On New Notions of Algorithmic Dimension, Immunity, and Medvedev Degree

dc.contributor.advisor Kjos-Hanssen, Bjoern
dc.contributor.author Webb, David J.
dc.contributor.department Mathematics
dc.date.accessioned 2022-10-19T22:36:00Z
dc.date.available 2022-10-19T22:36:00Z
dc.date.issued 2022
dc.description.degree Ph.D.
dc.identifier.uri https://hdl.handle.net/10125/103885
dc.subject Mathematics
dc.subject algorithmic dimension
dc.subject immunity
dc.subject Kolmogorov-Loveland randomness
dc.subject Medvedev degree
dc.title On New Notions of Algorithmic Dimension, Immunity, and Medvedev Degree
dc.type Thesis
dcterms.abstract We prove various results connected together by the common thread of computability theory. First, we investigate a new notion of algorithmic dimension, the inescapable dimension, which lies between the effective Hausdorff and packing dimensions. We also study its generalizations, obtaining an embedding of the Turing degrees into notions of dimension. We then investigate a new notion of computability theoretic immunity that arose in the course of the previous study, that of a set of natural numbers with no co-enumerable subsets. We demonstrate how this notion of Pi^0_1-immunity is connected to other immunity notions, and construct Pi^0_1-immune reals throughout the high/low and Ershov hierarchies. We also study those degrees that cannot compute or cannot co-enumerate a Pi^0_1-immune set. Finally, we discuss a recently discovered truth-table reduction for transforming a Kolmogorov-Loveland random input into a Martin-Löf random output by exploiting the fact that at least one half of such a KL-random is itself ML-random. We show that there is no better algorithm relying on this fact, i.e. there is no positive, linear, or bounded truth-table reduction which does this. We also generalize these results to the problem of outputting randomness from infinitely many inputs, only some of which are random.
dcterms.extent 75 pages
dcterms.language en
dcterms.publisher University of Hawai'i at Manoa
dcterms.rights All UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.type Text
local.identifier.alturi http://dissertations.umi.com/hawii:11456
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