74,302 research outputs found
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
NP-hardness of circuit minimization for multi-output functions
Can we design efficient algorithms for finding fast algorithms? This question is captured by various circuit minimization problems, and algorithms for the corresponding tasks have significant practical applications. Following the work of Cook and Levin in the early 1970s, a central question is whether minimizing the circuit size of an explicitly given function is NP-complete. While this is known to hold in restricted models such as DNFs, making progress with respect to more expressive classes of circuits has been elusive.
In this work, we establish the first NP-hardness result for circuit minimization of total functions in the setting of general (unrestricted) Boolean circuits. More precisely, we show that computing the minimum circuit size of a given multi-output Boolean function f : {0,1}^n ? {0,1}^m is NP-hard under many-one polynomial-time randomized reductions. Our argument builds on a simpler NP-hardness proof for the circuit minimization problem for (single-output) Boolean functions under an extended set of generators.
Complementing these results, we investigate the computational hardness of minimizing communication. We establish that several variants of this problem are NP-hard under deterministic reductions. In particular, unless ? = ??, no polynomial-time computable function can approximate the deterministic two-party communication complexity of a partial Boolean function up to a polynomial. This has consequences for the class of structural results that one might hope to show about the communication complexity of partial functions
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
The paper characterizes classes of functions for which deep learning can be
exponentially better than shallow learning. Deep convolutional networks are a
special case of these conditions, though weight sharing is not the main reason
for their exponential advantage
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