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Tarjan Transcript Final with Timestamps
A.M. Turing Award Oral History Interview with Robert (Bob) Endre Tarjan by Roy Levin San Mateo, California July 12, 2017 Levin: My name is Roy Levin. Today is July 12th, 2017, and I’m in San Mateo, California at the home of Robert Tarjan, where I’ll be interviewing him for the ACM Turing Award Winners project. Good afternoon, Bob, and thanks for spending the time to talk to me today. Tarjan: You’re welcome. Levin: I’d like to start by talking about your early technical interests and where they came from. When do you first recall being interested in what we might call technical things? Tarjan: Well, the first thing I would say in that direction is my mom took me to the public library in Pomona, where I grew up, which opened up a huge world to me. I started reading science fiction books and stories. Originally, I wanted to be the first person on Mars, that was what I was thinking, and I got interested in astronomy, started reading a lot of science stuff. I got to junior high school and I had an amazing math teacher. His name was Mr. Wall. I had him two years, in the eighth and ninth grade. He was teaching the New Math to us before there was such a thing as “New Math.” He taught us Peano’s axioms and things like that. It was a wonderful thing for a kid like me who was really excited about science and mathematics and so on. The other thing that happened was I discovered Scientific American in the public library and started reading Martin Gardner’s columns on mathematical games and was completely fascinated. -
Reducibility
Reducibility t REDUCIBILITY AMONG COMBINATORIAL PROBLEMS Richard M. Karp University of California at Berkeley Abstract: A large class of computational problems involve the determination of properties of graphs, digraphs, integers, arrays of integers, finite families of finite sets, boolean formulas and elements of other countable domains. Through simple encodings from such domains into the set of words over a finite alphabet these problems can be converted into language recognition problems, and we can inquire into their computational complexity. It is reasonable to consider such a problem satisfactorily solved when an algorithm for its solution is found which terminates within a . number of steps bounded by a polynomial in the length of the input. We show that a large number of classic unsolved problems of cover- ing. matching, packing, routing, assignment and sequencing are equivalent, in the sense that either each of them possesses a polynomial-bounded algorithm or none of them does. 1. INTRODUCTION All the general methods presently known for computing the chromatic number of a graph, deciding whether a graph has a Hamilton circuit. or solving a system of linear inequalities in which the variables are constrained to be 0 or 1, require a combinatorial search for which the worst case time requirement grows exponentially with the length of the input. In this paper we give theorems which strongly suggest, but do not imply, that these problems, as well as many others, will remain intractable perpetually. t This research was partially supported by National Science Founda- tion Grant GJ-474. 85 R. E. Miller et al. (eds.), Complexity of Computer Computations © Plenum Press, New York 1972 86 RICHARD M. -
Purely Functional Data Structures
Purely Functional Data Structures Chris Okasaki September 1996 CMU-CS-96-177 School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213 Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy. Thesis Committee: Peter Lee, Chair Robert Harper Daniel Sleator Robert Tarjan, Princeton University Copyright c 1996 Chris Okasaki This research was sponsored by the Advanced Research Projects Agency (ARPA) under Contract No. F19628- 95-C-0050. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of ARPA or the U.S. Government. Keywords: functional programming, data structures, lazy evaluation, amortization For Maria Abstract When a C programmer needs an efficient data structure for a particular prob- lem, he or she can often simply look one up in any of a number of good text- books or handbooks. Unfortunately, programmers in functional languages such as Standard ML or Haskell do not have this luxury. Although some data struc- tures designed for imperative languages such as C can be quite easily adapted to a functional setting, most cannot, usually because they depend in crucial ways on as- signments, which are disallowed, or at least discouraged, in functional languages. To address this imbalance, we describe several techniques for designing functional data structures, and numerous original data structures based on these techniques, including multiple variations of lists, queues, double-ended queues, and heaps, many supporting more exotic features such as random access or efficient catena- tion. In addition, we expose the fundamental role of lazy evaluation in amortized functional data structures. -
A Memorable Trip Abhisekh Sankaran Research Scholar, IIT Bombay
A Memorable Trip Abhisekh Sankaran Research Scholar, IIT Bombay It was my first trip to the US. It had not yet sunk in that I had been chosen by ACM India as one of two Ph.D. students from India to attend the big ACM Turing Centenary Celebration in San Francisco until I saw the familiar face of Stephen Cook enter a room in the hotel a short distance from mine; later, Moshe Vardi recognized me from his trip to IITB during FSTTCS, 2011. I recognized Nitin Saurabh from IMSc Chennai, the other student chosen by ACM-India; 11 ACM SIG©s had sponsored students and there were about 75 from all over the world. Registration started at 8am on 15th June, along with breakfast. Collecting my ©Student Scholar© badge and stuffing in some food, I entered a large hall with several hundred seats, a brightly lit podium with a large screen in the middle flanked by two others. The program began with a video giving a brief biography of Alan Turing from his boyhood to the dynamic young man who was to change the world forever. There were inaugural speeches by John White, CEO of ACM, and Vint Cerf, the 2004 Turing Award winner and incoming ACM President. The MC for the event, Paul Saffo, took over and the panel discussions and speeches commenced. A live Twitter feed made it possible for people in the audience and elsewhere to post questions/comments which were actually taken up in the discussions. Of the many sessions that took place in the next two days, I will describe three that I found most interesting. -
COT 5407:Introduction to Algorithms Author and Copyright: Giri Narasimhan Florida International University Lecture 1: August 28, 2007
COT 5407:Introduction to Algorithms Author and Copyright: Giri Narasimhan Florida International University Lecture 1: August 28, 2007. 1 Introduction The field of algorithms is the bedrock on which all of computer science rests. Would you jump into a business project without understanding what is in store for you, without know- ing what business strategies are needed, without understanding the nature of the market, and without evaluating the competition and the availability of skilled available workforce? In the same way, you should not undertake writing a program without thinking out a strat- egy (algorithm), without theoretically evaluating its performance (algorithm analysis), and without knowing what resources you will need and you have available. While there are broad principles of algorithm design, one of the the best ways to learn how to be an expert at designing good algorithms is to do an extensive survey of “case studies”. It provides you with a storehouse of strategies that have been useful for solving other problems. When posed with a new problem, the first step is to “model” your problem appropriately and cast it as a problem (or a variant) that has been previously studied or that can be easily solved. Often the problem is rather complex. In such cases, it is necessary to use general problem-solving techniques that one usually employs in modular programming. This involves breaking down the problem into smaller and easier subproblems. For each subproblem, it helps to start with a skeleton solution which is then refined and elaborated upon in a stepwise manner. Once a strategy or algorithm has been designed, it is important to think about several issues: why is it correct? does is solve all instances of the problem? is it the best possible strategy given the resource limitations and constraints? if not, what are the limits or bounds on the amount of resources used? are improved solutions possible? 2 History of Algorithms It is important for you to know the giants of the field, and the shoulders on which we all stand in order to see far. -
COMPUTERSCIENCE Science
BIBLIOGRAPHY DEPARTMENT OF COMPUTER SCIENCE TECHNICAL REPORTS, 1963- 1988 Talecn Marashian Nazarian Department ofComputer Science Stanford University Stanford, California 94305 1 X Abstract: This report lists, in chronological order, all reports published by the Stanford Computer Science Department (CSD) since 1963. Each report is identified by CSD number, author's name, title, number of pages, and date. If a given report is available from the department at the time of this Bibliography's printing, price is also listed. For convenience, an author index is included in the back of the text. Some reports are noted with a National Technical Information Service (NTIS) retrieval number (i.e., AD-XXXXXX), if available from the NTIS. Other reports are noted with Knowledge Systems Laboratory {KSL) or Computer Systems Laboratory (CSL) numbers (KSL-XX-XX; CSL-TR-XX-XX), and may be requested from KSL or (CSL), respectively. 2 INSTRUCTIONS In the Bibliography which follows, there is a listing for each Computer Science Department report published as of the date of this writing. Each listing contains the following information: " Report number(s) " Author(s) " Tide " Number ofpages " Month and yearpublished " NTIS number, ifknown " Price ofhardcopy version (standard price for microfiche: $2/copy) " Availability code AVAILABILITY CODES i. + hardcopy and microfiche 2. M microfiche only 3. H hardcopy only 4. * out-of-print All Computer Science Reports, if in stock, may be requested from following address: Publications Computer Science Department Stanford University Stanford, CA 94305 phone: (415) 723-4776 * 4 % > 3 ALTERNATIVE SOURCES Rising costs and restrictions on the use of research funds for printing reports have made it necessary to charge for all manuscripts. -
Alan Mathison Turing and the Turing Award Winners
Alan Turing and the Turing Award Winners A Short Journey Through the History of Computer TítuloScience do capítulo Luis Lamb, 22 June 2012 Slides by Luis C. Lamb Alan Mathison Turing A.M. Turing, 1951 Turing by Stephen Kettle, 2007 by Slides by Luis C. Lamb Assumptions • I assume knowlege of Computing as a Science. • I shall not talk about computing before Turing: Leibniz, Babbage, Boole, Gödel... • I shall not detail theorems or algorithms. • I shall apologize for omissions at the end of this presentation. • Comprehensive information about Turing can be found at http://www.mathcomp.leeds.ac.uk/turing2012/ • The full version of this talk is available upon request. Slides by Luis C. Lamb Alan Mathison Turing § Born 23 June 1912: 2 Warrington Crescent, Maida Vale, London W9 Google maps Slides by Luis C. Lamb Alan Mathison Turing: short biography • 1922: Attends Hazlehurst Preparatory School • ’26: Sherborne School Dorset • ’31: King’s College Cambridge, Maths (graduates in ‘34). • ’35: Elected to Fellowship of King’s College Cambridge • ’36: Publishes “On Computable Numbers, with an Application to the Entscheindungsproblem”, Journal of the London Math. Soc. • ’38: PhD Princeton (viva on 21 June) : “Systems of Logic Based on Ordinals”, supervised by Alonzo Church. • Letter to Philipp Hall: “I hope Hitler will not have invaded England before I come back.” • ’39 Joins Bletchley Park: designs the “Bombe”. • ’40: First Bombes are fully operational • ’41: Breaks the German Naval Enigma. • ’42-44: Several contibutions to war effort on codebreaking; secure speech devices; computing. • ’45: Automatic Computing Engine (ACE) Computer. Slides by Luis C. -
Biography Five Related and Significant Publications
GUY BLELLOCH Professor Computer Science Department Carnegie Mellon University Pittsburgh, PA 15213 [email protected], http://www.cs.cmu.edu/~guyb Biography Guy E. Blelloch received his B.S. and B.A. from Swarthmore College in 1983, and his M.S. and PhD from MIT in 1986, and 1988, respectively. Since then he has been on the faculty at Carnegie Mellon University, where he is now an Associate Professor. During the academic year 1997-1998 he was a visiting professor at U.C. Berkeley. He held the Finmeccanica Faculty Chair from 1991–1995 and received an NSF Young Investigator Award in 1992. He has been program chair for the ACM Symposium on Parallel Algorithms and Architectures, program co-chair for the IEEE International Parallel Processing Symposium, is on the editorial board of JACM, and has served on a dozen or so program committees. His research interests are in parallel programming languages and parallel algorithms, and in the interaction of the two. He has developed the NESL programming language under an ARPA contract and an NSF NYI award. His work on parallel algorithms includes work on sorting, computational geometry, and several pointer-based algorithms, including algorithms for list-ranking, set-operations, and graph connectivity. Five Related and Significant Publications 1. Guy Blelloch, Jonathan Hardwick, Gary L. Miller, and Dafna Talmor. Design and Implementation of a Practical Parallel Delaunay Algorithm. Algorithmica, 24(3/4), pp. 243–269, 1999. 2. Guy Blelloch, Phil Gibbons and Yossi Matias. Efficient Scheduling for Languages with Fine-Grained Parallelism. Journal of the ACM, 46(2), pp. -
Modularity Conserved During Evolution: Algorithms and Analysis
Modularity Conserved during Evolution: Algorithms and Analysis Luqman Hodgkinson Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2015-17 http://www.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-17.html May 1, 2015 Copyright © 2015, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Acknowledgement This thesis presents joint work with several collaborators at Berkeley. The work on algorithm design and analysis is joint work with Richard M. Karp, the work on reproducible computation is joint work with Eric Brewer and Javier Rosa, and the work on visualization of modularity is joint work with Nicholas Kong. I thank all of them for their contributions. They have been pleasant collaborators with stimulating ideas and good designs. Our descriptions of the projects in this thesis overlap somewhat with our published descriptions in [Hodgkinson and Karp, 2011a], [Hodgkinson and Karp, 2011b], [Hodgkinson and Karp, 2012], and [Hodgkinson et al., 2012]. Modularity Conserved during Evolution: Algorithms and Analysis by Luqman Hodgkinson B.A. Hon. (Hiram College) 2003 M.S. (Columbia University) 2007 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science with Designated Emphasis in Computational and Genomic Biology in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Richard M. -
Divide-And-Conquer Algorithms Part Four
Divide-and-Conquer Algorithms Part Four Announcements ● Problem Set 2 due right now. ● Can submit by Monday at 2:15PM using one late period. ● Problem Set 3 out, due July 22. ● Play around with divide-and-conquer algorithms and recurrence relations! ● Covers material up through and including today's lecture. Outline for Today ● The Selection Problem ● A problem halfway between searching and sorting. ● A Linear-Time Selection Algorithm ● A nonobvious algorithm with a nontrivial runtime. ● The Substitution Method ● Solving recurrences the Master Theorem can't handle. Order Statistics ● Given a collection of data, the kth order statistic is the kth smallest value in the data set. ● For the purposes of this course, we'll use zero-indexing, so the smallest element would be given by the 0th order statistic. ● To give a robust definition: the kth order statistic is the element that would appear at position k if the data were sorted. 1 6 1 8 0 3 3 9 The Selection Problem ● The selection problem is the following: Given a data set S (typically represented as an array) and a number k, return the kth order statistic of that set. ● Has elements of searching and sorting: Want to search for the kth-smallest element, but this is defined relative to a sorted ordering. 32 17 41 18 52 98 24 65 ● For today, we'll assume all values are distinct. An Initial Solution ● Any ideas how to solve this? ● Here is one simple solution: ● Sort the array. ● Return the element at the kth position. ● Unless we know something special about the array, this will run in time O(n log n). -
Memorial Resolution Robert W. Floyd (1936-2001
SenD#5513 MEMORIAL RESOLUTION ROBERT W. FLOYD (1936-2001 Robert W ``Bob'' Floyd, Professor Emeritus of Computer Science and one of the all-time great pioneers in that field, died on 25 September 2001 after a long illness. He was 65 years old, mourned by his numerous colleagues, his mother, two ex-wives, a daughter, and three sons. Although Bob was born in New York City on 8 June 1936, his parents moved more than a dozen times before he went to college. A child prodigy, he read every book he could get his hands on. His first grade teacher used him to teach fractions to the older children, in the small Southern schoolhouse where his education began. He built flying model airplanes, once carrying a large one on his lap during a very long train ride, so that it wouldn't be left behind in one of the frequent moves. He received a scholarship to enter the University of Chicago at age 15, in an experimental program for gifted children, and received a Bachelor of Arts degree in 1953 at age 17. Like almost every other child who has been in such accelerated programs, he soon lost his taste for school and continued to study only part time. Meanwhile he got a job at the Armour Research Foundation of Illinois Institute of Technology, first as a self-taught computer operator and then as a senior programmer and analyst. He received a B.S. degree in Physics from the University of Chicago in 1958, and published a conference paper about radio interference that same year. -
Jgrapht--A Java Library for Graph Data Structures and Algorithms
JGraphT - A Java library for graph data structures and algorithms Dimitrios Michail1, Joris Kinable2,3, Barak Naveh4, and John V Sichi5 1Dept. of Informatics and Telematics Harokopio University of Athens, Greece [email protected] 2Dept. of Industrial Engineering and Innovation Sciences Eindhoven University of Technology, The Netherlands [email protected] 3Robotics Institute Carnegie Mellon University, Pittsburgh, USA 4barak [email protected] 5The JGraphT project [email protected] Abstract Mathematical software and graph-theoretical algorithmic packages to efficiently model, analyze and query graphs are crucial in an era where large-scale spatial, societal and economic network data are abundantly available. One such package is JGraphT, a pro- gramming library which contains very efficient and generic graph data-structures along with a large collection of state-of-the-art algorithms. The library is written in Java with stability, interoperability and performance in mind. A distinctive feature of this library is its ability to model vertices and edges as arbitrary objects, thereby permitting natural representations of many common networks including transportation, social and biologi- cal networks. Besides classic graph algorithms such as shortest-paths and spanning-tree algorithms, the library contains numerous advanced algorithms: graph and subgraph iso- morphism; matching and flow problems; approximation algorithms for NP-hard problems arXiv:1904.08355v2 [cs.DS] 3 Feb 2020 such as independent set and TSP; and several more exotic algorithms such as Berge graph detection. Due to its versatility and generic design, JGraphT is currently used in large- scale commercial products, as well as non-commercial and academic research projects. In this work we describe in detail the design and underlying structure of the library, and discuss its most important features and algorithms.