1-Factorizations of Pseudorandom Graphs

1-Factorizations of Pseudorandom Graphs
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1-伪随机图的因式分解

DOI:
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发表时间:
2018
期刊:
IEEE Annual Symposium on Foundations of Computer Science
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通讯作者:
Vishesh Jain
Vishesh Jain
中科院分区:
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文献类型:
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作者:
Asaf Ferber;Vishesh Jain

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图G的1因子是Edge-disexind匹配的集合常规的匡威很容易被认为是错误的,我们会发现一个常规的伪和图形。图G(即,在N顶点上的d-regular图最多为λ,其第二大特征值最多为λ),只要n是偶数,c_0≤d≤n-n-1(其中c_0 = c_0)(其中c_0 = c_0(其中) ε)仅取决于ε),尤其是λ≤d^1-ε,因为(众所周知)典型的随机D-regrular d-regular Gragr G_N,D是这样的图形对于所有C_0≤d≤n-1,在典型的G_N中进行1次分化,从而扩展到Janson获得的所有D结果的所有可能值,并独立于Molloy,Robalewska,Robinson和Wormald延伸到固定d。此外,我们还获得了此类图G的不同1次拟合数的下限,该数字在已知的上限中降低了指数底部的2倍。即使在最简单的情况下,我们的证明是概率的,并且可以轻松地将其变成多项式时间(随机)算法, ^nd /2也比以前最著名的下限。
A 1-factorization of a graph G is a collection of edge-disjoint perfect matchings whose union is E(G). A trivial necessary condition for G to admit a 1-factorization is that |V(G)| is even and G is regular; the converse is easily seen to be false. In this paper, we consider the problem of finding 1-factorizations of regular, pseudorandom graphs. Specifically, we prove that for any ε > 0, an (n, d,λ)-graph G (that is, a d-regular graph on n vertices whose second largest eigenvalue in absolute value is at most λ) admits a 1-factorization provided that n is even, C_0 ≤ d ≤ n-1 (where C_0=C_0(ε) is a constant depending only on ε), and λ ≤ d^1-ε. In particular, since (as is well known) a typical random d-regular graph G_n, d is such a graph, we obtain the existence of a 1-factorization in a typical G_n, d for all C_0 ≤ d ≤ n-1, thereby extending to all possible values of d results obtained by Janson, and independently by Molloy, Robalewska, Robinson, and Wormald for fixed d. Moreover, we also obtain a lower bound for the number of distinct 1-factorizations of such graphs G which is off by a factor of 2 in the base of the exponent from the known upper bound. This lower bound is better by a factor of 2^nd/2 than the previously best known lower bounds, even in the simplest case where G is the complete graph. Our proofs are probabilistic and can be easily turned into polynomial time (randomized) algorithms.