Tight triangulations: a link between combinatorics and topology

Size: px
Start display at page:

Download "Tight triangulations: a link between combinatorics and topology"

Transcription

1 Tight tringultions: link between ombintoris nd topology Jonthn Spreer Melbourne, August 15, 2016

2 Topologil mnifolds (Geometri) Topology is study of mnifolds (surfes) up to ontinuous deformtion Complited deformtions mke reognition of mnifold diult (if not impossible) Reognition might still be esy for suiently nie deformtions

3 Convexity Topologil blls / spheres onvexity Limited use in geometry nd topology: mny objets re not blls / nnot be onvex Intuitive notion: mny objets look more onvex thn others

4 Tightness An embedding of topologil spe D into some Euliden spe E d is sid to be tight, if it is s onvex s possible given its topologil onstrints D is tight if nd only if interseting D with hlf spe h does not introdue new topologil fetures (eg. onneted omponents or holes) h H 0 (D) = F H 0 (D h) = F 2 D D Tightness generlises onvexity, nd minimises totl bsolute urvture. 1 1 Developed by Alexndrov, 1938; Milnor, Chern nd Lshof, Kuiper, 1950's.

5 Tightness

6 Morse theory Möbius. Theorie der elementren Verwndtshft, 1863

7 Morse theory Given mnifold M, look t funtion f M R f hs nitely mny ritil levels f 1 (α) f height funtion, f 1 (M) ontour lines Denes hndle deomposition of M nite desription of M Powerful: used to prove h-obordism theorem

8 Morse theory nd tightness Some height funtions re better thn others: fewer ritil levels mens more eient hndle deomposition How good n Morse funtion be? Theorem (Morse reltions) Let M be mnifold, nd let f M R be Morse funtion. Then µ(f ) d i=0 β i (M, Z 2 ) where β i (M, Z 2 ) is the i-th Betti number of M with Z 2 -oeients. If f ttins equlity, it is lled perfet Diretion long whih n objet ppers tight Embedding is tight if nd only if ll projetions re perfet Morse funtions. Deiding whether suh funtion exist is NP-omplete. 2 Is deiding whether ll Morse funtions re perfet onp-omplete? 2 Joswig nd Pfetsh 2006.

9 Finite desription of mnifolds Represent mnifolds (surfes) s simpliil omplexes ,2,4, 1,2,6, 1,3,4, 1,3,7, 1,5,6, 1,5,7, 2,3,5, 2,3,7, 2,4,5, 2,6,7, 3,4,6, 3,5,6, 4,5,7, 4,6, A PL tringultion of mnifold M is simpliil omplex C suh tht every vertex link is PL homeomorphi to the stndrd sphere stc(6) lkc(6) C 3 Question: n we link ombintoril properties of C to topologil properties of M? 2 b d

10 Finite desription of mnifolds Represent mnifolds (surfes) s simpliil omplexes ,2,4, 1,2,6, 1,3,4, 1,3,7, 1,5,6, 1,5,7, 2,3,5, 2,3,7, 2,4,5, 2,6,7, 3,4,6, 3,5,6, 4,5,7, 4,6, A PL tringultion of mnifold M is simpliil omplex C suh tht every vertex link is PL homeomorphi to the stndrd sphere stc(6) lkc(6) C 3 Question: n we link ombintoril properties of C to topologil properties of M? 2 b d Euler Chrteristi

11 Finite desription of mnifolds Represent mnifolds (surfes) s simpliil omplexes ,2,4, 1,2,6, 1,3,4, 1,3,7, 1,5,6, 1,5,7, 2,3,5, 2,3,7, 2,4,5, 2,6,7, 3,4,6, 3,5,6, 4,5,7, 4,6, A PL tringultion of mnifold M is simpliil omplex C suh tht every vertex link is PL homeomorphi to the stndrd sphere stc(6) lkc(6) C 3 Question: n we link ombintoril properties of C to topologil properties of M? Euler Chrteristi, rst Z 2 -Betti number(?) 2 b d

12 Abstrt disrete version of tightness Gol: intrinsi intertion between ombintoris nd topology. C onneted bstrt simpliil omplex with vertex set V (C). W V (C), C[W ] subomplex of C indued by W. C is tight if nd only if W V (C), C[W ] does not introdue ny new topologil fetures. 3 Tight tringultions re onjetured to be strongly miniml 4 3 Bnho 1970, Kühnel Kühnel Lutz

13 Tightness ounting topologil fetures New top. fetures non-perfet PL Morse funtion Compute verge no. of ritil levels per PL Morse funtion For eh subset W V (C), think of PL Morse funtions f C R, s.t. f () < f (b) for ll pirs W, b V (C) W Count ll suh Morse funtions Count topologil fetures of lkc (v) W, for ll v V (C) Morse reltion: Weighted sum (verge) must equl sum of Betti numbers

14 Seprtion nd µ index Denition C simpliil omplex with n verties, the µ index of C is given by where µ(c) = 1 n σ(lk C (v)) = σ(lk C (v)), v V (C) is lled the seprtion index of lk C (v). #(lk C (v)[w ]) 1 W V (C) ( n W ) Theorem (Combintoril Morse reltions) C tringultion of (onn.) mnifold M. Then µ(c) β 1 (M, Z 2 ).

15 Known tight tringultions dim(c) 2: C tight every pir of verties of C spns edge Surfe types dmitting tight tringultions: most orientble nd non-orientble surfes S with Euler hrteristi k Z, k > 3. χ(s) = 1 k(k 7), 6 Mnifolds known to dmit tight tringultions in dim > 2: K 3 surfe, CP 2, SU(3)/SO(3), innitely mny sphere bundles Fous of this tlk is dim(c) = 3: Equlity in ombintoril Morse reltion if nd only if C is tight. 5 whih we will show is purely ombintoril ondition

16 Known tight tringultions dim(c) 2: C tight every pir of verties of C spns edge Surfe types dmitting tight tringultions: most orientble nd non-orientble surfes S with Euler hrteristi k Z, k > 3. χ(s) = 1 k(k 7), 6 Mnifolds known to dmit tight tringultions in dim > 2: K 3 surfe, CP 2, SU(3)/SO(3), innitely mny sphere bundles Fous of this tlk is dim(c) = 3: Equlity in ombintoril Morse reltion if nd only if C is tight. Result: Tight tringultions of 3-mnifolds re strongly miniml, nd must hve stked 2-spheres s vertex links 5 5 whih we will show is purely ombintoril ondition

17 Stked spheres A stked (d + 1)-bll is dened reursively: A (d + 1)-simplex is stked (d + 1)-bll. A simpliil omplex obtined from stked (d + 1)-bll B by gluing (d + 1)-simplex long d-boundry fe of B is stked (d + 1)-bll. Stked (d 1)-sphere: boundry omplex of stked d-bll.

18 Stked spheres A stked (d + 1)-bll is dened reursively: A (d + 1)-simplex is stked (d + 1)-bll. A simpliil omplex obtined from stked (d + 1)-bll B by gluing (d + 1)-simplex long d-boundry fe of B is stked (d + 1)-bll. tringulted (d 1)-spheres boundries of simpliil d-polytopes stked (d 1)-spheres Stked (d 1)-sphere: boundry omplex of stked d-bll.

19 Stked spheres We know: µ is n upper bound for the rst Betti number Question: given simpliil omplex C with n verties, how lrge n µ(c) be? Mximise onneted omponents of subsets of links (i.e., mximise σ(lk C (v)) Intuition: mximum is ttined when number of edges is smll Theorem (Kli 1987) Let S be tringulted d-sphere, d 3. Then S hs t lest s mny edges s stked d-sphere with equlity i S is stked.

20 Stked spheres Vertex links lk C (v) re tringultions of the 2-sphere with n verties, e edges nd t tringles. 2e = 3t (every edge is ontined in extly two tringles) n e + t = 2 (Euler hrteristi) f (S) = (n, 3n 6, 2n 4) number of edges is lwys the sme

21 Stked spheres Vertex links lk C (v) re tringultions of the 2-sphere with n verties, e edges nd t tringles. 2e = 3t (every edge is ontined in extly two tringles) n e + t = 2 (Euler hrteristi) f (S) = (n, 3n 6, 2n 4) number of edges is lwys the sme

22 Stked spheres Vertex links lk C (v) re tringultions of the 2-sphere with n verties, e edges nd t tringles. 2e = 3t (every edge is ontined in extly two tringles) n e + t = 2 (Euler hrteristi) f (S) = (n, 3n 6, 2n 4) number of edges is lwys the sme Tringultion S σ(s) Tringultion S σ(s) 0 1/5 b b 27/35 5/7 b b 11/9 71/63 b b 23/21 22/21 b b

23 Stked spheres Theorem (Burton, Dtt, Singh, S. 2014) Let S be n n-vertex tringulted 2-sphere. Then (n 8)(n + 1) σ(s), 20 where equlity ours if nd only if S is stked sphere. Bound on σ

24 Stked spheres Theorem (Burton, Dtt, Singh, S. 2014) Let S be n n-vertex tringulted 2-sphere. Then (n 8)(n + 1) σ(s), 20 where equlity ours if nd only if S is stked sphere. Bound on σ Bound on µ

25 Stked spheres Theorem (Burton, Dtt, Singh, S. 2014) Let S be n n-vertex tringulted 2-sphere. Then (n 8)(n + 1) σ(s), 20 where equlity ours if nd only if S is stked sphere. Bound on σ Bound on µ Bound on β 1 (M, Z 2 )

26 Results Corollry (See lso Lutz, Sulnke, Swrtz, 2008) Let C be tringultion of 3-mnifold M with n verties. Then n 1 2 ( β 1 (M, Z 2 )) nd C is tight if equlity is ttined.

27 Results Corollry (See lso Lutz, Sulnke, Swrtz, 2008) Let C be tringultion of 3-mnifold M with n verties. Then n 1 2 ( β 1 (M, Z 2 )) nd C is tight if equlity is ttined. Theorem (Bghi, Dtt, S. 2016) Let C be tight tringultion of 3-mnifold with n verties. Then ll of its vertex links re (n 1)-vertex stked 2-spheres.

28 Results Corollry (See lso Lutz, Sulnke, Swrtz, 2008) Let C be tringultion of 3-mnifold M with n verties. Then n 1 2 ( β 1 (M, Z 2 )) nd C is tight if nd only if equlity is ttined. Theorem (Bghi, Dtt, S. 2016) Let C be tight tringultion of 3-mnifold with n verties. Then ll of its vertex links re (n 1)-vertex stked 2-spheres.

29 Results Corollry (See lso Lutz, Sulnke, Swrtz, 2008) Let C be tringultion of 3-mnifold M with n verties. Then n 1 2 ( β 1 (M, Z 2 )) nd C is tight if nd only if equlity is ttined. Theorem (Bghi, Dtt, S. 2016) Let C be tight tringultion of 3-mnifold with n verties. Then ll of its vertex links re (n 1)-vertex stked 2-spheres. Tightness for 3-mnifold is purely ombintoril

30 Results Corollry (See lso Lutz, Sulnke, Swrtz, 2008) Let C be tringultion of 3-mnifold M with n verties. Then n 1 2 ( β 1 (M, Z 2 )) nd C is tight if nd only if equlity is ttined. Theorem (Bghi, Dtt, S. 2016) Let C be tight tringultion of 3-mnifold with n verties. Then ll of its vertex links re (n 1)-vertex stked 2-spheres. Tightness for 3-mnifold is purely ombintoril Tight tringultions of 3-mnifolds re miniml, nd their rst Betti number is given by their number of verties

31 Thnk you B. Burton, B. Dtt, N. Singh, J. Spreer. Seprtion index of grphs nd stked 2-spheres. J. Combin. Theory (A), 136: , B. Bghi, B. Dtt, J. Spreer. A hrteriztion of tightly tringulted 3-mnifolds, rxiv:

Lecture 8: Graph-theoretic problems (again)

Lecture 8: Graph-theoretic problems (again) COMP36111: Advned Algorithms I Leture 8: Grph-theoreti prolems (gin) In Prtt-Hrtmnn Room KB2.38: emil: iprtt@s.mn..uk 2017 18 Reding for this leture: Sipser: Chpter 7. A grph is pir G = (V, E), where V

More information

10.2 Graph Terminology and Special Types of Graphs

10.2 Graph Terminology and Special Types of Graphs 10.2 Grph Terminology n Speil Types of Grphs Definition 1. Two verties u n v in n unirete grph G re lle jent (or neighors) in G iff u n v re enpoints of n ege e of G. Suh n ege e is lle inient with the

More information

12/9/14. CS151 Fall 20124Lecture (almost there) 12/6. Graphs. Seven Bridges of Königsberg. Leonard Euler

12/9/14. CS151 Fall 20124Lecture (almost there) 12/6. Graphs. Seven Bridges of Königsberg. Leonard Euler CS5 Fll 04Leture (lmost there) /6 Seven Bridges of Königserg Grphs Prof. Tny Berger-Wolf Leonrd Euler 707-783 Is it possile to wlk with route tht rosses eh ridge e Seven Bridges of Königserg Forget unimportnt

More information

Adjacency. Adjacency Two vertices u and v are adjacent if there is an edge connecting them. This is sometimes written as u v.

Adjacency. Adjacency Two vertices u and v are adjacent if there is an edge connecting them. This is sometimes written as u v. Terminology Adjeny Adjeny Two verties u nd v re djent if there is n edge onneting them. This is sometimes written s u v. v v is djent to nd ut not to. 2 / 27 Neighourhood Neighourhood The open neighourhood

More information

Chapter 4 Fuzzy Graph and Relation

Chapter 4 Fuzzy Graph and Relation Chpter 4 Fuzzy Grph nd Reltion Grph nd Fuzzy Grph! Grph n G = (V, E) n V : Set of verties(node or element) n E : Set of edges An edge is pir (x, y) of verties in V.! Fuzzy Grph ~ n ( ~ G = V, E) n V :

More information

Lesson 4.4. Euler Circuits and Paths. Explore This

Lesson 4.4. Euler Circuits and Paths. Explore This Lesson 4.4 Euler Ciruits nd Pths Now tht you re fmilir with some of the onepts of grphs nd the wy grphs onvey onnetions nd reltionships, it s time to egin exploring how they n e used to model mny different

More information

Lecture 12 : Topological Spaces

Lecture 12 : Topological Spaces Leture 12 : Topologil Spes 1 Topologil Spes Topology generlizes notion of distne nd loseness et. Definition 1.1. A topology on set X is olletion T of susets of X hving the following properties. 1. nd X

More information

6.045J/18.400J: Automata, Computability and Complexity. Quiz 2: Solutions. Please write your name in the upper corner of each page.

6.045J/18.400J: Automata, Computability and Complexity. Quiz 2: Solutions. Please write your name in the upper corner of each page. 6045J/18400J: Automt, Computbility nd Complexity Mrh 30, 2005 Quiz 2: Solutions Prof Nny Lynh Vinod Vikuntnthn Plese write your nme in the upper orner of eh pge Problem Sore 1 2 3 4 5 6 Totl Q2-1 Problem

More information

Unit #9 : Definite Integral Properties, Fundamental Theorem of Calculus

Unit #9 : Definite Integral Properties, Fundamental Theorem of Calculus Unit #9 : Definite Integrl Properties, Fundmentl Theorem of Clculus Gols: Identify properties of definite integrls Define odd nd even functions, nd reltionship to integrl vlues Introduce the Fundmentl

More information

If you are at the university, either physically or via the VPN, you can download the chapters of this book as PDFs.

If you are at the university, either physically or via the VPN, you can download the chapters of this book as PDFs. Lecture 5 Wlks, Trils, Pths nd Connectedness Reding: Some of the mteril in this lecture comes from Section 1.2 of Dieter Jungnickel (2008), Grphs, Networks nd Algorithms, 3rd edition, which is ville online

More information

CS311H: Discrete Mathematics. Graph Theory IV. A Non-planar Graph. Regions of a Planar Graph. Euler s Formula. Instructor: Işıl Dillig

CS311H: Discrete Mathematics. Graph Theory IV. A Non-planar Graph. Regions of a Planar Graph. Euler s Formula. Instructor: Işıl Dillig CS311H: Discrete Mthemtics Grph Theory IV Instructor: Işıl Dillig Instructor: Işıl Dillig, CS311H: Discrete Mthemtics Grph Theory IV 1/25 A Non-plnr Grph Regions of Plnr Grph The plnr representtion of

More information

Paradigm 5. Data Structure. Suffix trees. What is a suffix tree? Suffix tree. Simple applications. Simple applications. Algorithms

Paradigm 5. Data Structure. Suffix trees. What is a suffix tree? Suffix tree. Simple applications. Simple applications. Algorithms Prdigm. Dt Struture Known exmples: link tble, hep, Our leture: suffix tree Will involve mortize method tht will be stressed shortly in this ourse Suffix trees Wht is suffix tree? Simple pplitions History

More information

Width and Bounding Box of Imprecise Points

Width and Bounding Box of Imprecise Points Width nd Bounding Box of Impreise Points Vhideh Keikh Mrten Löffler Ali Mohdes Zhed Rhmti Astrt In this pper we study the following prolem: we re given set L = {l 1,..., l n } of prllel line segments,

More information

Line The set of points extending in two directions without end uniquely determined by two points. The set of points on a line between two points

Line The set of points extending in two directions without end uniquely determined by two points. The set of points on a line between two points Lines Line Line segment Perpendiulr Lines Prllel Lines Opposite Angles The set of points extending in two diretions without end uniquely determined by two points. The set of points on line between two

More information

and vertically shrinked by

and vertically shrinked by 1. A first exmple 1.1. From infinite trnsltion surfe mp to end-periodi mp. We begin with n infinite hlf-trnsltion surfe M 0 desribed s in Figure 1 nd n ffine mp f 0 defined s follows: the surfe is horizontlly

More information

Final Exam Review F 06 M 236 Be sure to look over all of your tests, as well as over the activities you did in the activity book

Final Exam Review F 06 M 236 Be sure to look over all of your tests, as well as over the activities you did in the activity book inl xm Review 06 M 236 e sure to loo over ll of your tests, s well s over the tivities you did in the tivity oo 1 1. ind the mesures of the numered ngles nd justify your wor. Line j is prllel to line.

More information

Journal of Combinatorial Theory, Series A

Journal of Combinatorial Theory, Series A Journl of Comintoril Theory, Series A 0 (0) Contents lists ville t SiVerse SieneDiret Journl of Comintoril Theory, Series A www.elsevier.om/lote/jt Spheril tiling y ongruent pentgons Hongho Go, Nn Shi,

More information

Duality in linear interval equations

Duality in linear interval equations Aville online t http://ijim.sriu..ir Int. J. Industril Mthemtis Vol. 1, No. 1 (2009) 41-45 Dulity in liner intervl equtions M. Movhedin, S. Slhshour, S. Hji Ghsemi, S. Khezerloo, M. Khezerloo, S. M. Khorsny

More information

Greedy Algorithm. Algorithm Fall Semester

Greedy Algorithm. Algorithm Fall Semester Greey Algorithm Algorithm 0 Fll Semester Optimiztion prolems An optimiztion prolem is one in whih you wnt to fin, not just solution, ut the est solution A greey lgorithm sometimes works well for optimiztion

More information

3D convex hulls. Convex Hull in 3D. convex polyhedron. convex polyhedron. The problem: Given a set P of points in 3D, compute their convex hull

3D convex hulls. Convex Hull in 3D. convex polyhedron. convex polyhedron. The problem: Given a set P of points in 3D, compute their convex hull Convex Hull in The rolem: Given set P of oints in, omute their onvex hull onvex hulls Comuttionl Geometry [si 3250] Lur Tom Bowoin College onvex olyheron 1 2 3 olygon olyheron onvex olyheron 4 5 6 Polyheron

More information

Lesson6: Modeling the Web as a graph Unit5: Linear Algebra for graphs

Lesson6: Modeling the Web as a graph Unit5: Linear Algebra for graphs Lesson6: Modeling the We s grph Unit5: Liner Alger for grphs Rene Pikhrdt Introdution to We Siene Prt 2 Emerging We Properties Rene Pikhrdt Institute CC-BY-SA-3. for We Siene nd Tehnologies Modeling the

More information

Lecture 13: Graphs I: Breadth First Search

Lecture 13: Graphs I: Breadth First Search Leture 13 Grphs I: BFS 6.006 Fll 2011 Leture 13: Grphs I: Bredth First Serh Leture Overview Applitions of Grph Serh Grph Representtions Bredth-First Serh Rell: Grph G = (V, E) V = set of verties (ritrry

More information

INTRODUCTION TO SIMPLICIAL COMPLEXES

INTRODUCTION TO SIMPLICIAL COMPLEXES INTRODUCTION TO SIMPLICIAL COMPLEXES CASEY KELLEHER AND ALESSANDRA PANTANO 0.1. Introduction. In this ctivity set we re going to introduce notion from Algebric Topology clled simplicil homology. The min

More information

Minimal Memory Abstractions

Minimal Memory Abstractions Miniml Memory Astrtions (As implemented for BioWre Corp ) Nthn Sturtevnt University of Alert GAMES Group Ferury, 7 Tlk Overview Prt I: Building Astrtions Minimizing memory requirements Performnes mesures

More information

MITSUBISHI ELECTRIC RESEARCH LABORATORIES Cambridge, Massachusetts. Introduction to Matroids and Applications. Srikumar Ramalingam

MITSUBISHI ELECTRIC RESEARCH LABORATORIES Cambridge, Massachusetts. Introduction to Matroids and Applications. Srikumar Ramalingam Cmrige, Msshusetts Introution to Mtrois n Applitions Srikumr Rmlingm MERL mm//yy Liner Alger (,0,0) (0,,0) Liner inepenene in vetors: v, v2,..., For ll non-trivil we hve s v s v n s, s2,..., s n 2v2...

More information

MATH 25 CLASS 5 NOTES, SEP

MATH 25 CLASS 5 NOTES, SEP MATH 25 CLASS 5 NOTES, SEP 30 2011 Contents 1. A brief diversion: reltively prime numbers 1 2. Lest common multiples 3 3. Finding ll solutions to x + by = c 4 Quick links to definitions/theorems Euclid

More information

COMP108 Algorithmic Foundations

COMP108 Algorithmic Foundations Grph Theory Prudene Wong http://www.s.liv..uk/~pwong/tehing/omp108/201617 How to Mesure 4L? 3L 5L 3L ontiner & 5L ontiner (without mrk) infinite supply of wter You n pour wter from one ontiner to nother

More information

Calculus Differentiation

Calculus Differentiation //007 Clulus Differentition Jeffrey Seguritn person in rowot miles from the nerest point on strit shoreline wishes to reh house 6 miles frther down the shore. The person n row t rte of mi/hr nd wlk t rte

More information

Ma/CS 6b Class 1: Graph Recap

Ma/CS 6b Class 1: Graph Recap M/CS 6 Clss 1: Grph Recp By Adm Sheffer Course Detils Adm Sheffer. Office hour: Tuesdys 4pm. dmsh@cltech.edu TA: Victor Kstkin. Office hour: Tuesdys 7pm. 1:00 Mondy, Wednesdy, nd Fridy. http://www.mth.cltech.edu/~2014-15/2term/m006/

More information

Pointwise convergence need not behave well with respect to standard properties such as continuity.

Pointwise convergence need not behave well with respect to standard properties such as continuity. Chpter 3 Uniform Convergence Lecture 9 Sequences of functions re of gret importnce in mny res of pure nd pplied mthemtics, nd their properties cn often be studied in the context of metric spces, s in Exmples

More information

MATH 2530: WORKSHEET 7. x 2 y dz dy dx =

MATH 2530: WORKSHEET 7. x 2 y dz dy dx = MATH 253: WORKSHT 7 () Wrm-up: () Review: polr coordintes, integrls involving polr coordintes, triple Riemnn sums, triple integrls, the pplictions of triple integrls (especilly to volume), nd cylindricl

More information

[SYLWAN., 158(6)]. ISI

[SYLWAN., 158(6)]. ISI The proposl of Improved Inext Isomorphi Grph Algorithm to Detet Design Ptterns Afnn Slem B-Brhem, M. Rizwn Jmeel Qureshi Fulty of Computing nd Informtion Tehnology, King Adulziz University, Jeddh, SAUDI

More information

Compact Drawings of 1-Planar Graphs with Right-Angle Crossings and Few Bends

Compact Drawings of 1-Planar Graphs with Right-Angle Crossings and Few Bends Compt Drwings of 1-Plnr Grphs with Right-Angle Crossings nd Few Bends Steven Chplik, Fbin Lipp, Alexnder Wolff, nd Johnnes Zink Lehrstuhl für Informtik I, Universität Würzburg, Germny http://www1.informtik.uni-wuerzburg.de/en/stff

More information

Directed Rectangle-Visibility Graphs have. Abstract. Visibility representations of graphs map vertices to sets in Euclidean space and

Directed Rectangle-Visibility Graphs have. Abstract. Visibility representations of graphs map vertices to sets in Euclidean space and Direted Retangle-Visibility Graphs have Unbounded Dimension Kathleen Romanik DIMACS Center for Disrete Mathematis and Theoretial Computer Siene Rutgers, The State University of New Jersey P.O. Box 1179,

More information

Correcting the Dynamic Call Graph Using Control Flow Constraints

Correcting the Dynamic Call Graph Using Control Flow Constraints Correting the Dynmi Cll Grph Using Control Flow Constrints Byeongheol Lee Kevin Resnik Mihel Bond Kthryn MKinley 1 Appered in CC2007 Motivtion Complexity of lrge objet oriented progrms Deompose the progrm

More information

Ma/CS 6b Class 1: Graph Recap

Ma/CS 6b Class 1: Graph Recap M/CS 6 Clss 1: Grph Recp By Adm Sheffer Course Detils Instructor: Adm Sheffer. TA: Cosmin Pohot. 1pm Mondys, Wednesdys, nd Fridys. http://mth.cltech.edu/~2015-16/2term/m006/ Min ook: Introduction to Grph

More information

CS 241 Week 4 Tutorial Solutions

CS 241 Week 4 Tutorial Solutions CS 4 Week 4 Tutoril Solutions Writing n Assemler, Prt & Regulr Lnguges Prt Winter 8 Assemling instrutions utomtilly. slt $d, $s, $t. Solution: $d, $s, nd $t ll fit in -it signed integers sine they re 5-it

More information

50 AMC LECTURES Lecture 2 Analytic Geometry Distance and Lines. can be calculated by the following formula:

50 AMC LECTURES Lecture 2 Analytic Geometry Distance and Lines. can be calculated by the following formula: 5 AMC LECTURES Lecture Anlytic Geometry Distnce nd Lines BASIC KNOWLEDGE. Distnce formul The distnce (d) between two points P ( x, y) nd P ( x, y) cn be clculted by the following formul: d ( x y () x )

More information

Solids. Solids. Curriculum Ready.

Solids. Solids. Curriculum Ready. Curriulum Rey www.mthletis.om This ooklet is ll out ientifying, rwing n mesuring solis n prisms. SOM CUES The Som Cue ws invente y Dnish sientist who went y the nme of Piet Hein. It is simple 3 # 3 #

More information

such that the S i cover S, or equivalently S

such that the S i cover S, or equivalently S MATH 55 Triple Integrls Fll 16 1. Definition Given solid in spce, prtition of consists of finite set of solis = { 1,, n } such tht the i cover, or equivlently n i. Furthermore, for ech i, intersects i

More information

Can Pythagoras Swim?

Can Pythagoras Swim? Overview Ativity ID: 8939 Mth Conepts Mterils Students will investigte reltionships etween sides of right tringles to understnd the Pythgoren theorem nd then use it to solve prolems. Students will simplify

More information

Bayesian Networks: Directed Markov Properties (Cont d) and Markov Equivalent DAGs

Bayesian Networks: Directed Markov Properties (Cont d) and Markov Equivalent DAGs Byesin Networks: Direte Mrkov Properties (Cont ) n Mrkov Equivlent DAGs Huizhen Yu jney.yu@s.helsinki.fi Dept. Computer Siene, Univ. of Helsinki Proilisti Moels, Spring, 2010 Huizhen Yu (U.H.) Byesin Networks:

More information

Graphing Conic Sections

Graphing Conic Sections Grphing Conic Sections Definition of Circle Set of ll points in plne tht re n equl distnce, clled the rdius, from fixed point in tht plne, clled the center. Grphing Circle (x h) 2 + (y k) 2 = r 2 where

More information

Union-Find Problem. Using Arrays And Chains. A Set As A Tree. Result Of A Find Operation

Union-Find Problem. Using Arrays And Chains. A Set As A Tree. Result Of A Find Operation Union-Find Problem Given set {,,, n} of n elements. Initilly ech element is in different set. ƒ {}, {},, {n} An intermixed sequence of union nd find opertions is performed. A union opertion combines two

More information

CS 551 Computer Graphics. Hidden Surface Elimination. Z-Buffering. Basic idea: Hidden Surface Removal

CS 551 Computer Graphics. Hidden Surface Elimination. Z-Buffering. Basic idea: Hidden Surface Removal CS 55 Computer Grphis Hidden Surfe Removl Hidden Surfe Elimintion Ojet preision lgorithms: determine whih ojets re in front of others Uses the Pinter s lgorithm drw visile surfes from k (frthest) to front

More information

V = set of vertices (vertex / node) E = set of edges (v, w) (v, w in V)

V = set of vertices (vertex / node) E = set of edges (v, w) (v, w in V) Definitions G = (V, E) V = set of verties (vertex / noe) E = set of eges (v, w) (v, w in V) (v, w) orere => irete grph (igrph) (v, w) non-orere => unirete grph igrph: w is jent to v if there is n ege from

More information

Answer Key Lesson 6: Workshop: Angles and Lines

Answer Key Lesson 6: Workshop: Angles and Lines nswer Key esson 6: tudent Guide ngles nd ines Questions 1 3 (G p. 406) 1. 120 ; 360 2. hey re the sme. 3. 360 Here re four different ptterns tht re used to mke quilts. Work with your group. se your Power

More information

F. R. K. Chung y. University ofpennsylvania. Philadelphia, Pennsylvania R. L. Graham. AT&T Labs - Research. March 2,1997.

F. R. K. Chung y. University ofpennsylvania. Philadelphia, Pennsylvania R. L. Graham. AT&T Labs - Research. March 2,1997. Forced convex n-gons in the plne F. R. K. Chung y University ofpennsylvni Phildelphi, Pennsylvni 19104 R. L. Grhm AT&T Ls - Reserch Murry Hill, New Jersey 07974 Mrch 2,1997 Astrct In seminl pper from 1935,

More information

1.5 Extrema and the Mean Value Theorem

1.5 Extrema and the Mean Value Theorem .5 Extrem nd the Men Vlue Theorem.5. Mximum nd Minimum Vlues Definition.5. (Glol Mximum). Let f : D! R e function with domin D. Then f hs n glol mximum vlue t point c, iff(c) f(x) for ll x D. The vlue

More information

Honors Thesis: Investigating the Algebraic Properties of Cayley Digraphs

Honors Thesis: Investigating the Algebraic Properties of Cayley Digraphs Honors Thesis: Investigting the Algebri Properties of Cyley Digrphs Alexis Byers, Wittenberg University Mthemtis Deprtment April 30, 2014 This pper utilizes Grph Theory to gin insight into the lgebri struture

More information

a < a+ x < a+2 x < < a+n x = b, n A i n f(x i ) x. i=1 i=1

a < a+ x < a+2 x < < a+n x = b, n A i n f(x i ) x. i=1 i=1 Mth 33 Volume Stewrt 5.2 Geometry of integrls. In this section, we will lern how to compute volumes using integrls defined by slice nlysis. First, we recll from Clculus I how to compute res. Given the

More information

Graph Searching & Perfect Graphs

Graph Searching & Perfect Graphs Grph Serhing & Perfet Grphs Lll Moutdid University of Toronto Astrt Perfet grphs, y definition, hve nie struture, tht grph serhing seems to extrt in, often non-inexpensive, mnner. We srth the surfe of

More information

CS553 Lecture Introduction to Data-flow Analysis 1

CS553 Lecture Introduction to Data-flow Analysis 1 ! Ide Introdution to Dt-flow nlysis!lst Time! Implementing Mrk nd Sweep GC!Tody! Control flow grphs! Liveness nlysis! Register llotion CS553 Leture Introdution to Dt-flow Anlysis 1 Dt-flow Anlysis! Dt-flow

More information

4.2 Simplicial Homology Groups

4.2 Simplicial Homology Groups 4.2. SIMPLICIAL HOMOLOGY GROUPS 93 4.2 Simplicial Homology Groups 4.2.1 Simplicial Complexes Let p 0, p 1,... p k be k + 1 points in R n, with k n. We identify points in R n with the vectors that point

More information

Class-XI Mathematics Conic Sections Chapter-11 Chapter Notes Key Concepts

Class-XI Mathematics Conic Sections Chapter-11 Chapter Notes Key Concepts Clss-XI Mthemtics Conic Sections Chpter-11 Chpter Notes Key Concepts 1. Let be fixed verticl line nd m be nother line intersecting it t fixed point V nd inclined to it t nd ngle On rotting the line m round

More information

1. SEQUENCES INVOLVING EXPONENTIAL GROWTH (GEOMETRIC SEQUENCES)

1. SEQUENCES INVOLVING EXPONENTIAL GROWTH (GEOMETRIC SEQUENCES) Numbers nd Opertions, Algebr, nd Functions 45. SEQUENCES INVOLVING EXPONENTIAL GROWTH (GEOMETRIC SEQUENCES) In sequence of terms involving eponentil growth, which the testing service lso clls geometric

More information

CS 340, Fall 2016 Sep 29th Exam 1 Note: in all questions, the special symbol ɛ (epsilon) is used to indicate the empty string.

CS 340, Fall 2016 Sep 29th Exam 1 Note: in all questions, the special symbol ɛ (epsilon) is used to indicate the empty string. CS 340, Fll 2016 Sep 29th Exm 1 Nme: Note: in ll questions, the speil symol ɛ (epsilon) is used to indite the empty string. Question 1. [10 points] Speify regulr expression tht genertes the lnguge over

More information

Exact discrete Morse functions on surfaces. To the memory of Professor Mircea-Eugen Craioveanu ( )

Exact discrete Morse functions on surfaces. To the memory of Professor Mircea-Eugen Craioveanu ( ) Stud. Univ. Babeş-Bolyai Math. 58(2013), No. 4, 469 476 Exact discrete Morse functions on surfaces Vasile Revnic To the memory of Professor Mircea-Eugen Craioveanu (1942-2012) Abstract. In this paper,

More information

Stained Glass Design. Teaching Goals:

Stained Glass Design. Teaching Goals: Stined Glss Design Time required 45-90 minutes Teching Gols: 1. Students pply grphic methods to design vrious shpes on the plne.. Students pply geometric trnsformtions of grphs of functions in order to

More information

Single-Layer Trunk Routing Using 45-Degree Lines within Critical Areas for PCB Routing

Single-Layer Trunk Routing Using 45-Degree Lines within Critical Areas for PCB Routing SASIMI 2010 Proeedings (R3-8) Single-Lyer Trunk Routing Using 45-Degree Lines within Critil Ares for PCB Routing Kyosuke SHINODA Yukihide KOHIRA Atsushi TAKAHASHI Tokyo Institute of Tehnology Dept. of

More information

Colouring contact graphs of squares and rectilinear polygons de Berg, M.T.; Markovic, A.; Woeginger, G.

Colouring contact graphs of squares and rectilinear polygons de Berg, M.T.; Markovic, A.; Woeginger, G. Colouring ontat graphs of squares and retilinear polygons de Berg, M.T.; Markovi, A.; Woeginger, G. Published in: nd European Workshop on Computational Geometry (EuroCG 06), 0 Marh - April, Lugano, Switzerland

More information

9 Graph Cutting Procedures

9 Graph Cutting Procedures 9 Grph Cutting Procedures Lst clss we begn looking t how to embed rbitrry metrics into distributions of trees, nd proved the following theorem due to Brtl (1996): Theorem 9.1 (Brtl (1996)) Given metric

More information

s 1 t 4 s 2 4 t 2 a b r 2 r 8 r10 g 4

s 1 t 4 s 2 4 t 2 a b r 2 r 8 r10 g 4 k-pirs Non-Crossing Shortest Pths in Simple Polgon Evnthi Ppdopoulou Northwestern Universit, Evnston, Illinois 60208, USA Astrt. This pper presents n O(n + k) time lgorithm to ompute the set of k non-rossing

More information

UNCORRECTED SAMPLE PAGES. Angle relationships and properties of 6geometrical figures 1. Online resources. What you will learn

UNCORRECTED SAMPLE PAGES. Angle relationships and properties of 6geometrical figures 1. Online resources. What you will learn Online resoures uto-mrked hpter pre-test Video demonstrtions of ll worked exmples Intertive widgets Intertive wlkthroughs Downlodle HOTsheets ess to ll HOTmths ustrlin urriulum ourses ess to the HOTmths

More information

Triple/Quadruple Patterning Layout Decomposition via Novel Linear Programming and Iterative Rounding

Triple/Quadruple Patterning Layout Decomposition via Novel Linear Programming and Iterative Rounding Triple/Qudruple Ptterning Lyout Deomposition vi Novel Liner Progrmming nd Itertive Rounding Yio Lin, Xioqing Xu, Bei Yu, Ross Bldik nd Dvid Z. Pn ECE Dept., University of Texs t Austin, Austin, TX USA

More information

Chapter 9. Greedy Technique. Copyright 2007 Pearson Addison-Wesley. All rights reserved.

Chapter 9. Greedy Technique. Copyright 2007 Pearson Addison-Wesley. All rights reserved. Chpter 9 Greey Tehnique Copyright 2007 Person Aison-Wesley. All rights reserve. Greey Tehnique Construts solution to n optimiztion prolem piee y piee through sequene of hoies tht re: fesile lolly optiml

More information

SOME EXAMPLES OF SUBDIVISION OF SMALL CATEGORIES

SOME EXAMPLES OF SUBDIVISION OF SMALL CATEGORIES SOME EXAMPLES OF SUBDIVISION OF SMALL CATEGORIES MARCELLO DELGADO Abstrct. The purpose of this pper is to build up the bsic conceptul frmework nd underlying motivtions tht will llow us to understnd ctegoricl

More information

Algorithms for graphs on surfaces

Algorithms for graphs on surfaces Algorithms for grphs on surfces Éric Colin de Verdière École normle supérieure, 20112012 ALGORITHMS FOR GRAPHS ON SURFACES Foreword nd introduction Foreword These notes re certinly not in nl shpe, nd comments

More information

4452 Mathematical Modeling Lecture 4: Lagrange Multipliers

4452 Mathematical Modeling Lecture 4: Lagrange Multipliers Mth Modeling Lecture 4: Lgrnge Multipliers Pge 4452 Mthemticl Modeling Lecture 4: Lgrnge Multipliers Lgrnge multipliers re high powered mthemticl technique to find the mximum nd minimum of multidimensionl

More information

Math 227 Problem Set V Solutions. f ds =

Math 227 Problem Set V Solutions. f ds = Mth 7 Problem Set V Solutions If is urve with prmetriztion r(t), t b, then we define the line integrl f ds b f ( r(t) ) dr dt (t) dt. Evlute the line integrl f(x,y,z)ds for () f(x,y,z) xosz, the urve with

More information

A METHOD FOR CHARACTERIZATION OF THREE-PHASE UNBALANCED DIPS FROM RECORDED VOLTAGE WAVESHAPES

A METHOD FOR CHARACTERIZATION OF THREE-PHASE UNBALANCED DIPS FROM RECORDED VOLTAGE WAVESHAPES A METHOD FOR CHARACTERIZATION OF THREE-PHASE UNBALANCED DIPS FROM RECORDED OLTAGE WAESHAPES M.H.J. Bollen, L.D. Zhng Dept. Eletri Power Engineering Chlmers University of Tehnology, Gothenurg, Sweden Astrt:

More information

Theory of Computation CSE 105

Theory of Computation CSE 105 $ $ $ Theory of Computtion CSE 105 Regulr Lnguges Study Guide nd Homework I Homework I: Solutions to the following problems should be turned in clss on July 1, 1999. Instructions: Write your nswers clerly

More information

AML710 CAD LECTURE 16 SURFACES. 1. Analytical Surfaces 2. Synthetic Surfaces

AML710 CAD LECTURE 16 SURFACES. 1. Analytical Surfaces 2. Synthetic Surfaces AML7 CAD LECTURE 6 SURFACES. Anlticl Surfces. Snthetic Surfces Surfce Representtion From CAD/CAM point of view surfces re s importnt s curves nd solids. We need to hve n ide of curves for surfce cretion.

More information

Computational geometry

Computational geometry Leture 23 Computtionl geometry Supplementl reding in CLRS: Chpter 33 exept 33.3 There re mny importnt prolems in whih the reltionships we wish to nlyze hve geometri struture. For exmple, omputtionl geometry

More information

Distance Computation between Non-convex Polyhedra at Short Range Based on Discrete Voronoi Regions

Distance Computation between Non-convex Polyhedra at Short Range Based on Discrete Voronoi Regions Distne Computtion etween Non-onvex Polyhedr t Short Rnge Bsed on Disrete Voronoi Regions Ktsuki Kwhi nd Hiroms Suzuki Deprtment of Preision Mhinery Engineering, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku,

More information

Introduction to Integration

Introduction to Integration Introduction to Integrtion Definite integrls of piecewise constnt functions A constnt function is function of the form Integrtion is two things t the sme time: A form of summtion. The opposite of differentition.

More information

a(e, x) = x. Diagrammatically, this is encoded as the following commutative diagrams / X

a(e, x) = x. Diagrammatically, this is encoded as the following commutative diagrams / X 4. Mon, Sept. 30 Lst time, we defined the quotient topology coming from continuous surjection q : X! Y. Recll tht q is quotient mp (nd Y hs the quotient topology) if V Y is open precisely when q (V ) X

More information

5 ANGLES AND POLYGONS

5 ANGLES AND POLYGONS 5 GLES POLYGOS urling rige looks like onventionl rige when it is extene. However, it urls up to form n otgon to llow ots through. This Rolling rige is in Pington sin in Lonon, n urls up every Friy t miy.

More information

Geometric Algorithms. Geometric Algorithms. Warning: Intuition May Mislead. Geometric Primitives

Geometric Algorithms. Geometric Algorithms. Warning: Intuition May Mislead. Geometric Primitives Geometri Algorithms Geometri Algorithms Convex hull Geometri primitives Closest pir of points Voronoi Applitions. Dt mining. VLSI design. Computer vision. Mthemtil models. Astronomil simultion. Geogrphi

More information

Section 10.4 Hyperbolas

Section 10.4 Hyperbolas 66 Section 10.4 Hyperbols Objective : Definition of hyperbol & hyperbols centered t (0, 0). The third type of conic we will study is the hyperbol. It is defined in the sme mnner tht we defined the prbol

More information

Fig.1. Let a source of monochromatic light be incident on a slit of finite width a, as shown in Fig. 1.

Fig.1. Let a source of monochromatic light be incident on a slit of finite width a, as shown in Fig. 1. Answer on Question #5692, Physics, Optics Stte slient fetures of single slit Frunhofer diffrction pttern. The slit is verticl nd illuminted by point source. Also, obtin n expression for intensity distribution

More information

1 The Definite Integral

1 The Definite Integral The Definite Integrl Definition. Let f be function defined on the intervl [, b] where

More information

Representation of Numbers. Number Representation. Representation of Numbers. 32-bit Unsigned Integers 3/24/2014. Fixed point Integer Representation

Representation of Numbers. Number Representation. Representation of Numbers. 32-bit Unsigned Integers 3/24/2014. Fixed point Integer Representation Representtion of Numbers Number Representtion Computer represent ll numbers, other thn integers nd some frctions with imprecision. Numbers re stored in some pproximtion which cn be represented by fixed

More information

10.5 Graphing Quadratic Functions

10.5 Graphing Quadratic Functions 0.5 Grphing Qudrtic Functions Now tht we cn solve qudrtic equtions, we wnt to lern how to grph the function ssocited with the qudrtic eqution. We cll this the qudrtic function. Grphs of Qudrtic Functions

More information

Error Numbers of the Standard Function Block

Error Numbers of the Standard Function Block A.2.2 Numers of the Stndrd Funtion Blok evlution The result of the logi opertion RLO is set if n error ours while the stndrd funtion lok is eing proessed. This llows you to rnh to your own error evlution

More information

FEEDBACK: The standard error of a regression is not an unbiased estimator for the standard deviation of the error in a multiple regression model.

FEEDBACK: The standard error of a regression is not an unbiased estimator for the standard deviation of the error in a multiple regression model. Introutory Eonometris: A Moern Approh 6th Eition Woolrige Test Bnk Solutions Complete ownlo: https://testbnkre.om/ownlo/introutory-eonometris-moern-pproh-6th-eition-jeffreym-woolrige-test-bnk/ Solutions

More information

2 Computing all Intersections of a Set of Segments Line Segment Intersection

2 Computing all Intersections of a Set of Segments Line Segment Intersection 15-451/651: Design & Anlysis of Algorithms Novemer 14, 2016 Lecture #21 Sweep-Line nd Segment Intersection lst chnged: Novemer 8, 2017 1 Preliminries The sweep-line prdigm is very powerful lgorithmic design

More information

Consistent Mesh Parameterizations

Consistent Mesh Parameterizations Sumitted for pulition, Jnury 2001 Consistent Mesh Prmeteriztions Emil Prun Prineton Wim Sweldens Bell Ls Peter Shröder Bell Ls + + + + + + + = Figure 1: When given set of hed models n ovious shpe to ompute

More information

Introduction. Example

Introduction. Example OMS0 Introution isjoint sets n minimum spnning trees In this leture we will strt by isussing t struture use for mintining isjoint subsets of some bigger set. This hs number of pplitions, inluing to mintining

More information

Kinetic Collision Detection: Algorithms and Experiments

Kinetic Collision Detection: Algorithms and Experiments Kineti Collision Detetion: Algorithms n Experiments Leonis J. Guis Feng Xie Li Zhng Computer Siene Deprtment, Stnfor University Astrt Effiient ollision etetion is importnt in mny rooti tsks, from high-level

More information

Small Business Networking

Small Business Networking Why network is n essentil productivity tool for ny smll business Effective technology is essentil for smll businesses looking to increse the productivity of their people nd business. Introducing technology

More information

Quadrilateral and Tetrahedral Mesh Stripification Using 2-Factor Partitioning of the Dual Graph

Quadrilateral and Tetrahedral Mesh Stripification Using 2-Factor Partitioning of the Dual Graph The Visul omputer mnusript No. (will e inserted y the editor) Plo Diz-Gutierrez M. Gopi Qudrilterl nd Tetrhedrl Mesh Stripifition Using 2-Ftor Prtitioning of the Dul Grph strt In order to find 2-ftor of

More information

1 Quad-Edge Construction Operators

1 Quad-Edge Construction Operators CS48: Computer Grphics Hndout # Geometric Modeling Originl Hndout #5 Stnford University Tuesdy, 8 December 99 Originl Lecture #5: 9 November 99 Topics: Mnipultions with Qud-Edge Dt Structures Scribe: Mike

More information

Compilers. Chapter 4: Syntactic Analyser. 3 er course Spring Term. Precedence grammars. Precedence grammars

Compilers. Chapter 4: Syntactic Analyser. 3 er course Spring Term. Precedence grammars. Precedence grammars Complers Chpter 4: yntt Anlyser er ourse prng erm Prt 4g: mple Preedene Grmmrs Alfonso Orteg: lfonso.orteg@um.es nrque Alfonse: enrque.lfonse@um.es Introduton A preedene grmmr ses the nlyss n the preedene

More information

CSCI1950 Z Computa4onal Methods for Biology Lecture 2. Ben Raphael January 26, hhp://cs.brown.edu/courses/csci1950 z/ Outline

CSCI1950 Z Computa4onal Methods for Biology Lecture 2. Ben Raphael January 26, hhp://cs.brown.edu/courses/csci1950 z/ Outline CSCI1950 Z Comput4onl Methods for Biology Lecture 2 Ben Rphel Jnury 26, 2009 hhp://cs.brown.edu/courses/csci1950 z/ Outline Review of trees. Coun4ng fetures. Chrcter bsed phylogeny Mximum prsimony Mximum

More information

[Prakash* et al., 5(8): August, 2016] ISSN: IC Value: 3.00 Impact Factor: 4.116

[Prakash* et al., 5(8): August, 2016] ISSN: IC Value: 3.00 Impact Factor: 4.116 [Prksh* et l 58: ugust 6] ISSN: 77-9655 I Vlue: Impt Ftor: 6 IJESRT INTERNTIONL JOURNL OF ENGINEERING SIENES & RESERH TEHNOLOGY SOME PROPERTIES ND THEOREM ON FUZZY SU-TRIDENT DISTNE Prveen Prksh* M Geeth

More information

Tiling Triangular Meshes

Tiling Triangular Meshes Tiling Tringulr Meshes Ming-Yee Iu EPFL I&C 1 Introdution Astrt When modelling lrge grphis senes, rtists re not epeted to model minute nd repetitive fetures suh s grss or snd with individul piees of geometry

More information

Polygonal Approximation of Voronoi Diagrams of a Set of Triangles in Three Dimensions Marek Teichmann Seth Teller MIT Computer Graphics Group Abstract

Polygonal Approximation of Voronoi Diagrams of a Set of Triangles in Three Dimensions Marek Teichmann Seth Teller MIT Computer Graphics Group Abstract Polygonl Approximtion of Voronoi Digrms of Set of Tringles in Three Dimensions Mrek Teihmnn Seth Teller MIT Computer Grphis Group Astrt We desrie roust dptive mrhing tetrhedr type lgorithm for onstruting

More information

Chapter 2. Chapter 2 5. Section segments: AB, BC, BD, BE. 32. N 53 E GEOMETRY INVESTIGATION Answers will vary. 34. (a) N. sunset.

Chapter 2. Chapter 2 5. Section segments: AB, BC, BD, BE. 32. N 53 E GEOMETRY INVESTIGATION Answers will vary. 34. (a) N. sunset. Chpter 2 5 Chpter 2 32. N 53 E GEOMETRY INVESTIGATION Answers will vry. 34. () N Setion 2.1 2. 4 segments: AB, BC, BD, BE sunset sunrise 4. 2 rys: CD (or CE ), CB (or CA ) 6. ED, EC, EB W Oslo, Norwy E

More information

Ray surface intersections

Ray surface intersections Ry surfce intersections Some primitives Finite primitives: polygons spheres, cylinders, cones prts of generl qudrics Infinite primitives: plnes infinite cylinders nd cones generl qudrics A finite primitive

More information