DOCSLIB.ORG

Cs6402 Design and Analysis of Algorithms

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Cs6402 Design and Analysis of Algorithms CS6402/DESIGN AND ANALYSIS OF ALGORITHMS M.I.E.T. ENGINEERING COLLEGE (Approved by AICTE and Affiliated to Anna University Chennai) TRICHY – PUDUKKOTTAI ROAD, TIRUCHIRAPPALLI – 620 007 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING COURSE MATERIAL CS6402 DESIGN AND ANALYSIS OF ALGORITHMS II YEAR - IV SEMESTER M.I.E.T. /CSE/ II /DESIGN AND ANALYSIS OF ALGORITHMS CS6402/DESIGN AND ANALYSIS OF ALGORITHMS M.I.E.T. ENGINEERING COLLEGE (Approved by AICTE and Affiliated to Anna University Chennai) TRICHY – PUDUKKOTTAI ROAD, TIRUCHIRAPPALLI – 620 007 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING SYLLABUS (THEORY) Sub. Code : CS6402 Branch / Year / Sem : CSE/II/IV Sub.Name : DESIGN AND ANALYSIS OF ALGORITHMS Staff Name : P.CHRISTOPHER CS6402 DESIGN AND ANALYSIS OF ALGORITHMS L T P C 3 0 0 3 UNIT I INTRODUCTION 9 Notion of an Algorithm Fundamentals of Algorithmic Problem Solving Important Problem Types Fundamentals of the Analysis of Algorithm Efficiency Analysis Framework Asymptotic Notations and– its properties Mathematical analysis for –Recursive and Non- recursive– algorithms. – – UNIT II BRUTE FORCE AND DIVIDE-AND-CONQUER– 9 Brute Force - Closest-Pair and Convex-Hull Problems-Exhaustive Search - Travelling Salesman Problem - Knapsack Problem - Assignment problem. Divide and conquer methodology Merge sort Quick sort Binary search Multiplication of Large Integers Multiplication-Closest-Pair and Convex-Hull Problems. – UNIT– III DYNAMIC– PROGRAMMING– AND GREEDY TECHNIQUE – Strassen’s 9 Matrix Computing a Binomial Coefficient Optimal Binary Search Trees Knapsack Problem and Memory functions. Greedy Technique - Kruskal's Algorithm- Dijkstra's Algorithm-Huffman– Warshall’s and Tr Floyd’ees. algorithm – UNIT IV– ITERATIVE IMPROVEMENT – Prim’s 9 algorithm The Simplex Method-The Maximum-Flow Problem Maximum Matching in Bipartite Graphs- The Stable marriage Problem. UNIT V COPING WITH THE LIMITATIONS OF ALGORIT– HM POWER 9 Limitations of Algorithm Power-Lower-Bound Arguments-Decision Trees-P, NP and NP- Complete Problems--Coping with the Limitations - Backtracking n-Queens problem Hamiltonian Circuit Problem Subset Sum Problem-Branch and Bound Assignment problem Knapsack Problem Travelling Salesman Problem- Approximation Algorithms– for NP Hard– Problems Travelling Salesman– problem Knapsack problem. – – – TOTAL: 45 PERIODS– TEXT BOOK:– – Education, 2012. Anany Levitin, Introduction to the Design and Analysis of Algorithms, Third Edition, Pearson REFERENCES: 1. Thomas H.Cormen, Charles E.Leiserson, Ronal d L. Rivest and Clifford Stein, Introduction to PearsonAlgorithms, Education, Third Edition, Reprint PHI2006. Learning Private Limited, . 3.. DonaldAlfred V.E. Aho,Knuth John E. Hopcroft and Jeffrey D. Ullman, Data Structures and Algorithms, 4. http://nptel.ac.in/, The Art of Computer Programming, Volumes & Pearson Education, . Steven S. Skiena, The Algorithm Design Manual, Second Edition, Springer, . SUBJECT IN-CHARGE HOD M.I.E.T. /CSE/ II /DESIGN AND ANALYSIS OF ALGORITHMS CS6402/DESIGN AND ANALYSIS OF ALGORITHMS M.I.E.T. ENGINEERING COLLEGE (Approved by AICTE and Affiliated to Anna University Chennai) TRICHY – PUDUKKOTTAI ROAD, TIRUCHIRAPPALLI – 620 007 DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING Sub. Code : CS6402 Branch/Year/Sem : CSE/II/IV Sub Name : DESIGN AND ANALYSIS OF ALGORITHMS Staff Name : P.CHRISTOPHER COURSE OBJECTIVE 1. Learn the algorithm analysis techniques. 2. Become familiar with the different algorithm design techniques. 3. Understand the limitations of Algorithm power. COURSE OUTCOMES 1. Interpret the fundamental needs of algorithms in problem solving. 2. Classify the different algorithm design techniques for problem solving. 3. Develop algorithms for various computing problems. 4. Analyze the time and space complexity of various algorithms. 5. Identify the limitations of algorithms in problem solving. 6. To identify the types of problem, formulate, analyze and compare the efficiency of algorithms. Prepared by Verified By STAFF NAME HOD (P.CHRISTOPHER) Approved by PRINCIPAL M.I.E.T. /CSE/ II /DESIGN AND ANALYSIS OF ALGORITHMS CS6402/DESIGN AND ANALYSIS OF ALGORITHMS M.I.E.T. ENGINEERING COLLEGE (Approved by AICTE and Affiliated to Anna University Chennai) TRICHY – PUDUKKOTTAI ROAD, TIRUCHIRAPPALLI – 620 007 UNIT I INTRODUCTION Notion of an Algorithm – Fundamentals of Algorithmic Problem Solving – Important Problem Types – Fundamentals of the Analysis of Algorithm Efficiency – Analysis Framework – Asymptotic Notations and its properties – Mathematical analysis for Recursive and Non-recursive algorithms. 1.1 NOTION OF AN ALGORITHM An algorithm is a sequence of unambiguous instructions for solving a problem. i.e., for obtaining a required output for any legitimate input in a finite amount of time. Problem Algorithm Input Computer output There are various methods to solve the same problem. The important points to be remembered are: 1. The non-ambiguity requirement for each step of an algorithm cannot be compromised. 2. The range of input for which an algorithm works has to be specified carefully. 3. The same algorithm can be represented in different ways. 4. Several algorithms for solving the same problem may exist. 5. Algorithms for the same problem can be based on very different ideas and can solve the problem with dramatically different speeds. The example here is to find the gcd of two integers with three different ways: The gcd of two nonnegative, not-both –zero integers m & n, denoted as gcd (m, n) is defined as the largest integer that divides both m & n evenly, i.e., with a remainder of zero. Euclid of Alexandria outlined an algorithm, for solving this problem in one of the volumes of his Elements. Gcd (m, n) = gcd (n, m mod n) is applied repeatedly until m mod n is equal to 0; since gcd (m, o) = m. {the last value of m is also the gcd of the initial m & n.} M.I.E.T. /CSE/ II /DESIGN AND ANALYSIS OF ALGORITHMS CS6402/DESIGN AND ANALYSIS OF ALGORITHMS The structured description of this algorithm is: Step 1: If n=0, return the value of m as the answer and stop; otherwise, proceed to step2. Step 2: Divide m by n and assign the value of the remainder to r. Step 3: Assign the value of n to m and the value of r to n. Go to step 1. 1.1.1 Euclid”s algorithm : ALGORITHM Euclid(m, n) //Computes gcd(m, n) by Euclid’s algorithm //Input: Two nonnegative, not-both-zero integers m and n //Output: Greatest common divisor of m and n while n _= 0 do r ←m mod n m←n n←r return m This algorithm comes to a stop, when the 2nd no becomes 0. The second number of the pair gets smaller with each iteration and it cannot become negative. Indeed, the new value of n on the next iteration is m mod n, which is always smaller than n. hence, the value of the second number in the pair eventually becomes 0, and the algorithm stops. Example: gcd (60, 24) = gcd (24,12) = gcd (12,0) = 12. The second method for the same problem is: obtained from the definition itself. i.e., gcd of m & n is the largest integer that divides both numbers evenly. Obviously, that number cannot be greater than the second number (or) smaller of these two numbers,which we will denote by t = min {m, n }. So start checking whether t divides both m and n: if it does t is the answer ; if it doesn’t t is decreased by 1 and try again. (Do this repeatedly till you reach 12 and then stop for the example given below) 1.1.2 Consecutive integer checking algorithm: Step 1: Assign the value of min {m, n} to t. Step 2: Divide m by t. If the remainder of this division is 0, go to step 3; otherwise go to step 4. Step 3: Divide n by t. If the remainder of this division is 0, return the value of t as the answer and stop; otherwise, proceed to step 4. Step 4: Decrease the value of t by 1. Go to step 2. Note: this algorithm, will not work when one of its input is zero. So we have to specify the range of input explicitly and carefully. M.I.E.T. /CSE/ II /DESIGN AND ANALYSIS OF ALGORITHMS CS6402/DESIGN AND ANALYSIS OF ALGORITHMS The third procedure is as follows: Step 1: Find the prime factors of m. Step 2: Find the prime factors of n. Step 3: Identify all the common factors in the two prime expansions found in step 1 & 2. (If p is a common factor occurring pm & pn times is m and n, respectively,it should be repeated min { pm, pn } times.). Step 4: Compute the product of all the common factors and return it as gcd of the numbers given. Example: 60 = 2.2.3.5 24 = 2.2.2.3 gcd (60,24) = 2.2.3 = 12 . This procedure is more complex and ambiguity arises since the prime factorization is not defined. So to make it as an efficient algorithm, incorporate the algorithm to find the prime factors. 1.2 FUNDAMENTALS OF ALGORITHMIC PROBLEM SOLVING Algorithms can be considered to be procedural solutions to problems. There are certain steps to be followed in designing and analyzing an algorithm. Code the algorithm Understand the problem Decide on: Computational means, exact vs.approximate problem solving, data structure, algorithm design technique Design an algorithm Prove the correctness Analyze the algorithm Code the algorithm M.I.E.T. /CSE/ II /DESIGN AND ANALYSIS OF ALGORITHMS CS6402/DESIGN AND ANALYSIS OF ALGORITHMS Understanding the problem ∗ An input to an algorithm specifies an instance of the problem the algorithm solves. It’s also important to specify exactly the range of instances the algorithm needs to handle. Before this we have to clearly understand the problem and clarify the doubts after leading the problems description. Correct algorithm should work for all possible inputs. Ascertaining the capabilities of a computational Device ∗ The second step is to ascertain the capabilities of a machine.
Load more

Recommended publications
  • Simulating Quantum Field Theory with a Quantum Computer
    Simulating quantum field theory with a quantum computer John Preskill Lattice 2018 28 July 2018 This talk has two parts (1) Near-term prospects for quantum computing. (2) Opportunities in quantum simulation of quantum field theory. Exascale digital computers will advance our knowledge of QCD, but some challenges will remain, especially concerning real-time evolution and properties of nuclear matter and quark-gluon plasma at nonzero temperature and chemical potential. Digital computers may never be able to address these (and other) problems; quantum computers will solve them eventually, though I’m not sure when. The physics payoff may still be far away, but today’s research can hasten the arrival of a new era in which quantum simulation fuels progress in fundamental physics. Frontiers of Physics short distance long distance complexity Higgs boson Large scale structure “More is different” Neutrino masses Cosmic microwave Many-body entanglement background Supersymmetry Phases of quantum Dark matter matter Quantum gravity Dark energy Quantum computing String theory Gravitational waves Quantum spacetime particle collision molecular chemistry entangled electrons A quantum computer can simulate efficiently any physical process that occurs in Nature. (Maybe. We don’t actually know for sure.) superconductor black hole early universe Two fundamental ideas (1) Quantum complexity Why we think quantum computing is powerful. (2) Quantum error correction Why we think quantum computing is scalable. A complete description of a typical quantum state of just 300 qubits requires more bits than the number of atoms in the visible universe. Why we think quantum computing is powerful We know examples of problems that can be solved efficiently by a quantum computer, where we believe the problems are hard for classical computers.
    [Show full text]
  • Geometric Algorithms
    Geometric Algorithms primitive operations convex hull closest pair voronoi diagram References: Algorithms in C (2nd edition), Chapters 24-25 http://www.cs.princeton.edu/introalgsds/71primitives http://www.cs.princeton.edu/introalgsds/72hull 1 Geometric Algorithms Applications. • Data mining. • VLSI design. • Computer vision. • Mathematical models. • Astronomical simulation. • Geographic information systems. airflow around an aircraft wing • Computer graphics (movies, games, virtual reality). • Models of physical world (maps, architecture, medical imaging). Reference: http://www.ics.uci.edu/~eppstein/geom.html History. • Ancient mathematical foundations. • Most geometric algorithms less than 25 years old. 2 primitive operations convex hull closest pair voronoi diagram 3 Geometric Primitives Point: two numbers (x, y). any line not through origin Line: two numbers a and b [ax + by = 1] Line segment: two points. Polygon: sequence of points. Primitive operations. • Is a point inside a polygon? • Compare slopes of two lines. • Distance between two points. • Do two line segments intersect? Given three points p , p , p , is p -p -p a counterclockwise turn? • 1 2 3 1 2 3 Other geometric shapes. • Triangle, rectangle, circle, sphere, cone, … • 3D and higher dimensions sometimes more complicated. 4 Intuition Warning: intuition may be misleading. • Humans have spatial intuition in 2D and 3D. • Computers do not. • Neither has good intuition in higher dimensions! Is a given polygon simple? no crossings 1 6 5 8 7 2 7 8 6 4 2 1 1 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 2 18 4 18 4 19 4 19 4 20 3 20 3 20 1 10 3 7 2 8 8 3 4 6 5 15 1 11 3 14 2 16 we think of this algorithm sees this 5 Polygon Inside, Outside Jordan curve theorem.
    [Show full text]
  • Introduction to Computational Social Choice
    1 Introduction to Computational Social Choice Felix Brandta, Vincent Conitzerb, Ulle Endrissc, J´er^omeLangd, and Ariel D. Procacciae 1.1 Computational Social Choice at a Glance Social choice theory is the field of scientific inquiry that studies the aggregation of individual preferences towards a collective choice. For example, social choice theorists|who hail from a range of different disciplines, including mathematics, economics, and political science|are interested in the design and theoretical evalu- ation of voting rules. Questions of social choice have stimulated intellectual thought for centuries. Over time the topic has fascinated many a great mind, from the Mar- quis de Condorcet and Pierre-Simon de Laplace, through Charles Dodgson (better known as Lewis Carroll, the author of Alice in Wonderland), to Nobel Laureates such as Kenneth Arrow, Amartya Sen, and Lloyd Shapley. Computational social choice (COMSOC), by comparison, is a very young field that formed only in the early 2000s. There were, however, a few precursors. For instance, David Gale and Lloyd Shapley's algorithm for finding stable matchings between two groups of people with preferences over each other, dating back to 1962, truly had a computational flavor. And in the late 1980s, a series of papers by John Bartholdi, Craig Tovey, and Michael Trick showed that, on the one hand, computational complexity, as studied in theoretical computer science, can serve as a barrier against strategic manipulation in elections, but on the other hand, it can also prevent the efficient use of some voting rules altogether. Around the same time, a research group around Bernard Monjardet and Olivier Hudry also started to study the computational complexity of preference aggregation procedures.
    [Show full text]
  • Mathematical Analysis of the Accordion Grating Illusion: a Differential Geometry Approach to Introduce the 3D Aperture Problem
    Neural Networks 24 (2011) 1093–1101 Contents lists available at SciVerse ScienceDirect Neural Networks journal homepage: www.elsevier.com/locate/neunet Mathematical analysis of the Accordion Grating illusion: A differential geometry approach to introduce the 3D aperture problem Arash Yazdanbakhsh a,b,∗, Simone Gori c,d a Cognitive and Neural Systems Department, Boston University, MA, 02215, United States b Neurobiology Department, Harvard Medical School, Boston, MA, 02115, United States c Universita' degli studi di Padova, Dipartimento di Psicologia Generale, Via Venezia 8, 35131 Padova, Italy d Developmental Neuropsychology Unit, Scientific Institute ``E. Medea'', Bosisio Parini, Lecco, Italy article info a b s t r a c t Keywords: When an observer moves towards a square-wave grating display, a non-rigid distortion of the pattern Visual illusion occurs in which the stripes bulge and expand perpendicularly to their orientation; these effects reverse Motion when the observer moves away. Such distortions present a new problem beyond the classical aperture Projection line Line of sight problem faced by visual motion detectors, one we describe as a 3D aperture problem as it incorporates Accordion Grating depth signals. We applied differential geometry to obtain a closed form solution to characterize the fluid Aperture problem distortion of the stripes. Our solution replicates the perceptual distortions and enabled us to design a Differential geometry nulling experiment to distinguish our 3D aperture solution from other candidate mechanisms (see Gori et al. (in this issue)). We suggest that our approach may generalize to other motion illusions visible in 2D displays. ' 2011 Elsevier Ltd. All rights reserved. 1. Introduction need for line-ends or intersections along the lines to explain the illusion.
    [Show full text]
  • Iasinstitute for Advanced Study
    IAInsti tSute for Advanced Study Faculty and Members 2012–2013 Contents Mission and History . 2 School of Historical Studies . 4 School of Mathematics . 21 School of Natural Sciences . 45 School of Social Science . 62 Program in Interdisciplinary Studies . 72 Director’s Visitors . 74 Artist-in-Residence Program . 75 Trustees and Officers of the Board and of the Corporation . 76 Administration . 78 Past Directors and Faculty . 80 Inde x . 81 Information contained herein is current as of September 24, 2012. Mission and History The Institute for Advanced Study is one of the world’s leading centers for theoretical research and intellectual inquiry. The Institute exists to encourage and support fundamental research in the sciences and human - ities—the original, often speculative thinking that produces advances in knowledge that change the way we understand the world. It provides for the mentoring of scholars by Faculty, and it offers all who work there the freedom to undertake research that will make significant contributions in any of the broad range of fields in the sciences and humanities studied at the Institute. Y R Founded in 1930 by Louis Bamberger and his sister Caroline Bamberger O Fuld, the Institute was established through the vision of founding T S Director Abraham Flexner. Past Faculty have included Albert Einstein, I H who arrived in 1933 and remained at the Institute until his death in 1955, and other distinguished scientists and scholars such as Kurt Gödel, George F. D N Kennan, Erwin Panofsky, Homer A. Thompson, John von Neumann, and A Hermann Weyl. N O Abraham Flexner was succeeded as Director in 1939 by Frank Aydelotte, I S followed by J.
    [Show full text]
  • Randnla: Randomized Numerical Linear Algebra
    review articles DOI:10.1145/2842602 generation mechanisms—as an algo- Randomization offers new benefits rithmic or computational resource for the develop ment of improved algo- for large-scale linear algebra computations. rithms for fundamental matrix prob- lems such as matrix multiplication, BY PETROS DRINEAS AND MICHAEL W. MAHONEY least-squares (LS) approximation, low- rank matrix approxi mation, and Lapla- cian-based linear equ ation solvers. Randomized Numerical Linear Algebra (RandNLA) is an interdisci- RandNLA: plinary research area that exploits randomization as a computational resource to develop improved algo- rithms for large-scale linear algebra Randomized problems.32 From a foundational per- spective, RandNLA has its roots in theoretical computer science (TCS), with deep connections to mathemat- Numerical ics (convex analysis, probability theory, metric embedding theory) and applied mathematics (scientific computing, signal processing, numerical linear Linear algebra). From an applied perspec- tive, RandNLA is a vital new tool for machine learning, statistics, and data analysis. Well-engineered implemen- Algebra tations have already outperformed highly optimized software libraries for ubiquitous problems such as least- squares,4,35 with good scalability in par- allel and distributed environments. 52 Moreover, RandNLA promises a sound algorithmic and statistical foundation for modern large-scale data analysis. MATRICES ARE UBIQUITOUS in computer science, statistics, and applied mathematics. An m × n key insights matrix can encode information about m objects ˽ Randomization isn’t just used to model noise in data; it can be a powerful (each described by n features), or the behavior of a computational resource to develop discretized differential operator on a finite element algorithms with improved running times and stability properties as well as mesh; an n × n positive-definite matrix can encode algorithms that are more interpretable in the correlations between all pairs of n objects, or the downstream data science applications.
    [Show full text]
  • Mechanism, Mentalism, and Metamathematics Synthese Library
    MECHANISM, MENTALISM, AND METAMATHEMATICS SYNTHESE LIBRARY STUDIES IN EPISTEMOLOGY, LOGIC, METHODOLOGY, AND PHILOSOPHY OF SCIENCE Managing Editor: JAAKKO HINTIKKA, Florida State University Editors: ROBER T S. COHEN, Boston University DONALD DAVIDSON, University o/Chicago GABRIEL NUCHELMANS, University 0/ Leyden WESLEY C. SALMON, University 0/ Arizona VOLUME 137 JUDSON CHAMBERS WEBB Boston University. Dept. 0/ Philosophy. Boston. Mass .• U.S.A. MECHANISM, MENT ALISM, AND MET AMA THEMA TICS An Essay on Finitism i Springer-Science+Business Media, B.V. Library of Congress Cataloging in Publication Data Webb, Judson Chambers, 1936- CII:J Mechanism, mentalism, and metamathematics. (Synthese library; v. 137) Bibliography: p. Includes indexes. 1. Metamathematics. I. Title. QA9.8.w4 510: 1 79-27819 ISBN 978-90-481-8357-9 ISBN 978-94-015-7653-6 (eBook) DOl 10.1007/978-94-015-7653-6 All Rights Reserved Copyright © 1980 by Springer Science+Business Media Dordrecht Originally published by D. Reidel Publishing Company, Dordrecht, Holland in 1980. Softcover reprint of the hardcover 1st edition 1980 No part of the material protected by this copyright notice may be reproduced or utilized in any form or by any means, electronic or mechanical, including photocopying, recording or by any informational storage and retrieval system, without written permission from the copyright owner TABLE OF CONTENTS PREFACE vii INTRODUCTION ix CHAPTER I / MECHANISM: SOME HISTORICAL NOTES I. Machines and Demons 2. Machines and Men 17 3. Machines, Arithmetic, and Logic 22 CHAPTER II / MIND, NUMBER, AND THE INFINITE 33 I. The Obligations of Infinity 33 2. Mind and Philosophy of Number 40 3. Dedekind's Theory of Arithmetic 46 4.
    [Show full text]
  • A Mathematical Analysis of Student-Generated Sorting Algorithms Audrey Nasar
    The Mathematics Enthusiast Volume 16 Article 15 Number 1 Numbers 1, 2, & 3 2-2019 A Mathematical Analysis of Student-Generated Sorting Algorithms Audrey Nasar Let us know how access to this document benefits ouy . Follow this and additional works at: https://scholarworks.umt.edu/tme Recommended Citation Nasar, Audrey (2019) "A Mathematical Analysis of Student-Generated Sorting Algorithms," The Mathematics Enthusiast: Vol. 16 : No. 1 , Article 15. Available at: https://scholarworks.umt.edu/tme/vol16/iss1/15 This Article is brought to you for free and open access by ScholarWorks at University of Montana. It has been accepted for inclusion in The Mathematics Enthusiast by an authorized editor of ScholarWorks at University of Montana. For more information, please contact [email protected]. TME, vol. 16, nos.1, 2&3, p. 315 A Mathematical Analysis of student-generated sorting algorithms Audrey A. Nasar1 Borough of Manhattan Community College at the City University of New York Abstract: Sorting is a process we encounter very often in everyday life. Additionally it is a fundamental operation in computer science. Having been one of the first intensely studied problems in computer science, many different sorting algorithms have been developed and analyzed. Although algorithms are often taught as part of the computer science curriculum in the context of a programming language, the study of algorithms and algorithmic thinking, including the design, construction and analysis of algorithms, has pedagogical value in mathematics education. This paper will provide an introduction to computational complexity and efficiency, without the use of a programming language. It will also describe how these concepts can be incorporated into the existing high school or undergraduate mathematics curriculum through a mathematical analysis of student- generated sorting algorithms.
    [Show full text]
  • Arxiv:1912.01885V1 [Math.DG]
    DIRAC-HARMONIC MAPS WITH POTENTIAL VOLKER BRANDING Abstract. We study the influence of an additional scalar potential on various geometric and analytic properties of Dirac-harmonic maps. We will create a mathematical wishlist of the pos- sible benefits from inducing the potential term and point out that the latter cannot be achieved in general. Finally, we focus on several potentials that are motivated from supersymmetric quantum field theory. 1. Introduction and Results The supersymmetric nonlinear sigma model has received a lot of interest in modern quantum field theory, in particular in string theory, over the past decades. At the heart of this model is an action functional whose precise structure is fixed by the invariance under various symmetry operations. In the physics literature the model is most often formulated in the language of supergeome- try which is necessary to obtain the invariance of the action functional under supersymmetry transformations. For the physics background of the model we refer to [1] and [18, Chapter 3.4]. However, if one drops the invariance under supersymmetry transformations the resulting action functional can be investigated within the framework of geometric variational problems and many substantial results in this area of research could be achieved in the last years. To formulate this mathematical version of the supersymmetric nonlinear sigma model one fixes a Riemannian spin manifold (M, g), a second Riemannian manifold (N, h) and considers a map φ: M → N. The central ingredients in the resulting action functional are the Dirichlet energy for the map φ and the Dirac action for a spinor defined along the map φ.
    [Show full text]
  • The Orthosymplectic Lie Supergroup in Harmonic Analysis Kevin Coulembier
    The orthosymplectic Lie supergroup in harmonic analysis Kevin Coulembier Department of Mathematical Analysis, Faculty of Engineering, Ghent University, Belgium Abstract. The study of harmonic analysis in superspace led to the Howe dual pair (O(m) × Sp(2n);sl2). This Howe dual pair is incomplete, which has several implications. These problems can be solved by introducing the orthosymplectic Lie supergroup OSp(mj2n). We introduce Lie supergroups in the supermanifold setting and show that everything is captured in the Harish-Chandra pair. We show the uniqueness of the supersphere integration as an orthosymplectically invariant linear functional. Finally we study the osp(mj2n)-representations of supersymmetric tensors for the natural module for osp(mj2n). Keywords: Howe dual pair, Lie supergroup, Lie superalgebra, harmonic analysis, supersphere PACS: 02.10.Ud, 20.30.Cj, 02.30.Px PRELIMINARIES Superspaces are spaces where one considers not only commuting but also anti-commuting variables. The 2n anti- commuting variables x`i generate the complex Grassmann algebra L2n. Definition 1. A supermanifold of dimension mj2n is a ringed space M = (M0;OM ), with M0 the underlying manifold of dimension m and OM the structure sheaf which is a sheaf of super-algebras with unity on M0. The sheaf OM satisfies the local triviality condition: there exists an open cover fUigi2I of M0 and isomorphisms of sheaves of super algebras T : Ui ! ¥ ⊗ L . i OM CUi 2n The sections of the structure sheaf are referred to as superfunctions on M . A morphism of supermanifolds ] ] F : M ! N is a morphism of ringed spaces (f;f ), f : M0 ! N0, f : ON ! f∗OM .
    [Show full text]
  • On P-Adic Mathematical Physics B
    ISSN 2070-0466, p-Adic Numbers, Ultrametric Analysis and Applications, 2009, Vol. 1, No. 1, pp. 1–17. c Pleiades Publishing, Ltd., 2009. REVIEW ARTICLES On p-Adic Mathematical Physics B. Dragovich1**, A. Yu. Khrennikov2***, S.V.Kozyrev3****,andI.V.Volovich3***** 1Institute of Physics, Pregrevica 118, 11080 Belgrade, Serbia 2Va¨xjo¨ University, Va¨xjo¨,Sweden 3 Steklov Mathematical Institute, ul. Gubkina 8, Moscow, 119991 Russia Received December 15, 2008 Abstract—A brief review of some selected topics in p-adic mathematical physics is presented. DOI: 10.1134/S2070046609010014 Key words: p-adic numbers, p-adic mathematical physics, complex systems, hierarchical structures, adeles, ultrametricity, string theory, quantum mechanics, quantum gravity, prob- ability, biological systems, cognitive science, genetic code, wavelets. 1. NUMBERS: RATIONAL, REAL, p-ADIC We present a brief review of some selected topics in p-adic mathematical physics. More details can be found in the references below and the other references are mainly contained therein. We hope that this brief introduction to some aspects of p-adic mathematical physics could be helpful for the readers of the first issue of the journal p-Adic Numbers, Ultrametric Analysis and Applications. The notion of numbers is basic not only in mathematics but also in physics and entire science. Most of modern science is based on mathematical analysis over real and complex numbers. However, it is turned out that for exploring complex hierarchical systems it is sometimes more fruitful to use analysis over p-adic numbers and ultrametric spaces. p-Adic numbers (see, e.g. [1]), introduced by Hensel, are widely used in mathematics: in number theory, algebraic geometry, representation theory, algebraic and arithmetical dynamics, and cryptography.
    [Show full text]
  • A History of Modern Numerical Linear Algebra
    Gene Golub, Stanford University 1 A History of Modern Numerical Linear Algebra Gene H. Golub Stanford University Gene Golub, Stanford University 2 What is Numerical Analysis? (a.k.a Scientific Computing) The definitions are becoming shorter... Webster’s New Collegiate Dictionary (1973): • ”The study of quantitative approximations to the solutions of mathematical problems including consideration of the errors and bounds to the errors involved.” The American Heritage Dictionary (1992): • ”The study of approximate solutions to mathematical problems, taking into account the extent of possible errors.” L. N. Trefethen, Oxford University (1992): • ”The study of algorithms for the problems of continuous mathematics.” Gene Golub, Stanford University 3 Numerical Analysis: How It All Started Numerical analysis motivated the development of the earliest computers. Ballistics • Solution of PDE’s • Data Analysis • Early pioneers included: J. von Neumann, A. M. Turing In the beginning... von Neumann & Goldstine (1947): ”Numerical Inversion of Matrices of High Order” Gene Golub, Stanford University 4 Numerical Linear Algebra Numerical Linear Algebra (NLA) is a small but active area of research: Less than 200 active, committed persons. But the community involves many scientists. Gene Golub, Stanford University 5 Top Ten Algorithms in Science (Jack Dongarra, 2000) 1. Metropolis Algorithm for Monte Carlo 2. Simplex Method for Linear Programming 3. Krylov Subspace Iteration Methods 4. The Decompositional Approach to Matrix Computations 5. The Fortran Optimizing Compiler 6. QR Algorithm for Computing Eigenvalues 7. Quicksort Algorithm for Sorting 8. Fast Fourier Transform 9. Integer Relation Detection 10. Fast Multipole Method Red: Algorithms within the exclusive domain of NLA research. • Blue: Algorithms strongly (though not exclusively) connected to NLA research.
    [Show full text]