Sensitivity and Query Complexity under Uncertainty

TL;DR

This paper investigates the query complexity of Boolean functions under uncertainty, introducing hazard-free extensions, and establishes bounds and methods for their computation in various models.

Contribution

It extends sensitivity theorems to uncertain models, improves bounds on query complexities, and develops conversion methods for hazard-free decision trees.

Findings

Deterministic query complexity is at most quadratic in randomized complexity.

Exponential gap between decision tree depth for Boolean functions and their hazard-free extensions.

Provides optimal conversion methods for decision trees with bounds based on input unknowns.

Abstract

In this paper, we study the query complexity of Boolean functions in the presence of uncertainty, motivated by parallel computation with an unlimited number of processors where inputs are allowed to be unknown. We allow each query to produce three results: zero, one, or unknown. The output could also be: zero, one, or unknown, with the constraint that we should output ''unknown'' only when we cannot determine the answer from the revealed input bits. Such an extension of a Boolean function is called its hazard-free extension. - We prove an analogue of Huang's celebrated sensitivity theorem [Annals of Mathematics, 2019] in our model of query complexity with uncertainty. - We show that the deterministic query complexity of the hazard-free extension of a Boolean function is at most quadratic in its randomized query complexity and quartic in its quantum query complexity, improving upon the best-known bounds in the Boolean world. - We exhibit an exponential gap between the smallest depth (size) of decision trees computing a Boolean function, and those computing its hazard-free extension. - We present general methods to convert decision trees for Boolean functions to those for their hazard-free counterparts, and show optimality of this construction. We also parameterize this result by the maximum number of unknown values in the input. - We show lower bounds on size complexity of decision trees for hazard-free extensions of Boolean functions in terms of the number of prime implicants and prime implicates of the underlying Boolean function.