Download or read book Algorithmic Randomness and Complexity for Continuous Measures written by Ming Yang Li and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic randomness is the study of random objects through computability theoretic means. In this dissertation, we study the concept of randomness with respect to continuous measures in two different approaches, measure theoretical randomness tests and algorithmic information complexity. Our main focus is the set NCR of infinite binary sequences that are not random with respect to any continuous measure. To this end, we first introduce a new, parameterized randomness test with respect to a continuous measure (Chapter 2). The main feature of the new test is that it applies and iterates the dissipation function of a measure. We prove our new test satisfies properties common for other, well-studied notions of randomness tests.We also show that, even though our test is strictly stronger than Martin-L\"{o}f tests for some individual measures, they coincide with Martin-L\"{o}f randomness when considering all continuous measures simultaneously ( Chapter 2 and Chapter 4). Next, we apply the new test notion to construct some new, previously unknown examples of NCR reals (Chapter 3). We constructively show that every Turing degree recursively enumerable above an NCR real contains an NCR real. We also construct an NCR real in every self-modulus degree. A direct corollary from either construction is that NCR reals exists in every $\Delta^0_2$ degree. Moreover, we also show that our constructive methods are versatile, by constructing examples like 1-generic NCR reals or NCR reals of effective packing dimension 1. We also construct a pair of never simultaneously continuously random reals neither of which is NCR. This answers a question by Adam Day and Andrew Marks. We then move on to investigating the complexity notion of NCR reals (Chapter 4). By using prefix-free complexity and a priori complexity as tools, we are able to show that NCR in Martin-L\"{o}f's sense and NCR in the sense of our new test notion have the same algorithmic complexity description and thus coincide. Finally, we study the descriptive complexity of NCR reals (Chapter 5). We prove that never random with respect to a $\Pi^0_1$ class of measures is arithmetically definable. As an application of our result, we show that the set of $\Delta^0_2$ NCR reals is arithmetic.
Download or read book Algorithmic Randomness and Complexity written by Rodney G. Downey and published by Springer Science & Business Media. This book was released on 2010-10-29 with total page 883 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computability and complexity theory are two central areas of research in theoretical computer science. This book provides a systematic, technical development of "algorithmic randomness" and complexity for scientists from diverse fields.
Download or read book Kolmogorov Complexity and Algorithmic Randomness written by A. Shen and published by American Mathematical Society. This book was released on 2022-05-18 with total page 511 pages. Available in PDF, EPUB and Kindle. Book excerpt: Looking at a sequence of zeros and ones, we often feel that it is not random, that is, it is not plausible as an outcome of fair coin tossing. Why? The answer is provided by algorithmic information theory: because the sequence is compressible, that is, it has small complexity or, equivalently, can be produced by a short program. This idea, going back to Solomonoff, Kolmogorov, Chaitin, Levin, and others, is now the starting point of algorithmic information theory. The first part of this book is a textbook-style exposition of the basic notions of complexity and randomness; the second part covers some recent work done by participants of the “Kolmogorov seminar” in Moscow (started by Kolmogorov himself in the 1980s) and their colleagues. This book contains numerous exercises (embedded in the text) that will help readers to grasp the material.
Download or read book Algorithmic Randomness written by Johanna N. Y. Franklin and published by Cambridge University Press. This book was released on 2020-05-07 with total page 371 pages. Available in PDF, EPUB and Kindle. Book excerpt: The last two decades have seen a wave of exciting new developments in the theory of algorithmic randomness and its applications to other areas of mathematics. This volume surveys much of the recent work that has not been included in published volumes until now. It contains a range of articles on algorithmic randomness and its interactions with closely related topics such as computability theory and computational complexity, as well as wider applications in areas of mathematics including analysis, probability, and ergodic theory. In addition to being an indispensable reference for researchers in algorithmic randomness, the unified view of the theory presented here makes this an excellent entry point for graduate students and other newcomers to the field.
Download or read book Information and Randomness written by Cristian Calude and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 252 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Algorithmic information theory (AIT) is the result of putting Shannon's information theory and Turing's computability theory into a cocktail shaker and shaking vigorously", says G.J. Chaitin, one of the fathers of this theory of complexity and randomness, which is also known as Kolmogorov complexity. It is relevant for logic (new light is shed on Gödel's incompleteness results), physics (chaotic motion), biology (how likely is life to appear and evolve?), and metaphysics (how ordered is the universe?). This book, benefiting from the author's research and teaching experience in Algorithmic Information Theory (AIT), should help to make the detailed mathematical techniques of AIT accessible to a much wider audience.
Download or read book The Discrepancy Method written by Bernard Chazelle and published by Cambridge University Press. This book was released on 2000 with total page 500 pages. Available in PDF, EPUB and Kindle. Book excerpt: The discrepancy method is the glue that binds randomness and complexity. It is the bridge between randomized computation and discrepancy theory, the area of mathematics concerned with irregularities in distributions. The discrepancy method has played a major role in complexity theory; in particular, it has caused a mini-revolution of sorts in computational geometry. This book tells the story of the discrepancy method in a few short independent vignettes. It is a varied tale which includes such topics as communication complexity, pseudo-randomness, rapidly mixing Markov chains, points on the sphere and modular forms, derandomization, convex hulls, Voronoi diagrams, linear programming and extensions, geometric sampling, VC-dimension theory, minimum spanning trees, linear circuit complexity, and multidimensional searching. The mathematical treatment is thorough and self-contained. In particular, background material in discrepancy theory is supplied as needed. Thus the book should appeal to students and researchers in computer science, operations research, pure and applied mathematics, and engineering.
Download or read book Randomness Through Computation written by Hector Zenil and published by World Scientific. This book was released on 2011 with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt: This review volume consists of an indispensable set of chapters written by leading scholars, scientists and researchers in the field of Randomness, including related subfields specially but not limited to the strong developed connections to the Computability and Recursion Theory. Highly respected, indeed renowned in their areas of specialization, many of these contributors are the founders of their fields. The scope of Randomness Through Computation is novel. Each contributor shares his personal views and anecdotes on the various reasons and motivations which led him to the study of the subject. They share their visions from their vantage and distinctive viewpoints. In summary, this is an opportunity to learn about the topic and its various angles from the leading thinkers.
Download or read book An Introduction to Kolmogorov Complexity and Its Applications written by Ming Li and published by Springer Science & Business Media. This book was released on 2009-03-18 with total page 809 pages. Available in PDF, EPUB and Kindle. Book excerpt: “The book is outstanding and admirable in many respects. ... is necessary reading for all kinds of readers from undergraduate students to top authorities in the field.” Journal of Symbolic Logic Written by two experts in the field, this is the only comprehensive and unified treatment of the central ideas and applications of Kolmogorov complexity. The book presents a thorough treatment of the subject with a wide range of illustrative applications. Such applications include the randomness of finite objects or infinite sequences, Martin-Loef tests for randomness, information theory, computational learning theory, the complexity of algorithms, and the thermodynamics of computing. It will be ideal for advanced undergraduate students, graduate students, and researchers in computer science, mathematics, cognitive sciences, philosophy, artificial intelligence, statistics, and physics. The book is self-contained in that it contains the basic requirements from mathematics and computer science. Included are also numerous problem sets, comments, source references, and hints to solutions of problems. New topics in this edition include Omega numbers, Kolmogorov–Loveland randomness, universal learning, communication complexity, Kolmogorov's random graphs, time-limited universal distribution, Shannon information and others.
Download or read book An Introduction to Kolmogorov Complexity and Its Applications written by Ming Li and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Briefly, we review the basic elements of computability theory and prob ability theory that are required. Finally, in order to place the subject in the appropriate historical and conceptual context we trace the main roots of Kolmogorov complexity. This way the stage is set for Chapters 2 and 3, where we introduce the notion of optimal effective descriptions of objects. The length of such a description (or the number of bits of information in it) is its Kolmogorov complexity. We treat all aspects of the elementary mathematical theory of Kolmogorov complexity. This body of knowledge may be called algo rithmic complexity theory. The theory of Martin-Lof tests for random ness of finite objects and infinite sequences is inextricably intertwined with the theory of Kolmogorov complexity and is completely treated. We also investigate the statistical properties of finite strings with high Kolmogorov complexity. Both of these topics are eminently useful in the applications part of the book. We also investigate the recursion theoretic properties of Kolmogorov complexity (relations with Godel's incompleteness result), and the Kolmogorov complexity version of infor mation theory, which we may call "algorithmic information theory" or "absolute information theory. " The treatment of algorithmic probability theory in Chapter 4 presup poses Sections 1. 6, 1. 11. 2, and Chapter 3 (at least Sections 3. 1 through 3. 4).
Download or read book Kolmogorov Complexity and Computational Complexity written by Osamu Watanabe and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: The mathematical theory of computation has given rise to two important ap proaches to the informal notion of "complexity": Kolmogorov complexity, usu ally a complexity measure for a single object such as a string, a sequence etc., measures the amount of information necessary to describe the object. Compu tational complexity, usually a complexity measure for a set of objects, measures the compuational resources necessary to recognize or produce elements of the set. The relation between these two complexity measures has been considered for more than two decades, and may interesting and deep observations have been obtained. In March 1990, the Symposium on Theory and Application of Minimal Length Encoding was held at Stanford University as a part of the AAAI 1990 Spring Symposium Series. Some sessions of the symposium were dedicated to Kolmogorov complexity and its relations to the computational complexity the ory, and excellent expository talks were given there. Feeling that, due to the importance of the material, some way should be found to share these talks with researchers in the computer science community, I asked the speakers of those sessions to write survey papers based on their talks in the symposium. In response, five speakers from the sessions contributed the papers which appear in this book.
Download or read book Algorithmic Randomness and Kolmogorov Complexity for Qubits written by Tejas Shekhar Bhojraj and published by . This book was released on 2021 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work extends the theories of algorithmic randomness and Kolmogorov complexity of bitstrings to the quantum realm. Nies and Scholz defined quantum Martin-Löf randomness (q-MLR): the first notion of algorithmic randomness to be defined for infinite sequences of qubits, which are called states. We define a notion of quantum Solovay randomness and show it to be equivalent to q-MLR using purely linear algebraic methods. Quantum Schnorr randomness is then introduced. A quantum analogue of the law of large numbers is shown to hold for quantum Schnorr random states. We next turn to a quantum analogue of Kolmogorov complexity. We introduce quantum-K (QK), a measure of the descriptive complexity of density matrices using classical prefix-free Turing machines and show that the initial segments of weak Solovay random and quantum Schnorr random states are incompressible in the sense of QK. Many properties enjoyed by prefix-free Kolmogorov complexity (K) have analogous versions for QK; notably a counting condition. Several connections between Solovay randomness and (K), including the Chaitin type characterization of Solovay randomness, carry over to those between weak Solovay randomness and QK. Schnorr randomness has a Levin\textendash Schnorr characterization using KcC; a version of K defined using an arbitrary computable measure machine, C. We similarly define QKc, a version of QK. Quantum Schnorr randomness is shown to have a Levin\textendash Schnorr and a Chaitin type characterization using QKc. We then show how classical randomness can be generated from a computable, non-quantum random state. We formalize how `measurement' of a state induces a probability measure on the space of infinite bitstrings. A state is `measurement random' (mR) if the measure induced by it, under any computable basis, assigns probability one to the set of Martin-Löf randoms. I.e., measuring a mR state produces a Martin-Löf random bitstring with probability one. While quantum-Martin-Löf random states are mR, we show that the converse fails by defining a computable mR state p which is not quantum-Martin-Löf random. In fact, something stronger is true. Measuring p in any computable basis yields an arithmetically random sequence with probability one. The work concludes by studying the asymptotic von Neumann entropy of computable states.
Download or read book Handbook of Computability and Complexity in Analysis written by Vasco Brattka and published by Springer Nature. This book was released on 2021-06-04 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computable analysis is the modern theory of computability and complexity in analysis that arose out of Turing's seminal work in the 1930s. This was motivated by questions such as: which real numbers and real number functions are computable, and which mathematical tasks in analysis can be solved by algorithmic means? Nowadays this theory has many different facets that embrace topics from computability theory, algorithmic randomness, computational complexity, dynamical systems, fractals, and analog computers, up to logic, descriptive set theory, constructivism, and reverse mathematics. In recent decades computable analysis has invaded many branches of analysis, and researchers have studied computability and complexity questions arising from real and complex analysis, functional analysis, and the theory of differential equations, up to (geometric) measure theory and topology. This handbook represents the first coherent cross-section through most active research topics on the more theoretical side of the field. It contains 11 chapters grouped into parts on computability in analysis; complexity, dynamics, and randomness; and constructivity, logic, and descriptive complexity. All chapters are written by leading experts working at the cutting edge of the respective topic. Researchers and graduate students in the areas of theoretical computer science and mathematical logic will find systematic introductions into many branches of computable analysis, and a wealth of information and references that will help them to navigate the modern research literature in this field.
Download or read book Lecture Notes on Descriptional Complexity and Randomness written by Peter Gacs and published by . This book was released on 2014-11-11 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lecture notes on descriptional complexity and randomnessBy Peter Gacs
Download or read book Algorithmic Randomness Physical Entropy Measurements and the Second Law written by and published by . This book was released on 1989 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: Algorithmic information content is equal to the size -- in the number of bits -- of the shortest program for a universal Turing machine which can reproduce a state of a physical system. In contrast to the statistical Boltzmann-Gibbs-Shannon entropy, which measures ignorance, the algorithmic information content is a measure of the available information. It is defined without a recourse to probabilities and can be regarded as a measure of randomness of a definite microstate. I suggest that the physical entropy S -- that is, the quantity which determines the amount of the work [Delta]W which can be extracted in the cyclic isothermal expansion process through the equation [Delta]W = k{sub B}T[Delta]S -- is a sum of two contributions: the mission information measured by the usual statistical entropy and the known randomness measured by the algorithmic information content. The sum of these two contributions is a constant of motion'' in the process of a dissipation less measurement on an equilibrium ensemble. This conservation under a measurement, which can be traced back to the noiseless coding theorem of Shannon, is necessary to rule out existence of a successful Maxwell's demon. 17 refs., 3 figs.
Download or read book Computational Complexity written by Sanjeev Arora and published by Cambridge University Press. This book was released on 2009-04-20 with total page 609 pages. Available in PDF, EPUB and Kindle. Book excerpt: New and classical results in computational complexity, including interactive proofs, PCP, derandomization, and quantum computation. Ideal for graduate students.
Download or read book Encyclopedia of Computer Science and Technology written by Allen Kent and published by CRC Press. This book was released on 1999-05-14 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Approach to Complexity from a Human-Centered Artificial Intelligence Perspective to The Virtual Workplace
Download or read book Computational Prospects Of Infinity Part I Tutorials written by Chi Tat Chong and published by World Scientific. This book was released on 2008-05-02 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents the written versions of the tutorial lectures given at the Workshop on Computational Prospects of Infinity, held from 18 June to 15 August 2005 at the Institute for Mathematical Sciences, National University of Singapore. It consists of articles by four of the leading experts in recursion theory (computability theory) and set theory. The survey paper of Rod Downey provides a comprehensive introduction to algorithmic randomness, one of the most active areas of current research in recursion theory. Theodore A Slaman's article is the first printed account of the ground-breaking work of Slaman-Woodin and Slaman-Shore on the definability of the Turing jump. John Steel presents some results on the properties of derived models of mice, and on the existence of mice with large derived models. The study was motivated by some of the well-known Holy Grails in inner model theory, including the Mouse Set Conjecture. In his presentation, W Hugh Woodin gives an outline of an expanded version (unpublished) on suitable extender sequences, a subject that was developed in the attempt to understand inner model theory for large cardinals beyond the level of superstrong cardinals.The volume serves as a useful guide for graduate students and researchers in recursion theory and set theory to some of the most important and significant developments in these subjects in recent years.
