Under the auspices of the Computational Complexity Foundation (CCF)

REPORTS > KEYWORD > BOOLEAN FUNCTIONS:
Reports tagged with Boolean Functions:
TR95-004 | 1st January 1995
Martin Dietzfelbinger, Miroslaw Kutylowski, Rüdiger Reischuk

#### Feasible Time-Optimal Algorithms for Boolean Functions on Exclusive-Write PRAMs

It was shown some years ago that the computation time for many important
Boolean functions of n arguments on concurrent-read exclusive-write
parallel random-access machines
(CREW PRAMs) of unlimited size is at least f(n) = 0.72 log n.
On the other hand, it ... more >>>

TR96-033 | 6th March 1996
Bernd Borchert, Desh Ranjan, Frank Stephan

#### On the Computational Complexity of some Classical Equivalence Relations on Boolean Functions

The paper analyzes in terms of polynomial time many-one reductions
the computational complexity of several natural equivalence
relations on Boolean functions which derive from replacing
variables by expressions. Most of these computational problems
turn out to be between co-NP and Sigma^p_2.

more >>>

TR96-036 | 28th May 1996
Petr Savicky, Stanislav Zak

#### A large lower bound for 1-branching programs

Revisions: 1

Branching programs (b.p.'s) or decision diagrams are a general
graph-based model of sequential computation. B.p.'s of polynomial
size are a nonuniform counterpart of LOG. Lower bounds for
different kinds of restricted b.p.'s are intensively investigated.
An important restriction are so called 1-b.p.'s, where each
computation reads each input bit at ... more >>>

TR98-004 | 13th January 1998
Farid Ablayev, Marek Karpinski

#### On the Power of Randomized Ordered Branching Programs

We introduce a model of a {\em randomized branching program}
in a natural way similar to the definition of a randomized circuit.
We exhibit an explicit boolean function
$f_{n}:\{0,1\}^{n}\to\{0,1\}$ for which we prove that:

1) $f_{n}$ can be computed by a polynomial size randomized
... more >>>

TR98-011 | 29th January 1998
Farid Ablayev, Marek Karpinski

#### A Lower Bound for Integer Multiplication on Randomized Read-Once Branching Programs

We prove an exponential lower bound ($2^{\Omega(n/\log n)}$) on the
size of any randomized ordered read-once branching program
computing integer multiplication. Our proof depends on proving
a new lower bound on Yao's randomized one-way communication
complexity of certain boolean functions. It generalizes to some
other ... more >>>

TR98-038 | 9th July 1998
Marek Karpinski

#### On the Computational Power of Randomized Branching Programs

We survey some upper and lower bounds established recently on
the sizes of randomized branching programs computing explicit
boolean functions. In particular, we display boolean
functions on which randomized read-once ordered branching
programs are exponentially more powerful than deterministic
or nondeterministic read-$k$-times branching programs for ... more >>>

TR99-009 | 26th March 1999
Marek Karpinski, Rustam Mubarakzjanov

#### A Note on Las Vegas OBDDs

We prove that the error-free (Las Vegas) randomized OBDDs
are computationally equivalent to the deterministic OBDDs.
In contrast, it is known the same is not true for the

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TR00-085 | 19th September 2000
Rustam Mubarakzjanov

#### Probabilistic OBDDs: on Bound of Width versus Bound of Error

Ordered binary decision diagrams (OBDDs) are well established tools to
represent Boolean functions. There are a lot of results concerning
different types of generalizations of OBDDs. The same time, the power
of the most general form of OBDD, namely probabilistic (without bounded
error) OBDDs, is not studied enough. In ... more >>>

TR02-002 | 3rd January 2002
Howard Barnum, Michael Saks

#### A lower bound on the quantum query complexity of read-once functions

We establish a lower bound of $\Omega{(\sqrt{n})}$ on the bounded-error quantum query complexity of read-once Boolean functions, providing evidence for the conjecture that $\Omega(\sqrt{D(f)})$ is a lower bound for all Boolean functions.Our technique extends a result of Ambainis, based on the idea that successful computation of a function requires decoherence'' ... more >>>

TR03-083 | 21st November 2003
Jan Arpe, Andreas Jakoby, Maciej Liskiewicz

#### One-Way Communication Complexity of Symmetric Boolean Functions

We study deterministic one-way communication complexity
of functions with Hankel communication matrices.
Some structural properties of such matrices are established
and applied to the one-way two-party communication complexity
of symmetric Boolean functions.
It is shown that the number of required communication bits
does not depend on ... more >>>

TR04-022 | 31st March 2004
Nayantara Bhatnagar, Parikshit Gopalan

#### The Degree of Threshold Mod 6 and Diophantine Equations

We continue the study of the degree of polynomials representing threshold functions modulo 6 initiated by Barrington, Beigel and Rudich. We use the framework established by the authors relating representations by symmetric polynomials to simultaneous protocols. We show that proving bounds on the degree of Threshold functions is equivalent to ... more >>>

TR05-023 | 16th February 2005
Robert H. Sloan, Balázs Szörényi, György Turán

#### On k-term DNF with largest number of prime implicants

It is known that a k-term DNF can have at most 2^k ? 1 prime implicants and this bound is sharp. We determine all k-term DNF having the maximal number of prime implicants. It is shown that a DNF is maximal if and only if it corresponds to a non-repeating ... more >>>

TR07-028 | 12th February 2007
Daniel Sawitzki

#### Implicit Simulation of FNC Algorithms

Implicit algorithms work on their input's characteristic functions and should solve problems heuristically by as few and as efficient functional operations as possible. Together with an appropriate data structure to represent the characteristic functions they yield heuristics which are successfully applied in numerous areas. It is known that implicit algorithms ... more >>>

TR07-129 | 25th October 2007
Jeffrey C. Jackson, Homin Lee, Rocco Servedio, Andrew Wan

#### Learning Random Monotone DNF

We give an algorithm that with high probability properly learns random monotone t(n)-term
DNF under the uniform distribution on the Boolean cube {0, 1}^n. For any polynomially bounded function t(n) <= poly(n) the algorithm runs in time poly(n, 1/eps) and with high probability outputs an eps accurate monotone DNF ... more >>>

TR08-012 | 20th November 2007
Arnab Bhattacharyya

#### A Note on the Distance to Monotonicity of Boolean Functions

Given a boolean function, let epsilon_M(f) denote the smallest distance between f and a monotone function on {0,1}^n. Let delta_M(f) denote the fraction of hypercube edges where f violates monotonicity. We give an alternative proof of the tight bound: delta_M(f) >= 2/n eps_M(f) for any boolean function f. This was ... more >>>

TR09-048 | 29th May 2009
Parikshit Gopalan, Shachar Lovett, Amir Shpilka

#### On the Complexity of Boolean Functions in Different Characteristics

Every Boolean function on $n$ variables can be expressed as a unique multivariate polynomial modulo $p$ for every prime $p$. In this work, we study how the degree of a function in one characteristic affects its complexity in other characteristics. We establish the following general principle: functions with low degree ... more >>>

TR11-075 | 6th May 2011
Arnab Bhattacharyya, Elena Grigorescu, Prasad Raghavendra, Asaf Shapira

#### Testing Odd-Cycle-Freeness in Boolean Functions

Call a function $f: \mathbb{F}_2^n \to \{0,1\}$ odd-cycle-free if there are no $x_1, \dots, x_k \in \mathbb{F}_2^n$ with $k$ an odd integer such that $f(x_1) = \cdots = f(x_k) = 1$ and $x_1 + \cdots + x_k = 0$. We show that one can distinguish odd-cycle-free functions from those $\epsilon$-far ... more >>>

TR11-116 | 17th August 2011
Andris Ambainis, Xiaoming Sun

#### New separation between $s(f)$ and $bs(f)$

In this note we give a new separation between sensitivity and block sensitivity of Boolean functions: $bs(f)=\frac{2}{3}s(f)^2-\frac{1}{3}s(f)$.

more >>>

TR12-089 | 7th July 2012
Meena Boppana

#### Lattice Variant of the Sensitivity Conjecture

The Sensitivity Conjecture, posed in 1994, states that the fundamental measures known as the sensitivity and block sensitivity of a Boolean function $f$, $s(f)$ and $bs(f)$ respectively, are polynomially related. It is known that $bs(f)$ is polynomially related to important measures in computer science including the decision-tree depth, polynomial degree, ... more >>>

TR12-099 | 5th August 2012
Nikos Leonardos

#### An improved lower bound for the randomized decision tree complexity of recursive majority

Revisions: 1

We prove that the randomized decision tree complexity of the recursive majority-of-three is $\Omega(2.6^d)$, where $d$ is the depth of the recursion. The proof is by a bottom up induction, which is same in spirit as the one in the proof of Saks and Wigderson in their FOCS 1986 paper ... more >>>

TR13-029 | 19th February 2013

#### A {\huge ${o(n)}$} monotonicity tester for Boolean functions over the hypercube

Revisions: 1

Given oracle access to a Boolean function $f:\{0,1\}^n \mapsto \{0,1\}$, we design a randomized tester that takes as input a parameter $\eps>0$, and outputs {\sf Yes} if the function is monotone, and outputs {\sf No} with probability $>2/3$, if the function is $\eps$-far from monotone. That is, $f$ needs to ... more >>>

TR13-164 | 28th November 2013
Scott Aaronson, Andris Ambainis, Kaspars Balodis, Mohammad Bavarian

#### Weak Parity

We study the query complexity of Weak Parity: the problem of computing the parity of an n-bit input string, where one only has to succeed on a 1/2+eps fraction of input strings, but must do so with high probability on those inputs where one does succeed. It is well-known that ... more >>>

TR13-191 | 26th December 2013
Petr Savicky

#### Boolean functions with a vertex-transitive group of automorphisms

A Boolean function is called vertex-transitive, if the partition of the Boolean cube into the preimage of 0 and the preimage of 1 is invariant under a vertex-transitive group of isometric transformations of the Boolean cube. Several constructions of vertex-transitive functions and some of their properties are presented.

more >>>

TR14-027 | 21st February 2014
Andris Ambainis, Krisjanis Prusis

#### A Tight Lower Bound on Certificate Complexity in Terms of Block Sensitivity and Sensitivity

Revisions: 1

Sensitivity, certificate complexity and block sensitivity are widely used Boolean function complexity measures. A longstanding open problem, proposed by Nisan and Szegedy, is whether sensitivity and block sensitivity are polynomially related. Motivated by the constructions of functions which achieve the largest known separations, we study the relation between 1-certificate complexity ... more >>>

TR14-077 | 2nd June 2014
Andris Ambainis, Jevgenijs Vihrovs

#### Size of Sets with Small Sensitivity: a Generalization of Simon's Lemma

Revisions: 2

We study the structure of sets $S\subseteq\{0, 1\}^n$ with small sensitivity. The well-known Simon's lemma says that any $S\subseteq\{0, 1\}^n$ of sensitivity $s$ must be of size at least $2^{n-s}$. This result has been useful for proving lower bounds on sensitivity of Boolean functions, with applications to the theory of ... more >>>

TR14-144 | 30th October 2014
Eric Blais, Clement Canonne, Igor Carboni Oliveira, Rocco Servedio, Li-Yang Tan

#### Learning circuits with few negations

Monotone Boolean functions, and the monotone Boolean circuits that compute them, have been intensively studied in complexity theory. In this paper we study the structure of Boolean functions in terms of the minimum number of negations in any circuit computing them, a complexity measure that interpolates between monotone functions and ... more >>>

TR15-011 | 22nd January 2015
Subhash Khot, Dor Minzer, Muli Safra

#### On Monotonicity Testing and Boolean Isoperimetric type Theorems

We show a directed and robust analogue of a boolean isoperimetric type theorem of Talagrand. As an application, we
give a monotonicity testing algorithm that makes $\tilde{O}(\sqrt{n}/\epsilon^2)$ non-adaptive queries to a function
$f:\{0,1\}^n \mapsto \{0,1\}$, always accepts a monotone function and rejects a function that is $\epsilon$-far from
being monotone ... more >>>

TR15-131 | 10th August 2015
Parikshit Gopalan, Noam Nisan, Rocco Servedio, Kunal Talwar, Avi Wigderson

#### Smooth Boolean functions are easy: efficient algorithms for low-sensitivity functions

A natural measure of smoothness of a Boolean function is its sensitivity (the largest number of Hamming neighbors of a point which differ from it in function value). The structure of smooth or equivalently low-sensitivity functions is still a mystery. A well-known conjecture states that every such Boolean function can ... more >>>

TR16-084 | 23rd May 2016
Shalev Ben-David

#### Low-Sensitivity Functions from Unambiguous Certificates

We provide new query complexity separations against sensitivity for total Boolean functions: a power 3 separation between deterministic (and even randomized or quantum) query complexity and sensitivity, and a power 2.1 separation between certificate complexity and sensitivity. We get these separations by using a new connection between sensitivity and a ... more >>>

TR16-132 | 23rd August 2016
Mitali Bafna, Satyanarayana V. Lokam, Sébastien Tavenas, Ameya Velingker

#### On the Sensitivity Conjecture for Read-k Formulas

Various combinatorial/algebraic parameters are used to quantify the complexity of a Boolean function. Among them, sensitivity is one of the simplest and block sensitivity is one of the most useful. Nisan (1989) and Nisan and Szegedy (1991) showed that block sensitivity and several other parameters, such as certificate complexity, decision ... more >>>

TR16-136 | 31st August 2016
Clement Canonne, Elena Grigorescu, Siyao Guo, Akash Kumar, Karl Wimmer

#### Testing k-Monotonicity

Revisions: 1

A Boolean $k$-monotone function defined over a finite poset domain ${\cal D}$ alternates between the values $0$ and $1$ at most $k$ times on any ascending chain in ${\cal D}$. Therefore, $k$-monotone functions are natural generalizations of the classical monotone functions, which are the $1$-monotone functions.

Motivated by the ... more >>>

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