On the Composition of Randomized Query Complexity and Approximate Degree
Sourav Chakraborty, Chandrima Kayal, Rajat Mittal, Manaswi Paraashar,, Swagato Sanyal, Nitin Saurabh

TL;DR
This paper investigates the composition properties of randomized query complexity and approximate degree, extending known results to broader classes of functions and establishing new conditions under which these measures compose.
Contribution
It extends the class of functions for which randomized query complexity and approximate degree compose, providing new theoretical bounds and relations between these complexity measures.
Findings
When R(f) = Θ(n), noisyR(f) = Θ(R(f))
R and noisyR compose under the same outer functions
Approximate degree composes when it is asymptotically equal to √block sensitivity of f
Abstract
For any Boolean functions and , the question whether , is known as the composition question for the randomized query complexity. Similarly, the composition question for the approximate degree asks whether . These questions are two of the most important and well-studied problems, and yet we are far from answering them satisfactorily. It is known that the measures compose if one assumes various properties of the outer function (or inner function ). This paper extends the class of outer functions for which and compose. A recent landmark result (Ben-David and Blais, 2020) showed that . This implies that composition holds whenever . We…
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Taxonomy
TopicsComplexity and Algorithms in Graphs · Machine Learning and Algorithms · Algorithms and Data Compression
