A shortest cycle for each vertex of a graph

We present an algorithm that finds, for each vertex of an undirected graph, a shortest cycle containing it. While for directed graphs this problem reduces to the All-Pairs Shortest Paths problem, this is not known to be the case for undirected graphs.We present a truly sub-cubic randomized algorithm for the undirected case. Given an undirected graph with n vertices and integer weights in $1,\dots ,M$, it runs in $\stackrel{?}{O}(\sqrt{M}{n}^{(\omega +3)/2})$ time where $\omega <2.376$ is the exponent of matrix multiplication. As a by-product, our algorithm can be used to determine which vertices lie on cycles of length at most t in $\stackrel{?}{O}(M{n}^{\omega }t)$ time. For the case of bounded real edge weights, a variant of our algorithm solves the problem up to an additive error of ? in $\stackrel{?}{O}(n^{(\omega +6)/3})$ time.     

On the advice complexity of online bipartite matching and online stable marriage

In this paper, we study the advice complexity of the online bipartite matching problem and the online stable marriage problem. We show that for both problems, $\lceil {\log }_2(n!)\rceil$ bits of advice are necessary and sufficient for a deterministic online algorithm to be optimal, where n denotes the number of vertices in one bipartition in the former problem, and the number of men in the latter.     

Efficient algorithms for the round-trip 1-center and 1-median problems

This paper presents improved algorithms for the round-trip single-facility location problem on a general graph, in which a set A of collection depots is given and the service distance of a customer is defined to be the distance from the server, to the customer, then to a depot, and back to the server. Each customer i is associated with a subset $A^i\subseteq A$ of depots that i can potentially select from and use. When $A^i=A$ for each customer i, the problem is unrestricted; otherwise it is restricted. For the restricted round-trip 1-center problem, we give an $O(mn\lg n)$-time algorithm. For the restricted 1-median problem, we give an $O(mn\lg (|A|/m)+n^2\lg n)$-time algorithm. For the unrestricted 1-median problem, we give an $O(mn+n^2\lg \lg n)$-time algorithm.     

Approximation algorithms for the Weighted t-Uniform Sparsest Cut and some other graph partitioning problems

We study the Weighted t-Uniform Sparsest Cut (Weighted t-USC) and other related problems. In an instance of the Weighted t-USC problem, a parameter t and an undirected graph $G=(V,E)$ with edge-weights $w:E\to {\mathbb{R}}_{\ge 0}$ and vertex-weights $\eta :V\to {\mathbb{R}}_{+}$ are given. The goal is to find a vertex set $S\subseteq V$ with $|S|\le t$ while minimizing $w(S,V\backslash S)/\eta (S)$, where $w(S,V\backslash S)$ is the total weight of the edges with exactly one endpoint in S and $\eta (S)={\sum }_{v\in S}\eta (v)$. For this problem, we present a $(O(\log t),1+ϵ)$ factor bicriteria approximation algorithm. Our algorithm outperforms the current best algorithm when $t=n^{o(1)}$. We also present better approximation algorithms for Weighted ρ-Unbalanced Cut and Min–Max k-Partitioning problems.     

Online scheduling of equal-length jobs with incompatible families on multiple batch machines to maximize the weighted number of early jobs

This paper studies the online scheduling of equal-length jobs with incompatible families on multiple batch machines which can process the jobs from a common family in batches, where each batch has a capacity b with $b=\infty$ in the unbounded batching and $b<\infty$ in the bounded batching. Each job J has an equal-length integral processing time $p>0$, an integral release time $r(J)?0$, an integral deadline $d(J)?0$ and a real weight $w(J)?0$. The goal is to determine a preemptive schedule with restart which maximizes the weighted number of early jobs. When $p=1$, we show that a simple greedy online algorithm has a competitive ratio 2, and establish the lower bound $2?1/b$. This means that the greedy algorithm is of the best possible for $b=\infty$. When p is any positive integer, we provide an online algorithm of competitive ratio $3+2\sqrt{2}$ for both $b=\infty$ and $b<\infty$. This is the first online algorithm for the problem with a constant competitive ratio.     

Testing hypergraph colorability

We study the problem of testing properties of hypergraphs. The goal of property testing is to distinguish between the case whether a given object has a certain property or is “far away” from the property. We prove that the fundamental problem of $\ell$-colorability of k-uniform hypergraphs can be tested in time independent of the size of the hypergraph. We present a testing algorithm that examines only $(k\ell /\epsilon {)}^{\mathrm{O}(k)}$ entries of the adjacency matrix of the input hypergraph, where $\epsilon$ is a distance parameter independent of the size of the hypergraph. The algorithm tests only a constant number of entries in the adjacency matrix provided that $\ell$, k, and $\epsilon$ are constants. This result is a generalization of previous results about testing graph colorability.     

The Steiner traveling salesman problem with online advanced edge blockages

The package delivery in an urban road network is formulated as an online Steiner traveling salesman problem, where the driver (i.e. the salesman) receives road (i.e. edge) blockage messages when he is at a certain distance to the respective blocked edges. Such road blockages are referred to as advanced information. With these online advanced road blockages, the driver wishes to deliver all the packages to their respective customers and returns back to the service depot through a shortest route. During the entire delivery process, there will be at most k road blockages, and they are non-recoverable. When the driver knows about road blockages at a distance $\alpha \mathrm{OPT}$, where $\alpha \in [0,1]$ is referred to as the forecasting ratio and OPT denotes the length of the offline shortest route, we first prove that $\max \left\{\right(1-2\alpha )k+1,1\}$ is a lower bound on the competitive ratio. We then present a polynomial time online algorithm with a competitive ratio very close to this lower bound. Computational results show that our algorithm is efficient and produces near optimal solutions. Similar results for a variation, in which the driver does not need to return to the service depot, are also achieved.