On the distribution of sensitivities of symmetric Boolean functions

06/26/2023
by   Jon T. Butler, et al.
0

A Boolean function f(x⃗) is sensitive to bit x_i if there is at least one input vector x⃗ and one bit x_i in x⃗, such that changing x_i changes f. A function has sensitivity s if among all input vectors, the largest number of bits to which f is sensitive is s. We count the n-variable symmetric Boolean functions that have maximum sensitivity. We show that most such functions have the largest possible sensitivity, n. This suggests sensitivity is limited as a complexity measure for symmetric Boolean functions.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/29/2022

On Symmetric Pseudo-Boolean Functions: Factorization, Kernels and Applications

A symmetric pseudo-Boolean function is a map from Boolean tuples to real...
research
01/29/2019

Boolean Functions with Biased Inputs: Approximation and Noise Sensitivity

This paper considers the problem of approximating a Boolean function f u...
research
04/21/2021

Sensitivity as a Complexity Measure for Sequence Classification Tasks

We introduce a theoretical framework for understanding and predicting th...
research
11/11/2021

Enhanced Fast Boolean Matching based on Sensitivity Signatures Pruning

Boolean matching is significant to digital integrated circuits design. A...
research
02/05/2023

Level-p-complexity of Boolean functions using Thinning, Memoization, and Polynomials

This paper describes a purely functional library for computing level-p-c...
research
08/18/2023

Noise Sensitivity and Stability of Deep Neural Networks for Binary Classification

A first step is taken towards understanding often observed non-robustnes...
research
01/05/2022

ADRA: Extending Digital Computing-in-Memory with Asymmetric Dual-Row-Activation

Computing in-memory (CiM) has emerged as an attractive technique to miti...

Please sign up or login with your details

Forgot password? Click here to reset