A note on (<Emphasis Type="Italic">s</Emphasis>, <Emphasis Type="Italic">t</Emphasis>)-relaxed <Emphasis Type="Italic">L</Emphasis>(2, 1)-labeling of graphs

Let \(G=(V, E)\) be a graph. For two vertices u and v in G, we denote \(d_G(u, v)\) the distance between u and v. A vertex v is called an i-neighbor of u if \(d_G(u,v)=i\). Let s, t and k be nonnegative integers. An (s, t)-relaxed k-L(2, 1)-labeling of a graph G is an assignment of labels from \(\{0, 1, \ldots , k\}\) to the vertices of G if the following three conditions are met: (1) adjacent vertices get different labels; (2) for any vertex u of G, there are at most s 1-neighbors of u receiving labels from \(\{f(u)-1,f(u)+1\}\); (3) for any vertex u of G, the number of 2-neighbors of u assigned the label f(u) is at most t. The (s, t)-relaxed L(2, 1)-labeling number \(\lambda _{2,1}^{s,t}(G)\) of G is the minimum k such that G admits an (s, t)-relaxed k-L(2, 1)-labeling. In this article, we refute Conjecture 4 and Conjecture 5 stated in (Lin in J Comb Optim. doi: 10.1007/s10878-014-9746-9, 2013).     

Approximating <Emphasis Type="Italic">k</Emphasis>-generalized connectivity via collapsing HSTs

An instance of the k -generalized connectivity problem consists of an undirected graph G=(V,E), whose edges are associated with non-negative costs, and a collection \({\mathcal{D}}=\{(S_{1},T_{1}),\ldots,(S_{d},T_{d})\}\) of distinct demands, each of which comprises a pair of disjoint vertex sets. We say that a subgraph ??G connects a demand (S _i ,T _i ) when it contains a path with one endpoint in S _i and the other in T _i . Given an integer parameter k, the goal is to identify a minimum cost subgraph that connects at least k demands in \({\mathcal{D}}\).Alon, Awerbuch, Azar, Buchbinder and Naor (SODA ’04) seem to have been the first to consider the generalized connectivity paradigm as a unified machinery for incorporating multiple-choice decisions into network formation settings. Their main contribution in this context was to devise a multiplicative-update online algorithm for computing log-competitive fractional solutions, and to propose provably-good rounding procedures for important special cases. Nevertheless, approximating the generalized connectivity problem in its unconfined form, where one makes no structural assumptions about the underlying graph and collection of demands, has remained an open question up until a recent O(log?² nlog?² d) approximation due to Chekuri, Even, Gupta and Segev (SODA ’08). Unfortunately, the latter result does not extend to connecting a pre-specified number of demands. Furthermore, even the simpler case of singleton demands has been established as a challenging computational task, when Hajiaghayi and Jain (SODA ’06) related its inapproximability to that of dense k -subgraph.In this paper, we present the first non-trivial approximation algorithm for k-generalized connectivity, which is derived by synthesizing several techniques originating in probabilistic embeddings of finite metrics, network design, and randomization. Specifically, our algorithm constructs, with constant probability, a feasible subgraph whose cost is within a factor of O(n ^2/3?polylog(n,k)) of optimal. We believe that the fundamental approach illustrated in the current writing is of independent interest, and may be applicable in other settings as well.     

On the online multi-agent O–D <Emphasis Type="Italic">k</Emphasis>-Canadian Traveler Problem

In this article, we present new results on the online multi-agent O–D k-Canadian Traveler Problem, in which there are multiple agents and an input graph with a given source node O and a destination node D together with edge costs such that at most k edges are blocked. The blocked edges are not known a priori and are not recoverable. All of the agents are initially located at O. The objective is to find an online strategy such that at least one of the agents finds a route from the initial location O to a given destination D with minimum total cost. We focus on the case where communication among the agents is limited in the sense that some travelers can both send and receive information while the others can only receive information. We formalize the definition of agents’ intelligence by specifying three levels. We introduce two online strategies which utilize higher levels of agents’ intelligence to provide updated lower bounds to this problem. We show that one of our strategies is optimal in both cases with complete and limited communication in the special case of O–D edge-disjoint graphs and highest level of agents’ intelligence.     

An executive hierarchy analysis of stock options: Does gender matter<Emphasis Type="Italic">?</Emphasis>

This study takes a step forward in addressing the influence of stock options on executive risk-taking behavior, examining the moderating role of the executive hierarchy—CEOs versus non-CEO executives—and the gender effect within these corporate positions. Panel data analysis for matched samples of S&P 1500 listed firms between 2006 and 2011 confirms both hierarchical and gender differences in the relationship between executive stock options (ESOs) and risk taking. The maximum wealth at risk at which risk-increasing behavior changes to risk-reducing behavior—in the inverted U-shaped relationship—is higher for CEOs than for non-CEO executives, while gender differences in the ESO risk-taking effect are stronger at the level of CEOs. Thus, our evidence shows the importance of considering executive’s decision-making freedom (by means of hierarchy) in order to predict risk preferences according to executive gender.     

On irreducible no-hole <Emphasis Type="Italic">L</Emphasis>(2, 1)-coloring of subdivision of graphs

An L(2, 1)-coloring (or labeling) of a graph G is a mapping \(f:V(G) \rightarrow \mathbb {Z}^{+}\bigcup \{0\}\) such that \(|f(u)-f(v)|\ge 2\) for all edges uv of G, and \(|f(u)-f(v)|\ge 1\) if u and v are at distance two in G. The span of an L(2, 1)-coloring f, denoted by span f, is the largest integer assigned by f to some vertex of the graph. The span of a graph G, denoted by \(\lambda (G)\), is min {span \(f: f\text {is an }L(2,1)\text {-coloring of } G\}\). If f is an L(2, 1)-coloring of a graph G with span k then an integer l is a hole in f, if \(l\in (0,k)\) and there is no vertex v in G such that \(f(v)=l\). A no-hole coloring is defined to be an L(2, 1)-coloring with span k which uses all the colors from \(\{0,1,\ldots ,k\}\), for some integer k not necessarily the span of the graph. An L(2, 1)-coloring is said to be irreducible if colors of no vertices in the graph can be decreased and yield another L(2, 1)-coloring of the same graph. An irreducible no-hole coloring of a graph G, also called inh-coloring of G, is an L(2, 1)-coloring of G which is both irreducible and no-hole. The lower inh-span or simply inh-span of a graph G, denoted by \(\lambda _{inh}(G)\), is defined as \(\lambda _{inh}(G)=\min ~\{\)span f : f is an inh-coloring of G}. Given a graph G and a function h from E(G) to \(\mathbb {N}\), the h-subdivision of G, denoted by \(G_{(h)}\), is the graph obtained from G by replacing each edge uv in G with a path of length h(uv). In this paper we show that \(G_{(h)}\) is inh-colorable for \(h(e)\ge 2\), \(e\in E(G)\), except the case \(\Delta =3\) and \(h(e)=2\) for at least one edge but not for all. Moreover we find the exact value of \(\lambda _{inh}(G_{(h)})\) in several cases and give upper bounds of the same in the remaining.     

On the <Emphasis Type="Italic">L</Emphasis>(2, 1)-labeling conjecture for brick product graphs

Let \(G=(V, E)\) be a graph. Denote \(d_G(u, v)\) the distance between two vertices u and v in G. An L(2, 1)-labeling of G is a function \(f: V \rightarrow \{0,1,\ldots \}\) such that for any two vertices u and v, \(|f(u)-f(v)| \ge 2\) if \(d_G(u, v) = 1\) and \(|f(u)-f(v)| \ge 1\) if \(d_G(u, v) = 2\). The span of f is the difference between the largest and the smallest number in f(V). The \(\lambda \)-number \(\lambda (G)\) of G is the minimum span over all L(2, 1)-labelings of G. In this paper, we conclude that the \(\lambda \)-number of each brick product graph is 5 or 6, which confirms Conjecture 6.1 stated in Li et al. (J Comb Optim 25:716–736, 2013).     

A primal–dual online algorithm for the <Emphasis Type="Italic">k</Emphasis>-server problem on weighted HSTs

In this paper, we show that there is a \(\frac{5}{2}\ell \cdot \ln (1+k)\)-competitive randomized algorithm for the k-sever problem on weighted Hierarchically Separated Trees (HSTs) with depth \(\ell \) when \(n=k+1\) where n is the number of points in the metric space, which improved previous best competitive ratio \(12 \ell \ln (1+4\ell (1+k))\) by Bansal et al. (FOCS, pp 267–276, 2011).     

New algorithms for online rectangle filling with <Emphasis Type="BoldItalic">k</Emphasis>-lookahead

We study the online rectangle filling problem which arises in channel aware scheduling of wireless networks, and present deterministic and randomized results for algorithms that are allowed a k-lookahead for k≥2. Our main result is a deterministic min {1.848,1+2/(k−1)}-competitive online algorithm. This is the first algorithm for this problem with a competitive ratio approaching 1 as k approaches +∞. The previous best-known solution for this problem has a competitive ratio of 2 for any k≥2. We also present a randomized online algorithm with a competitive ratio of 1+1/(k+1). Our final result is a closely matching lower bound (also proved in this paper) of $1+1/(\sqrt{k+2}+\sqrt{k+1})^{2}>1+1/(4(k+2))$1+1/(\sqrt{k+2}+\sqrt{k+1})^{2}>1+1/(4(k+2)) on the competitive ratio of any randomized online algorithm against an oblivious adversary. These are the first known results for randomized algorithms for this problem.     

Packing <Emphasis Type="Bold">[1, Δ]</Emphasis>-factors in graphs of small degree

Given an undirected, connected graph G with maximum degree Δ, we introduce the concept of a [1, Δ]-factor k-packing in G, defined as a set of k edge-disjoint subgraphs of G such that every vertex of G has an incident edge in at least one subgraph. The problem of deciding whether a graph admits a [1,Δ]-factor k-packing is shown to be solvable in linear time for k = 2, but NP-complete for all k≥ 3. For k = 2, the optimisation problem of minimising the total number of edges of the subgraphs of the packing is NP-hard even when restricted to subcubic planar graphs, but can in general be approximated within a factor of by reduction to the Maximum 2-Edge-Colorable Subgraph problem. Finally, we discuss implications of the obtained results for the problem of fault-tolerant guarding of a grid, which provides the main motivation for research.     

A linear programming duality approach to analyzing strictly nonblocking <Emphasis Type="Italic">d</Emphasis>-ary multilog networks under general crosstalk constraints

When a switching network topology is used for constructing optical cross-connects, as in the circuit switching case, no two routes are allowed to share a link. However, if two routes share too many switching elements, then crosstalk introduced at those switching elements degrades signal quality. Vaez and Lea (IEEE Trans. Commun. 48:(2)316–323, 2000) introduced a parameter c which is the maximum number of distinct switching elements a route can share with other routes in the network. This is called the general crosstalk constraint. This paper presents a new method of analyzing strictly nonblocking multi-log networks under this general crosstalk constraint using linear programming duality.