Bridging Computational Notions of Depth: Turing Functionals, Semimeasures, and More

Written by computational | Published 2025/01/16
Tech Story Tags: turing-functional | semimeasures | initial-segment-complexity | continuous-semimeasures | depth-notions | randomness | bennet | martin-lof-random-sequences

TLDRHere's what you need to know about Turing functionals, semimeasures, and initial segment complexity.via the TL;DR App

Table of Links

Abstract and 1 Introduction

2 Background

3 On the slow growth law

4 Members of Deep Π0 1 classes

5 Strong depth is Negligible

6 Variants of Strong Depth

References

Appendix A. Proof of Lemma 3

2. Background

Lemma 1 ([BDM23]).

In addition, we need the following theorem (see, e.g. [JLL94, Theorem 4.3(2)]).

Note that by combining Lemma 1(iii) and Theorem 2, we obtain a resource-bounded analogue of Levin’s coding theorem.

In the rest of the paper we will sometimes use an equivalent characterization of order-depth, given by the following lemma.

The proof of this lemma is technical; for the sake of readability, we defer it to the appendix.

The slow growth law also holds for order-depth.

This paper is available on arxiv under CC BY 4.0 DEED license.

Authors:

(1) Laurent Bienvenu;

(2) Christopher P. Porter.


Written by computational | Computational: We take random inputs, follow complex steps, and hope the output makes sense. And then blog about it.
Published by HackerNoon on 2025/01/16