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Alan Frieze

Publications and source records attributed to Alan Frieze.

At least 163 records · Page 9Linked to original sources
arXiv2012

A greedy algorithm for finding a large 2-matching on a random cubic graph

A 2-matching of a graph $G$ is a spanning subgraph with maximum degree two. The size of a 2-matching $U$ is the number of edges in $U$ and this is at least $n-\k(U)$ where $n$ is the number of vertices of $G$ and $\k$ denotes the number of components. In this paper, we analyze the performance of a greedy algorithm \textsc{2greedy} for finding a large 2-matching on a random 3-regular graph. We prove that with high probability, the algorithm outputs a 2-matching $U$ with $\k(U) = \tildeΘ\of{n^{1/5}}$.

math.CO↗
arXiv2012

Random triangle removal

Starting from a complete graph on $n$ vertices, repeatedly delete the edges of a uniformly chosen triangle. This stochastic process terminates once it arrives at a triangle-free graph, and the fundamental question is to estimate the final number of edges (equivalently, the time it takes the process to finish, or how many edge-disjoint triangles are packed via the random greedy algorithm). Bollobás and Erdős (1990) conjectured that the expected final number of edges has order $n^{3/2}$, motivated by the study of the Ramsey number $R(3,t)$. An upper bound of $o(n^2)$ was shown by Spencer (1995) and independently by Rödl and Thoma (1996). Several bounds were given for variants and generalizations (e.g., Alon, Kim and Spencer (1997) and Wormald (1999)), while the best known upper bound for the original question of Bollobás and Erdős was $n^{7/4+o(1)}$ due to Grable (1997). No nontrivial lower bound was available. Here we prove that with high probability the final number of edges in random triangle removal is equal to $n^{3/2+o(1)}$, thus confirming the 3/2 exponent conjectured by Bollobás and Erdős and matching the predictions of Spencer et al. For the upper bound, for any fixed $ε>0$ we construct a family of $\exp(O(1/ε))$ graphs by gluing $O(1/ε)$ triangles sequentially in a prescribed manner, and dynamically track all homomorphisms from them, rooted at any two vertices, up to the point where $n^{3/2+ε}$ edges remain. A system of martingales establishes concentration for these random variables around their analogous means in a random graph with corresponding edge density, and a key role is played by the self-correcting nature of the process. The lower bound builds on the estimates at that very point to show that the process will typically terminate with at least $n^{3/2-o(1)}$ edges left.

math.CO↗
arXiv2011

Cops and Robbers on Geometric Graphs

Cops and robbers is a turn-based pursuit game played on a graph $G$. One robber is pursued by a set of cops. In each round, these agents move between vertices along the edges of the graph. The cop number $c(G)$ denotes the minimum number of cops required to catch the robber in finite time. We study the cop number of geometric graphs. For points $x_1, ..., x_n \in \R^2$, and $r \in \R^+$, the vertex set of the geometric graph $G(x_1, ..., x_n; r)$ is the graph on these $n$ points, with $x_i, x_j$ adjacent when $ \norm{x_i -x_j} \leq r$. We prove that $c(G) \leq 9$ for any connected geometric graph $G$ in $R^2$ and we give an example of a connected geometric graph with $c(G) = 3$. We improve on our upper bound for random geometric graphs that are sufficiently dense. Let $G(n,r)$ denote the probability space of geometric graphs with $n$ vertices chosen uniformly and independently from $[0,1]^2$. For $G \in G(n,r)$, we show that with high probability (whp), if $r \geq K_1 (\log n/n)^{1/4}$, then $c(G) \leq 2$, and if $r \geq K_2(\log n/n)^{1/5}$, then $c(G) = 1$ where $K_1, K_2 > 0$ are absolute constants. Finally, we provide a lower bound near the connectivity regime of $G(n,r)$: if $r \leq K_3 \log n / \sqrt{n} $ then $c(G) > 1$ whp, where $K_3 > 0$ is an absolute constant.

math.CO↗
arXiv2011

On a Greedy 2-Matching Algorithm and Hamilton Cycles in Random Graphs with Minimum Degree at Least Three

We describe and analyse a simple greedy algorithm \2G\ that finds a good 2-matching $M$ in the random graph $G=G_{n,cn}^{\d\geq 3}$ when $c\geq 15$. A 2-matching is a spanning subgraph of maximum degree two and $G$ is drawn uniformly from graphs with vertex set $[n]$, $cn$ edges and minimum degree at least three. By good we mean that $M$ has $O(\log n)$ components. We then use this 2-matching to build a Hamilton cycle in $O(n^{1.5+o(1)})$ time \whp.

math.CO↗
arXiv2011

Tight Hamilton Cycles in Random Uniform Hypergraphs

In this paper we show that $e/n$ is the sharp threshold for the existence of tight Hamilton cycles in random $k$-uniform hypergraphs, for all $k\ge 4$. When $k=3$ we show that $1/n$ is an asymptotic threshold. We also determine thresholds for the existence of other types of Hamilton cycles.

math.CO↗
arXiv2011

High Degree Vertices, Eigenvalues and Diameter of Random Apollonian Networks

In this work we analyze basic properties of Random Apollonian Networks \cite{zhang,zhou}, a popular stochastic model which generates planar graphs with power law properties. Specifically, let $k$ be a constant and $Δ_1 \geq Δ_2 \geq .. \geq Δ_k$ be the degrees of the $k$ highest degree vertices. We prove that at time $t$, for any function $f$ with $f(t) \rightarrow +\infty$ as $t \rightarrow +\infty$, $\frac{t^{1/2}}{f(t)} \leq Δ_1 \leq f(t)t^{1/2}$ and for $i=2,...,k=O(1)$, $\frac{t^{1/2}}{f(t)} \leq Δ_i \leq Δ_{i-1} - \frac{t^{1/2}}{f(t)}$ with high probability (\whp). Then, we show that the $k$ largest eigenvalues of the adjacency matrix of this graph satisfy $λ_k = (1\pm o(1))Δ_k^{1/2}$ \whp. Furthermore, we prove a refined upper bound on the asymptotic growth of the diameter, i.e., that \whp the diameter $d(G_t)$ at time $t$ satisfies $d(G_t) \leq ρ\log{t}$ where $\frac{1}ρ=η$ is the unique solution greater than 1 of the equation $η- 1 - \logη = \log{3}$. Finally, we investigate other properties of the model.

cs.SI↗
arXiv2011

Covering the edges of a random graph by cliques

The clique cover number of a graph G is the minimum number of cliques required to cover the edges of graph G. In this paper we consider the random graph G(n,p), for p constant. We prove that with probability 1-o(1), the clique number of G(n,p) is Theta(n^2/\log^2n).

math.CO↗
arXiv2011

Component structure of the vacant set induced by a random walk on a random graph

We consider random walks on several classes of graphs and explore the likely structure of the vacant set, i.e. the set of unvisited vertices. Let Γ(t) be the subgraph induced by the vacant set of the walk at step t. We show that for random graphs G_{n,p} (above the connectivity threshold) and for random regular graphs G_r, r \geq 3, the graph Γ(t) undergoes a phase transition in the sense of the well-known Erdos-Renyi phase transition. Thus for t \leq (1-ε)t^*, there is a unique giant component, plus components of size O(log n), and for t \geq (1+ε)t^* all components are of size O(log n). For G_{n,p} and G_r we give the value of t^*, and the size of Γ(t). For G_r, we also give the degree sequence of Γ(t), the size of the giant component (if any) of Γ(t) and the number of tree components of Γ(t) of a given size k=O(log n). We also show that for random digraphs D_{n,p} above the strong connectivity threshold, there is a similar directed phase transition. Thus for t\leq (1-ε)t^*, there is a unique strongly connected giant component, plus strongly connected components of size O(log n), and for t\geq (1+ε)t^* all strongly connected components are of size O(log n).

math.CO↗
arXiv2011

Stationary distribution and cover time of random walks on random digraphs

We study properties of a simple random walk on the random digraph D_{n,p} when np={d\log n},\; d>1. We prove that whp the stationary probability pi_v of a vertex v is asymptotic to deg^-(v)/m where deg^-(v) is the in-degree of v and m=n(n-1)p is the expected number of edges of D_{n,p}. If d=d(n) tends to infinity with n, the stationary distribution is asymptotically uniform whp. Using this result we prove that, for d>1, whp the cover time of D_{n,p} is asymptotic to d\log (d/(d-1))n\log n. If d=d(n) tends to infinity with n, then the cover time is asymptotic to n\log n.

math.CO↗
arXiv2011

Loose Hamilton Cycles in Random Uniform Hypergraphs

In the random hypergraph $H_{n,p;k}$ each possible $k$-tuple appears independently with probability $p$. A loose Hamilton cycle is a cycle in which every pair of adjacent edges intersects in a single vertex. We prove that if $p n^{k-1}/\log n$ tends to infinity with $n$ then $$\lim_{\substack{n\to \infty 2(k-1) |n}}\Pr(H_{n,p;k}\ contains\ a\ loose\ Hamilton\ cycle)=1.$$ This is asymptotically best possible.

math.CO↗
arXiv2011

Packing tight Hamilton cycles in uniform hypergraphs

We say that a $k$-uniform hypergraph $C$ is a Hamilton cycle of type $\ell$, for some $1\le \ell \le k$, if there exists a cyclic ordering of the vertices of $C$ such that every edge consists of $k$ consecutive vertices and for every pair of consecutive edges $E_{i-1},E_i$ in $C$ (in the natural ordering of the edges) we have $|E_{i-1}\setminus E_i|=\ell$. We define a class of $(\e,p)$-regular hypergraphs, that includes random hypergraphs, for which we can prove the existence of a decomposition of almost all edges into type $\ell$ Hamilton cycles, where $\ell<k/2$.

math.CO↗
arXiv2010

Rainbow Hamilton cycles in random graphs

One of the most famous results in the theory of random graphs establishes that the threshold for Hamiltonicity in the Erdos-Renyi random graph G_{n,p} is around p ~ (log n + log log n) / n. Much research has been done to extend this to increasingly challenging random structures. In particular, a recent result by Frieze determined the asymptotic threshold for a loose Hamilton cycle in the random 3-uniform hypergraph by connecting 3-uniform hypergraphs to edge-colored graphs. In this work, we consider that setting of edge-colored graphs, and prove a result which achieves the best possible first order constant. Specifically, when the edges of G_{n,p} are randomly colored from a set of (1 + o(1)) n colors, with p = (1 + o(1)) (log n) / n, we show that one can almost always find a Hamilton cycle which has the further property that all edges are distinctly colored (rainbow).

math.CO↗
arXiv2010

Packing tight Hamilton cycles in 3-uniform hypergraphs

Let H be a 3-uniform hypergraph with N vertices. A tight Hamilton cycle C \subset H is a collection of N edges for which there is an ordering of the vertices v_1, ..., v_N such that every triple of consecutive vertices {v_i, v_{i+1}, v_{i+2}} is an edge of C (indices are considered modulo N). We develop new techniques which enable us to prove that under certain natural pseudo-random conditions, almost all edges of H can be covered by edge-disjoint tight Hamilton cycles, for N divisible by 4. Consequently, we derive the corollary that random 3-uniform hypergraphs can be almost completely packed with tight Hamilton cycles w.h.p., for N divisible by 4 and P not too small. Along the way, we develop a similar result for packing Hamilton cycles in pseudo-random digraphs with even numbers of vertices.

math.CO↗
arXiv2010

Average case performance of heuristics for multi-dimensional assignment problems

We consider multi-dimensional assignment problems in a probabilistic setting. Our main results are: (i) A new efficient algorithm for the 3-dimensional planar problem, based on enumerating and selecting from a set of "alternating-path trees"; (ii) A new efficient matching-based algorithm for the 3-dimensional axial problem.

cs.DM↗
arXiv2010

A note on the random greedy triangle-packing algorithm

The random greedy algorithm for constructing a large partial Steiner-Triple-System is defined as follows. We begin with a complete graph on $n$ vertices and proceed to remove the edges of triangles one at a time, where each triangle removed is chosen uniformly at random from the collection of all remaining triangles. This stochastic process terminates once it arrives at a triangle-free graph. In this note we show that with high probability the number of edges in the final graph is at most $ O\big( n^{7/4}\log^{5/4}n \big) $.

math.CO↗
arXiv2010

Variations on Cops and Robbers

We consider several variants of the classical Cops and Robbers game. We treat the version where the robber can move R > 1 edges at a time, establishing a general upper bound of N / α^{(1-o(1))\sqrt{log_α N}}, where α = 1 + 1/R, thus generalizing the best known upper bound for the classical case R = 1 due to Lu and Peng. We also show that in this case, the cop number of an N-vertex graph can be as large as N^{1 - 1/(R-2)} for finite R, but linear in N if R is infinite. For R = 1, we study the directed graph version of the problem, and show that the cop number of any strongly connected digraph on N vertices is at most O(N(log log N)^2/log N). Our approach is based on expansion.

math.CO↗
arXiv2010

Loose Hamilton Cycles in Random 3-Uniform Hypergraphs

In the random hypergraph H=H(n,p;3) each possible triple appears independently with probability p. A loose Hamilton cycle can be described as a sequence of edges {x_i,y_i,x_{i+1}\} for i=1,2,...,n/2. We prove that there exists an absolute constant K>0 such that if p>K\log n/n^2 then lim_{n->oo 4 |n}}Pr(H(n,p;3) contains a loose Hamilton cycle)=1.

math.CO↗