1 Asymptotically optimal K k -packings of dense graphs via fractional K k -decompositions Raphael Yuster University of Haifa.

Slides:



Advertisements
Similar presentations
Lower Bounds for Additive Spanners, Emulators, and More David P. Woodruff MIT and Tsinghua University To appear in FOCS, 2006.
Advertisements

Long cycles, short cycles, min-degree subgraphs, and feedback arc sets in Eulerian digraphs Raphael Yuster joint work with Asaf Shapira Eilat 2012.
Inapproximability of Hypergraph Vertex-Cover. A k-uniform hypergraph H= : V – a set of vertices E - a collection of k-element subsets of V Example: k=3.
On Complexity, Sampling, and -Nets and -Samples. Range Spaces A range space is a pair, where is a ground set, it’s elements called points and is a family.
Inapproximability of MAX-CUT Khot,Kindler,Mossel and O ’ Donnell Moshe Ben Nehemia June 05.
1 Decomposing Hypergraphs with Hypertrees Raphael Yuster University of Haifa - Oranim.
Thresholds for Ackermannian Ramsey Numbers Authors: Menachem Kojman Gyesik Lee Eran Omri Andreas Weiermann.
On the Density of a Graph and its Blowup Raphael Yuster Joint work with Asaf Shapira.
Approximation Algorithms Chapter 14: Rounding Applied to Set Cover.
Approximation, Chance and Networks Lecture Notes BISS 2005, Bertinoro March Alessandro Panconesi University La Sapienza of Rome.
Upper bounds for asymmetric Ramsey properties of random graphs Reto Spöhel, ETH Zürich Joint work with Yoshiharu Kohayakawa, Universidade de São Paulo.
Parallel Scheduling of Complex DAGs under Uncertainty Grzegorz Malewicz.
Combinatorial Algorithms
Every H-decomposition of K n has a nearly resolvable alternative Wilson: e(H) | n(n-1)/2 and gcd(H) | n-1 n>> then there exists an H-decomposition of K.
The number of edge-disjoint transitive triples in a tournament.
1 By Gil Kalai Institute of Mathematics and Center for Rationality, Hebrew University, Jerusalem, Israel presented by: Yair Cymbalista.
1 List Coloring and Euclidean Ramsey Theory TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box.: A A A A Noga Alon, Tel Aviv.
1 Discrete Structures & Algorithms Graphs and Trees: II EECE 320.
Graph Sparsifiers by Edge-Connectivity and Random Spanning Trees Nick Harvey University of Waterloo Department of Combinatorics and Optimization Joint.
Asymmetric Ramsey Properties of Random Graphs involving Cliques Reto Spöhel Joint work with Martin Marciniszyn, Jozef Skokan, and Angelika Steger TexPoint.
2007 Kézdy André Kézdy Department of Mathematics University of Louisville * Preliminary report.  More -valuations for trees via the combinatorial nullstellensatz*
1 2 Introduction In last chapter we saw a few consistency tests. In this chapter we are going to prove the properties of Plane-vs.- Plane test: Thm[RaSa]:
Definition Hamiltonian graph: A graph with a spanning cycle (also called a Hamiltonian cycle). Hamiltonian graph Hamiltonian cycle.
1 Packing directed cycles efficiently Zeev Nutov Raphael Yuster.
Online Vertex Colorings of Random Graphs Without Monochromatic Subgraphs Reto Spöhel, ETH Zurich Joint work with Martin Marciniszyn.
Matching Polytope, Stable Matching Polytope Lecture 8: Feb 2 x1 x2 x3 x1 x2 x3.
1 Separator Theorems for Planar Graphs Presented by Shira Zucker.
Distributed Combinatorial Optimization
1 Introduction to Approximation Algorithms Lecture 15: Mar 5.
(work appeared in SODA 10’) Yuk Hei Chan (Tom)
Antimagic Labellings of Graphs Torsten Mütze Joint work with Dan Hefetz and Justus Schwartz.
Packing Element-Disjoint Steiner Trees Mohammad R. Salavatipour Department of Computing Science University of Alberta Joint with Joseph Cheriyan Department.
Online Ramsey Games in Random Graphs Reto Spöhel, ETH Zürich Joint work with Martin Marciniszyn and Angelika Steger.
Dana Moshkovitz, MIT Joint work with Subhash Khot, NYU.
Fractional decompositions of dense hypergraphs Raphael Yuster University of Haifa.
Primal-Dual Meets Local Search: Approximating MST’s with Non-uniform Degree Bounds Author: Jochen Könemann R. Ravi From CMU CS 3150 Presentation by Dan.
The Quasi-Randomness of Hypergraph Cut Properties Asaf Shapira & Raphael Yuster.
The Effect of Induced Subgraphs on Quasi-randomness Asaf Shapira & Raphael Yuster.
Discrete Mathematics, 1st Edition Kevin Ferland
1 Introduction to Approximation Algorithms. 2 NP-completeness Do your best then.
The Turán number of sparse spanning graphs Raphael Yuster joint work with Noga Alon Banff 2012.
Topics in Algorithms 2005 Constructing Well-Connected Networks via Linear Programming and Primal Dual Algorithms Ramesh Hariharan.
Online Vertex-Coloring Games in Random Graphs Reto Spöhel (joint work with Martin Marciniszyn; appeared at SODA ’07)
Edge-disjoint induced subgraphs with given minimum degree Raphael Yuster 2012.
Chapter 9: Geometric Selection Theorems 11/01/2013
Discrete Structures Lecture 12: Trees Ji Yanyan United International College Thanks to Professor Michael Hvidsten.
1 The number of orientations having no fixed tournament Noga Alon Raphael Yuster.
1 Rainbow Decompositions Raphael Yuster University of Haifa Proc. Amer. Math. Soc. (2008), to appear.
Ramsey Properties of Random Graphs; A Sharp Threshold Proven via A Hypergraph Regularity Lemma. Ehud Friedgut, Vojtech Rödl, Andrzej Rucinski, Prasad.
CSE 589 Part VI. Reading Skiena, Sections 5.5 and 6.8 CLR, chapter 37.
1 Decomposition into bipartite graphs with minimum degree 1. Raphael Yuster.
Linear Program Set Cover. Given a universe U of n elements, a collection of subsets of U, S = {S 1,…, S k }, and a cost function c: S → Q +. Find a minimum.
1 Quasi-randomness is determined by the distribution of copies of a graph in equicardinal large sets Raphael Yuster University of Haifa.
1 Use graphs and not pure logic Variables represented by nodes and dependencies by edges. Common in our language: “threads of thoughts”, “lines of reasoning”,
Unique Games Approximation Amit Weinstein Complexity Seminar, Fall 2006 Based on: “Near Optimal Algorithms for Unique Games" by M. Charikar, K. Makarychev,
Dense graphs with a large triangle cover have a large triangle packing Raphael Yuster SIAM DM’10.
Introduction to Graph Theory Lecture 13: Graph Coloring: Edge Coloring.
Introduction to Graph Theory
1 Covering Non-uniform Hypergraphs Endre Boros Yair Caro Zoltán Füredi Raphael Yuster.
Chromatic Coloring with a Maximum Color Class Bor-Liang Chen Kuo-Ching Huang Chih-Hung Yen* 30 July, 2009.
Approximation Algorithms Duality My T. UF.
Hongyu Liang Institute for Theoretical Computer Science Tsinghua University, Beijing, China The Algorithmic Complexity.
Theory of Computational Complexity M1 Takao Inoshita Iwama & Ito Lab Graduate School of Informatics, Kyoto University.
CHAPTER SIX T HE P ROBABILISTIC M ETHOD M1 Zhang Cong 2011/Nov/28.
Theory of Computational Complexity Probability and Computing Chapter Hikaru Inada Iwama and Ito lab M1.
Antimagic Labellings of Graphs
Additive Combinatorics and its Applications in Theoretical CS
Sum-product theorems over finite fields
Packing directed cycles efficiently
Integer and fractional packing of graph families
Presentation transcript:

1 Asymptotically optimal K k -packings of dense graphs via fractional K k -decompositions Raphael Yuster University of Haifa

2 Fractional decompositions of dense hypergraphs Raphael Yuster University of Haifa

3 Definitions, notations and background Let H 0 be a fixed hypergraph. A fractional H 0 -decomposition of a hypergraph H is an assignment of nonnegative real weights to the copies of H 0 in H such that for each e  E(H) the sum of the weights of copies of H 0 containing e is 1. K(k,r) will denote the complete r-graph with k vertices. We think of k and r as fixed. We prove (for n > n 0 ): There exists a positive constant α=α(k,r) so that every r-graph in which every (r-1)-set is contained in at least n(1-α) edges has a fractional K(k,r)-decomposition. In fact: α(k,r) > 6 -kr α(k,2) > 0.1k -10 α(3,2) > 10 -4

4 From fractional to integral Combined with the following result of Haxell, Nagle and Rödl, our result has consequences for integral packing. Let ν(H 0,H) denote the maximum number of edge-disjoint copies of H 0 in H (the H 0 -packing number of H). Let ν * (H 0,H) denote the fractional relaxation. Trivially, ν * (H 0,H) ≥ ν(H 0,H). If H is an r-graph with n vertices (r=2,3) it has been proved by Haxell, Nagle and Rödl that ν * (H 0,H) < ν(H 0,H) + o(n r ).

5 Corollaries for graphs If G is a graph with n vertices, and δ(G) > (1- 0.1k -10 )n then G has an asymptotically optimal K k -packing. Same theorem holds for k-vertex graphs. For triangles (k=3), δ(G) > n suffices. The previously best known bound (for the missing degree) in the triangles case was (Gustavsson). The previously best known bound for K k was k -94 (Gustavsson). However, Gustavsson guarantees a decomposition in case the appropriate divisibility conditions holds.

6 Corollary for 3-graphs If H is a 3-graph with n vertices and minimum co-degree ( k )n then H has an asymptotically optimal K(k,3)-packing. Same theorem holds for k-vertex 3-graphs. The previously best known bound (for the missing co-degree) was 0 (Rödl).

7 Tools used in the proof Some linear algebra. Kahn’s Theorem: For every r* > 1 and every γ > 0 there exists a positive constant ρ=ρ(r*,γ) such that the following statement is true: If U is an r*-graph with: maxdeg < D maxcodeg < ρD then there is a proper coloring of the edges of U with at most (1+γ)D colors. Several probabilistic arguments. Hall’s Theorem for hypergraphs by Aharoni and Haxell (topological proof): Let U ={U 1,…,U m } be a family of p-graphs. If for every W  U there is a matching in U U  W U of size greater than p(| W |-1) then U has an SDR.

8 The proof Recall the goal There exists a positive constant α=α(k,r) so that every r-graph in which every (r-1)-set is contained in at least n(1-α) edges has a fractional K(k,r)-decomposition. Let t=k(r+1) Consider the 3 r-graphs: F(k,r) = { K(k,r), K(t,r), H(t,r) } H(t,r) is a K(t,r) missing one edge. K(k,r) fractionally decomposes each element of F(k,r). (To show that K(k,r) fractionally decomposes H(t,r) requires some work. Here we use some linear algebra.) For r=2 it suffices to take t=2k-1 and the proof is easy. E.g. K 5 - fractionally decomposes K 3.

9 The proof – cont. It suffices to prove the stronger theorem There exists a positive constant α=α(k,r) so that every r-graph in which every (r-1)-set is contained in at least n(1-α) edges has an integral F(k,r)-decomposition. Let ε = ε(k,r) be chosen later. Let η = (2 -H(ε) 0.9) 1/ε. H(ε) the entropy function. Let α = min{ (η/2) 2, ε 2 /(t 2 4 t+1 ) } Let γ satisfy (1-αt2 t )(1-γ)/(1+γ) 2 > 1-2αt2 t Let r* = Let ρ = ρ(r*,γ) be the constant from Kahn’s theorem. Assume n is suff. large as a function of all these constants. Let δ d (H) and Δ d (H) denote the min and max d-degrees of H, 0 < d < r, resp.

10 The proof – cont. Our r-graph H satisfies δ d (H) > It is not difficult to prove (induction) that every edge of H lies on “many” K(t,r). In fact, if c(e) denotes the number of K(t,r) containing e then n t-r > c(e) (t-r)! > n t-r (1-αt2 t ) Color the edges of H randomly using q=n 1/(4r*-4) colors (that’s many colors). Let H i be the spanning r-graph colored with i. Easy (Chernoff): δ d (H i ) very close to δ d (H)/q Not so easy: we would also like to show that c i (e) is very close to its expectation c(e)n -1/4. Note that two K(t,r) that contain e may share other edges as well – a lot of dependence.

11 The proof – cont. Still, we can prove it by partitioning the c(e) events to many (but not too many) subsets such that all events in the same part are independent, show large deviation on each part and the sum of slacks is still negligible. Thus, (1+γ)n t-r-1/4 > c i (e) (t-r)! > (1-γ)n t-r-1/4 (1-αt2 t ) We fix the coloring with q colors satisfying the above. For each H i we create another r*-graph U i as follows: - the vertices of U i are the edges of H i - the edges of U i are the copies of K(t,r) in H i Notice that Δ(U i ) < D=(1+γ)n t-r-1/4 (t-r)! -1 Notice that Δ 2 (U i ) < n t-r-1 << ρD

12 The proof – cont. By Kahn’s theorem this means that the K(t,r) copies of H i can be partitioned into at most D(1+γ) packings. We pick one of these packings at random. Denote it by L i. The set L=L 1 U…U L q is a K(t,r) packing of H. Let M denote the edges of H not belonging to any element of L. Let p = A p-subset {S 1,…,S p } of L is good for e  M if we can select one edge from each S i such that, together with p, we have a K(k,r). We say that L is good if for each e  M we can select a good p-subset, and all |M| selections are disjoint.

13 Example: being good a b b c L M a c S 100 S 700 {S 100,S 700 } is good for (a,c) k=3 r=2 So: t=5 p=2

14 The proof – cont. Recall F(k,r) = { K(k,r), K(t,r), H(t,r) }. Clearly: L is good → H has an F k -decomposition. It remains to show that there exists a good L. We will show that with positive probability, the random selection of the q packings L 1 U…U L q yields a good L. We use Hall’s theorem for hypergraphs. Let M={e 1,…,e m }. Let U ={U 1,…,U m } be a family of p-graphs defined as follows: The vertex set of U i is L (i.e, K(t,r) copies) The edge set of U i are the p-subsets of L that are good for e i U has an SDR → L is good.

15 The proof – cont. Thus, it suffices to show that the random selection of the q packings L 1 U…U L q guarantees that, with positive probability, for every W  U there is a matching in U U  W U of size greater than p(| W |-1). It turns out that the only thing needed to guarantee this is to show that with positive probability, for all 1 < d < r: Δ d (H[M]) < 2ε Once this is established, the remainder of the claim is deterministic, namely Δ d (H[M]) < 2ε → U has an SDR. (purely combinatorial proof, but not so easy).

16 Open problems Determine the correct value of α(k,r). The simplest case is α(3,2) (triangles). We currently have α(3,2) > A construction shows that α(3,2) ≤ ¼. More generally, a construction given in the paper shows that α(k,2) ≤ 1/(k+1). We conjecture α(k,2) = 1/(k+1). For hypergraphs we don’t even know what to conjecture.