Profile of Random Exponential Binary Trees

We introduce the random exponential binary tree (EBT) and study its profile. As customary, the tree is extended by padding each leaf node (considered internal), with the appropriate number of external nodes, so that the outdegree of every internal node is made equal to 2. In a random EBT, at every step, each external node is promoted to an internal node with probability p, stays unchanged with probability 1 - p, and the resulting tree is extended. We study the internal and external profiles of a random EBT and get exact expectations for the numbers of internal and external nodes at each level. Asymptotic analysis shows that the average external profile is richest at level \(\frac {2p}{p+1}n\), and it experiences phase transitions at levels a n, where the a’s are the solutions to an algebraic equation. The rates of convergence themselves go through an infinite number of phase changes in the sublinear range, and then again at the nearly linear levels.     

The distribution of the size of the ancestor‐tree and of the induced spanning subtree for random trees

We consider two tree statistics that extend in a natural way the parameters depth of a node resp. distance between two nodes. The ancestor‐tree of p given nodes in a rooted tree T is the subtree of T, spanned by the root and these p nodes and generalizes the depth (ancestor‐tree of a single node), whereas the spanning subtree induced by p given nodes in a tree T generalizes the distance (induced spanning subtree of two nodes). We study the random variables size of the ancestor‐tree resp. spanning subtree size for two tree families, the simply generated trees and the recursive trees. We will assume here the random tree model and also that all () possibilities of selecting p nodes in a tree of size n are equally likely. For random simply generated trees we can then characterize for a fixed number p of chosen nodes the limiting distribution of both parameters as generalized Gamma distributions, where we prove the convergence of the moments too. For some specific simply generated tree families we can give exact formulæ for the first moments. In the instance of random recursive trees, we will show that the considered parameters are asymptotically normally distributed, where we can give also exact formulæ for the expectation and the variance. © 2004 Wiley Periodicals, Inc. Random Struct. Alg., 2004     

Shortest Path Auction Algorithm Without Contractions Using Virtual Source Concept

In this paper, the problem of finding a shortest path tree rooted at a given source node on a directed graph (SPT) is considered. A new efficient algorithm based on a primal-dual approach is presented, which improves both the convergence and the complexity of the best known auction-like algorithm. It uses the virtual source (VS) concept based on the following consideration: when a node i is visited for the first time by any algorithm which preserves verified the dual admissibility conditions, then the shortest path (SP) from the source node to i is found. Therefore, the SP from the source to the remaining nodes may be computed by considering i as a virtual source.We propose a very efficient implementation of an auction-like algorithm that uses this concept and enables us to obtain a computational cost of O(n ²), where n is the number of nodes.Numerical experimentsare reported showing that the new method outdoes previously proposed auction-like algorithms and is highly competitive with other state-of-art SP approaches.     

Weighted heuristic search in networks

The evaluation function used in heuristic search algorithms commonly has the form , where n is any node in the network, is the cost of the best path currently known from the start node to n, and is the heuristic estimate associated with node n. A more general form of the evaluation function can be obtained by incorporating a weighting parameter α:

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, where 0≤ α ≤1. Such an evaluation function has been used in some recent experimental investigations of the 8-puzzle problem. In this paper a theoretical framework is developed for the analysis of the worst-case behavior of weighted heuristic search algorithms. A new algorithm is proposed whose worst-case performance characteristics are greatly superior to those of earlier algorithms in terms of the following two measures: how good is the solution, and how many nodes are expanded. Bounds are also obtained on some useful network parameters for both general and special types of heuristic estimate functions.     

Persistence of centrality in random growing trees

We investigate properties of node centrality in random growing tree models. We focus on a measure of centrality that computes the maximum subtree size of the tree rooted at each node, with the most central node being the tree centroid. For random trees grown according to a preferential attachment model, a uniform attachment model, or a diffusion processes over a regular tree, we prove that a single node persists as the tree centroid after a finite number of steps, with probability 1. Furthermore, this persistence property generalizes to the top K ≥ 1 nodes with respect to the same centrality measure. We also establish necessary and sufficient conditions for the size of an initial seed graph required to ensure persistence of a particular node with probability , as a function of ϵ: In the case of preferential and uniform attachment models, we derive bounds for the size of an initial hub constructed around the special node. In the case of a diffusion process over a regular tree, we derive bounds for the radius of an initial ball centered around the special node. Our necessary and sufficient conditions match up to constant factors for preferential attachment and diffusion tree models.     

Solving linear optimization over arithmetic constraint formula

Since Balas extended the classical linear programming problem to the disjunctive programming (DP) problem where the constraints are combinations of both logic AND and OR, many researchers explored this optimization problem under various theoretical or application scenarios such as generalized disjunctive programming (GDP), optimization modulo theories (OMT), robot path planning, real-time systems, etc. However, the possibility of combining these differently-described but form-equivalent problems into a single expression remains overlooked. The contribution of this paper is two folded. First, we convert the linear DP/GDP model, linear-arithmetic OMT problem and related application problems into an equivalent form, referred to as the linear optimization over arithmetic constraint formula (LOACF). Second, a tree-search-based algorithm named RS-LPT is proposed to solve LOACF. RS-LPT exploits the techniques of interval analysis and nonparametric estimation for reducing the search tree and lowering the number of visited nodes. Also, RS-LPT alleviates bad construction of search tree by backtracking and pruning dynamically. We evaluate RS-LPT against two most common DP/GDP methods, three state-of-the-art OMT solvers and the disjunctive transformation based method on optimization benchmarks with different types and scales. Our results favor RS-LPT as compared to existing competing methods, especially for large scale cases.     

All Colors Shortest Path problem on trees

Given an edge weighted tree T(V, E), rooted at a designated base vertex \(r \in V\), and a color from a set of colors \(C=\{1,\ldots ,k\}\) assigned to every vertex \(v \in V\), All Colors Shortest Path problem on trees (ACSP-t) seeks the shortest, possibly non-simple, path starting from r in T such that at least one node from every distinct color in C is visited. We show that ACSP-t is NP-hard, and also prove that it does not have a constant factor approximation. We give an integer linear programming formulation of ACSP-t. Based on a linear programming relaxation of this formulation, an iterative rounding heuristic is proposed. The paper also explores genetic algorithm and tabu search to develop alternative heuristic solutions for ACSP-t. The performance of all the proposed heuristics are evaluated experimentally for a wide range of trees that are generated parametrically.     

A short note on locating facilities on a path to minimize load range equity measure

This work considers the problem of locating \(p\) facilities on the nodes of a path such that the range of the weights assigned to facilities will be as small as possible. We present a polynomial progressive search algorithm to solve the problem.     

A minmax regret median problem on a tree under uncertain locations of the demand points

This paper deals with the location of a new facility on a tree according to the minimization of the weighted distance to the customers. The weights represent demands of the set of nodes. The exact location of each customer will be assumed unknown but close   to its corresponding node. In this context, an algorithm to find a minmax regret median is proposed and its complexity is shown to be O(nlog(n))$O(nlog\left(n\right))$, where n$n$ is the number of nodes of the tree     

On weighted heights of random trees

Consider the family treeT of a branching process starting from a single progenitor and conditioned to havev=v(T) edges (total progeny). To each edge <e> we associate a weightW(e). The weights are i.i.d. random variables and independent ofT. The weighted height of a self-avoiding path inT starting at the root is the sum of the weights associated with the path. We are interested in the asymptotic distribution of the maximum weighted path height in the limit asv=n. Depending on the tail of the weight distribution, we obtain the limit in three cases. In particular ify ² P(W(e)> y)0, then the limit distribution depends strongly on the tree and, in fact, is the distribution of the maximum of a Brownian excursion. If the tail of the weight distribution is regularly varying with exponent 0<2, then the weight swamps the tree and the answer is the asymptotic distribution of the maximum edge weight in the tree. There is a borderline case, namely,P(W(e)> y)cy ^–2 asy, in which the limit distribution exists but involves both the tree and the weights in a more complicated way.     

Non-interactive correlation distillation, inhomogeneous Markov chains, and the reverse Bonami-Beckner inequality

In this paper we studynon-interactive correlation distillation (NICD), a generalization of noise sensitivity previously considered in [5, 31, 39]. We extend the model toNICD on trees. In this model there is a fixed undirected tree with players at some of the nodes. One node is given a uniformly random string and this string is distributed throughout the network, with the edges of the tree acting as independent binary symmetric channels. The goal of the players is to agree on a shared random bit without communicating. Our new contributions include the following:

? In the case of ak-leaf star graph (the model considered in [31]), we resolve the open question of whether the success probability must go to zero ask » ∞. We show that this is indeed the case and provide matching upper and lower bounds on the asymptotically optimal rate (a slowly-decaying polynomial).

? In the case of thek-vertex path graph, we show that it is always optimal for all players to use the same 1-bit function.

? In the general case we show that all players should use monotone functions. We also show, somewhat surprisingly, that for certain trees it is better if not all players use the same function.

Our techniques include the use of thereverse Bonami-Beckner inequality. Although the usual Bonami-Beckner has been frequently used before, its reverse counterpart seems not to be well known. To demonstrate its strength, we use it to prove a new isoperimetric inequality for the discrete cube and a new result on the mixing of short random walks on the cube. Another tool that we need is a tight bound on the probability that a Markov chain stays inside certain sets; we prove a new theorem generalizing and strengthening previous such bounds [2, 3, 6]. On the probabilistic side, we use the “reflection principle” and the FKG and related inequalities in order to study the problem on general trees.     

Spanning cactus of a graph: Existence,extension, optimization,and approximation

We show that testing if an undirected graph contains a bridgeless spanning cactus is NP-hard. As a consequence, the minimum spanning cactus problem (MSCP) on an undirected graph with 0–1 edge weights is NP-hard. For any subgraph $S$ of $K_n$, we give polynomially testable necessary and sufficient conditions for $S$ to be extendable to a cactus in $K_n$ and the weighted version of this problem is shown to be NP-hard. A spanning tree is shown to be extendable to a cactus in $K_n$ if and only if it has at least one node of even degree. When $S$ is a spanning tree, we show that the weighted version can also be solved in polynomial time. Further, we give an $O\left(n^3\right)$ algorithm for computing a minimum cost spanning tree with at least one vertex of even degree on a graph on $n$ nodes. Finally, we show that for a complete graph with edge-costs satisfying the triangle inequality, the MSCP is equivalent to a general class of optimization problems that properly includes the traveling salesman problem and they all have the same approximation hardness.     

The Power of Choice in the Construction of Recursive Trees

The power of choice is known to change the character of random structures and produce desirable optimization effects. We discuss generalizations of random recursive trees, grown under the choice to meet optimization criteria. Specifically, we discuss the random k-minimal (k-maximal) label recursive tree, where a set of k candidate parents, instead of one as in the usual recursive tree, is selected and the node with minimal (maximal) label among them is assigned as parent for the next node. These models are proposed as alternatives for D’Souza et al. (Eur Phys J B59:535–543, 2007) minimal and maximal depth models. The advantage of the label models is that they are tractable and at the same time provide approximations and bounds for the depth models. For the depth of nodes in label models we give the average behavior and exact distributions involving Stirling’s numbers and derive Gaussian limit laws.