7,435 research outputs found
The Multiplicative Complexity of 6-variable Boolean Functions
The multiplicative complexity of a Boolean function is the minimum number of AND gates that are necessary and sufficient to implement the function over the basis (AND, XOR, NOT). Finding the multiplicative complexity of a given function is computationally intractable, even for functions with small number of inputs. Turan et al. showed that -variable Boolean functions can be implemented with at most AND gates for . A counting argument can be used to show that, for , there exist -variable Boolean functions with multiplicative complexity of at least . In this work, we propose a method to find the multiplicative complexity of Boolean functions by analyzing circuits with a particular number of AND gates and utilizing the affine equivalence of functions. We use this method to study the multiplicative complexity of 6-variable Boolean functions, and calculate the multiplicative complexities of all 150357 affine equivalence classes. We show that any 6-variable Boolean function can be implemented using at most 6 AND gates.
Additionally, we exhibit specific 6-variable Boolean functions which have multiplicative complexity 6
MALL proof equivalence is Logspace-complete, via binary decision diagrams
Proof equivalence in a logic is the problem of deciding whether two proofs
are equivalent modulo a set of permutation of rules that reflects the
commutative conversions of its cut-elimination procedure. As such, it is
related to the question of proofnets: finding canonical representatives of
equivalence classes of proofs that have good computational properties. It can
also be seen as the word problem for the notion of free category corresponding
to the logic.
It has been recently shown that proof equivalence in MLL (the multiplicative
with units fragment of linear logic) is PSPACE-complete, which rules out any
low-complexity notion of proofnet for this particular logic.
Since it is another fragment of linear logic for which attempts to define a
fully satisfactory low-complexity notion of proofnet have not been successful
so far, we study proof equivalence in MALL- (multiplicative-additive without
units fragment of linear logic) and discover a situation that is totally
different from the MLL case. Indeed, we show that proof equivalence in MALL-
corresponds (under AC0 reductions) to equivalence of binary decision diagrams,
a data structure widely used to represent and analyze Boolean functions
efficiently.
We show these two equivalent problems to be LOGSPACE-complete. If this
technically leaves open the possibility for a complete solution to the question
of proofnets for MALL-, the established relation with binary decision diagrams
actually suggests a negative solution to this problem.Comment: in TLCA 201
A strong direct product theorem for quantum query complexity
We show that quantum query complexity satisfies a strong direct product
theorem. This means that computing copies of a function with less than
times the quantum queries needed to compute one copy of the function implies
that the overall success probability will be exponentially small in . For a
boolean function we also show an XOR lemma---computing the parity of
copies of with less than times the queries needed for one copy implies
that the advantage over random guessing will be exponentially small.
We do this by showing that the multiplicative adversary method, which
inherently satisfies a strong direct product theorem, is always at least as
large as the additive adversary method, which is known to characterize quantum
query complexity.Comment: V2: 19 pages (various additions and improvements, in particular:
improved parameters in the main theorems due to a finer analysis of the
output condition, and addition of an XOR lemma and a threshold direct product
theorem in the boolean case). V3: 19 pages (added grant information
On the Complexity of Computing Two Nonlinearity Measures
We study the computational complexity of two Boolean nonlinearity measures:
the nonlinearity and the multiplicative complexity. We show that if one-way
functions exist, no algorithm can compute the multiplicative complexity in time
given the truth table of length , in fact under the same
assumption it is impossible to approximate the multiplicative complexity within
a factor of . When given a circuit, the problem of
determining the multiplicative complexity is in the second level of the
polynomial hierarchy. For nonlinearity, we show that it is #P hard to compute
given a function represented by a circuit
Optimal Bounds on Approximation of Submodular and XOS Functions by Juntas
We investigate the approximability of several classes of real-valued
functions by functions of a small number of variables ({\em juntas}). Our main
results are tight bounds on the number of variables required to approximate a
function within -error over
the uniform distribution: 1. If is submodular, then it is -close
to a function of variables.
This is an exponential improvement over previously known results. We note that
variables are necessary even for linear
functions. 2. If is fractionally subadditive (XOS) it is -close
to a function of variables. This result holds for all
functions with low total -influence and is a real-valued analogue of
Friedgut's theorem for boolean functions. We show that
variables are necessary even for XOS functions.
As applications of these results, we provide learning algorithms over the
uniform distribution. For XOS functions, we give a PAC learning algorithm that
runs in time . For submodular functions we give
an algorithm in the more demanding PMAC learning model (Balcan and Harvey,
2011) which requires a multiplicative factor approximation with
probability at least over the target distribution. Our uniform
distribution algorithm runs in time .
This is the first algorithm in the PMAC model that over the uniform
distribution can achieve a constant approximation factor arbitrarily close to 1
for all submodular functions. As follows from the lower bounds in (Feldman et
al., 2013) both of these algorithms are close to optimal. We also give
applications for proper learning, testing and agnostic learning with value
queries of these classes.Comment: Extended abstract appears in proceedings of FOCS 201
Multiplicative-Additive Proof Equivalence is Logspace-complete, via Binary Decision Trees
Given a logic presented in a sequent calculus, a natural question is that of
equivalence of proofs: to determine whether two given proofs are equated by any
denotational semantics, ie any categorical interpretation of the logic
compatible with its cut-elimination procedure. This notion can usually be
captured syntactically by a set of rule permutations.
Very generally, proofnets can be defined as combinatorial objects which
provide canonical representatives of equivalence classes of proofs. In
particular, the existence of proof nets for a logic provides a solution to the
equivalence problem of this logic. In certain fragments of linear logic, it is
possible to give a notion of proofnet with good computational properties,
making it a suitable representation of proofs for studying the cut-elimination
procedure, among other things.
It has recently been proved that there cannot be such a notion of proofnets
for the multiplicative (with units) fragment of linear logic, due to the
equivalence problem for this logic being Pspace-complete.
We investigate the multiplicative-additive (without unit) fragment of linear
logic and show it is closely related to binary decision trees: we build a
representation of proofs based on binary decision trees, reducing proof
equivalence to decision tree equivalence, and give a converse encoding of
binary decision trees as proofs. We get as our main result that the complexity
of the proof equivalence problem of the studied fragment is Logspace-complete.Comment: arXiv admin note: text overlap with arXiv:1502.0199
Constructive Relationships Between Algebraic Thickness and Normality
We study the relationship between two measures of Boolean functions;
\emph{algebraic thickness} and \emph{normality}. For a function , the
algebraic thickness is a variant of the \emph{sparsity}, the number of nonzero
coefficients in the unique GF(2) polynomial representing , and the normality
is the largest dimension of an affine subspace on which is constant. We
show that for , any function with algebraic thickness
is constant on some affine subspace of dimension
. Furthermore, we give an algorithm
for finding such a subspace. We show that this is at most a factor of
from the best guaranteed, and when restricted to the
technique used, is at most a factor of from the best
guaranteed. We also show that a concrete function, majority, has algebraic
thickness .Comment: Final version published in FCT'201
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