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This note is an attempt to unconditionally prove the existence of weak one way functions (OWF). Starting from a provably intractable decision problem $L_D$ (whose existence is nonconstructively assured from the well-known discrete…

Computational Complexity · Computer Science 2023-07-19 Stefan Rass

We prove that for any decision tree calculating a boolean function $f:\{-1,1\}^n\to\{-1,1\}$, \[ \Var[f] \le \sum_{i=1}^n \delta_i \Inf_i(f), \] where $\delta_i$ is the probability that the $i$th input variable is read and $\Inf_i(f)$ is…

Computational Complexity · Computer Science 2007-05-23 Ryan O'Donnell , Michael Saks , Oded Schramm , Rocco A. Servedio

The threshold degree of a Boolean function $f\colon\{0,1\}^n\to\{0,1\}$ is the minimum degree of a real polynomial $p$ that represents $f$ in sign: $\mathrm{sgn}\; p(x)=(-1)^{f(x)}.$ A related notion is sign-rank, defined for a Boolean…

Computational Complexity · Computer Science 2019-01-07 Alexander A. Sherstov , Pei Wu

Let $\mathsf{TH}_k$ denote the $k$-out-of-$n$ threshold function: given $n$ input Boolean variables, the output is $1$ if and only if at least $k$ of the inputs are $1$. We consider the problem of computing the $\mathsf{TH}_k$ function…

Data Structures and Algorithms · Computer Science 2024-12-24 Ziao Wang , Nadim Ghaddar , Banghua Zhu , Lele Wang

A function defined on the Boolean hypercube is $k$-Fourier-sparse if it has at most $k$ nonzero Fourier coefficients. For a function $f: \mathbb{F}_2^n \rightarrow \mathbb{R}$ and parameters $k$ and $d$, we prove a strong upper bound on the…

Data Structures and Algorithms · Computer Science 2015-04-08 Ishay Haviv , Oded Regev

We establish nearly tight bounds on the expected shrinkage of decision lists and DNF formulas under the $p$-random restriction $\mathbf R_p$ for all values of $p \in [0,1]$. For a function $f$ with domain $\{0,1\}^n$, let $\mathrm{DL}(f)$…

Computational Complexity · Computer Science 2020-12-29 Benjamin Rossman

The approximate degree of a Boolean function $f \colon \{-1, 1\}^n \rightarrow \{-1, 1\}$ is the least degree of a real polynomial that approximates $f$ pointwise to error at most $1/3$. We introduce a generic method for increasing the…

Computational Complexity · Computer Science 2017-03-20 Mark Bun , Justin Thaler

We prove that the Fourier dimension of any Boolean function with Fourier sparsity $s$ is at most $O\left(s^{2/3}\right)$. Our proof method yields an improved bound of $\widetilde{O}(\sqrt{s})$ assuming a conjecture of…

Computational Complexity · Computer Science 2014-07-15 Swagato Sanyal

A function $f:\ \{-1,1\}^n\rightarrow \mathbb{R}$ is called pseudo-Boolean. It is well-known that each pseudo-Boolean function $f$ can be written as $f(x)=\sum_{I\in {\cal F}}\hat{f}(I)\chi_I(x),$ where ${\cal F}\subseteq \{I:\ I\subseteq…

Discrete Mathematics · Computer Science 2012-12-04 Gregory Gutin , Anders Yeo

We study a natural complexity measure of Boolean functions known as the rational degree. Denoted $\textrm{rdeg}(f)$, it is the minimal degree of a rational function that is equal to $f$ on the Boolean hypercube. For total functions $f$, it…

Computational Complexity · Computer Science 2025-04-16 Vishnu Iyer , Siddhartha Jain , Robin Kothari , Matt Kovacs-Deak , Vinayak M. Kumar , Luke Schaeffer , Daochen Wang , Michael Whitmeyer

We investigate the randomized decision tree complexity of a specific class of read-once threshold functions. A read-once threshold formula can be defined by a rooted tree, every internal node of which is labeled by a threshold function…

Computational Complexity · Computer Science 2023-10-19 Nikos Leonardos

The $\epsilon$-approximate degree of a Boolean function $f: \{-1, 1\}^n \to \{-1, 1\}$ is the minimum degree of a real polynomial that approximates $f$ to within $\epsilon$ in the $\ell_\infty$ norm. We prove several lower bounds on this…

Computational Complexity · Computer Science 2014-03-25 Mark Bun , Justin Thaler

We study the probabilistic degree over reals of the OR function on $n$ variables. For an error parameter $\epsilon$ in (0,1/3), the $\epsilon$-error probabilistic degree of any Boolean function $f$ over reals is the smallest non-negative…

Computational Complexity · Computer Science 2022-11-24 Siddharth Bhandari , Prahladh Harsha , Tulasimohan Molli , Srikanth Srinivasan

Let $\mathcal{F}_{n}^*$ be the set of Boolean functions depending on all $n$ variables. We prove that for any $f\in \mathcal{F}_{n}^*$, $f|_{x_i=0}$ or $f|_{x_i=1}$ depends on the remaining $n-1$ variables, for some variable $x_i$. This…

Computational Complexity · Computer Science 2015-02-05 Chia-Jung Lee , Satya V. Lokam , Shi-Chun Tsai , Ming-Chuan Yang

The \emph{Chow parameters} of a Boolean function $f: \{-1,1\}^n \to \{-1,1\}$ are its $n+1$ degree-0 and degree-1 Fourier coefficients. It has been known since 1961 (Chow, Tannenbaum) that the (exact values of the) Chow parameters of any…

Computational Complexity · Computer Science 2012-06-06 Anindya De , Ilias Diakonikolas , Vitaly Feldman , Rocco A. Servedio

The Fourier-Walsh expansion of a Boolean function $f \colon \{0,1\}^n \rightarrow \{0,1\}$ is its unique representation as a multilinear polynomial. The Kindler-Safra theorem (2002) asserts that if in the expansion of $f$, the total weight…

Combinatorics · Mathematics 2019-01-28 Nathan Keller , Ohad Klein

The {\em Total Influence} ({\em Average Sensitivity) of a discrete function is one of its fundamental measures. We study the problem of approximating the total influence of a monotone Boolean function \ifnum\plusminus=1 $f: \{\pm1\}^n…

Data Structures and Algorithms · Computer Science 2011-01-28 Dana Ron , Ronitt Rubinfeld , Muli Safra , Omri Weinstein

We show tight upper and lower bounds for switching lemmas obtained by the action of random $p$-restrictions on boolean functions that can be expressed as decision trees in which every vertex is at a distance of at most $t$ from some leaf,…

Computational Complexity · Computer Science 2017-03-02 Jenish C. Mehta

Let x be a random vector coming from any k-wise independent distribution over {-1,1}^n. For an n-variate degree-2 polynomial p, we prove that E[sgn(p(x))] is determined up to an additive epsilon for k = poly(1/epsilon). This answers an open…

Computational Complexity · Computer Science 2010-02-18 Ilias Diakonikolas , Daniel M. Kane , Jelani Nelson

The approximate degree of a Boolean function $f(x_{1},x_{2},\ldots,x_{n})$ is the minimum degree of a real polynomial that approximates $f$ pointwise within $1/3$. Upper bounds on approximate degree have a variety of applications in…

Computational Complexity · Computer Science 2018-01-16 Alexander A. Sherstov