Fundamental Data Structures
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Programming for Engineers Pointers in C Programming: Part 02
Programming For Engineers Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff1, Ahmad Fakhri Ab. Nasir2 Faculty of Manufacturing Engineering [email protected], [email protected] PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 0.0 Chapter’s Information • Expected Outcomes – To further use pointers in C programming • Contents 1.0 Pointer and Array 2.0 Pointer and String 3.0 Pointer and dynamic memory allocation PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array • We will review array data type first and later we will relate array with pointer. • Previously, we learn about basic data types such as integer, character and floating numbers. In C programming language, if we have 5 test scores and would like to average the scores, we may code in the following way. PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. Nasir 1.0 Pointer and Array • This program is manageable if the scores are only 5. What should we do if we have 100,000 scores? In such case, we need an efficient way to represent a collection of similar data type1. In C programming, we usually use array. • Array is a fixed-size sequence of elements of the same data type.1 • In C programming, we declare an array like the following statement: PFE – Pointers in C Programming: Part 02 by Wan Azhar Wan Yusoff and Ahmad Fakhri Ab. -
Communications of the Acm
COMMUNICATIONS CACM.ACM.ORG OF THEACM 11/2014 VOL.57 NO.11 Scene Understanding by Labeling Pixels Evolution of the Product Manager The Data on Diversity On Facebook, Most Ties Are Weak Keeping Online Reviews Honest Association for Computing Machinery tvx-full-page.pdf-newest.pdf 1 11/10/2013 12:03 3-5 JUNE, 2015 BRUSSELS, BELGIUM Course and Workshop C proposals by M 15 November 2014 Y CM Paper Submissions by MY 12 January 2015 CY CMY K Work in Progress, Demos, DC, & Industrial Submissions by 2 March 2015 Welcoming Submissions on Content Production Systems & Infrastructures Devices & Interaction Techniques Experience Design & Evaluation Media Studies Data Science & Recommendations Business Models & Marketing Innovative Concepts & Media Art TVX2015.COM [email protected] ACM Books M MORGAN& CLAYPOOL &C PUBLISHERS Publish your next book in the ACM Digital Library ACM Books is a new series of advanced level books for the computer science community, published by ACM in collaboration with Morgan & Claypool Publishers. I’m pleased that ACM Books is directed by a volunteer organization headed by a dynamic, informed, energetic, visionary Editor-in-Chief (Tamer Özsu), working closely with a forward-looking publisher (Morgan and Claypool). —Richard Snodgrass, University of Arizona books.acm.org ACM Books ◆ will include books from across the entire spectrum of computer science subject matter and will appeal to computing practitioners, researchers, educators, and students. ◆ will publish graduate level texts; research monographs/overviews of established and emerging fields; practitioner-level professional books; and books devoted to the history and social impact of computing. ◆ will be quickly and attractively published as ebooks and print volumes at affordable prices, and widely distributed in both print and digital formats through booksellers and to libraries and individual ACM members via the ACM Digital Library platform. -
C Programming: Data Structures and Algorithms
C Programming: Data Structures and Algorithms An introduction to elementary programming concepts in C Jack Straub, Instructor Version 2.07 DRAFT C Programming: Data Structures and Algorithms, Version 2.07 DRAFT C Programming: Data Structures and Algorithms Version 2.07 DRAFT Copyright © 1996 through 2006 by Jack Straub ii 08/12/08 C Programming: Data Structures and Algorithms, Version 2.07 DRAFT Table of Contents COURSE OVERVIEW ........................................................................................ IX 1. BASICS.................................................................................................... 13 1.1 Objectives ...................................................................................................................................... 13 1.2 Typedef .......................................................................................................................................... 13 1.2.1 Typedef and Portability ............................................................................................................. 13 1.2.2 Typedef and Structures .............................................................................................................. 14 1.2.3 Typedef and Functions .............................................................................................................. 14 1.3 Pointers and Arrays ..................................................................................................................... 16 1.4 Dynamic Memory Allocation ..................................................................................................... -
Benchmarks for IP Forwarding Tables
Reviewers James Abello Richard Cleve Vassos Hadzilacos Dimitris Achilioptas James Clippinger Jim Hafner Micah Adler Anne Condon Torben Hagerup Oswin Aichholzer Stephen Cook Armin Haken William Aiello Tom Cormen Shai Halevi Donald Aingworth Dan Dooly Eric Hansen Susanne Albers Oliver Duschka Refael Hassin Eric Allender Martin Dyer Johan Hastad Rajeev Alur Ran El-Yaniv Lisa Hellerstein Andris Ambainis David Eppstein Monika Henzinger Amihood Amir Jeff Erickson Tom Henzinger Artur Andrzejak Kousha Etessami Jeremy Horwitz Boris Aronov Will Evans Russell Impagliazzo Sanjeev Arora Guy Even Piotr Indyk Amotz Barnoy Ron Fagin Sandra Irani Yair Bartal Michalis Faloutsos Ken Jackson Julien Basch Martin Farach-Colton David Johnson Saugata Basu Uri Feige John Jozwiak Bob Beals Joan Feigenbaum Bala Kalyandasundaram Paul Beame Stefan Felsner Ming-Yang Kao Steve Bellantoni Faith Fich Haim Kaplan Micahel Ben-Or Andy Fingerhut Bruce Kapron Josh Benaloh Paul Fischer Michael Kaufmann Charles Bennett Lance Fortnow Michael Kearns Marshall Bern Steve Fortune Sanjeev Khanna Nikolaj Bjorner Alan Frieze Samir Khuller Johannes Blomer Anna Gal Joe Kilian Avrim Blum Naveen Garg Valerie King Dan Boneh Bernd Gartner Philip Klein Andrei Broder Rosario Gennaro Spyros Kontogiannis Nader Bshouty Ashish Goel Gilad Koren Adam Buchsbaum Michel Goemans Dexter Kozen Lynn Burroughs Leslie Goldberg Dina Kravets Ran Canetti Paul Goldberg S. Ravi Kumar Pei Cao Oded Goldreich Eyal Kushilevitz Moses Charikar John Gray Stephen Kwek Chandra Chekuri Dan Greene Larry Larmore Yi-Jen Chiang -
Data Structure
EDUSAT LEARNING RESOURCE MATERIAL ON DATA STRUCTURE (For 3rd Semester CSE & IT) Contributors : 1. Er. Subhanga Kishore Das, Sr. Lect CSE 2. Mrs. Pranati Pattanaik, Lect CSE 3. Mrs. Swetalina Das, Lect CA 4. Mrs Manisha Rath, Lect CA 5. Er. Dillip Kumar Mishra, Lect 6. Ms. Supriti Mohapatra, Lect 7. Ms Soma Paikaray, Lect Copy Right DTE&T,Odisha Page 1 Data Structure (Syllabus) Semester & Branch: 3rd sem CSE/IT Teachers Assessment : 10 Marks Theory: 4 Periods per Week Class Test : 20 Marks Total Periods: 60 Periods per Semester End Semester Exam : 70 Marks Examination: 3 Hours TOTAL MARKS : 100 Marks Objective : The effectiveness of implementation of any application in computer mainly depends on the that how effectively its information can be stored in the computer. For this purpose various -structures are used. This paper will expose the students to various fundamentals structures arrays, stacks, queues, trees etc. It will also expose the students to some fundamental, I/0 manipulation techniques like sorting, searching etc 1.0 INTRODUCTION: 04 1.1 Explain Data, Information, data types 1.2 Define data structure & Explain different operations 1.3 Explain Abstract data types 1.4 Discuss Algorithm & its complexity 1.5 Explain Time, space tradeoff 2.0 STRING PROCESSING 03 2.1 Explain Basic Terminology, Storing Strings 2.2 State Character Data Type, 2.3 Discuss String Operations 3.0 ARRAYS 07 3.1 Give Introduction about array, 3.2 Discuss Linear arrays, representation of linear array In memory 3.3 Explain traversing linear arrays, inserting & deleting elements 3.4 Discuss multidimensional arrays, representation of two dimensional arrays in memory (row major order & column major order), and pointers 3.5 Explain sparse matrices. -
Full Functional Verification of Linked Data Structures
Full Functional Verification of Linked Data Structures Karen Zee Viktor Kuncak Martin C. Rinard MIT CSAIL, Cambridge, MA, USA EPFL, I&C, Lausanne, Switzerland MIT CSAIL, Cambridge, MA, USA ∗ [email protected] viktor.kuncak@epfl.ch [email protected] Abstract 1. Introduction We present the first verification of full functional correctness for Linked data structures such as lists, trees, graphs, and hash tables a range of linked data structure implementations, including muta- are pervasive in modern software systems. But because of phenom- ble lists, trees, graphs, and hash tables. Specifically, we present the ena such as aliasing and indirection, it has been a challenge to de- use of the Jahob verification system to verify formal specifications, velop automated reasoning systems that are capable of proving im- written in classical higher-order logic, that completely capture the portant correctness properties of such data structures. desired behavior of the Java data structure implementations (with the exception of properties involving execution time and/or mem- 1.1 Background ory consumption). Given that the desired correctness properties in- In principle, standard specification and verification approaches clude intractable constructs such as quantifiers, transitive closure, should work for linked data structure implementations. But in prac- and lambda abstraction, it is a challenge to successfully prove the tice, many of the desired correctness properties involve logical generated verification conditions. constructs such transitive closure and -
Advanced Data Structures
Advanced Data Structures PETER BRASS City College of New York CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521880374 © Peter Brass 2008 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published in print format 2008 ISBN-13 978-0-511-43685-7 eBook (EBL) ISBN-13 978-0-521-88037-4 hardback Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Contents Preface page xi 1 Elementary Structures 1 1.1 Stack 1 1.2 Queue 8 1.3 Double-Ended Queue 16 1.4 Dynamical Allocation of Nodes 16 1.5 Shadow Copies of Array-Based Structures 18 2 Search Trees 23 2.1 Two Models of Search Trees 23 2.2 General Properties and Transformations 26 2.3 Height of a Search Tree 29 2.4 Basic Find, Insert, and Delete 31 2.5ReturningfromLeaftoRoot35 2.6 Dealing with Nonunique Keys 37 2.7 Queries for the Keys in an Interval 38 2.8 Building Optimal Search Trees 40 2.9 Converting Trees into Lists 47 2.10 -
Programmer's Guide
Programmer’s Guide Release 2.2.0 January 16, 2016 CONTENTS 1 Introduction 1 1.1 Documentation Roadmap...............................1 1.2 Related Publications..................................2 2 Overview 3 2.1 Development Environment..............................3 2.2 Environment Abstraction Layer............................4 2.3 Core Components...................................4 2.4 Ethernet* Poll Mode Driver Architecture.......................6 2.5 Packet Forwarding Algorithm Support........................6 2.6 librte_net........................................6 3 Environment Abstraction Layer7 3.1 EAL in a Linux-userland Execution Environment..................7 3.2 Memory Segments and Memory Zones (memzone)................ 11 3.3 Multiple pthread.................................... 12 3.4 Malloc.......................................... 14 4 Ring Library 19 4.1 References for Ring Implementation in FreeBSD*................. 20 4.2 Lockless Ring Buffer in Linux*............................ 20 4.3 Additional Features.................................. 20 4.4 Use Cases....................................... 21 4.5 Anatomy of a Ring Buffer............................... 21 4.6 References....................................... 28 5 Mempool Library 31 5.1 Cookies......................................... 31 5.2 Stats.......................................... 31 5.3 Memory Alignment Constraints............................ 31 5.4 Local Cache...................................... 32 5.5 Use Cases....................................... 33 6 -
Podcast Ch23a
Podcast Ch23a • Title: Bit Arrays • Description: Overview; bit operations in Java; BitArray class • Participants: Barry Kurtz (instructor); John Helfert and Tobie Williams (students) • Textbook: Data Structures for Java; William H. Ford and William R. Topp Bit Arrays • Applications such as compiler code generation and compression algorithms create data that includes specific sequences of bits. – Many applications, such as compilers, generate specific sequences of bits. Bit Arrays (continued) • Java binary bit handling operators |, &, and ^ act on pairs of bits and return the new value. The unary operator ~ inverts the bits of its operand. BitBit OperationsOperations x y ~x x | y x & y x ^ y 0 0 1 0 0 0 0 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 1 0 Bit Arrays (continued) Bit Arrays (continued) • Operator << shifts integer or char values to the left. Operators >> and >>> shift values to the right using signed or unsigned arithmetic, respectively. Assume x and y are 32-bit integers. x = 0...10110110 x << 2 = 0...1011011000 x = 101...11011100 x >> 3 = 111101...11011 x = 101...11011100 x >>> 3 = 000101...11011 Bit Arrays (continued) Before performing the bitwise operator |, &, or ^, Java performs binary numeric promotion on the operands. The type of the bitwise operator expression is the promoted type of the operands. The rules of promotion are as follows: • If either operand is of type long, the other is converted to long. • Otherwise, both operands are converted to type int. In the case of the unary operator ~, Java converts a byte, char, short to int before applying the operator, and the resulting value is an int. -
Supplementary Material HR19008 AC
Historical Records of Australian Science © Australian Academy of Science 2020 https://doi.org/10.1071/HR19008_AC Supplementary Material: Historical Records of Australian Science, 2020, 31(1), 17–25 Supplementary material. A bibliography of Australian mathematics to 1960 with observations relating to the history of Australian mathematics Graeme L. Cohen Formerly, School of Mathematical Sciences, University of Technology Sydney, NSW 2007, Australia. Email: [email protected] File S1. Lists List 1—Chronological List of Publications to 1900 2 List 2—Alphabetical by Author List of Publications from 1901 16 to 1960 (Excluding Theses and Most School Books) List 3—Alphabetical by Author List of Theses Leading to the 37 Award of a Higher Degree, to 1960 Index 45 References (further to those in the main text) 56 1 Historical Records of Australian Science © Australian Academy of Science 2020 https://doi.org/10.1071/HR19008_AC Supplementary Material: Historical Records of Australian Science, 2020, 31(1), 17–25 LISTS The main item, article HR19008, should be read first for a full understanding of what is included here. The superscript CAI before an author’s name indicates that biographical information on that author is to be found in Counting Australia In.1 List 1— Chronological List of Publications to 1900 1. RUSSELL, JOHN WM. (18-?) Exercises and examples in expert arithmetic, Kealy & Philip, Sydney. 2. BOWDEN, THOMAS (1812) Bowden’s tables, Ferguson I, 532. [‘Their object is to ascertain at a momentary glance, the difference between sterling and currency … They are calculated with a degree of precision that must have occupied considerable time and attention …’, according to Ferguson. -
Data Structure Invariants
Lecture 8 Notes Data Structures 15-122: Principles of Imperative Computation (Spring 2016) Frank Pfenning, André Platzer, Rob Simmons 1 Introduction In this lecture we introduce the idea of imperative data structures. So far, the only interfaces we’ve used carefully are pixels and string bundles. Both of these interfaces had the property that, once we created a pixel or a string bundle, we weren’t interested in changing its contents. In this lecture, we’ll talk about an interface that mimics the arrays that are primitively available in C0. To implement this interface, we’ll need to round out our discussion of types in C0 by discussing pointers and structs, two great tastes that go great together. We will discuss using contracts to ensure that pointer accesses are safe. Relating this to our learning goals, we have Computational Thinking: We illustrate the power of abstraction by con- sidering both the client-side and library-side of the interface to a data structure. Algorithms and Data Structures: The abstract arrays will be one of our first examples of abstract datatypes. Programming: Introduction of structs and pointers, use and design of in- terfaces. 2 Structs So far in this course, we’ve worked with five different C0 types — int, bool, char, string, and arrays t[] (there is a array type t[] for every type t). The character, Boolean and integer values that we manipulate, store LECTURE NOTES Data Structures L8.2 locally, and pass to functions are just the values themselves. For arrays (and strings), the things we store in assignable variables or pass to functions are addresses, references to the place where the data stored in the array can be accessed. -
Binary Index Trees a Cumulative Frequency Array Allows Us To
Binary Index Trees A cumulative frequency array allows us to calculate the sum of the range of values in O(1), as long as there are no changes to the data once the queries start. But consider situations where we might change the value at an index in an array, then query for the sum of a range of values in the array, followed by some more changes and more queries. In this sort of situation, a cumulative frequency array would still give us O(1) query times, but it would take O(n) time to update after each change!!! (Basically, if we change one index in a cumulative frequency array, all other indexes above it would have to have this value added to it as well.) Here is a quick illustration: Current cumulative frequency array: Index 0 1 2 3 4 5 6 7 8 Value 2 4 4 4 6 8 11 13 17 Now, consider adding the value 2 to the data, recalling that index i stores the number of values less than or equal to i. The adjusted array is: Index 0 1 2 3 4 5 6 7 8 Value 2 4 5 5 7 9 12 14 18 We had to edit each index 2 or greater, which would take O(n) for a cumulative frequency array of size n. Thus, we want a new arrangement where both the query AND the update are relatively fast. The creative insight here is that perhaps if we store data in a different way, perhaps we can reduce the update time by quite a bit while incurring only a modest increase in query time.