32 research outputs found
Inapproximability of the Standard Pebble Game and Hard to Pebble Graphs
Pebble games are single-player games on DAGs involving placing and moving
pebbles on nodes of the graph according to a certain set of rules. The goal is
to pebble a set of target nodes using a minimum number of pebbles. In this
paper, we present a possibly simpler proof of the result in [CLNV15] and
strengthen the result to show that it is PSPACE-hard to determine the minimum
number of pebbles to an additive term for all , which improves upon the currently known additive constant hardness of
approximation [CLNV15] in the standard pebble game. We also introduce a family
of explicit, constant indegree graphs with nodes where there exists a graph
in the family such that using constant pebbles requires moves
to pebble in both the standard and black-white pebble games. This independently
answers an open question summarized in [Nor15] of whether a family of DAGs
exists that meets the upper bound of moves using constant pebbles
with a different construction than that presented in [AdRNV17].Comment: Preliminary version in WADS 201
On the Hardness of Red-Blue Pebble Games
Red-blue pebble games model the computation cost of a two-level memory
hierarchy. We present various hardness results in different red-blue pebbling
variants, with a focus on the oneshot model. We first study the relationship
between previously introduced red-blue pebble models (base, oneshot, nodel). We
also analyze a new variant (compcost) to obtain a more realistic model of
computation. We then prove that red-blue pebbling is NP-hard in all of these
model variants. Furthermore, we show that in the oneshot model, a
-approximation algorithm for is only possible if the unique
games conjecture is false. Finally, we show that greedy algorithms are not good
candidates for approximation, since they can return significantly worse
solutions than the optimum
Definable inapproximability: New challenges for duplicator
AbstractWe consider the hardness of approximation of optimization problems from the point of view of definability. For many -hard optimization problems it is known that, unless , no polynomial-time algorithm can give an approximate solution guaranteed to be within a fixed constant factor of the optimum. We show, in several such instances and without any complexity theoretic assumption, that no algorithm that is expressible in fixed-point logic with counting (FPC) can compute an approximate solution. Since important algorithmic techniques for approximation algorithms (such as linear or semidefinite programming) are expressible in FPC, this yields lower bounds on what can be achieved by such methods. The results are established by showing lower bounds on the number of variables required in first-order logic with counting to separate instances with a high optimum from those with a low optimum for fixed-size instances.</jats:p
Approximating Cumulative Pebbling Cost Is Unique Games Hard
The cumulative pebbling complexity of a directed acyclic graph is defined
as , where the minimum is taken over all
legal (parallel) black pebblings of and denotes the number of
pebbles on the graph during round . Intuitively, captures
the amortized Space-Time complexity of pebbling copies of in parallel.
The cumulative pebbling complexity of a graph is of particular interest in
the field of cryptography as is tightly related to the
amortized Area-Time complexity of the Data-Independent Memory-Hard Function
(iMHF) [AS15] defined using a constant indegree directed acyclic
graph (DAG) and a random oracle . A secure iMHF should have
amortized Space-Time complexity as high as possible, e.g., to deter brute-force
password attacker who wants to find such that . Thus, to
analyze the (in)security of a candidate iMHF , it is crucial to
estimate the value but currently, upper and lower bounds for
leading iMHF candidates differ by several orders of magnitude. Blocki and Zhou
recently showed that it is -Hard to compute , but
their techniques do not even rule out an efficient
-approximation algorithm for any constant . We
show that for any constant , it is Unique Games hard to approximate
to within a factor of .
(See the paper for the full abstract.)Comment: 28 pages, updated figures and corrected typo
Hardness of Approximation in PSPACE and Separation Results for Pebble Games
We consider the pebble game on DAGs with bounded fan-in introduced in
[Paterson and Hewitt '70] and the reversible version of this game in [Bennett
'89], and study the question of how hard it is to decide exactly or
approximately the number of pebbles needed for a given DAG in these games. We
prove that the problem of eciding whether ~pebbles suffice to reversibly
pebble a DAG is PSPACE-complete, as was previously shown for the standard
pebble game in [Gilbert, Lengauer and Tarjan '80]. Via two different graph
product constructions we then strengthen these results to establish that both
standard and reversible pebbling space are PSPACE-hard to approximate to within
any additive constant. To the best of our knowledge, these are the first
hardness of approximation results for pebble games in an unrestricted setting
(even for polynomial time). Also, since [Chan '13] proved that reversible
pebbling is equivalent to the games in [Dymond and Tompa '85] and [Raz and
McKenzie '99], our results apply to the Dymond--Tompa and Raz--McKenzie games
as well, and from the same paper it follows that resolution depth is
PSPACE-hard to determine up to any additive constant. We also obtain a
multiplicative logarithmic separation between reversible and standard pebbling
space. This improves on the additive logarithmic separation previously known
and could plausibly be tight, although we are not able to prove this. We leave
as an interesting open problem whether our additive hardness of approximation
result could be strengthened to a multiplicative bound if the computational
resources are decreased from polynomial space to the more common setting of
polynomial time
Constructing Hard Examples for Graph Isomorphism.
We describe a method for generating graphs that provide difficult examples for practical Graph Isomorphism testers. We first give the theoretical construction, showing that we can have a family of graphs without any
non-trivial automorphisms which also have high Weisfeiler-Leman dimension. The construction is based on properties of random 3XOR-formulas.
We describe how to convert such a formula into a graph which has the
desired properties with high probability. We validate the method by experimental implementations. We construct random formulas and validate
them with a SAT solver to filter through suitable ones, and then convert
them into graphs. Experimental results demonstrate that the resulting
graphs do provide hard examples that match the hardest known benchmarks for graph isomorphism
Static-Memory-Hard Functions, and Modeling the Cost of Space vs. Time
A series of recent research starting with (Alwen and Serbinenko, STOC 2015) has deepened our understanding of the notion of memory-hardness in cryptography â a useful property of hash functions for deterring large-scale password-cracking attacks â and has shown memory-hardness to have intricate connections with the theory of graph pebbling. Definitions of memory-hardness are not yet unified in the somewhat nascent field of memory-hardness, however, and the guarantees proven to date are with respect to a range of proposed definitions. In this paper, we observe two significant and practical considerations that are not analyzed by existing models of memory-hardness, and propose new models to capture them, accompanied by constructions based on new hard-to-pebble graphs. Our contribution is two-fold, as follows.
First, existing measures of memory-hardness only account for dynamic memory usage (i.e., memory read/written at runtime), and do not consider static memory usage (e.g., memory on disk). Among other things, this means that memory requirements considered by prior models are inherently upper-bounded by a hash functionâs runtime; in contrast, counting static memory would potentially allow quantification of much larger memory requirements, decoupled from runtime. We propose a new definition of static-memory-hard function (SHF) which takes static memory into account: we model static memory usage by oracle access to a large preprocessed string, which may be considered part of the hash function description. Static memory requirements are complementary to dynamic memory requirements: neither can replace the other, and to deter large-scale password-cracking attacks, a hash function will benefit from being both dynamic memory-hard and static-memory-hard. We give two SHF constructions based on pebbling. To prove static-memory-hardness, we define a new pebble game (âblack-magic pebble gameâ), and new graph constructions with optimal complexity under our proposed measure. Moreover, we provide a prototype implementation of our first SHF construction (which is based on pebbling of a simple âcylinderâ graph), providing an initial demonstration of practical feasibility for a limited range of parameter settings.
Secondly, existing memory-hardness models implicitly assume that the cost of space and time are more or less on par: they consider only linear ratios between the costs of time and space. We propose a new model to capture nonlinear time-space trade-offs: e.g., how is the adversary impacted when space is quadratically more expensive than time? We prove that nonlinear tradeoffs can in fact cause adversaries to employ different strategies from linear tradeoffs. Finally, as an additional contribution of independent interest, we present an asymptotically tight graph construction that achieves the best possible space complexity up to log log n-factors for an existing memory-hardness measure called cumulative complexity in the sequential pebbling model