7 research outputs found
Pebbling, Entropy and Branching Program Size Lower Bounds
We contribute to the program of proving lower bounds on the size of branching
programs solving the Tree Evaluation Problem introduced by Cook et. al. (2012).
Proving a super-polynomial lower bound for the size of nondeterministic thrifty
branching programs (NTBP) would separate from for thrifty models
solving the tree evaluation problem. First, we show that {\em Read-Once NTBPs}
are equivalent to whole black-white pebbling algorithms thus showing a tight
lower bound (ignoring polynomial factors) for this model.
We then introduce a weaker restriction of NTBPs called {\em Bitwise
Independence}. The best known NTBPs (of size ) for the tree
evaluation problem given by Cook et. al. (2012) are Bitwise Independent. As our
main result, we show that any Bitwise Independent NTBP solving
must have at least states. Prior to this work, lower
bounds were known for NTBPs only for fixed heights (See Cook et. al.
(2012)). We prove our results by associating a fractional black-white pebbling
strategy with any bitwise independent NTBP solving the Tree Evaluation Problem.
Such a connection was not known previously even for fixed heights.
Our main technique is the entropy method introduced by Jukna and Z{\'a}k
(2001) originally in the context of proving lower bounds for read-once
branching programs. We also show that the previous lower bounds given by Cook
et. al. (2012) for deterministic branching programs for Tree Evaluation Problem
can be obtained using this approach. Using this method, we also show tight
lower bounds for any -way deterministic branching program solving Tree
Evaluation Problem when the instances are restricted to have the same group
operation in all internal nodes.Comment: 25 Pages, Manuscript submitted to Journal in June 2013 This version
includes a proof for tight size bounds for (syntactic) read-once NTBPs. The
proof is in the same spirit as the proof for size bounds for bitwise
independent NTBPs present in the earlier version of the paper and is included
in the journal version of the paper submitted in June 201
EXTREMELY UNIFORM BRANCHING PROGRAMS
We propose a new descriptive complexity notion of uniformity for branching programs solving problems defined on structured data. We observe that FO[=]-uniform (n-way) branching programs are unable to solve the tree evaluation problem studied by Cook, McKenzie, Wehr, Braverman and Santhanam [8] because such programs possess a variant of their thriftiness property. Similarly, FO[=]-uniform (n-way) branching programs are unable to solve the P-complete GEN problem because such programs possess the incremental property studied by Gál, Kouck´y and McKenzie [10]. 1
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
Pebbling Arguments for Tree Evaluation
The Tree Evaluation Problem was introduced by Cook et al. in 2010 as a
candidate for separating P from L and NL. The most general space lower bounds
known for the Tree Evaluation Problem require a semantic restriction on the
branching programs and use a connection to well-known pebble games to generate
a bottleneck argument. These bounds are met by corresponding upper bounds
generated by natural implementations of optimal pebbling algorithms. In this
paper we extend these ideas to a variety of restricted families of both
deterministic and non-deterministic branching programs, proving tight lower
bounds under these restricted models. We also survey and unify known lower
bounds in our "pebbling argument" framework