49 research outputs found
Separation of Circulating Tokens
Self-stabilizing distributed control is often modeled by token abstractions.
A system with a single token may implement mutual exclusion; a system with
multiple tokens may ensure that immediate neighbors do not simultaneously enjoy
a privilege. For a cyber-physical system, tokens may represent physical objects
whose movement is controlled. The problem studied in this paper is to ensure
that a synchronous system with m circulating tokens has at least d distance
between tokens. This problem is first considered in a ring where d is given
whilst m and the ring size n are unknown. The protocol solving this problem can
be uniform, with all processes running the same program, or it can be
non-uniform, with some processes acting only as token relays. The protocol for
this first problem is simple, and can be expressed with Petri net formalism. A
second problem is to maximize d when m is given, and n is unknown. For the
second problem, the paper presents a non-uniform protocol with a single
corrective process.Comment: 22 pages, 7 figures, epsf and pstricks in LaTe
Finding Connected Dense -Subgraphs
Given a connected graph on vertices and a positive integer ,
a subgraph of on vertices is called a -subgraph in . We design
combinatorial approximation algorithms for finding a connected -subgraph in
such that its density is at least a factor
of the density of the densest -subgraph
in (which is not necessarily connected). These particularly provide the
first non-trivial approximations for the densest connected -subgraph problem
on general graphs
Approximating k-Forest with Resource Augmentation: A Primal-Dual Approach
In this paper, we study the -forest problem in the model of resource
augmentation. In the -forest problem, given an edge-weighted graph ,
a parameter , and a set of demand pairs , the
objective is to construct a minimum-cost subgraph that connects at least
demands. The problem is hard to approximate---the best-known approximation
ratio is . Furthermore, -forest is as hard to
approximate as the notoriously-hard densest -subgraph problem.
While the -forest problem is hard to approximate in the worst-case, we
show that with the use of resource augmentation, we can efficiently approximate
it up to a constant factor.
First, we restate the problem in terms of the number of demands that are {\em
not} connected. In particular, the objective of the -forest problem can be
viewed as to remove at most demands and find a minimum-cost subgraph that
connects the remaining demands. We use this perspective of the problem to
explain the performance of our algorithm (in terms of the augmentation) in a
more intuitive way.
Specifically, we present a polynomial-time algorithm for the -forest
problem that, for every , removes at most demands and has
cost no more than times the cost of an optimal algorithm
that removes at most demands
Reordering buffer management with advice
In the reordering buffer management problem, a sequence of colored items arrives at a service station to be processed. Each color change between two consecutively processed items generates some cost. A reordering buffer of capacity k items can be used to preprocess the input sequence in order to decrease the number of color changes. The goal is to find a scheduling strategy that, using the reordering buffer, minimizes the number of color changes in the given sequence of items. We consider the problem in the setting of online computation with advice. In this model, the color of an item becomes known only at the time when the item enters the reordering buffer. Additionally, together with each item entering the buffer, we get a fixed number of advice bits, which can be seen as information about the future or as information about an optimal solution (or an approximation thereof) for the whole input sequence. We show that for any ε>0 there is a (1+ε)-competitive algorithm for the problem which uses only a constant (depending on ε) number of advice bits per input item. This also immediately implies a (1+ε)-approximation algorithm which has 2O(nlog1/ε) running time (this should be compared to the trivial optimal algorithm which has a running time of kO(n)). We complement the above result by presenting a lower bound of Ω(logk) bits of advice per request for any 1-competitive algorithm
On finding dense subgraphs
Abstract. Given an undirected graph G = (V, E), the density of a subgraph on vertex set S is defined as d(S) = |E(S)|, where E(S) is the set of edges |S| in the subgraph induced by nodes in S. Finding subgraphs of maximum density is a very well studied problem. One can also generalize this notion to directed graphs. For a directed graph one notion of density given by Kannan and Vinay [12] is as follows: given subsets S and T of vertices, the density of the subgrap