Algorithms in Modern Mathematics and Computer Science
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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. -
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. -
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. -
Anna Lysyanskaya Curriculum Vitae
Anna Lysyanskaya Curriculum Vitae Computer Science Department, Box 1910 Brown University Providence, RI 02912 (401) 863-7605 email: [email protected] http://www.cs.brown.edu/~anna Research Interests Cryptography, privacy, computer security, theory of computation. Education Massachusetts Institute of Technology Cambridge, MA Ph.D. in Computer Science, September 2002 Advisor: Ronald L. Rivest, Viterbi Professor of EECS Thesis title: \Signature Schemes and Applications to Cryptographic Protocol Design" Massachusetts Institute of Technology Cambridge, MA S.M. in Computer Science, June 1999 Smith College Northampton, MA A.B. magna cum laude, Highest Honors, Phi Beta Kappa, May 1997 Appointments Brown University, Providence, RI Fall 2013 - Present Professor of Computer Science Brown University, Providence, RI Fall 2008 - Spring 2013 Associate Professor of Computer Science Brown University, Providence, RI Fall 2002 - Spring 2008 Assistant Professor of Computer Science UCLA, Los Angeles, CA Fall 2006 Visiting Scientist at the Institute for Pure and Applied Mathematics (IPAM) Weizmann Institute, Rehovot, Israel Spring 2006 Visiting Scientist Massachusetts Institute of Technology, Cambridge, MA 1997 { 2002 Graduate student IBM T. J. Watson Research Laboratory, Hawthorne, NY Summer 2001 Summer Researcher IBM Z¨urich Research Laboratory, R¨uschlikon, Switzerland Summers 1999, 2000 Summer Researcher 1 Teaching Brown University, Providence, RI Spring 2008, 2011, 2015, 2017, 2019; Fall 2012 Instructor for \CS 259: Advanced Topics in Cryptography," a seminar course for graduate students. Brown University, Providence, RI Spring 2012 Instructor for \CS 256: Advanced Complexity Theory," a graduate-level complexity theory course. Brown University, Providence, RI Fall 2003,2004,2005,2010,2011 Spring 2007, 2009,2013,2014,2016,2018 Instructor for \CS151: Introduction to Cryptography and Computer Security." Brown University, Providence, RI Fall 2016, 2018 Instructor for \CS 101: Theory of Computation," a core course for CS concentrators. -
What Every Computer Scientist Should Know About Floating-Point Arithmetic
What Every Computer Scientist Should Know About Floating-Point Arithmetic DAVID GOLDBERG Xerox Palo Alto Research Center, 3333 Coyote Hill Road, Palo Alto, CalLfornLa 94304 Floating-point arithmetic is considered an esotoric subject by many people. This is rather surprising, because floating-point is ubiquitous in computer systems: Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions such as overflow This paper presents a tutorial on the aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating-point standard, and concludes with examples of how computer system builders can better support floating point, Categories and Subject Descriptors: (Primary) C.0 [Computer Systems Organization]: General– instruction set design; D.3.4 [Programming Languages]: Processors —compders, optirruzatzon; G. 1.0 [Numerical Analysis]: General—computer arithmetic, error analysis, numerzcal algorithms (Secondary) D. 2.1 [Software Engineering]: Requirements/Specifications– languages; D, 3.1 [Programming Languages]: Formal Definitions and Theory —semantZcs D ,4.1 [Operating Systems]: Process Management—synchronization General Terms: Algorithms, Design, Languages Additional Key Words and Phrases: denormalized number, exception, floating-point, floating-point standard, gradual underflow, guard digit, NaN, overflow, relative error, rounding error, rounding mode, ulp, underflow INTRODUCTION tions of addition, subtraction, multipli- cation, and division. It also contains Builders of computer systems often need background information on the two information about floating-point arith- methods of measuring rounding error, metic. -
Cryptography
Security Engineering: A Guide to Building Dependable Distributed Systems CHAPTER 5 Cryptography ZHQM ZMGM ZMFM —G. JULIUS CAESAR XYAWO GAOOA GPEMO HPQCW IPNLG RPIXL TXLOA NNYCS YXBOY MNBIN YOBTY QYNAI —JOHN F. KENNEDY 5.1 Introduction Cryptography is where security engineering meets mathematics. It provides us with the tools that underlie most modern security protocols. It is probably the key enabling technology for protecting distributed systems, yet it is surprisingly hard to do right. As we’ve already seen in Chapter 2, “Protocols,” cryptography has often been used to protect the wrong things, or used to protect them in the wrong way. We’ll see plenty more examples when we start looking in detail at real applications. Unfortunately, the computer security and cryptology communities have drifted apart over the last 20 years. Security people don’t always understand the available crypto tools, and crypto people don’t always understand the real-world problems. There are a number of reasons for this, such as different professional backgrounds (computer sci- ence versus mathematics) and different research funding (governments have tried to promote computer security research while suppressing cryptography). It reminds me of a story told by a medical friend. While she was young, she worked for a few years in a country where, for economic reasons, they’d shortened their medical degrees and con- centrated on producing specialists as quickly as possible. One day, a patient who’d had both kidneys removed and was awaiting a transplant needed her dialysis shunt redone. The surgeon sent the patient back from the theater on the grounds that there was no urinalysis on file. -
Theoretical Computer Science Knowledge a Selection of Latest Research Visit Springer.Com for an Extensive Range of Books and Journals in the field
ABCD springer.com Theoretical Computer Science Knowledge A Selection of Latest Research Visit springer.com for an extensive range of books and journals in the field Theory of Computation Classical and Contemporary Approaches Topics and features include: Organization into D. C. Kozen self-contained lectures of 3-7 pages 41 primary lectures and a handful of supplementary lectures Theory of Computation is a unique textbook that covering more specialized or advanced topics serves the dual purposes of covering core material in 12 homework sets and several miscellaneous the foundations of computing, as well as providing homework exercises of varying levels of difficulty, an introduction to some more advanced contempo- many with hints and complete solutions. rary topics. This innovative text focuses primarily, although by no means exclusively, on computational 2006. 335 p. 75 illus. (Texts in Computer Science) complexity theory. Hardcover ISBN 1-84628-297-7 $79.95 Theoretical Introduction to Programming B. Mills page, this book takes you from a rudimentary under- standing of programming into the world of deep Including well-digested information about funda- technical software development. mental techniques and concepts in software construction, this book is distinct in unifying pure 2006. XI, 358 p. 29 illus. Softcover theory with pragmatic details. Driven by generic ISBN 1-84628-021-4 $69.95 problems and concepts, with brief and complete illustrations from languages including C, Prolog, Java, Scheme, Haskell and HTML. This book is intended to be both a how-to handbook and easy reference guide. Discussions of principle, worked Combines theory examples and exercises are presented. with practice Densely packed with explicit techniques on each springer.com Theoretical Computer Science Knowledge Complexity Theory Exploring the Limits of Efficient Algorithms computer science. -
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. -
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. -
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. -
The Computer Scientist As Toolsmith—Studies in Interactive Computer Graphics
Frederick P. Brooks, Jr. Fred Brooks is the first recipient of the ACM Allen Newell Award—an honor to be presented annually to an individual whose career contributions have bridged computer science and other disciplines. Brooks was honored for a breadth of career contributions within computer science and engineering and his interdisciplinary contributions to visualization methods for biochemistry. Here, we present his acceptance lecture delivered at SIGGRAPH 94. The Computer Scientist Toolsmithas II t is a special honor to receive an award computer science. Another view of computer science named for Allen Newell. Allen was one of sees it as a discipline focused on problem-solving sys- the fathers of computer science. He was tems, and in this view computer graphics is very near especially important as a visionary and a the center of the discipline. leader in developing artificial intelligence (AI) as a subdiscipline, and in enunciating A Discipline Misnamed a vision for it. When our discipline was newborn, there was the What a man is is more important than what he usual perplexity as to its proper name. We at Chapel Idoes professionally, however, and it is Allen’s hum- Hill, following, I believe, Allen Newell and Herb ble, honorable, and self-giving character that makes it Simon, settled on “computer science” as our depart- a double honor to be a Newell awardee. I am pro- ment’s name. Now, with the benefit of three decades’ foundly grateful to the awards committee. hindsight, I believe that to have been a mistake. If we Rather than talking about one particular research understand why, we will better understand our craft. -
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.