Asymptotics of Pattern Avoidance in the Klazar Set Partition and Permutation-Tuple Settings Permutation Patterns 2017 Abstract

Size: px
Start display at page:

Download "Asymptotics of Pattern Avoidance in the Klazar Set Partition and Permutation-Tuple Settings Permutation Patterns 2017 Abstract"

Transcription

1 Asymptotics of Patter Avoiace i the Klazar Set Partitio a Permutatio-Tuple Settigs Permutatio Patters 2017 Abstract Bejami Guby Departmet of Mathematics Harvar Uiversity Cambrige, Massachusetts, U.S.A. bguby@g.harvar.eu May 30, 2017 This research is joit work with Dömötör Pálvölgyi. We first itrouce set partitios a the otio of Klazar-type set partitio patter avoiace. Defiitio. A set partitio is a partitio of the set [] for some ito ay umber of oempty sets, where the orer withi the partitio is irrelevat. We call these sets blocks of the partitio. The umber of set partitios of [] is the Bell umber B. Ofte, whe writig specific set partitios, we will write the partitio [] = S 1 S k as S 1 / /S k, where the S i are i icreasig orer of smallest elemet, a are writte as strigs of umbers from least to greatest; for example, [5] = {2, 4} {1, 3, 5} woul be writte 135/24. To carry over our otios of avoiace, we efie patter cotaimet o set partitios. Defiitio. Let π a π be set partitios of [] a [m], respectively. We say that π cotais (respectively avois) π if there exists (respectively oes ot exist) a strictly icreasig fuctio f : [m] [] such that for ay i, j [m], i a j are i the same block of π if a oly if f(i) a f(j) are i the same block of π. (Note that this is istict from RGF -type set partitio avoiace as stuie i [5], where, for example, 145/23 woul avoi 12/34, as i RGF -type cotaimet the orer of blocks must be preserve.) We will be cocere with the asymptotics of patter classes of set partitios. I aalogy to the permutatio case, where all patter classes grow as! or are boue above by a expoetial, we fi that we ca similarly classify the growth rate of patter classes of set partitios to withi a expoetial factor. Specifically, we have the followig. 1

2 Theorem 1. Let P be a oempty patter class of set partitios, a as usual let P be the partitios of [] i P. The oe of the followig is true. 1. P cotais every set partitio; that is, P = B. 2. There exists a positive iteger a real costats c 2 > c 1 > 0 such that for every, c 1 (1 1 ) P c 2 (1 1 ) To explai whe the patter class falls ito a particular asymptotic rage, we relate partitios to tuples of permutatios a efie the permutability statistic. Defiitio. Let σ 1,..., σ S be permutatios. We the efie [σ 1,..., σ ] to be the set partitio of [( + 1)] cotaiig blocks B 1,..., B, where B i = {i, + σ 1 (i), 2 + σ 2 (i),..., + σ (i)}. Defiitio. Let π be a set partitio of []. The permutability of π, eote pm(π), is the miimal such that there exists m Z + a σ 1,..., σ S m such that [σ 1,..., σ ] cotais π. We ow have the termiology to escribe which patter classes correspo to which i Theorem 1: is the smallest permutability of a set partitio ot i P. (We igore the case where this is 0 this correspos also to = 1.) We ow outlie the methos of proof of the lower a upper bous. For the lower bou, ote that by assumptio, all partitios of permutability 1 are i P. This will iclue all partitios of the form [σ 1,..., σ 1 ]. O [], there are (!) 1 such partitios, which alreay gives the esire lower bou. As usual, the upper bou is far more ifficult; we will simply escribe some of the techiques a lemmas ivolve. It suffices to show the result for classes give by avoiig oe elemet [σ 1,..., σ ], as every patter class with the correct asymptotic is cotaie i such a class. As with permutatios, where ofte it is ecessary to geeralize to 0 1-matrices, we may geeralize from set partitios to orere hypergraphs. Just as 0 1 matrices correspo to bipartite graphs, with permutatio matrices correspoig to matchigs or 1-regular bipartite graphs, set partitios correspo to 1-regular orere hypergraphs. I particular, the eges of a 1-regular orere hypergraph give a partitio of the vertex set []. This implies that we shoul look for some Fürei-Hajal type bou o hypergraphs that avoi a hypergraph of the esire form. We first make several efiitios. Defiitio. The orere hypergraph G cotais (respectively avois) the orere hypergraph H if there exists (respectively oes ot exist) a orer-preservig ijectio i V : V (H) V (G) a a a ijectio i E : E(H) E(G) that are compatible, i the sese that if v e E(H), the i V (v) i E (e). A -permutatio hypergraph is a hypergraph correspoig to some set partitio [σ 1,..., σ 1 ]. 2

3 Note that the efiitio esures that a -permutatio hypergraph is -uiform. We may ow state our Fürei-Hajal type lemma. Lemma 1. For a orere hypergraph G, efie i(g) := E. Let H be a -permutatio E E(G) hypergraph. The there exists a costat c such that for all Z + a orere hypergraphs G o [] avoiig H, i(g) c 1. The proof of the lemma is a techical iuctio, first provig a stroger statemet for t-uiform hypergraphs a the usig that to obtai the result for geeral graphs. Note that Lemma 1 is a geeralizatio of two results of Klazar a Marcus i [4], oe of which is simply the = 2 case of Lemma 1, a the other eals with -imesioal 0 1 matrices, which give a subset of the cases whe G is -uiform. Remark. This result is quite iterestig i its ow right, a prompts the questio of etermiig asymptotics of i(g) for various H (or to geeralize to patter classes of max G avois H V (G)=[] orere hypergraphs). From Lemma 1 it is ot ifficult to see that if H is 1-regular, correspoig to some set partitio π, the this is withi a costat o either sie of pm(π). For o-1-regular hypergraphs, the aswer may be more complicate; for example, if G is restricte to be a graph, Klazar shows i [3] that it is possible to achieve a fuctio that grows very slightly faster tha liearly. From Lemma 1 it is a routie recursive erivatio that the umber of orere hypergraphs G o [] that avoi some -permutatio hypergraph H is boue above asymptotically by c 1 for some costat c. Ufortuately, this is far greater tha the esire (1 1) 1, as we are ow cosierig geeral hypergraphs a ot just set partitios. However, a fial trick fiishes the solutio; give a set partitio i the form of a 1-regular hypergraph G o [], we may ivie the vertices ito s itervals of size. For each ege s E of the origial graph, we ca create a ege E of the ew graph cotaiig exactly those vertices correspoig to itervals i which E cotaie at least oe vertex. (We the elete uplicate eges.) If the resultig graph is G, we kow that we oly have c s 1 choices for G. The, we may bou how may set partitios correspo to a particular choice of G. Optimizig for s leas after some algebra to the esire bou. We may ow apply this result to permutatio-tuple patter avoiace, which we ow efie. Defiitio. Fix Z +. Let σ 1,..., σ S a σ 1,..., σ S m be permutatios. The the -tuple (σ 1,..., σ ) cotais (respectively avois) the -tuple (σ 1,..., σ ) if there exist (respectively o ot exist) iices i 1,..., i m [] with i 1 < < i m satisfyig the property that for ay j, σ j (i 1 ) σ j (i m ) has the same relative orerig as σ j(1) σ j(m). I other wors, a -tuple T 1 cotais aother -tuple T 2 if a oly if each permutatio i T 1 cotais the correspoig permutatio i T 2 at the same locatio. 3

4 It is ot ifficult to see that -permutatio-tuple avoiace is equivalet to set partitio avoiace where the set partitios are restricte to the form [σ 1,..., σ ]. That is, [σ 1,..., σ ] cotais [σ 1,..., σ ] if a oly if (σ 1,..., σ ) cotais (σ 1,..., σ ). This immeiately gives a upper bou o the umber of elemets of S that avoi a particular -tuple (σ 1,..., σ ): it is at most the umber of set partitios of [( + 1)] that ( avoi [σ 1,..., σ ]. By our earlier result this is at most c for some costat c. Itriguigly, the lower bou i this case also hols (this follows quickly from results i [1]) that is, the umber of elemets of S that avoi (σ 1,..., σ ) is at least c some costat c > 0. I sum, we have the followig result. ( for Theorem 2. Fix, m Z +, a σ 1,..., σ S m. For a particular, let S (σ 1,..., σ ) be the set of -tuples of permutatios i S that avoi (σ 1,..., σ ). The there exist costats c 2 > c 1 > 0 so that for all, c 1 ( S (σ 1,..., σ ) < c 2 ( 2 1 Whe = 1, Theorem 2 simply returs Staley-Wilf. For higher this is highly otrivial eve i simple cases for example, there oes ot seem to be a close form for the umber of pairs of permutatios i S that avoi (12, 12). Eve the asymptotics here are ot well boue: by Theorem 2 we kow that it will be boue withi a expoetial of (!) 3 2 (ote that i ay of these theorems, by Stirlig Approximatio, we ca replace by!), but the base of that expoetial factor is oly curretly kow (to the author s kowlege) to be boue betwee 1 a 3 3(3 log 3 4 log 2) 3.76, as prove i [2]. (This special case is the sequece of umber of pairs (σ 1, σ 2 ) S 2 with σ 1 σ 2 i the weak Bruhat orer.) There are several questios that remai here; most immeiately, i the case of -tuples, we have ot show ay classificatio of growth rates of patter classes like the oe we showe i the set partitio case. Of course, the patter class of every -tuple (that is, basis size 0) grows as (!) (or, to use the kis of expressios above, withi expoetially of ); Theorem 2 shows that ay patter class of -tuples of permutatios with exactly oe ( (otrivial) basis elemet grows withi expoetially of. Other growth orers are possible; we ca take the prouct of a patter class of 1 -tuples a a patter class of 2 - tuples to get a patter class of tuples. This immeiately implies that we may obtai k growth rates withi expoetial of c 1 where c =, where k, 1,..., k Z + with i=1 i k. Are there ay other posible growth rates? It is ot clear what we might expect from, for example, a arbitrary patter class of pairs of permutatios (so = 2) with basis size 2. The permutability statistic of partitios oes ot seem to be istribute accorig to other kow statistics of partitios. Determiig this istributio might help specify the costats i Theorem 1, as P is boue below by the umber of set partitios of [] permutability at most 1. ). 4

5 Refereces [1] Graham Brightwell, Raom k-dimesioal Orers: With a Number of Liear Extesios, Orer, Vol. 9, Iss. 4, pp , [2] Ewar Crae a Nic Georgiou, Notes o Atichais i the Raom k-dimesioal Orer, Bristol Workshop o Raom Atichais i k-dimesioal Partial Orers (2011), receive via persoal commuicatio with Aam Hammett 8/18/2016. [3] Marti Klazar, Extremal Problems for Orere (Hyper)graphs: Applicatios of Daveport-Schizel Sequeces, Europea Joural of Combiatorics, Vol. 25, Iss. 1, pp , [4] Marti Klazar a Aam Marcus, Extesios of the Liear Bou i the Fürei-Hajal Cojecture, Avaces i Applie Mathematics, Vol. 38, Iss. 2, pp , [5] Toufik Masour, Combiatorics of Set Partitios, CRC Press,

Irregularity strength of regular graphs

Irregularity strength of regular graphs Irregularity stregth of regular graphs Jakub Przyby lo AGH Uiversity of Sciece a Techology Al. Mickiewicza 30, 30-059 Kraków, Pola przybylo@wms.mat.agh.eu.pl Submitte: Nov 1, 007; Accepte: Ju 9, 00; Publishe:

More information

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a

n n B. How many subsets of C are there of cardinality n. We are selecting elements for such a 4. [10] Usig a combiatorial argumet, prove that for 1: = 0 = Let A ad B be disjoit sets of cardiality each ad C = A B. How may subsets of C are there of cardiality. We are selectig elemets for such a subset

More information

1 Graph Sparsfication

1 Graph Sparsfication CME 305: Discrete Mathematics ad Algorithms 1 Graph Sparsficatio I this sectio we discuss the approximatio of a graph G(V, E) by a sparse graph H(V, F ) o the same vertex set. I particular, we cosider

More information

On (K t e)-saturated Graphs

On (K t e)-saturated Graphs Noame mauscript No. (will be iserted by the editor O (K t e-saturated Graphs Jessica Fuller Roald J. Gould the date of receipt ad acceptace should be iserted later Abstract Give a graph H, we say a graph

More information

CIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8)

CIS 121 Data Structures and Algorithms with Java Fall Big-Oh Notation Tuesday, September 5 (Make-up Friday, September 8) CIS 11 Data Structures ad Algorithms with Java Fall 017 Big-Oh Notatio Tuesday, September 5 (Make-up Friday, September 8) Learig Goals Review Big-Oh ad lear big/small omega/theta otatios Practice solvig

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

Perhaps the method will give that for every e > U f() > p - 3/+e There is o o-trivial upper boud for f() ad ot eve f() < Z - e. seems to be kow, where

Perhaps the method will give that for every e > U f() > p - 3/+e There is o o-trivial upper boud for f() ad ot eve f() < Z - e. seems to be kow, where ON MAXIMUM CHORDAL SUBGRAPH * Paul Erdos Mathematical Istitute of the Hugaria Academy of Scieces ad Reu Laskar Clemso Uiversity 1. Let G() deote a udirected graph, with vertices ad V(G) deote the vertex

More information

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs

CHAPTER IV: GRAPH THEORY. Section 1: Introduction to Graphs CHAPTER IV: GRAPH THEORY Sectio : Itroductio to Graphs Sice this class is called Number-Theoretic ad Discrete Structures, it would be a crime to oly focus o umber theory regardless how woderful those topics

More information

Combination Labelings Of Graphs

Combination Labelings Of Graphs Applied Mathematics E-Notes, (0), - c ISSN 0-0 Available free at mirror sites of http://wwwmaththuedutw/ame/ Combiatio Labeligs Of Graphs Pak Chig Li y Received February 0 Abstract Suppose G = (V; E) is

More information

Big-O Analysis. Asymptotics

Big-O Analysis. Asymptotics Big-O Aalysis 1 Defiitio: Suppose that f() ad g() are oegative fuctios of. The we say that f() is O(g()) provided that there are costats C > 0 ad N > 0 such that for all > N, f() Cg(). Big-O expresses

More information

Lecture 2: Spectra of Graphs

Lecture 2: Spectra of Graphs Spectral Graph Theory ad Applicatios WS 20/202 Lecture 2: Spectra of Graphs Lecturer: Thomas Sauerwald & He Su Our goal is to use the properties of the adjacecy/laplacia matrix of graphs to first uderstad

More information

Streaming, Network Flow

Streaming, Network Flow CS 38 Itrouctio to Algorithms Week 8 Recitatio Notes TA: Joey Hog jhhog@caltech.eu) 1 Streamig Algorithms We ca imagie a situatio i which a stream of ata is beig recieve but there is too much ata comig

More information

The Adjacency Matrix and The nth Eigenvalue

The Adjacency Matrix and The nth Eigenvalue Spectral Graph Theory Lecture 3 The Adjacecy Matrix ad The th Eigevalue Daiel A. Spielma September 5, 2012 3.1 About these otes These otes are ot ecessarily a accurate represetatio of what happeed i class.

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Ruig Time of a algorithm Ruig Time Upper Bouds Lower Bouds Examples Mathematical facts Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite

More information

The isoperimetric problem on the hypercube

The isoperimetric problem on the hypercube The isoperimetric problem o the hypercube Prepared by: Steve Butler November 2, 2005 1 The isoperimetric problem We will cosider the -dimesioal hypercube Q Recall that the hypercube Q is a graph whose

More information

Lower Bounds for Sorting

Lower Bounds for Sorting Liear Sortig Topics Covered: Lower Bouds for Sortig Coutig Sort Radix Sort Bucket Sort Lower Bouds for Sortig Compariso vs. o-compariso sortig Decisio tree model Worst case lower boud Compariso Sortig

More information

. Written in factored form it is easy to see that the roots are 2, 2, i,

. Written in factored form it is easy to see that the roots are 2, 2, i, CMPS A Itroductio to Programmig Programmig Assigmet 4 I this assigmet you will write a java program that determies the real roots of a polyomial that lie withi a specified rage. Recall that the roots (or

More information

condition w i B i S maximum u i

condition w i B i S maximum u i ecture 10 Dyamic Programmig 10.1 Kapsack Problem November 1, 2004 ecturer: Kamal Jai Notes: Tobias Holgers We are give a set of items U = {a 1, a 2,..., a }. Each item has a weight w i Z + ad a utility

More information

University of Waterloo Department of Electrical and Computer Engineering ECE 250 Algorithms and Data Structures

University of Waterloo Department of Electrical and Computer Engineering ECE 250 Algorithms and Data Structures Uiversity of Waterloo Departmet of Electrical ad Computer Egieerig ECE 250 Algorithms ad Data Structures Midterm Examiatio ( pages) Istructor: Douglas Harder February 7, 2004 7:30-9:00 Name (last, first)

More information

Ones Assignment Method for Solving Traveling Salesman Problem

Ones Assignment Method for Solving Traveling Salesman Problem Joural of mathematics ad computer sciece 0 (0), 58-65 Oes Assigmet Method for Solvig Travelig Salesma Problem Hadi Basirzadeh Departmet of Mathematics, Shahid Chamra Uiversity, Ahvaz, Ira Article history:

More information

A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH

A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH J. Appl. Math. & Computig Vol. 21(2006), No. 1-2, pp. 233-238 Website: http://jamc.et A RELATIONSHIP BETWEEN BOUNDS ON THE SUM OF SQUARES OF DEGREES OF A GRAPH YEON SOO YOON AND JU KYUNG KIM Abstract.

More information

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 9. Pointers and Dynamic Arrays. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 9 Poiters ad Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 9.1 Poiters 9.2 Dyamic Arrays Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Slide 9-3

More information

MAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS

MAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS Fura Uiversity Electroic Joural of Udergraduate Matheatics Volue 00, 1996 6-16 MAXIMUM MATCHINGS IN COMPLETE MULTIPARTITE GRAPHS DAVID SITTON Abstract. How ay edges ca there be i a axiu atchig i a coplete

More information

New Results on Energy of Graphs of Small Order

New Results on Energy of Graphs of Small Order Global Joural of Pure ad Applied Mathematics. ISSN 0973-1768 Volume 13, Number 7 (2017), pp. 2837-2848 Research Idia Publicatios http://www.ripublicatio.com New Results o Eergy of Graphs of Small Order

More information

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract

On Infinite Groups that are Isomorphic to its Proper Infinite Subgroup. Jaymar Talledo Balihon. Abstract O Ifiite Groups that are Isomorphic to its Proper Ifiite Subgroup Jaymar Talledo Baliho Abstract Two groups are isomorphic if there exists a isomorphism betwee them Lagrage Theorem states that the order

More information

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance

Pseudocode ( 1.1) Analysis of Algorithms. Primitive Operations. Pseudocode Details. Running Time ( 1.1) Estimating performance Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Pseudocode ( 1.1) High-level descriptio of a algorithm More structured

More information

INTERSECTION CORDIAL LABELING OF GRAPHS

INTERSECTION CORDIAL LABELING OF GRAPHS INTERSECTION CORDIAL LABELING OF GRAPHS G Meea, K Nagaraja Departmet of Mathematics, PSR Egieerig College, Sivakasi- 66 4, Virudhuagar(Dist) Tamil Nadu, INDIA meeag9@yahoocoi Departmet of Mathematics,

More information

Matrix Partitions of Split Graphs

Matrix Partitions of Split Graphs Matrix Partitios of Split Graphs Tomás Feder, Pavol Hell, Ore Shklarsky Abstract arxiv:1306.1967v2 [cs.dm] 20 Ju 2013 Matrix partitio problems geeralize a umber of atural graph partitio problems, ad have

More information

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis

Analysis Metrics. Intro to Algorithm Analysis. Slides. 12. Alg Analysis. 12. Alg Analysis Itro to Algorithm Aalysis Aalysis Metrics Slides. Table of Cotets. Aalysis Metrics 3. Exact Aalysis Rules 4. Simple Summatio 5. Summatio Formulas 6. Order of Magitude 7. Big-O otatio 8. Big-O Theorems

More information

A Note on Chromatic Transversal Weak Domination in Graphs

A Note on Chromatic Transversal Weak Domination in Graphs Iteratioal Joural of Mathematics Treds ad Techology Volume 17 Number 2 Ja 2015 A Note o Chromatic Trasversal Weak Domiatio i Graphs S Balamuruga 1, P Selvalakshmi 2 ad A Arivalaga 1 Assistat Professor,

More information

c-dominating Sets for Families of Graphs

c-dominating Sets for Families of Graphs c-domiatig Sets for Families of Graphs Kelsie Syder Mathematics Uiversity of Mary Washigto April 6, 011 1 Abstract The topic of domiatio i graphs has a rich history, begiig with chess ethusiasts i the

More information

Computational Geometry

Computational Geometry Computatioal Geometry Chapter 4 Liear programmig Duality Smallest eclosig disk O the Ageda Liear Programmig Slides courtesy of Craig Gotsma 4. 4. Liear Programmig - Example Defie: (amout amout cosumed

More information

FURTHER INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS)

FURTHER INTEGRATION TECHNIQUES (TRIG, LOG, EXP FUNCTIONS) Mathematics Revisio Guides More Trigoometric ad Log Itegrals Page of 7 MK HOME TUITION Mathematics Revisio Guides Level: AS / A Level AQA : C Edexcel: C OCR: C OCR MEI: C FURTHER INTEGRATION TECHNIQUES

More information

Homework 1 Solutions MA 522 Fall 2017

Homework 1 Solutions MA 522 Fall 2017 Homework 1 Solutios MA 5 Fall 017 1. Cosider the searchig problem: Iput A sequece of umbers A = [a 1,..., a ] ad a value v. Output A idex i such that v = A[i] or the special value NIL if v does ot appear

More information

Average Connectivity and Average Edge-connectivity in Graphs

Average Connectivity and Average Edge-connectivity in Graphs Average Coectivity ad Average Edge-coectivity i Graphs Jaehoo Kim, Suil O July 1, 01 Abstract Coectivity ad edge-coectivity of a graph measure the difficulty of breakig the graph apart, but they are very

More information

Counting the Number of Minimum Roman Dominating Functions of a Graph

Counting the Number of Minimum Roman Dominating Functions of a Graph Coutig the Number of Miimum Roma Domiatig Fuctios of a Graph SHI ZHENG ad KOH KHEE MENG, Natioal Uiversity of Sigapore We provide two algorithms coutig the umber of miimum Roma domiatig fuctios of a graph

More information

Lecture 5. Counting Sort / Radix Sort

Lecture 5. Counting Sort / Radix Sort Lecture 5. Coutig Sort / Radix Sort T. H. Corme, C. E. Leiserso ad R. L. Rivest Itroductio to Algorithms, 3rd Editio, MIT Press, 2009 Sugkyukwa Uiversity Hyuseug Choo choo@skku.edu Copyright 2000-2018

More information

1.2 Binomial Coefficients and Subsets

1.2 Binomial Coefficients and Subsets 1.2. BINOMIAL COEFFICIENTS AND SUBSETS 13 1.2 Biomial Coefficiets ad Subsets 1.2-1 The loop below is part of a program to determie the umber of triagles formed by poits i the plae. for i =1 to for j =

More information

Octahedral Graph Scaling

Octahedral Graph Scaling Octahedral Graph Scalig Peter Russell Jauary 1, 2015 Abstract There is presetly o strog iterpretatio for the otio of -vertex graph scalig. This paper presets a ew defiitio for the term i the cotext of

More information

Sorting in Linear Time. Data Structures and Algorithms Andrei Bulatov

Sorting in Linear Time. Data Structures and Algorithms Andrei Bulatov Sortig i Liear Time Data Structures ad Algorithms Adrei Bulatov Algorithms Sortig i Liear Time 7-2 Compariso Sorts The oly test that all the algorithms we have cosidered so far is compariso The oly iformatio

More information

Thompson s Group F (p + 1) is not Minimally Almost Convex

Thompson s Group F (p + 1) is not Minimally Almost Convex Thompso s Group F (p + ) is ot Miimally Almost Covex Claire Wladis Thompso s Group F (p + ). A Descriptio of F (p + ) Thompso s group F (p + ) ca be defied as the group of piecewiseliear orietatio-preservig

More information

Algorithms Chapter 3 Growth of Functions

Algorithms Chapter 3 Growth of Functions Algorithms Chapter 3 Growth of Fuctios Istructor: Chig Chi Li 林清池助理教授 chigchi.li@gmail.com Departmet of Computer Sciece ad Egieerig Natioal Taiwa Ocea Uiversity Outlie Asymptotic otatio Stadard otatios

More information

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis

Outline and Reading. Analysis of Algorithms. Running Time. Experimental Studies. Limitations of Experiments. Theoretical Analysis Outlie ad Readig Aalysis of Algorithms Iput Algorithm Output Ruig time ( 3.) Pseudo-code ( 3.2) Coutig primitive operatios ( 3.3-3.) Asymptotic otatio ( 3.6) Asymptotic aalysis ( 3.7) Case study Aalysis

More information

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved.

Chapter 11. Friends, Overloaded Operators, and Arrays in Classes. Copyright 2014 Pearson Addison-Wesley. All rights reserved. Chapter 11 Frieds, Overloaded Operators, ad Arrays i Classes Copyright 2014 Pearso Addiso-Wesley. All rights reserved. Overview 11.1 Fried Fuctios 11.2 Overloadig Operators 11.3 Arrays ad Classes 11.4

More information

Algorithm. Counting Sort Analysis of Algorithms

Algorithm. Counting Sort Analysis of Algorithms Algorithm Coutig Sort Aalysis of Algorithms Assumptios: records Coutig sort Each record cotais keys ad data All keys are i the rage of 1 to k Space The usorted list is stored i A, the sorted list will

More information

An Efficient Algorithm for Graph Bisection of Triangularizations

An Efficient Algorithm for Graph Bisection of Triangularizations A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045 Oe Brookigs Drive St. Louis, Missouri 63130-4899, USA jaegerg@cse.wustl.edu

More information

Lecture 1: Introduction and Strassen s Algorithm

Lecture 1: Introduction and Strassen s Algorithm 5-750: Graduate Algorithms Jauary 7, 08 Lecture : Itroductio ad Strasse s Algorithm Lecturer: Gary Miller Scribe: Robert Parker Itroductio Machie models I this class, we will primarily use the Radom Access

More information

Recursive Estimation

Recursive Estimation Recursive Estimatio Raffaello D Adrea Sprig 2 Problem Set: Probability Review Last updated: February 28, 2 Notes: Notatio: Uless otherwise oted, x, y, ad z deote radom variables, f x (x) (or the short

More information

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence

9.1. Sequences and Series. Sequences. What you should learn. Why you should learn it. Definition of Sequence _9.qxd // : AM Page Chapter 9 Sequeces, Series, ad Probability 9. Sequeces ad Series What you should lear Use sequece otatio to write the terms of sequeces. Use factorial otatio. Use summatio otatio to

More information

Recursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle:

Recursion. Recursion. Mathematical induction: example. Recursion. The sum of the first n odd numbers is n 2 : Informal proof: Principle: Recursio Recursio Jordi Cortadella Departmet of Computer Sciece Priciple: Reduce a complex problem ito a simpler istace of the same problem Recursio Itroductio to Programmig Dept. CS, UPC 2 Mathematical

More information

15-859E: Advanced Algorithms CMU, Spring 2015 Lecture #2: Randomized MST and MST Verification January 14, 2015

15-859E: Advanced Algorithms CMU, Spring 2015 Lecture #2: Randomized MST and MST Verification January 14, 2015 15-859E: Advaced Algorithms CMU, Sprig 2015 Lecture #2: Radomized MST ad MST Verificatio Jauary 14, 2015 Lecturer: Aupam Gupta Scribe: Yu Zhao 1 Prelimiaries I this lecture we are talkig about two cotets:

More information

From last week. Lecture 5. Outline. Principles of programming languages

From last week. Lecture 5. Outline. Principles of programming languages Priciples of programmig laguages From last week Lecture 5 http://few.vu.l/~silvis/ppl/2007 Natalia Silvis-Cividjia e-mail: silvis@few.vu.l ML has o assigmet. Explai how to access a old bidig? Is & for

More information

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1

COSC 1P03. Ch 7 Recursion. Introduction to Data Structures 8.1 COSC 1P03 Ch 7 Recursio Itroductio to Data Structures 8.1 COSC 1P03 Recursio Recursio I Mathematics factorial Fiboacci umbers defie ifiite set with fiite defiitio I Computer Sciece sytax rules fiite defiitio,

More information

CS211 Fall 2003 Prelim 2 Solutions and Grading Guide

CS211 Fall 2003 Prelim 2 Solutions and Grading Guide CS11 Fall 003 Prelim Solutios ad Gradig Guide Problem 1: (a) obj = obj1; ILLEGAL because type of referece must always be a supertype of type of object (b) obj3 = obj1; ILLEGAL because type of referece

More information

Chapter 3 Classification of FFT Processor Algorithms

Chapter 3 Classification of FFT Processor Algorithms Chapter Classificatio of FFT Processor Algorithms The computatioal complexity of the Discrete Fourier trasform (DFT) is very high. It requires () 2 complex multiplicatios ad () complex additios [5]. As

More information

Math 10C Long Range Plans

Math 10C Long Range Plans Math 10C Log Rage Plas Uits: Evaluatio: Homework, projects ad assigmets 10% Uit Tests. 70% Fial Examiatio.. 20% Ay Uit Test may be rewritte for a higher mark. If the retest mark is higher, that mark will

More information

Area As A Limit & Sigma Notation

Area As A Limit & Sigma Notation Area As A Limit & Sigma Notatio SUGGESTED REFERENCE MATERIAL: As you work through the problems listed below, you should referece Chapter 5.4 of the recommeded textbook (or the equivalet chapter i your

More information

How do we evaluate algorithms?

How do we evaluate algorithms? F2 Readig referece: chapter 2 + slides Algorithm complexity Big O ad big Ω To calculate ruig time Aalysis of recursive Algorithms Next time: Litterature: slides mostly The first Algorithm desig methods:

More information

Takashi Tsuboi Graduate School of Mathematical Sciences, the University of Tokyo, Japan

Takashi Tsuboi Graduate School of Mathematical Sciences, the University of Tokyo, Japan TOPOLOGY Takashi Tsuboi Graduate School of Mathematical Scieces, the Uiversity of Tokyo, Japa Keywords: eighborhood, ope sets, metric space, covergece, cotiuity, homeomorphism, homotopy type, compactess,

More information

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved.

Chapter 1. Introduction to Computers and C++ Programming. Copyright 2015 Pearson Education, Ltd.. All rights reserved. Chapter 1 Itroductio to Computers ad C++ Programmig Copyright 2015 Pearso Educatio, Ltd.. All rights reserved. Overview 1.1 Computer Systems 1.2 Programmig ad Problem Solvig 1.3 Itroductio to C++ 1.4 Testig

More information

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming

Lecture Notes 6 Introduction to algorithm analysis CSS 501 Data Structures and Object-Oriented Programming Lecture Notes 6 Itroductio to algorithm aalysis CSS 501 Data Structures ad Object-Orieted Programmig Readig for this lecture: Carrao, Chapter 10 To be covered i this lecture: Itroductio to algorithm aalysis

More information

Data Structures and Algorithms. Analysis of Algorithms

Data Structures and Algorithms. Analysis of Algorithms Data Structures ad Algorithms Aalysis of Algorithms Outlie Ruig time Pseudo-code Big-oh otatio Big-theta otatio Big-omega otatio Asymptotic algorithm aalysis Aalysis of Algorithms Iput Algorithm Output

More information

Running Time. Analysis of Algorithms. Experimental Studies. Limitations of Experiments

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

More information

Propagation time for probabilistic zero forcing

Propagation time for probabilistic zero forcing Propagatio time for probabilistic zero forcig Jesse Geeso Leslie Hogbe arxiv:181.1076v1 [math.co] Dec 018 December 1, 018 Abstract Zero forcig is a colorig game playe o a graph that was itrouce more tha

More information

Running Time ( 3.1) Analysis of Algorithms. Experimental Studies. Limitations of Experiments

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

More information

Analysis of Algorithms

Analysis of Algorithms Aalysis of Algorithms Iput Algorithm Output A algorithm is a step-by-step procedure for solvig a problem i a fiite amout of time. Ruig Time Most algorithms trasform iput objects ito output objects. The

More information

Numerical Methods Lecture 6 - Curve Fitting Techniques

Numerical Methods Lecture 6 - Curve Fitting Techniques Numerical Methods Lecture 6 - Curve Fittig Techiques Topics motivatio iterpolatio liear regressio higher order polyomial form expoetial form Curve fittig - motivatio For root fidig, we used a give fuctio

More information

Strong Complementary Acyclic Domination of a Graph

Strong Complementary Acyclic Domination of a Graph Aals of Pure ad Applied Mathematics Vol 8, No, 04, 83-89 ISSN: 79-087X (P), 79-0888(olie) Published o 7 December 04 wwwresearchmathsciorg Aals of Strog Complemetary Acyclic Domiatio of a Graph NSaradha

More information

A graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n))

A graphical view of big-o notation. c*g(n) f(n) f(n) = O(g(n)) ca see that time required to search/sort grows with size of We How do space/time eeds of program grow with iput size? iput. time: cout umber of operatios as fuctio of iput Executio size operatio Assigmet:

More information

2. ALGORITHM ANALYSIS

2. ALGORITHM ANALYSIS 2. ALGORITHM ANALYSIS computatioal tractability survey of commo ruig times 2. ALGORITHM ANALYSIS computatioal tractability survey of commo ruig times Lecture slides by Kevi Waye Copyright 2005 Pearso-Addiso

More information

Protected points in ordered trees

Protected points in ordered trees Applied Mathematics Letters 008 56 50 www.elsevier.com/locate/aml Protected poits i ordered trees Gi-Sag Cheo a, Louis W. Shapiro b, a Departmet of Mathematics, Sugkyukwa Uiversity, Suwo 440-746, Republic

More information

3D Model Retrieval Method Based on Sample Prediction

3D Model Retrieval Method Based on Sample Prediction 20 Iteratioal Coferece o Computer Commuicatio ad Maagemet Proc.of CSIT vol.5 (20) (20) IACSIT Press, Sigapore 3D Model Retrieval Method Based o Sample Predictio Qigche Zhag, Ya Tag* School of Computer

More information

Consider the following population data for the state of California. Year Population

Consider the following population data for the state of California. Year Population Assigmets for Bradie Fall 2016 for Chapter 5 Assigmet sheet for Sectios 5.1, 5.3, 5.5, 5.6, 5.7, 5.8 Read Pages 341-349 Exercises for Sectio 5.1 Lagrage Iterpolatio #1, #4, #7, #13, #14 For #1 use MATLAB

More information

arxiv: v2 [cs.ds] 24 Mar 2018

arxiv: v2 [cs.ds] 24 Mar 2018 Similar Elemets ad Metric Labelig o Complete Graphs arxiv:1803.08037v [cs.ds] 4 Mar 018 Pedro F. Felzeszwalb Brow Uiversity Providece, RI, USA pff@brow.edu March 8, 018 We cosider a problem that ivolves

More information

NTH, GEOMETRIC, AND TELESCOPING TEST

NTH, GEOMETRIC, AND TELESCOPING TEST NTH, GEOMETRIC, AND TELESCOPING TEST Sectio 9. Calculus BC AP/Dual, Revised 08 viet.dag@humbleisd.et /4/08 0:0 PM 9.: th, Geometric, ad Telescopig Test SUMMARY OF TESTS FOR SERIES Lookig at the first few

More information

South Slave Divisional Education Council. Math 10C

South Slave Divisional Education Council. Math 10C South Slave Divisioal Educatio Coucil Math 10C Curriculum Package February 2012 12 Strad: Measuremet Geeral Outcome: Develop spatial sese ad proportioal reasoig It is expected that studets will: 1. Solve

More information

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem

Exact Minimum Lower Bound Algorithm for Traveling Salesman Problem Exact Miimum Lower Boud Algorithm for Travelig Salesma Problem Mohamed Eleiche GeoTiba Systems mohamed.eleiche@gmail.com Abstract The miimum-travel-cost algorithm is a dyamic programmig algorithm to compute

More information

Ch 9.3 Geometric Sequences and Series Lessons

Ch 9.3 Geometric Sequences and Series Lessons Ch 9.3 Geometric Sequeces ad Series Lessos SKILLS OBJECTIVES Recogize a geometric sequece. Fid the geeral, th term of a geometric sequece. Evaluate a fiite geometric series. Evaluate a ifiite geometric

More information

Some cycle and path related strongly -graphs

Some cycle and path related strongly -graphs Some cycle ad path related strogly -graphs I. I. Jadav, G. V. Ghodasara Research Scholar, R. K. Uiversity, Rajkot, Idia. H. & H. B. Kotak Istitute of Sciece,Rajkot, Idia. jadaviram@gmail.com gaurag ejoy@yahoo.co.i

More information

Counting Regions in the Plane and More 1

Counting Regions in the Plane and More 1 Coutig Regios i the Plae ad More 1 by Zvezdelia Stakova Berkeley Math Circle Itermediate I Group September 016 1. Overarchig Problem Problem 1 Regios i a Circle. The vertices of a polygos are arraged o

More information

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein

Lecture 6. Lecturer: Ronitt Rubinfeld Scribes: Chen Ziv, Eliav Buchnik, Ophir Arie, Jonathan Gradstein 068.670 Subliear Time Algorithms November, 0 Lecture 6 Lecturer: Roitt Rubifeld Scribes: Che Ziv, Eliav Buchik, Ophir Arie, Joatha Gradstei Lesso overview. Usig the oracle reductio framework for approximatig

More information

Module 8-7: Pascal s Triangle and the Binomial Theorem

Module 8-7: Pascal s Triangle and the Binomial Theorem Module 8-7: Pascal s Triagle ad the Biomial Theorem Gregory V. Bard April 5, 017 A Note about Notatio Just to recall, all of the followig mea the same thig: ( 7 7C 4 C4 7 7C4 5 4 ad they are (all proouced

More information

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0

Polynomial Functions and Models. Learning Objectives. Polynomials. P (x) = a n x n + a n 1 x n a 1 x + a 0, a n 0 Polyomial Fuctios ad Models 1 Learig Objectives 1. Idetify polyomial fuctios ad their degree 2. Graph polyomial fuctios usig trasformatios 3. Idetify the real zeros of a polyomial fuctio ad their multiplicity

More information

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS)

CSC165H1 Worksheet: Tutorial 8 Algorithm analysis (SOLUTIONS) CSC165H1, Witer 018 Learig Objectives By the ed of this worksheet, you will: Aalyse the ruig time of fuctios cotaiig ested loops. 1. Nested loop variatios. Each of the followig fuctios takes as iput a

More information

An Efficient Algorithm for Graph Bisection of Triangularizations

An Efficient Algorithm for Graph Bisection of Triangularizations Applied Mathematical Scieces, Vol. 1, 2007, o. 25, 1203-1215 A Efficiet Algorithm for Graph Bisectio of Triagularizatios Gerold Jäger Departmet of Computer Sciece Washigto Uiversity Campus Box 1045, Oe

More information

CMPT 125 Assignment 2 Solutions

CMPT 125 Assignment 2 Solutions CMPT 25 Assigmet 2 Solutios Questio (20 marks total) a) Let s cosider a iteger array of size 0. (0 marks, each part is 2 marks) it a[0]; I. How would you assig a poiter, called pa, to store the address

More information

THE COMPETITION NUMBERS OF JOHNSON GRAPHS

THE COMPETITION NUMBERS OF JOHNSON GRAPHS Discussioes Mathematicae Graph Theory 30 (2010 ) 449 459 THE COMPETITION NUMBERS OF JOHNSON GRAPHS Suh-Ryug Kim, Boram Park Departmet of Mathematics Educatio Seoul Natioal Uiversity, Seoul 151 742, Korea

More information

Optimum Solution of Quadratic Programming Problem: By Wolfe s Modified Simplex Method

Optimum Solution of Quadratic Programming Problem: By Wolfe s Modified Simplex Method Volume VI, Issue III, March 7 ISSN 78-5 Optimum Solutio of Quadratic Programmig Problem: By Wolfe s Modified Simple Method Kalpaa Lokhade, P. G. Khot & N. W. Khobragade, Departmet of Mathematics, MJP Educatioal

More information

Planar graphs. Definition. A graph is planar if it can be drawn on the plane in such a way that no two edges cross each other.

Planar graphs. Definition. A graph is planar if it can be drawn on the plane in such a way that no two edges cross each other. Plaar graphs Defiitio. A graph is plaar if it ca be draw o the plae i such a way that o two edges cross each other. Example: Face 1 Face 2 Exercise: Which of the followig graphs are plaar? K, P, C, K,m,

More information

CSE 417: Algorithms and Computational Complexity

CSE 417: Algorithms and Computational Complexity Time CSE 47: Algorithms ad Computatioal Readig assigmet Read Chapter of The ALGORITHM Desig Maual Aalysis & Sortig Autum 00 Paul Beame aalysis Problem size Worst-case complexity: max # steps algorithm

More information

Pattern Recognition Systems Lab 1 Least Mean Squares

Pattern Recognition Systems Lab 1 Least Mean Squares Patter Recogitio Systems Lab 1 Least Mea Squares 1. Objectives This laboratory work itroduces the OpeCV-based framework used throughout the course. I this assigmet a lie is fitted to a set of poits usig

More information

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13

CIS 121 Data Structures and Algorithms with Java Spring Stacks and Queues Monday, February 12 / Tuesday, February 13 CIS Data Structures ad Algorithms with Java Sprig 08 Stacks ad Queues Moday, February / Tuesday, February Learig Goals Durig this lab, you will: Review stacks ad queues. Lear amortized ruig time aalysis

More information

Python Programming: An Introduction to Computer Science

Python Programming: An Introduction to Computer Science Pytho Programmig: A Itroductio to Computer Sciece Chapter 1 Computers ad Programs 1 Objectives To uderstad the respective roles of hardware ad software i a computig system. To lear what computer scietists

More information

2 X = 2 X. The number of all permutations of a set X with n elements is. n! = n (n 1) (n 2) nn e n

2 X = 2 X. The number of all permutations of a set X with n elements is. n! = n (n 1) (n 2) nn e n 1 Discrete Mathematics revisited. Facts to remember Give set X, the umber of subsets of X is give by X = X. The umber of all permutatios of a set X with elemets is! = ( 1) ( )... 1 e π. The umber ( ) k

More information

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1

CS200: Hash Tables. Prichard Ch CS200 - Hash Tables 1 CS200: Hash Tables Prichard Ch. 13.2 CS200 - Hash Tables 1 Table Implemetatios: average cases Search Add Remove Sorted array-based Usorted array-based Balaced Search Trees O(log ) O() O() O() O(1) O()

More information

Mathematical Stat I: solutions of homework 1

Mathematical Stat I: solutions of homework 1 Mathematical Stat I: solutios of homework Name: Studet Id N:. Suppose we tur over cards simultaeously from two well shuffled decks of ordiary playig cards. We say we obtai a exact match o a particular

More information

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA

Creating Exact Bezier Representations of CST Shapes. David D. Marshall. California Polytechnic State University, San Luis Obispo, CA , USA Creatig Exact Bezier Represetatios of CST Shapes David D. Marshall Califoria Polytechic State Uiversity, Sa Luis Obispo, CA 93407-035, USA The paper presets a method of expressig CST shapes pioeered by

More information

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method

A New Morphological 3D Shape Decomposition: Grayscale Interframe Interpolation Method A ew Morphological 3D Shape Decompositio: Grayscale Iterframe Iterpolatio Method D.. Vizireau Politehica Uiversity Bucharest, Romaia ae@comm.pub.ro R. M. Udrea Politehica Uiversity Bucharest, Romaia mihea@comm.pub.ro

More information

On Ryser s conjecture for t-intersecting and degree-bounded hypergraphs arxiv: v2 [math.co] 9 Dec 2017

On Ryser s conjecture for t-intersecting and degree-bounded hypergraphs arxiv: v2 [math.co] 9 Dec 2017 O Ryser s cojecture for t-itersectig ad degree-bouded hypergraphs arxiv:1705.1004v [math.co] 9 Dec 017 Zoltá Király Departmet of Computer Sciece ad Egerváry Research Group (MTA-ELTE) Eötvös Uiversity Pázmáy

More information

The Magma Database file formats

The Magma Database file formats The Magma Database file formats Adrew Gaylard, Bret Pikey, ad Mart-Mari Breedt Johaesburg, South Africa 15th May 2006 1 Summary Magma is a ope-source object database created by Chris Muller, of Kasas City,

More information