15,557 research outputs found
Time vs. Information Tradeoffs for Leader Election in Anonymous Trees
The leader election task calls for all nodes of a network to agree on a
single node. If the nodes of the network are anonymous, the task of leader
election is formulated as follows: every node of the network must output a
simple path, coded as a sequence of port numbers, such that all these paths end
at a common node, the leader. In this paper, we study deterministic leader
election in anonymous trees.
Our aim is to establish tradeoffs between the allocated time and the
amount of information that has to be given to the nodes to
enable leader election in time in all trees for which leader election in
this time is at all possible. Following the framework of , this information (a single binary string) is provided to all
nodes at the start by an oracle knowing the entire tree. The length of this
string is called the . For an allocated time ,
we give upper and lower bounds on the minimum size of advice sufficient to
perform leader election in time .
We consider -node trees of diameter . While leader election
in time can be performed without any advice, for time we give
tight upper and lower bounds of . For time we give
tight upper and lower bounds of for even values of ,
and tight upper and lower bounds of for odd values of .
For the time interval for constant ,
we prove an upper bound of and a lower bound of
, the latter being valid whenever is odd or when
the time is at most . Finally, for time for any
constant (except for the case of very small diameters), we give
tight upper and lower bounds of
In pursuit of the dynamic optimality conjecture
In 1985, Sleator and Tarjan introduced the splay tree, a self-adjusting
binary search tree algorithm. Splay trees were conjectured to perform within a
constant factor as any offline rotation-based search tree algorithm on every
sufficiently long sequence---any binary search tree algorithm that has this
property is said to be dynamically optimal. However, currently neither splay
trees nor any other tree algorithm is known to be dynamically optimal. Here we
survey the progress that has been made in the almost thirty years since the
conjecture was first formulated, and present a binary search tree algorithm
that is dynamically optimal if any binary search tree algorithm is dynamically
optimal.Comment: Preliminary version of paper to appear in the Conference on Space
Efficient Data Structures, Streams and Algorithms to be held in August 2013
in honor of Ian Munro's 66th birthda
Deterministic and Probabilistic Binary Search in Graphs
We consider the following natural generalization of Binary Search: in a given
undirected, positively weighted graph, one vertex is a target. The algorithm's
task is to identify the target by adaptively querying vertices. In response to
querying a node , the algorithm learns either that is the target, or is
given an edge out of that lies on a shortest path from to the target.
We study this problem in a general noisy model in which each query
independently receives a correct answer with probability (a
known constant), and an (adversarial) incorrect one with probability .
Our main positive result is that when (i.e., all answers are
correct), queries are always sufficient. For general , we give an
(almost information-theoretically optimal) algorithm that uses, in expectation,
no more than queries, and identifies the target correctly with probability at
leas . Here, denotes the
entropy. The first bound is achieved by the algorithm that iteratively queries
a 1-median of the nodes not ruled out yet; the second bound by careful repeated
invocations of a multiplicative weights algorithm.
Even for , we show several hardness results for the problem of
determining whether a target can be found using queries. Our upper bound of
implies a quasipolynomial-time algorithm for undirected connected
graphs; we show that this is best-possible under the Strong Exponential Time
Hypothesis (SETH). Furthermore, for directed graphs, or for undirected graphs
with non-uniform node querying costs, the problem is PSPACE-complete. For a
semi-adaptive version, in which one may query nodes each in rounds, we
show membership in in the polynomial hierarchy, and hardness
for
Remarks on Category-Based Routing in Social Networks
It is well known that individuals can route messages on short paths through
social networks, given only simple information about the target and using only
local knowledge about the topology. Sociologists conjecture that people find
routes greedily by passing the message to an acquaintance that has more in
common with the target than themselves, e.g. if a dentist in Saarbr\"ucken
wants to send a message to a specific lawyer in Munich, he may forward it to
someone who is a lawyer and/or lives in Munich. Modelling this setting,
Eppstein et al. introduced the notion of category-based routing. The goal is to
assign a set of categories to each node of a graph such that greedy routing is
possible. By proving bounds on the number of categories a node has to be in we
can argue about the plausibility of the underlying sociological model. In this
paper we substantially improve the upper bounds introduced by Eppstein et al.
and prove new lower bounds.Comment: 21 page
Neighborhoods of trees in circular orderings
In phylogenetics, a common strategy used to construct an evolutionary tree for a set of species X is to search in the space of all such trees for one that optimizes some given score function (such as the minimum evolution, parsimony or likelihood score). As this can be computationally intensive, it was recently proposed to restrict such searches to the set of all those trees that are compatible with some circular ordering of the set X. To inform the design of efficient algorithms to perform such searches, it is therefore of interest to find bounds for the number of trees compatible with a fixed ordering in the neighborhood of a tree that is determined by certain tree operations commonly used to search for trees: the nearest neighbor interchange (nni), the subtree prune and regraft (spr) and the tree bisection and reconnection (tbr) operations. We show that the size of such a neighborhood of a binary tree associated with the nni operation is independent of the tree’s topology, but that this is not the case for the spr and tbr operations. We also give tight upper and lower bounds for the size of the neighborhood of a binary tree for the spr and tbr operations and characterize those trees for which these bounds are attained
- …