise the v k connected comonents. Let x i 1 be numbe of edges of the ith-comonent then these numbes solve the equation x 1 + x 2 + + x v k = k. Let y i 1 be numbe of edges of the ga between ith-comonent and the next one then these numbes solve equation y 1 + y 2 + + y v k = n k. 4

y 3 x 3 x 1 y 2 x 2 y 1 Hence n times the numbe of the all ositive integal solutions of the evious equations gives v k times (the fist comonent is labeled the numbe C(C n,k,v. Theefoe C(C n,k,v = n ( ( k 1 n k 1 = n ( ( k n k 1 v k v k 1 v k 1 k v k v k 1 and the numbe of edge coveings with k edges is C(C n,k = C(C n,k,n = n ( k = [x n y k 2 xy ] k n k 1 yx yx 2. The sequence is tiangula with esect to the double index (n,k since C(C n,k can be consideed zeo when k > n, and it aeas in Sloane s Encycloedia [7] as A113214. It is inteesting to note that the total numbe of edge coveings of C n is the n-th Lucas numbe (A32 ( n k C(C n = C(C n,k = = L n. k n k k=1 This is not a eal suise because the edge coveings of C n ae in bijective coeondence with the monome-dime tilings (no ovelaing of C n : elace any vetex coveed by two edges with a monome and then fill the est with dimes. k=1 The following identities ae well known (see, fo examle, [5] and we include the oofs hee fo comleteness. 5

Lemma 2.1. i Fo any ositive intege n ii Fo any ositive integes n and Poof. As egads identity (6 and 1 (1 x n 1 1 x 1 Now identity (7, 1 ( ( 1 n 1 1 n = nh n. (6 = dx = (1 x n 1 1 x x 1 (1 x n 1 dx = and on the othe hand ( ( 1 n 1 = 1 ( n 1 1 n 1 ( 1 ( n 1 x 1 dx = 1 t n 1 1 dx = t 1 dx = 1 x 1 = ( 1 ( n 1 1 n 2 (7 ( ( 1 n 1 t dt = H n 1. = x dx = =, ( ( 1 n 1, 1 x 1 (1 x n 1 dx = B( 1,n 1 = ( 1!(n 1! (n 1! = 1 ( n 1. We ae now able to find an exlicit fomula fo the mean cove time fo the cycle gah. Theoem 2.2. n/2 E[τ Cn ] = nh n n =1 ( n ( n 1 1. 6

Poof. By (1 E[τ Cn ] = 1 Ĉ(C n, n = 1 Ĉ(C n,n n n ( ( k n k = 1 ( 1 k n+ n k n k k=n 1 ( ( k 1 k = 1 n ( 1 k n+ n 1 n k k=n 1 ( ( n 1 n = 1 n ( 1 = ( ( 1 n 1 ( n ( ( 1 n 1 = 1 n n. Theefoe, by the evious lemma E[τ Cn ] = nh n n =1 ( n ( n 1 =1 = nh n n = n/2 =1 ( n ( n 1. The exact values of E[τ Cn ] fo N = n = 2, 3, 4, 5, 6, 7, 8 ae 1, 5 2, 11 3, 31 6, 67 1, 167 2, 151 15. 3 The cyclic ladde C n K 2 The cyclic ladde is a 3-egula gah obtained by taking the gah catesian oduct of the cycle gah C n and the comlete gah K 2. It has N = 2n vetice (n on the oute cicle and n in the inne cicle and M = 3n edges (n on each cicle and the n ungs. 7

The numbe of coveings of C n K 2 without ungs is L 2 n because the inne and the oute cicles ae coveed indeendently. Assume that the coveing has 1 ungs then we label the fist one and we let x i + 1 1 be the numbe of edges (on one cicle between the i-th ung and the next one. Since the numbe of coveings of a linea gah with x i + 2 vetices with the end vetices aleady coveed is the Fibonacci numbe F xi +3 (just the numbe of monome-dime tilings of the (x i + 2-sti then the numbe of coveings with 1 ungs is given by the -convolution (n/ Fx 2 i +3. Theefoe C(C n K 2 = L 2 n + x 1 + +x =n (n/ i=1 x 1 + +x =n Now it is easy to find the geneating function: since h(x = L 2 nx n 4 7x x 2 = and g(x = (1 + x(1 3x + x 2 n= it follows that ( (xg(x f(x = h(x + (xd Fx 2 i +3. i=1 Fn+3x 2 n = n= ( ( = h(x + (xd log 4 + x x 2 (1 + x(1 3x + x 2, 1 1 xg(x 4 15x 18x 2 x 3 = (1 + x(1 6x 3x 2 + 2x 3 = 4 + 5x + 43x 2 + 263x 3 + 1699x 4 + 1895x 5 + 69943x 6 + 448943x 7 + o(x 7 and C(C n K 2 = [x n ]f(x is the sequence A12334. Letting 4 (4y + 3y 2 x y 3 x 2 h(x,y = 1 (y + y 2 x (y 2 + y 3 x 2 + y 3 x 3 and g(x = 1 + 2y + y2 + ( y + y 2 + y 3 x y 3 x 2, 1 (y + y 2 x (y 2 + y 3 x 2 + y 3 x 3 by a simila agument, we can show that C(C n K 2,k = [x n y k ]f(x,y whee ( f(x,y = h(x,y + x ( ( 1 log x 1 xyg(x,y = 4 (3y + 9y2 + 3y 3 x (4y 2 + 1y 3 + 4y 4 x 2 + (y 3 y 4 y 5 x 3. (1 + xy(1 (2y + 3y 2 + y 3 x (2y 3 + y 4 x 2 + (y 3 + y 4 x 3 By (1, the exact values of E[τ Cn K 2 ] fo N = 2n = 4, 6, 8, 1 ae 18 5, 1919 28, 788 77, 334283 2424. 8

4 K n and K n,n Thee ae two othe imotant egula gahs whose edge coveings ae counted by sequences contained in the Sloane s Encycloedia [7]: the comlete gah K n and the comlete biatite gah K n,n. All the fomulas can be veified by alying the inclusion-exclusion incile. The tiangula sequence C(K n,k fo k M = ( n 2 is A54548 and C(Kn is A6129: C(K n,k = ( n ( 1 j j j= (( n j 2 k and C(K n = By (1, the exact values of E[τ Kn ] fo N = n = 2, 3, 4, 5, 6 ae 1, 5 2, 19 5, 671 126, 97 14. ( n ( 1 j 2 (n j 2. j The tiangula sequence C(K n,n,k fo k M = n 2 is A55599 and C(K n,n is A48291 and C(K n,n,k = j= ( ( ( n n (n j(n i ( 1 i ( 1 j i j k i= C(K n,n = j= j= ( n ( 1 j (2 n j 1 n. j By (1, the exact values of E[τ Kn,n ] fo N = 2n = 2, 4, 6, 8, 1 ae 1, 11 3, 199 28, 4687 455, 144789 1313. Refeences [1] M. Adle, E. Halein, R. Ka and V. Vaziani, A stochastic ocess on the hyecube with alications to ee-to-ee netwoks, Poc. STOC 23, 575 584. [2] N. Alon and J. H. Sence, The Pobabilistic Method, 2nd Edition, Wiley, 2. [3] C. Cooe and A. Fieze, The cove time of andom egula gahs, SIAM J. Discete Math., 18 (25, 728 74. [4] N. Dimitov and C. Plaxton, Otimal cove time fo a gah-based couon collecto ocess, Poc. ICALP 25, 72 716. [5] H. W. Gould, Combinatoial Identities, Mogantown, West Viginia, 1972. [6] A. N. Myes and H. Wilf, Some new asects of the couon collecto s oblem, SIAM J. Discete Math., 17 (23, 1 17. 9

[7] N. J. A. Sloane, The On-Line Encycloedia of Intege Sequences, ublished electonically at htt://www.eseach.att.com/ njas/sequences/. 2 Mathematics Subject Classification: Pimay 6C5. Keywods: couon collecto s oblem, gah, edge cove. (Concened with sequences A32, A6129, A48291, A54548, A55599, A113214, and A12334. Received Octobe 23 26; evised vesion eceived May 24 28; Setembe 3 28. Published in Jounal of Intege Sequences, Octobe 4 28. Retun to Jounal of Intege Sequences home age. 1