Title Statement of Responsibility a Computational Introduction to Number Theory and Algebra Victor Shoup
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500 Natural Sciences and Mathematics
500 500 Natural sciences and mathematics Natural sciences: sciences that deal with matter and energy, or with objects and processes observable in nature Class here interdisciplinary works on natural and applied sciences Class natural history in 508. Class scientific principles of a subject with the subject, plus notation 01 from Table 1, e.g., scientific principles of photography 770.1 For government policy on science, see 338.9; for applied sciences, see 600 See Manual at 231.7 vs. 213, 500, 576.8; also at 338.9 vs. 352.7, 500; also at 500 vs. 001 SUMMARY 500.2–.8 [Physical sciences, space sciences, groups of people] 501–509 Standard subdivisions and natural history 510 Mathematics 520 Astronomy and allied sciences 530 Physics 540 Chemistry and allied sciences 550 Earth sciences 560 Paleontology 570 Biology 580 Plants 590 Animals .2 Physical sciences For astronomy and allied sciences, see 520; for physics, see 530; for chemistry and allied sciences, see 540; for earth sciences, see 550 .5 Space sciences For astronomy, see 520; for earth sciences in other worlds, see 550. For space sciences aspects of a specific subject, see the subject, plus notation 091 from Table 1, e.g., chemical reactions in space 541.390919 See Manual at 520 vs. 500.5, 523.1, 530.1, 919.9 .8 Groups of people Add to base number 500.8 the numbers following —08 in notation 081–089 from Table 1, e.g., women in science 500.82 501 Philosophy and theory Class scientific method as a general research technique in 001.4; class scientific method applied in the natural sciences in 507.2 502 Miscellany 577 502 Dewey Decimal Classification 502 .8 Auxiliary techniques and procedures; apparatus, equipment, materials Including microscopy; microscopes; interdisciplinary works on microscopy Class stereology with compound microscopes, stereology with electron microscopes in 502; class interdisciplinary works on photomicrography in 778.3 For manufacture of microscopes, see 681. -
Computational Method for Phase Space Transport with Applications to Lobe Dynamics and Rate of Escape
Computational Method for Phase Space Transport with Applications to Lobe Dynamics and Rate of Escape Shibabrat Naik1, Francois Lekien2, and Shane D. Ross3 1,3Engineering Mechanics Program, Department of Biomedical Engineering & Mechanics, Virginia Tech, Blacksburg, VA-24061, USA 2Ecole´ Polytechnique, Universite´ Libre de Bruxelles, B-1050 Brussels, Belgium Abstract Lobe dynamics and escape from a potential well are general frameworks introduced to study phase space transport in chaotic dynamical systems. While the former approach studies how regions of phase space are transported by reducing the flow to a two-dimensional map, the latter approach studies the phase space structures that lead to critical events by crossing periodic orbit around saddles. Both of these frameworks require computation with curves represented by millions of points—computing intersection points between these curves and area bounded by the segments of these curves—for quantifying the transport and escape rate. We present a theory for computing these intersection points and the area bounded between the segments of these curves based on a classification of the intersection points using equivalence class. We also present an alternate theory for curves with nontransverse intersections and a method to increase the density of points on the curves for locating the intersection points accurately. The numerical implementation of the theory presented herein is available as an open source software called Lober. We used this package to demonstrate the application of the theory -
Math 788M: Computational Number Theory (Instructor’S Notes)
Math 788M: Computational Number Theory (Instructor’s Notes) The Very Beginning: • A positive integer n can be written in n steps. • The role of numerals (now O(log n) steps) • Can we do better? (Example: The largest known prime contains 258716 digits and doesn’t take long to write down. It’s 2859433 − 1.) Running Time of Algorithms: • A positive integer n in base b contains [logb n] + 1 digits. • Big-Oh & Little-Oh Notation (as well as , , ∼, ) Examples 1: log (1 + (1/n)) = O(1/n) Examples 2: [logb n] + 1 log n Examples 3: 1 + 2 + ··· + n n2 These notes are for a course taught by Michael Filaseta in the Spring of 1996 and being updated for Fall of 2007. Computational Number Theory Notes 2 Examples 4: f a polynomial of degree k =⇒ f(n) = O(nk) Examples 5: (r + 1)π ∼ rπ • We will want algorithms to run quickly (in a small number of steps) in comparison to the length of the input. For example, we may ask, “How quickly can we factor a positive integer n?” One considers the length of the input n to be of order log n (corresponding to the number of binary digits n has). An algorithm runs in polynomial time if the number of steps it takes is bounded above by a polynomial in the length of the input. An algorithm to factor n in polynomial time would require that it take O (log n)k steps (and that it factor n). Addition and Subtraction (of n and m): • We are taught how to do binary addition and subtraction in O(log n + log m) steps. -
Number Theory
“mcs-ftl” — 2010/9/8 — 0:40 — page 81 — #87 4 Number Theory Number theory is the study of the integers. Why anyone would want to study the integers is not immediately obvious. First of all, what’s to know? There’s 0, there’s 1, 2, 3, and so on, and, oh yeah, -1, -2, . Which one don’t you understand? Sec- ond, what practical value is there in it? The mathematician G. H. Hardy expressed pleasure in its impracticality when he wrote: [Number theorists] may be justified in rejoicing that there is one sci- ence, at any rate, and that their own, whose very remoteness from or- dinary human activities should keep it gentle and clean. Hardy was specially concerned that number theory not be used in warfare; he was a pacifist. You may applaud his sentiments, but he got it wrong: Number Theory underlies modern cryptography, which is what makes secure online communication possible. Secure communication is of course crucial in war—which may leave poor Hardy spinning in his grave. It’s also central to online commerce. Every time you buy a book from Amazon, check your grades on WebSIS, or use a PayPal account, you are relying on number theoretic algorithms. Number theory also provides an excellent environment for us to practice and apply the proof techniques that we developed in Chapters 2 and 3. Since we’ll be focusing on properties of the integers, we’ll adopt the default convention in this chapter that variables range over the set of integers, Z. 4.1 Divisibility The nature of number theory emerges as soon as we consider the divides relation a divides b iff ak b for some k: D The notation, a b, is an abbreviation for “a divides b.” If a b, then we also j j say that b is a multiple of a. -
A Taste of Set Theory for Philosophers
Journal of the Indian Council of Philosophical Research, Vol. XXVII, No. 2. A Special Issue on "Logic and Philosophy Today", 143-163, 2010. Reprinted in "Logic and Philosophy Today" (edited by A. Gupta ans J.v.Benthem), College Publications vol 29, 141-162, 2011. A taste of set theory for philosophers Jouko Va¨an¨ anen¨ ∗ Department of Mathematics and Statistics University of Helsinki and Institute for Logic, Language and Computation University of Amsterdam November 17, 2010 Contents 1 Introduction 1 2 Elementary set theory 2 3 Cardinal and ordinal numbers 3 3.1 Equipollence . 4 3.2 Countable sets . 6 3.3 Ordinals . 7 3.4 Cardinals . 8 4 Axiomatic set theory 9 5 Axiom of Choice 12 6 Independence results 13 7 Some recent work 14 7.1 Descriptive Set Theory . 14 7.2 Non well-founded set theory . 14 7.3 Constructive set theory . 15 8 Historical Remarks and Further Reading 15 ∗Research partially supported by grant 40734 of the Academy of Finland and by the EUROCORES LogICCC LINT programme. I Journal of the Indian Council of Philosophical Research, Vol. XXVII, No. 2. A Special Issue on "Logic and Philosophy Today", 143-163, 2010. Reprinted in "Logic and Philosophy Today" (edited by A. Gupta ans J.v.Benthem), College Publications vol 29, 141-162, 2011. 1 Introduction Originally set theory was a theory of infinity, an attempt to understand infinity in ex- act terms. Later it became a universal language for mathematics and an attempt to give a foundation for all of mathematics, and thereby to all sciences that are based on mathematics. -
Revealing the Phase Space Structure of Hamiltonian Systems Using the Action
Revealing the phase space structure of Hamiltonian systems using the action Francisco Gonzalez Montoya, Makrina Agaoglou, and Matthaios Katsanikas School of Mathematics, University of Bristol, Fry Building, Woodland Road, Bristol, BS8 1UG, United Kingdom. Abstract In this work, we analyse the properties of the Maupertuis' action as a tool to reveal the phase space structure for Hamiltonian systems. We construct a scalar field with the action's values along the trajectories in the phase space. The different behaviour of the trajectories around important phase space objects like unstable periodic orbits, their stable and unstable manifolds, and KAM islands generate characteristic patterns on the scalar field constructed with the action. Using these different patterns is possible to identify the skeleton of the phase space and understand the dynamics. Also, we present a simple argument based on the conservation of the energy and the behaviour of the trajectories to understand the values of their actions. In order to show how this tool reveals the phase space structures and its effectiveness, we compare the scalar field constructed with the actions with Poincare maps for the same set of initial conditions in the phase space of an open Hamiltonian system with 2 degrees of freedom. The study of phase space structure is a fundamental problem in dynamical systems. It is essential to understand the trajectories' behaviour and properties from a theoretical and practical perspective. The traditional tools to visualise the phase space structure like the projection of the trajectories in a plane or Poincare maps are important tools to understand many properties of ODE systems' phase space with 3 dimensions. -
Complexity Theory
Complexity Theory Course Notes Sebastiaan A. Terwijn Radboud University Nijmegen Department of Mathematics P.O. Box 9010 6500 GL Nijmegen the Netherlands [email protected] Copyright c 2010 by Sebastiaan A. Terwijn Version: December 2017 ii Contents 1 Introduction 1 1.1 Complexity theory . .1 1.2 Preliminaries . .1 1.3 Turing machines . .2 1.4 Big O and small o .........................3 1.5 Logic . .3 1.6 Number theory . .4 1.7 Exercises . .5 2 Basics 6 2.1 Time and space bounds . .6 2.2 Inclusions between classes . .7 2.3 Hierarchy theorems . .8 2.4 Central complexity classes . 10 2.5 Problems from logic, algebra, and graph theory . 11 2.6 The Immerman-Szelepcs´enyi Theorem . 12 2.7 Exercises . 14 3 Reductions and completeness 16 3.1 Many-one reductions . 16 3.2 NP-complete problems . 18 3.3 More decision problems from logic . 19 3.4 Completeness of Hamilton path and TSP . 22 3.5 Exercises . 24 4 Relativized computation and the polynomial hierarchy 27 4.1 Relativized computation . 27 4.2 The Polynomial Hierarchy . 28 4.3 Relativization . 31 4.4 Exercises . 32 iii 5 Diagonalization 34 5.1 The Halting Problem . 34 5.2 Intermediate sets . 34 5.3 Oracle separations . 36 5.4 Many-one versus Turing reductions . 38 5.5 Sparse sets . 38 5.6 The Gap Theorem . 40 5.7 The Speed-Up Theorem . 41 5.8 Exercises . 43 6 Randomized computation 45 6.1 Probabilistic classes . 45 6.2 More about BPP . 48 6.3 The classes RP and ZPP . -
Representation Theory* Its Rise and Its Role in Number Theory
Representation Theory* Its Rise and Its Role in Number Theory Introduction By representation theory we understand the representation of a group by linear transformations of a vector space. Initially, the group is finite, as in the researches of Dedekind and Frobenius, two of the founders of the subject, or a compact Lie group, as in the theory of invariants and the researches of Hurwitz and Schur, and the vector space finite-dimensional, so that the group is being represented as a group of matrices. Under the combined influences of relativity theory and quantum mechanics, in the context of relativistic field theories in the nineteen-thirties, the Lie group ceased to be compact and the space to be finite-dimensional, and the study of infinite-dimensional representations of Lie groups began. It never became, so far as I can judge, of any importance to physics, but it was continued by mathematicians, with strictly mathematical, even somewhat narrow, goals, and led, largely in the hands of Harish-Chandra, to a profound and unexpectedly elegant theory that, we now appreciate, suggests solutions to classical problems in domains, especially the theory of numbers, far removed from the concerns of Dirac and Wigner, in whose papers the notion first appears. Physicists continue to offer mathematicians new notions, even within representation theory, the Virasoro algebra and its representations being one of the most recent, that may be as fecund. Predictions are out of place, but it is well to remind ourselves that the representation theory of noncompact Lie groups revealed its force and its true lines only after an enormous effort, over two decades and by one of the very best mathematical minds of our time, to establish rigorously and in general the elements of what appeared to be a somewhat peripheral subject. -
![Arxiv:2107.08429V1 [Math.DS] 18 Jul 2021](https://docslib.b-cdn.net/cover/6185/arxiv-2107-08429v1-math-ds-18-jul-2021-696185.webp)
Arxiv:2107.08429V1 [Math.DS] 18 Jul 2021
Support vector machines for learning reactive islands Shibabrat Naik,a) Vladim´ır Krajˇn´ak,b) and Stephen Wigginsc) School of Mathematics, University of Bristol Fry building, Woodland Road, Bristol BS8 1UG, United Kingdom (Dated: 20 July 2021) We develop a machine learning framework that can be applied to data sets derived from the trajectories of Hamilton's equations. The goal is to learn the phase space structures that play the governing role for phase space transport relevant to particu- lar applications. Our focus is on learning reactive islands in two degrees-of-freedom Hamiltonian systems. Reactive islands are constructed from the stable and unsta- ble manifolds of unstable periodic orbits and play the role of quantifying transition dynamics. We show that support vector machines (SVM) is an appropriate machine learning framework for this purpose as it provides an approach for finding the bound- aries between qualitatively distinct dynamical behaviors, which is in the spirit of the phase space transport framework. We show how our method allows us to find re- active islands directly in the sense that we do not have to first compute unstable periodic orbits and their stable and unstable manifolds. We apply our approach to the H´enon-HeilesHamiltonian system, which is a benchmark system in the dynamical systems community. We discuss different sampling and learning approaches and their advantages and disadvantages. arXiv:2107.08429v1 [math.DS] 18 Jul 2021 a)Corresponding author: [email protected] b)[email protected] c)[email protected] 1 Prediction and control of transition between potential wells across index-one saddles are of importance in a diverse array of non-linear systems in physics, chemistry, and engineering. -
Strong Flows of Dilute Suspensions of Microstructure Andrew J
Strong flows of dilute suspensions of microstructure Andrew J. Szeri* Department of MechanicaI and Environmentat Engineering, University of California, Santa Barbara, Calljrrnia 93 IO6 Stephen Wiggins Applied Mechanics, California Institute of Technology, Pasadena, Cal@rnia 91125 and Center for Nonlinear Studies, Los Alamos National Laboratory, Las Alamos, New Mexico 87545 L. Gary Lea1 Department of Chemical and Nuclear Engineering, University of California, Santa Barbara, California 93106 (Received 28 August 1990) We consider dilute suspensions that have a microstructure that may be characterized by an axial state vector. Examples include axisymmetric particles, line elements of the fluid itself, or, as an approximation, droplets of fluid or polymer molecules. Past studies, in which sufficient conditions for stretch or coherent orientation of the microstructure are obtained for steady flows with homogeneous velocity gradient. tensors are shown not to apply to the general situation. Instead, a careful analysis of the microdynamical equations reveals that stretching and orientation of the microstructure by the flow must be analyzed over a time interval. Using techniques from the theory of dynamical systems, a quantitative measure is developed to determine orientations and/or stretched lengths of the microstructure, that are robust and attractive to nearby states. This leads to a strongJlow criterion for unsteady flows with inhomogeneous velocity gradient tensors in which the effects of history dependence are apparent. A particular model system is treated in the case of general two-dimensional flow. The sensitivity of the results to changes in the modeling assumptions is investigated. 1438 Phys. Fluids A 3 (5), May 1991 0899-8213/91/051438-01$02.00 @ 1991 American Institute of Physics 1438 Downloaded 02 Apr 2006 to 131.215.240.9. -
Interactions of Computational Complexity Theory and Mathematics
Interactions of Computational Complexity Theory and Mathematics Avi Wigderson October 22, 2017 Abstract [This paper is a (self contained) chapter in a new book on computational complexity theory, called Mathematics and Computation, whose draft is available at https://www.math.ias.edu/avi/book]. We survey some concrete interaction areas between computational complexity theory and different fields of mathematics. We hope to demonstrate here that hardly any area of modern mathematics is untouched by the computational connection (which in some cases is completely natural and in others may seem quite surprising). In my view, the breadth, depth, beauty and novelty of these connections is inspiring, and speaks to a great potential of future interactions (which indeed, are quickly expanding). We aim for variety. We give short, simple descriptions (without proofs or much technical detail) of ideas, motivations, results and connections; this will hopefully entice the reader to dig deeper. Each vignette focuses only on a single topic within a large mathematical filed. We cover the following: • Number Theory: Primality testing • Combinatorial Geometry: Point-line incidences • Operator Theory: The Kadison-Singer problem • Metric Geometry: Distortion of embeddings • Group Theory: Generation and random generation • Statistical Physics: Monte-Carlo Markov chains • Analysis and Probability: Noise stability • Lattice Theory: Short vectors • Invariant Theory: Actions on matrix tuples 1 1 introduction The Theory of Computation (ToC) lays out the mathematical foundations of computer science. I am often asked if ToC is a branch of Mathematics, or of Computer Science. The answer is easy: it is clearly both (and in fact, much more). Ever since Turing's 1936 definition of the Turing machine, we have had a formal mathematical model of computation that enables the rigorous mathematical study of computational tasks, algorithms to solve them, and the resources these require. -
On Bell's Arithmetic of Boolean Algebra*
ON BELL'S ARITHMETIC OF BOOLEAN ALGEBRA* BY WALLIE ABRAHAM HURWITZ 1. Introduction. Bellf has constructed an arithmetic for an algebra of logic or (following the terminology of Shefferî) a Boolean algebra, which presents gratifying analogies to the arithmetics of rational and other fields. In only one detail is the similarity less close than seems appropriate to the difference in the structures of the algebras themselves—namely, in the properties of the concept congruence. In this note I shall show that a slightly more general definition retains all the properties of the congruence given by Bell and restores several analogies to rational arithmetic lost by the Bell definition. All the notation and terminology of the paper of Bell (to which reference should be made for results not here repeated) other than those relating to congruence are followed in the present treatment. In particular, we utilize the two (dual) interpretations of arithmetic operations and relations in ?: Name Symbol Interpretation I Interpretation II (s) Arithmetic sum: asß: a+ß, aß; (p) Arithmetic product: apß: aß, ct+ß; (g) G. CD.: agß: a+ß, aß; (l) L. C. M.: alß: aß, a+ß; (f) Arithmetic zero: f: <o, e; (v) Arithmetic unity: v. t, «; (d) a divides/3: adß: a\ß, ß\a. 2. Residuals. It will be convenient to amplify slightly Bell's treatment of residuals. By the residual of b with respect to a in Si, bra, is meant the quotient of a by the G. C. D. of a and b. Transforming this into a form equivalent for 21and suitable, by the non-appearance of the concept quotient, for analogy in ?, Bell uses for 2 substantially the following: ßra is the G.