Proof of the 1-factorization and Hamilton decomposition conjectures · 2013-07-21 ·...

Proof of the 1-factorization and Hamiltondecomposition conjectures

Allan Lo

University of Birmingham

LMS–EPSRC Durham Symposium22 July 2013

Joint work withBela Csaba (Szeged), Daniela Kuhn (Birmingham),

Deryk Osthus (Birmingham) and Andrew Treglown (QMUL)

Allan Lo Proof of the 1-factorization and Hamilton decomposition conjectures

1-factorization

A 1-factorization of a graph G is a decomposition into edge-disjointperfect matchings.

If G contains a 1-factorization, then |G| is even and G is D-regular.⇒ χ ′(G) = D.

Question

Does every D-regular graph G with |G|= n even and D ≥ n/2 containa 1-factorization?

1-factorization

A 1-factorization of a graph G is a decomposition into edge-disjointperfect matchings.

If G contains a 1-factorization, then |G| is even and G is D-regular.⇒ χ ′(G) = D.

Question

Does every D-regular graph G with |G|= n even and D ≥ n/2 containa 1-factorization?

1-factorization conjecture

1-factorization conjecture (Chetwynd and Hilton 1985, Dirac 1950s)

Every D-regular graph G with |G|= n even and

{n/2−1 if n = 0 (mod 4)

n/2 if n = 2 (mod 4),

contains a 1-factorization. Equivalently, χ ′(G) = D.

The bound is best possible.“An odd component contains no perfect matching.”

Kn/2 Kn/2

n = 2 (mod 4)

Kn/2+1−Cn/2+1 Kn/2−1

n = 0 (mod 4)

Same bound for the existence of a single perfect matching in D-regulargraphs.

{n/2−1 if n = 0 (mod 4)

n/2 if n = 2 (mod 4),

Kn/2 Kn/2

n = 2 (mod 4)

Kn/2+1−Cn/2+1 Kn/2−1

n = 0 (mod 4)

{n/2−1 if n = 0 (mod 4)

n/2 if n = 2 (mod 4),

Kn/2 Kn/2

n = 2 (mod 4)

Kn/2+1−Cn/2+1 Kn/2−1

n = 0 (mod 4)

{n/2−1 if n = 0 (mod 4)

n/2 if n = 2 (mod 4),

Some partial results:

True for D = n−1, i.e. complete graphs.

Chetwynd and Hilton (1989), and independently Niessen andVolkmann (1990), for D ≥ (

√7−1)n/2≈ 0.82n.

Perkovic and Reed (1997) for D ≥ (1/2+ ε)n with ε > 0.

Theorem (Csaba, Kuhn, L, Osthus, Treglown 2013+)

The 1-factorization conjecture holds for large n.

{n/2−1 if n = 0 (mod 4)

n/2 if n = 2 (mod 4),

True for D = n−1, i.e. complete graphs.

Chetwynd and Hilton (1989), and independently Niessen andVolkmann (1990), for D ≥ (

√7−1)n/2≈ 0.82n.

Perkovic and Reed (1997) for D ≥ (1/2+ ε)n with ε > 0.

The 1-factorization conjecture holds for large n.

Hamilton decomposition

A Hamilton decomposition of a graph G is a decomposition intoedge-disjoint Hamilton cycles.

If G contains a Hamilton decomposition, then G is D-regular and D iseven.

Question

Does every D-regular graph on n vertices with D ≥ n/2 even contain aHamilton decomposition?

Hamilton decomposition

A Hamilton decomposition of a graph G is a decomposition intoedge-disjoint Hamilton cycles.

If G contains a Hamilton decomposition, then G is D-regular and D iseven.

Question

Does every D-regular graph on n vertices with D ≥ n/2 even contain aHamilton decomposition?

Hamilton decomposition conjecture

Hamilton decomposition conjecture (Nash-Williams 1970)

Every D-regular graph on n vertices with D ≥ bn/2c can bedecomposed into edge-disjoint Hamilton cycles and at most oneperfect matching.

The bound is best possible.“No disconnected graph contains a Hamilton cycle.”

dn/2ebn/2c

Same bound for the existence of a single Hamilton cycle in D-regulargraphs.

ObservationIf n is even, then ‘a Hamilton cycle = two perfect matchings’.Hamilton decomposition⇒ 1-factorization.But Hamilton decomposition conjecture 6⇒ 1-factorization conjecture.

dn/2ebn/2c

ObservationIf n is even, then ‘a Hamilton cycle = two perfect matchings’.Hamilton decomposition⇒ 1-factorization.

But Hamilton decomposition conjecture 6⇒ 1-factorization conjecture.

dn/2ebn/2c

Walecki (1890) Kn has a Hamilton decomposition.Nash-Williams (1969), D ≥ bn/2c implies a Hamilton cycle.Jackson (1979), D/2−n/6 edge-disjoint Hamilton cyclesChristofides, Kuhn and Osthus (2012) if D ≥ n/2+ εn, then Gcontains (1− ε ′)D/2 edge-disjoint Hamilton cyclesKuhn and Osthus (2013) D ≥ n/2+ εn implies Hamiltondecomposition.

The Hamilton decomposition conjecture holds for large n.

Walecki (1890) Kn has a Hamilton decomposition.Nash-Williams (1969), D ≥ bn/2c implies a Hamilton cycle.Jackson (1979), D/2−n/6 edge-disjoint Hamilton cyclesChristofides, Kuhn and Osthus (2012) if D ≥ n/2+ εn, then Gcontains (1− ε ′)D/2 edge-disjoint Hamilton cyclesKuhn and Osthus (2013) D ≥ n/2+ εn implies Hamiltondecomposition.

The Hamilton decomposition conjecture holds for large n.

Robust expander

Definition

Given 0 < ν < τ < 1, we say that a graph G on n vertices is a robust(ν ,τ)-expander, if for all S ⊆ V (G) with τn ≤ |S| ≤ (1− τ)n thenumber of vertices that have at least νn neighbours in S is at least|S|+νn.

S RNν(S)≥ νn

“G is still an expander after removing a sparse subgraph.”

Theorem (Kuhn and Osthus 2013)

For 1/n� ν � τ � α , every αn-regular robust (ν ,τ)-expander G onn vertices can be decomposed into edge-disjoint Hamilton cycles andat most one perfect matching.

Robust expander

Definition

Given 0 < ν < τ < 1, we say that a graph G on n vertices is a robust(ν ,τ)-expander, if for all S ⊆ V (G) with τn ≤ |S| ≤ (1− τ)n thenumber of vertices that have at least νn neighbours in S is at least|S|+νn.

S RNν(S)≥ νn

“G is still an expander after removing a sparse subgraph.”

Theorem (Kuhn and Osthus 2013)

For 1/n� ν � τ � α , every αn-regular robust (ν ,τ)-expander G onn vertices can be decomposed into edge-disjoint Hamilton cycles andat most one perfect matching.

Extremal structure

Structural Lemma

Let G be a D-regular graph with |G|= n and D ≥ n/2−1. Then either

(i) G is a robust expander;

(ii) G is ε-close to complete bipartite graph Kn/2,n/2;

(iii) G is ε-close to union of two complete graphs Kn/2.

robust expander

n/2 n/2

≈ Kn/2 ≈ Kn/2

n/2 n/2

G is ε-close to H if G can be transformed to H by adding/removing atmost εn2 edges.

Extremal structure

Structural Lemma

(i) G is a robust expander; X

robust expander

X n/2 n/2

≈ Kn/2 ≈ Kn/2

n/2 n/2

Extremal structure

Structural Lemma

(i) G is a robust expander; X

robust expander

X n/2 n/2

≈ Kn/2 ≈ Kn/2

n/2 n/2

First attempt : Finding many Hamilton cycles

A B≈ Kn/2 ≈ Kn/2

1 Find many Hamilton cycles in G[A] and G[B].‘Easy’, because G[A] and G[B] are almost complete.

2 ‘Connect’ cycles on to get a Hamilton decomposition.Hard, because G[A,B] is sparse.

Actually, we need to first construct the connections, then extend eachconnection into a Hamilton cycle.

A B≈ Kn/2 ≈ Kn/2

A B≈ Kn/2 ≈ Kn/2???

2 ‘Connect’ cycles on to get a Hamilton decomposition.

Hard, because G[A,B] is sparse.

A B≈ Kn/2 ≈ Kn/2???

Vertex partition

G[A] and G[B] are almost complete.

V0 = {bad vertices} and |V0| ≤ εn.e.g. If v has εn neighbours in A and in B, then v is bad.

A connecting subgraph J will cover V0, connect A and B, hasendpoints in A∪B.

Vertex partition

G[A] and G[B] are almost complete.

V0 = {bad vertices} and |V0| ≤ εn.e.g. If v has εn neighbours in A and in B, then v is bad.

A connecting subgraph J will cover V0, connect A and B, hasendpoints in A∪B.

A key property of connecting subgraphs

‘Contract’ all paths incident with V0.Replace AB-edges with AA-edges and BB-edges.Call resulting green graph J∗.

‘Contract’ all paths incident with V0.

Replace AB-edges with AA-edges and BB-edges.Call resulting green graph J∗.

‘Contract’ all paths incident with V0.Replace AB-edges with AA-edges and BB-edges.

Call resulting green graph J∗.

J∗CA CB

Properties of J∗

Let CA be a spanning cycle on A and CB be a spanning cycle on B.Suppose that J∗ ⊆ CA +CB (in a suitable order).If we replace J∗ with J, then we get a Hamilton cycle.

Properties of J∗

Finding a Hamilton decomposition

1 Decompose edges not in A and not in B into connectingsubgraphs, J1,J2, . . . ,JD/2.

2 Replace each Ji with J∗i .

Let G∗ = G[A]+G[B]+ J∗1 + · · ·+ J∗D/2.Note that G∗[A] and G∗[B] are D-regular multigraphs.

3 ‘Hamilton decompose’ G∗[A]4 Replace each J∗i with Ji .5 G contains a Hamilton decomposition. �

2 Replace each Ji with J∗i .Let G∗ = G[A]+G[B]+ J∗1 + · · ·+ J∗D/2.Note that G∗[A] and G∗[B] are D-regular multigraphs.

3 ‘Hamilton decompose’ G∗[A]

4 Replace each J∗i with Ji .5 G contains a Hamilton decomposition. �

3 ‘Hamilton decompose’ G∗[A] and G∗[B].

4 Replace each J∗i with Ji .5 G contains a Hamilton decomposition. �

3 ‘Hamilton decompose’ G∗[A] and G∗[B].4 Replace each J∗i with Ji .

5 G contains a Hamilton decomposition. �

3 ‘Hamilton decompose’ G∗[A] and G∗[B].4 Replace each J∗i with Ji .5 G contains a Hamilton decomposition. �

1 Decompose edges not in A and not in B into connectingsubgraphs, J1,J2, . . . ,JD/2. Hard

3 ‘Hamilton decompose’ G∗[A] and G∗[B]. Hard4 Replace each J∗i with Ji .5 G contains a Hamilton decomposition. �

Packing Hamilton cycles with large δ (G)

Question

Suppose that G is a graph on n vertices with δ (G)≥ n/2 (notnecessarily regular). How many edge-disjoint Hamilton cycles arecontained in G?

Dirac (1957) one Hamilton cycle.

Nash-Williams (1971) b5n/224c edge-disjoint Hamilton cycles

Conjecture (Nash-Williams 1971)

Every graph G on n vertices with δ (G)≥ n/2 contains (n−2)/8edge-disjoint Hamilton cycles.

Question

Babai’s construction:

|A|= 4k +2 and |B|= |A|−2G[A] is a matching of size 2k +1

ObservationEvery Hamilton cycles contains at least 2 edges from A⇒ G has at most e(A)/2 edge-disjoint Hamilton cycles⇒ G has ≤ k edge-disjoint Hamilton cycles

Babai’s construction:

|A|= 4k +2 and |B|= |A|−2G[A] is a matching of size 2k +1

ObservationEvery Hamilton cycles contains at least 2 edges from A⇒ G has at most e(A)/2 edge-disjoint Hamilton cycles⇒ G has ≤ k edge-disjoint Hamilton cycles

Question

Theorem (Csaba, Kuhn, Lapinskas, L, Osthus, Treglown 2013+)

The Nash-Williams conjecture is true for large n.

Open problem

Let regeven(G) be the degree of the largest even-regular spanningsubgraph in G.

Observation

Every graph G on n vertices contains ≤ regeven(G)/2 edge-disjointHamilton cycles.

Conjecture (Kuhn, Lapinskas and Osthus 2013)

Every graph G on n vertices with δ (G)≥ n/2 contains regeven(G)/2edge-disjoint Hamilton cycles.

Some partial results by

Ferber, Krivelevich and Sudakov (2013+)

Kuhn and Osthus (2013+)

Open problem

Observation

Open problem

Observation

Open problem

Observation

Proof of the 1-factorization and Hamilton decomposition conjectures · 2013-07-21 ·...

Documents

Transcript of Proof of the 1-factorization and Hamilton decomposition conjectures · 2013-07-21 ·...

lmpiementation of Parallel Matrix Decomposition for NIKE3D .../67531/metadc... · . time, exploration of the parallel version of the popular direct matrix factorization algorithm

matchings (Kozen).pdf

Properties of Stable Matchings

Sparse PCA via Bipartite Matchings - GitHub Pagesmegasthenis.github.io/repository/NIPS2015-SPCAviaBipartiteMatchings-Poster.pdfSparse PCA via Bipartite Matchings Megasthenis Asteris,

LU Decomposition (Factorization) LU ﻪﯾﺰﺠﺗ شورresearch.iaun.ac.ir/pd/s_emami/pdfs/UploadFile_8856.pdfAdvanced Numerical Methods 52 سﻮﮔ فﺬﺣ شور زا هدﺎﻔﺘﺳا

Weighted Matchings & Applications in Scientific Computing

LU Decomposition 1. Introduction Another way of solving a system of equations is by using a factorization technique for matrices called LU decomposition.

INTERPOLATIVE DECOMPOSITION BUTTERFLY FACTORIZATION

Numerical linear algebra - Vrije Universiteit Brusselhomepages.vub.ac.be/~imarkovs/laa/l2.pdf · Lecture 2: Numerical linear algebra • QR factorization • Eigenvalue decomposition

REVIEW Lecture 10 - Massachusetts Institute of … Lecture 10: • Direct Methods for solving (linear) algebraic equations – Gauss Elimination – LU decomposition/factorization

Private Matchings and Allocations

web.mat.bham.ac.uk › D.Kuehn › SeparableGraphPacking_2Oct.pdf · A BANDWIDTH THEOREM FOR APPROXIMATE DECOMPOSITIONSof the Hamilton decomposition conjecture and the 1-factorization

Covers, Dominations, Independent Sets and Matchings

QR Factorization and Singular Value Decomposition · Last time • Solving non-linear least squares – Newton, Gauss-Newton, Levenberg-Marquardt methods – Intro to logistic regresion

Singular Value Decomposition (matrix factorization)

REVIEW Lecture 5: 3 2 2 O n n On ( ) ( ) 3 · LU Decomposition / Factorization: Variations • Doolittle decomposition: – mii=1(implied but could be stored in L) • Crout decomposition:

15 - 12 Feb - Stable Matchings

The QR Decomposition - BYU ACME QR decomposition of a matrix A is a factorization A = QR, ... fact, this approach is usually faster and more accurate than Gram-Schmidt methods.

Singular Value Decomposition (matrix factorization) · 2020. 5. 6. · Singular Value Decomposition The SVD is a factorization of a !×#matrix into $=&’(! ... Example: Image compression

Approximating Graphic TSP by Matchings