189 research outputs found
Pebbling in Semi-2-Trees
Graph pebbling is a network model for transporting discrete resources that
are consumed in transit. Deciding whether a given configuration on a particular
graph can reach a specified target is -complete, even for diameter
two graphs, and deciding whether the pebbling number has a prescribed upper
bound is -complete. Recently we proved that the pebbling number
of a split graph can be computed in polynomial time. This paper advances the
program of finding other polynomial classes, moving away from the large tree
width, small diameter case (such as split graphs) to small tree width, large
diameter, continuing an investigation on the important subfamily of chordal
graphs called -trees. In particular, we provide a formula, that can be
calculated in polynomial time, for the pebbling number of any semi-2-tree,
falling shy of the result for the full class of 2-trees.Comment: Revised numerous arguments for clarity and added technical lemmas to
support proof of main theorem bette
PEBBLING IN SPLIT GRAPHS
abstract: Graph pebbling is a network optimization model for transporting discrete resources that are consumed in transit: the movement of 2 pebbles across an edge consumes one of the pebbles. The pebbling number of a graph is the fewest number of pebbles t so that, from any initial configuration of t pebbles on its vertices, one can place a pebble on any given target vertex via such pebbling steps. It is known that deciding whether a given configuration on a particular graph can reach a specified target is NP-complete, even for diameter 2 graphs, and that deciding whether the pebbling number has a prescribed upper bound is Π[P over 2]-complete. On the other hand, for many families of graphs there are formulas or polynomial algorithms for computing pebbling numbers; for example, complete graphs, products of paths (including cubes), trees, cycles, diameter 2 graphs, and more. Moreover, graphs having minimum pebbling number are called Class 0, and many authors have studied which graphs are Class 0 and what graph properties guarantee it, with no characterization in sight. In this paper we investigate an important family of diameter 3 chordal graphs called split graphs; graphs whose vertex set can be partitioned into a clique and an independent set. We provide a formula for the pebbling number of a split graph, along with an algorithm for calculating it that runs in O(n[superscript β]) time, where β = 2ω/(ω + 1) [= over ∼] 1.41 and ω [= over ∼] 2.376 is the exponent of matrix multiplication. Furthermore we determine that all split graphs with minimum degree at least 3 are Class 0
The Cover Pebbling Number of Graphs
A pebbling move on a graph consists of taking two pebbles off of one vertex
and placing one pebble on an adjacent vertex. In the traditional pebbling
problem we try to reach a specified vertex of the graph by a sequence of
pebbling moves. In this paper we investigate the case when every vertex of the
graph must end up with at least one pebble after a series of pebbling moves.
The cover pebbling number of a graph is the minimum number of pebbles such that
however the pebbles are initially placed on the vertices of the graph we can
eventually put a pebble on every vertex simultaneously. We find the cover
pebbling numbers of trees and some other graphs. We also consider the more
general problem where (possibly different) given numbers of pebbles are
required for the vertices.Comment: 12 pages. Submitted to Discrete Mathematic
Modified Linear Programming and Class 0 Bounds for Graph Pebbling
Given a configuration of pebbles on the vertices of a connected graph , a
\emph{pebbling move} removes two pebbles from some vertex and places one pebble
on an adjacent vertex. The \emph{pebbling number} of a graph is the
smallest integer such that for each vertex and each configuration of
pebbles on there is a sequence of pebbling moves that places at least
one pebble on .
First, we improve on results of Hurlbert, who introduced a linear
optimization technique for graph pebbling. In particular, we use a different
set of weight functions, based on graphs more general than trees. We apply this
new idea to some graphs from Hurlbert's paper to give improved bounds on their
pebbling numbers.
Second, we investigate the structure of Class 0 graphs with few edges. We
show that every -vertex Class 0 graph has at least
edges. This disproves a conjecture of Blasiak et al. For diameter 2 graphs, we
strengthen this lower bound to , which is best possible. Further, we
characterize the graphs where the bound holds with equality and extend the
argument to obtain an identical bound for diameter 2 graphs with no cut-vertex.Comment: 19 pages, 8 figure
A linear optimization technique for graph pebbling
Graph pebbling is a network model for studying whether or not a given supply
of discrete pebbles can satisfy a given demand via pebbling moves. A pebbling
move across an edge of a graph takes two pebbles from one endpoint and places
one pebble at the other endpoint; the other pebble is lost in transit as a
toll. It has been shown that deciding whether a supply can meet a demand on a
graph is NP-complete. The pebbling number of a graph is the smallest t such
that every supply of t pebbles can satisfy every demand of one pebble. Deciding
if the pebbling number is at most k is \Pi_2^P-complete. In this paper we
develop a tool, called the Weight Function Lemma, for computing upper bounds
and sometimes exact values for pebbling numbers with the assistance of linear
optimization. With this tool we are able to calculate the pebbling numbers of
much larger graphs than in previous algorithms, and much more quickly as well.
We also obtain results for many families of graphs, in many cases by hand, with
much simpler and remarkably shorter proofs than given in previously existing
arguments (certificates typically of size at most the number of vertices times
the maximum degree), especially for highly symmetric graphs. Here we apply the
Weight Function Lemma to several specific graphs, including the Petersen,
Lemke, 4th weak Bruhat, Lemke squared, and two random graphs, as well as to a
number of infinite families of graphs, such as trees, cycles, graph powers of
cycles, cubes, and some generalized Petersen and Coxeter graphs. This partly
answers a question of Pachter, et al., by computing the pebbling exponent of
cycles to within an asymptotically small range. It is conceivable that this
method yields an approximation algorithm for graph pebbling
Non-malleable codes for space-bounded tampering
Non-malleable codes—introduced by Dziembowski, Pietrzak and Wichs at ICS 2010—are key-less coding schemes in which mauling attempts to an encoding of a given message, w.r.t. some class of tampering adversaries, result in a decoded value that is either identical or unrelated to the original message. Such codes are very useful for protecting arbitrary cryptographic primitives against tampering attacks against the memory. Clearly, non-malleability is hopeless if the class of tampering adversaries includes the decoding and encoding algorithm. To circumvent this obstacle, the majority of past research focused on designing non-malleable codes for various tampering classes, albeit assuming that the adversary is unable to decode. Nonetheless, in many concrete settings, this assumption is not realistic
Pebbling and Branching Programs Solving the Tree Evaluation Problem
We study restricted computation models related to the Tree Evaluation
Problem}. The TEP was introduced in earlier work as a simple candidate for the
(*very*) long term goal of separating L and LogDCFL. The input to the problem
is a rooted, balanced binary tree of height h, whose internal nodes are labeled
with binary functions on [k] = {1,...,k} (each given simply as a list of k^2
elements of [k]), and whose leaves are labeled with elements of [k]. Each node
obtains a value in [k] equal to its binary function applied to the values of
its children, and the output is the value of the root. The first restricted
computation model, called Fractional Pebbling, is a generalization of the
black/white pebbling game on graphs, and arises in a natural way from the
search for good upper bounds on the size of nondeterministic branching programs
(BPs) solving the TEP - for any fixed h, if the binary tree of height h has
fractional pebbling cost at most p, then there are nondeterministic BPs of size
O(k^p) solving the height h TEP. We prove a lower bound on the fractional
pebbling cost of d-ary trees that is tight to within an additive constant for
each fixed d. The second restricted computation model we study is a semantic
restriction on (non)deterministic BPs solving the TEP - Thrifty BPs.
Deterministic (resp. nondeterministic) thrifty BPs suffice to implement the
best known algorithms for the TEP, based on black (resp. fractional) pebbling.
In earlier work, for each fixed h a lower bound on the size of deterministic
thrifty BPs was proved that is tight for sufficiently large k. We give an
alternative proof that achieves the same bound for all k. We show the same
bound still holds in a less-restricted model, and also that gradually weaker
lower bounds can be obtained for gradually weaker restrictions on the model.Comment: Written as one of the requirements for my MSc. 29 pages, 6 figure
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