Preface... 1 The Boost C++ Libraries Overview... 5 Math Toolkit: Special Functions Math Toolkit: Orthogonal Functions... 29

 Mary Manning
 8 months ago
 Views:
Transcription
1 Preface... 1 Goals of this Book... 1 Structure of the Book... 1 For whom is this Book?... 1 Using the Boost Libraries... 2 Practical Hints and Guidelines... 2 What s Next? The Boost C++ Libraries Overview Library Classification Essential Libraries Supporting Libraries Math Toolkit: Special Functions Introduction and Objectives An Overview of the Math Toolkit Special Functions Gamma Functions Gamma Function Incomplete Gamma Functions and their Inverses Beta and Error Functions Incomplete Beta Functions and their Inverses Factorials and Binomial Coefficients The Error Function and its Inverse Bessel Functions Elliptic Integral Functions Elliptic Integrals of the First, Second and Third Kinds Complete Elliptic Integrals Other Functions Zeta Function Exponential Integrals Inverse Hyperbolic Functions Sinus Cardinal and Hyperbolic Sinus Cardinal Functions Rounding, Truncation and Integer Conversions Applications and Relationships with STL and Boost Summary and Conclusions Math Toolkit: Orthogonal Functions Introduction and Objectives An Introduction to Orthogonal Polynomials Common Properties Laguerre and Hermite Polynomials Spherical Harmonics Chebychev Polynomials Computing the Roots of Bessel Functions Statistics Distributions Summary and Conclusions Date and Time Introduction and Objectives Overview of Concepts and Functionality Gregorian Time Date... 44
2 ii Table of Contents Date Duration Date Period Date Iterators Creating Userdefined Utilities A generic Date Iterator using Boost Variant General Schedules of Date Period International Money Market (IMM) Dates in Trading Systems Posix Time System Local Time Date Generators and Algorithms Date/Time I/O and Serialisation SetLike Operations Application Areas Summary and Conclusions Some Building Block Data Structures and Libraries Introduction and Objectives Timer Library Uuid (Universally Unique Identifiers) Creating Uuids Other Functionality Dynamic Bitset and STL Bitsets Boolean Operations Type Conversions dynamic_bitset Applications of Dynamic Bitsets Circular Buffer The Boost Circular Buffer Class Using Circular Buffer: ProducerConsumer Pattern Summary and Conclusions Matrix Algebra in Boost Part I: ublas Data Structures Introduction and Objectives BLAS (Basic Linear Algebra Subprograms) BLAS Level BLAS Level BLAS Level Dense Vectors Creating and Accessing Dense Vectors Special Dense Vectors Sparse Vectors Mapped Vector Compressed Vector Coordinate Vector Dense Matrices Creating and Accessing Dense Matrices Special Dense Matrices Other Kinds of Matrices Sparse Matrices Triangular Matrices Triangular Adaptor Summary and Conclusions... 95
3 iii 7 Matrix Algebra in Boost Part II: Advanced Features and Applications Introduction and Objectives Patterned Matrices Symmetric Matrices Hermitian Matrices Banded Matrices Vector and Matrix Proxies Vector Range Vector Slice Matrix Proxies: Rows and Columns Matrix Views: What are Options? Vector Expressions Matrix Expressions Applying ublas: Solving Linear Systems of Equations Conjugate Gradient Method (CGM) LU Decomposition Cholesky Decomposition Applications of ublas Summary and Conclusions An Introduction to Network Programming Concepts and Protocols Introduction and Objectives Overview of OSI and TCP/IP Protocols and Services Internet Addresses Some Special IP Addresses Internet Addresses in Boost Implementing Endpoints Domain Name System (DNS) ClientServer Model of Interaction Connectionless and ConnectionOriented Servers The Socket Interface Protocol for Acknowledgement and Retransmission Summary and Conclusions Boost ASIO: Synchronous Operations Introduction Testing Network Applications and Troubleshooting DNS Reverse DNS Buffers UDP Example: UDP Echo Server Example: UDP Echo Client TCP Example: TCP Echo Server Example: TCP Echo Client Using Socket IOStreams to improve Ease of Use Summary and Conclusions Boost ASIO: Asynchronous Operations Introduction Timers Synchronous Deadline Timer
4 iv Table of Contents Asynchronous Deadline Timer Binding Arguments Binding to Member Functions Thread Pooling and Synchronising Threads Asynchronous UDP Server Asynchronous TCP Server The ASyncEchoServer Class The ASyncConnection Class ThreadPooled Asynchronous TCP Server CRC Checksums and Timeouts Message and CRC Heartbeats and Timeouts Sending messages Echo Client Implementation Summary and Conclusions Boost Interprocess: IPC Mechanisms Introduction and Objectives Persistence of IPC Mechanisms Shared Memory Memory Mapped File Advanced Mapped Regions Pointers in Mapped Regions Static Members in Mapped Regions Managed Memory Segments Managed Shared Memory Managed Memory Mapped File Allocating Memory Fragments in Managed Memory Allocating Objects in Managed Memory Synchronisation of Object Construction and Retrieving Composite Objects Synchronising Composite Object Creation Using Allocators Other Allocators STL Compatible Containers Managed External Buffer and Managed Heap Memory Other Managed Segment Functionality Summary and Conclusions Boost Interprocess II: Process Synchronisation Introduction and Objectives Mutexes Mutex Operations Named Mutex Anonymous Mutex Scoped Lock Condition Variables Message Queue Semaphores Upgradable Mutexes Introduction to Upgradable Mutexes Upgradable Mutexes in Boost Interprocess
5 v Lock Transfer Summary and Conclusions Interval Arithmetic Introduction and Objectives What is Interval Analysis, Interval Arithmetic, Interval Mathematics? Interval Arithmetic: Mathematical Foundations Boost Interval Library: Functionality and Initial Examples Application: Matrix Computations with Intervals Function Evaluation in Interval Advanced Functions and Related Data Structures Solution of Nonlinear Equations Summary and Conclusions Userdefined Memory Allocation: Boost Pool Introduction and Objectives Dynamic Memory Allocation in C++ and STL Allocator Requirements Pool Concepts Simple Segregated Storage Concept Pool Singleton Pool Object Pool Pool Allocator and Fast Pool Allocator Summary and Conclusions An Introduction to Graph Theory and Graph Algorithms Introduction and Objectives Directed and Undirected Graphs; Terms and Definitions Further Properties of Graphs and Digraphs Paths and Connectivity Special Types of Graphs Graph Data Structures Operations on Graphs Minimum Spanning Tree (MST) Problems DepthFirst and BreadthFirst Searches in Graphs Shortest Path Problems Connected Components Applications of Graph Theory Project Planning Some Specific Graphs Eulerian, de Bruijn and Hamiltonian Digraphs Random Graphs Flow Networks Summary and Conclusions The Boost Graph Library Data Structures and Fundamental Algorithms Introduction and Objectives An Overview of the Functionality in BGL Boost Property Map Library Boost Property Map Category Tags and Traits Property Map Types An Introduction to Data Structures in BGL Auxiliary Classes
6 vi Table of Contents 16.6 Minimum Spanning Tree (MST) Algorithms Kruskal Algorithm Prim Algorithm Shortest Path Algorithms Dijkstra s Algorithm BellmanFord Algorithm Summary and Conclusions The Boost Graph Library (BGL) Advanced Algorithms Introduction and Objectives More ShortestPath Algorithms FloydWarshall Algorithm Johnson Algorithm Transitive Closure Connected Component Algorithms Connected Components Biconnected Components and Articulation Points Incremental Connected Components Strong Components Graph Structure Comparison Isomorphic Graphs Extending Algorithms with Visitor Basic Graph Algorithms Other Graph Algorithms in BGL Sparse Matrix Ordering Algorithms Random Graphs Summary and Conclusions Interval Container Library Introduction and Objectives Overview of the ICL What Kinds of Intervals? Statically Bound Interval Types Modelling Intervals by Parameter Variation Intervals and Temporal Types Interval Sets and Interval Maps Interval Combining Styles Splitting Interval Containers Separating Interval Containers Iterators for Interval Sets and Maps Some 101 Examples Applying ICL to Managements Information Systems (MIS) Daily Planning Summary and Conclusions Boost Functional Factory Introduction and Objectives An Overview of GOF Patterns Strengths and Limitations of GOF Patterns An Example: Traditional Windows Factories Boost Functional Factory Function Factory with Smart Pointers RValue Arguments
7 vii 19.7 Allocators Value Factory Factory with Algorithms Summary and Conclusions Bibliography Index Book Registration Form Boost Volume II User Agreement
Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest. Introduction to Algorithms
Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Introduction to Algorithms Preface xiii 1 Introduction 1 1.1 Algorithms 1 1.2 Analyzing algorithms 6 1.3 Designing algorithms 1 1 1.4 Summary 1 6
More information4.1.2 Merge Sort Sorting Lower Bound Counting Sort Sorting in Practice Solving Problems by Sorting...
Contents 1 Introduction... 1 1.1 What is Competitive Programming?... 1 1.1.1 Programming Contests.... 2 1.1.2 Tips for Practicing.... 3 1.2 About This Book... 3 1.3 CSES Problem Set... 5 1.4 Other Resources...
More informationIntroduction to Algorithms Third Edition
Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest Clifford Stein Introduction to Algorithms Third Edition The MIT Press Cambridge, Massachusetts London, England Preface xiü I Foundations Introduction
More informationContents. Preface xvii Acknowledgments. CHAPTER 1 Introduction to Parallel Computing 1. CHAPTER 2 Parallel Programming Platforms 11
Preface xvii Acknowledgments xix CHAPTER 1 Introduction to Parallel Computing 1 1.1 Motivating Parallelism 2 1.1.1 The Computational Power Argument from Transistors to FLOPS 2 1.1.2 The Memory/Disk Speed
More informationIntroduction to Algorithms
Thomas H. Carmen Charles E. Leiserson Ronald L. Rivest Clifford Stein Introduction to Algorithms Third Edition The MIT Press Cambridge, Massachusetts London, England Contents Preface xiii  I Foundations
More informationQuestion Paper Code : 97044
Reg. No. : Question Paper Code : 97044 B.E./B.Tech. DEGREE EXAMINATION NOVEMBER/DECEMBER 2014 Third Semester Computer Science and Engineering CS 6301 PROGRAMMING AND DATA STRUCTURESII (Regulation 2013)
More informationChapter 9 Graph Algorithms
Chapter 9 Graph Algorithms 2 Introduction graph theory useful in practice represent many reallife problems can be if not careful with data structures 3 Definitions an undirected graph G = (V, E) is a
More informationR10 SET  1. Code No: R II B. Tech I Semester, Supplementary Examinations, May
www.jwjobs.net R10 SET  1 II B. Tech I Semester, Supplementary Examinations, May  2012 (Com. to CSE, IT, ECC ) Time: 3 hours Max Marks: 75 ************* 1. a) Which of the given options provides the
More informationChapter 9 Graph Algorithms
Introduction graph theory useful in practice represent many reallife problems can be if not careful with data structures Chapter 9 Graph s 2 Definitions Definitions an undirected graph is a finite set
More informationCSC 8301 Design & Analysis of Algorithms: Warshall s, Floyd s, and Prim s algorithms
CSC 8301 Design & Analysis of Algorithms: Warshall s, Floyd s, and Prim s algorithms Professor Henry Carter Fall 2016 Recap Spacetime tradeoffs allow for faster algorithms at the cost of space complexity
More informationChapter 9 Graph Algorithms
Chapter 9 Graph Algorithms 2 Introduction graph theory useful in practice represent many reallife problems can be slow if not careful with data structures 3 Definitions an undirected graph G = (V, E)
More informationData Structures and Algorithm Analysis in C++
INTERNATIONAL EDITION Data Structures and Algorithm Analysis in C++ FOURTH EDITION Mark A. Weiss Data Structures and Algorithm Analysis in C++, International Edition Table of Contents Cover Title Contents
More informationn 2 ( ) ( ) + n is in Θ n logn
CSE Test Spring Name Last Digits of Mav ID # Multiple Choice. Write your answer to the LEFT of each problem. points each. The time to multiply an m n matrix and a n p matrix is in: A. Θ( n) B. Θ( max(
More informationSolving problems on graph algorithms
Solving problems on graph algorithms Workshop Organized by: ACM Unit, Indian Statistical Institute, Kolkata. Tutorial3 Date: 06.07.2017 Let G = (V, E) be an undirected graph. For a vertex v V, G {v} is
More informationENGINEERING PROBLEM SOLVING WITH C++
ENGINEERING PROBLEM SOLVING WITH C++ Second Edition Delores M. Etter Electrical Engineering Department United States Naval Academy Jeanine A. Ingber Training Consultant Sandia National Laboratories Upper
More informationIntroduction to Parallel & Distributed Computing Parallel Graph Algorithms
Introduction to Parallel & Distributed Computing Parallel Graph Algorithms Lecture 16, Spring 2014 Instructor: 罗国杰 gluo@pku.edu.cn In This Lecture Parallel formulations of some important and fundamental
More informationGraph Algorithms. Parallel and Distributed Computing. Department of Computer Science and Engineering (DEI) Instituto Superior Técnico.
Graph Algorithms Parallel and Distributed Computing Department of Computer Science and Engineering (DEI) Instituto Superior Técnico May, 0 CPD (DEI / IST) Parallel and Distributed Computing 000 / Outline
More informationLecture 13. Reading: Weiss, Ch. 9, Ch 8 CSE 100, UCSD: LEC 13. Page 1 of 29
Lecture 13 Connectedness in graphs Spanning trees in graphs Finding a minimal spanning tree Time costs of graph problems and NPcompleteness Finding a minimal spanning tree: Prim s and Kruskal s algorithms
More informationAnany Levitin 3RD EDITION. Arup Kumar Bhattacharjee. mmmmm Analysis of Algorithms. Soumen Mukherjee. Introduction to TllG DCSISFI &
Introduction to TllG DCSISFI & mmmmm Analysis of Algorithms 3RD EDITION Anany Levitin Villa nova University International Edition contributions by Soumen Mukherjee RCC Institute of Information Technology
More informationShortest path problems
Next... Shortest path problems Singlesource shortest paths in weighted graphs ShortestPath Problems Properties of Shortest Paths, Relaxation Dijkstra s Algorithm BellmanFord Algorithm ShortestPaths
More informationLecture 4: Graph Algorithms
Lecture 4: Graph Algorithms Definitions Undirected graph: G =(V, E) V finite set of vertices, E finite set of edges any edge e = (u,v) is an unordered pair Directed graph: edges are ordered pairs If e
More informationLINUX INTERNALS & NETWORKING Weekend Workshop
Here to take you beyond LINUX INTERNALS & NETWORKING Weekend Workshop Linux Internals & Networking Weekend workshop Objectives: To get you started with writing system programs in Linux Build deeper view
More informationHonors Precalculus: Solving equations and inequalities graphically and algebraically. Page 1
Solving equations and inequalities graphically and algebraically 1. Plot points on the Cartesian coordinate plane. P.1 2. Represent data graphically using scatter plots, bar graphs, & line graphs. P.1
More informationCLASS: II YEAR / IV SEMESTER CSE CS 6402DESIGN AND ANALYSIS OF ALGORITHM UNIT I INTRODUCTION
CLASS: II YEAR / IV SEMESTER CSE CS 6402DESIGN AND ANALYSIS OF ALGORITHM UNIT I INTRODUCTION 1. What is performance measurement? 2. What is an algorithm? 3. How the algorithm is good? 4. What are the
More informationMinimum Spanning Trees
Minimum Spanning Trees Problem A town has a set of houses and a set of roads. A road connects 2 and only 2 houses. A road connecting houses u and v has a repair cost w(u, v). Goal: Repair enough (and no
More informationLecture 6 Basic Graph Algorithms
CS 491 CAP Intro to Competitive Algorithmic Programming Lecture 6 Basic Graph Algorithms Uttam Thakore University of Illinois at UrbanaChampaign September 30, 2015 Updates ICPC Regionals teams will be
More informationGraphs & Digraphs Tuesday, November 06, 2007
Graphs & Digraphs Tuesday, November 06, 2007 10:34 PM 16.1 Directed Graphs (digraphs) like a tree but w/ no root node & no guarantee of paths between nodes consists of: nodes/vertices  a set of elements
More informationHiQ Analysis, Visualization, and Report Generation
Visually Organize Your Analysis Projects in an Interactive Notebook is an interactive problemsolving environment where you analyze, visualize, and document realworld science and engineering problems.
More informationWeighted Graph Algorithms Presented by Jason Yuan
Weighted Graph Algorithms Presented by Jason Yuan Slides: Zachary Friggstad Programming Club Meeting Weighted Graphs struct Edge { int u, v ; int w e i g h t ; // can be a double } ; Edge ( int uu = 0,
More informationMicrosoft. Microsoft Visual C# Step by Step. John Sharp
Microsoft Microsoft Visual C# 2010 Step by Step John Sharp Table of Contents Acknowledgments Introduction xvii xix Part I Introducing Microsoft Visual C# and Microsoft Visual Studio 2010 1 Welcome to
More informationMA 252: Data Structures and Algorithms Lecture 36. Partha Sarathi Mandal. Dept. of Mathematics, IIT Guwahati
MA 252: Data Structures and Algorithms Lecture 36 http://www.iitg.ernet.in/psm/indexing_ma252/y12/index.html Partha Sarathi Mandal Dept. of Mathematics, IIT Guwahati The AllPairs Shortest Paths Problem
More information( D. Θ n. ( ) f n ( ) D. Ο%
CSE 0 Name Test Spring 0 Multiple Choice. Write your answer to the LEFT of each problem. points each. The time to run the code below is in: for i=n; i>=; i) for j=; j
More informationParallel Graph Algorithms
Parallel Graph Algorithms Design and Analysis of Parallel Algorithms 5DV050 Spring 202 Part I Introduction Overview Graphsdenitions, properties, representation Minimal spanning tree Prim's algorithm Shortest
More informationCL020  Advanced Linux and UNIX Programming
Corder Enterprises International Building World Class MIS Teams, for you! CL020  Advanced Linux and UNIX Programming Course Description: Indepth training for software developers on Linux and UNIX system
More information2. True or false: even though BFS and DFS have the same space complexity, they do not always have the same worst case asymptotic time complexity.
1. T F: Consider a directed graph G = (V, E) and a vertex s V. Suppose that for all v V, there exists a directed path in G from s to v. Suppose that a DFS is run on G, starting from s. Then, true or false:
More informationAdvanced R. V!aylor & Francis Group. Hadley Wickham. ~ CRC Press
~ CRC Press V!aylor & Francis Group Advanced R Hadley Wickham ')'l If trlro r r 1 Introduction 1 1.1 Who should read this book 3 1.2 What you will get out of this book 3 1.3 Metatechniques... 4 1.4 Recommended
More informationAnalysis of Algorithms, I
Analysis of Algorithms, I CSOR W4231.002 Eleni Drinea Computer Science Department Columbia University Thursday, March 8, 2016 Outline 1 Recap Singlesource shortest paths in graphs with real edge weights:
More informationSERVICEORIENTED COMPUTING
THIRD EDITION (REVISED PRINTING) SERVICEORIENTED COMPUTING AND WEB SOFTWARE INTEGRATION FROM PRINCIPLES TO DEVELOPMENT YINONG CHEN AND WEITEK TSAI ii Table of Contents Preface (This Edition)...xii Preface
More informationEECS 281 Homework 4 Key Fall 2004
EECS 281 Homework 4 Key Fall 2004 Assigned: Due: 30NO04 14DE04, 6pm, EECS 281 box in room 2420 EECS Graph Algorithm Selection (10/50 points) You are planning a trip to Los Angeles, California and will
More informationThe Shortest Path Problem. The Shortest Path Problem. Mathematical Model. Integer Programming Formulation
The Shortest Path Problem jla,jc@imm.dtu.dk Department of Management Engineering Technical University of Denmark The Shortest Path Problem Given a directed network G = (V,E,w) for which the underlying
More informationCS/ENGRD 2110 ObjectOriented Programming and Data Structures Spring 2012 Thorsten Joachims. Lecture 25: Review and Open Problems
CS/ENGRD 2110 ObjectOriented Programming and Data Structures Spring 2012 Thorsten Joachims Lecture 25: Review and Open Problems Course Overview Programming Concepts ObjectOriented Programming Interfaces
More informationCLASSIC DATA STRUCTURES IN JAVA
CLASSIC DATA STRUCTURES IN JAVA Timothy Budd Oregon State University Boston San Francisco New York London Toronto Sydney Tokyo Singapore Madrid Mexico City Munich Paris Cape Town Hong Kong Montreal CONTENTS
More informationGRAPHS: THEORY AND ALGORITHMS
GRAPHS: THEORY AND ALGORITHMS K. THULASIRAMAN M. N. S. SWAMY Concordia University Montreal, Canada A WileyInterscience Publication JOHN WILEY & SONS, INC. New York / Chichester / Brisbane / Toronto /
More information( ) D. Θ ( ) ( ) Ο f ( n) ( ) Ω. C. T n C. Θ. B. n logn Ο
CSE 0 Name Test Fall 0 Multiple Choice. Write your answer to the LEFT of each problem. points each. The expected time for insertion sort for n keys is in which set? (All n! input permutations are equally
More informationNETWORK PROGRAMMING AND MANAGEMENT 1 KINGS DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK
NETWORK PROGRAMMING AND MANAGEMENT 1 KINGS COLLEGE OF ENGINEERING DEPARTMENT OF INFORMATION TECHNOLOGY QUESTION BANK Subject Code & Name: Network Programming and Management Year / Sem : III / VI UNIT
More informationData Structures and Algorithms for Engineers
463 Data Structures and Algorithms for Engineers David Vernon Carnegie Mellon University Africa vernon@cmuedu wwwvernoneu Data Structures and Algorithms for Engineers 1 Carnegie Mellon University Africa
More informationTwo hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE. Date: Thursday 1st June 2017 Time: 14:0016:00
COMP 26120 Two hours UNIVERSITY OF MANCHESTER SCHOOL OF COMPUTER SCIENCE Algorithms and Imperative Programming Date: Thursday 1st June 2017 Time: 14:0016:00 Please answer THREE Questions from the FOUR
More informationn 2 ( ) ( ) Ο f ( n) ( ) Ω B. n logn Ο
CSE 220 Name Test Fall 20 Last 4 Digits of Mav ID # Multiple Choice. Write your answer to the LEFT of each problem. 4 points each. The time to compute the sum of the n elements of an integer array is in:
More informationWITH C+ + William Ford University of the Pacific. William Topp University of the Pacific. Prentice Hall, Englewood Cliffs, New Jersey 07632
DATA STRUCTURES WITH C+ + William Ford University of the Pacific William Topp University of the Pacific Prentice Hall, Englewood Cliffs, New Jersey 07632 CONTENTS Preface xvii CHAPTER 1 INTRODUCTION 1
More informationAMS526: Numerical Analysis I (Numerical Linear Algebra)
AMS526: Numerical Analysis I (Numerical Linear Algebra) Lecture 5: Sparse Linear Systems and Factorization Methods Xiangmin Jiao Stony Brook University Xiangmin Jiao Numerical Analysis I 1 / 18 Sparse
More informationSUBJECT: INFORMATION TECHNOLOGY
NOTICE DATED: 02.02.2017 The syllabus for conducting Written Test [Multiple Choice Questions (MCQs)] in Information Technology for the posts of Lecturer (10+2) in School Education Department is notified
More informationGIAN Course on Distributed Network Algorithms. Spanning Tree Constructions
GIAN Course on Distributed Network Algorithms Spanning Tree Constructions Stefan Schmid @ TLabs, 2011 Spanning Trees Attactive infrastructure : sparse subgraph ( loopfree backbone ) connecting all nodes.
More informationShortest Path Algorithms
Shortest Path Algorithms Andreas Klappenecker [based on slides by Prof. Welch] 1 Single Source Shortest Path Given: a directed or undirected graph G = (V,E) a source node s in V a weight function w: E
More informationCS/COE 1501 cs.pitt.edu/~bill/1501/ Graphs
CS/COE 1501 cs.pitt.edu/~bill/1501/ Graphs 5 3 2 4 1 0 2 Graphs A graph G = (V, E) Where V is a set of vertices E is a set of edges connecting vertex pairs Example: V = {0, 1, 2, 3, 4, 5} E = {(0, 1),
More informationASSIGNMENTS. Progra m Outcom e. Chapter Q. No. Outcom e (CO) I 1 If f(n) = Θ(g(n)) and g(n)= Θ(h(n)), then proof that h(n) = Θ(f(n))
ASSIGNMENTS Chapter Q. No. Questions Course Outcom e (CO) Progra m Outcom e I 1 If f(n) = Θ(g(n)) and g(n)= Θ(h(n)), then proof that h(n) = Θ(f(n)) 2 3. What is the time complexity of the algorithm? 4
More informationShortest Path Problem
Shortest Path Problem CLRS Chapters 24.1 3, 24.5, 25.2 Shortest path problem Shortest path problem (and variants) Properties of shortest paths Algorithmic framework BellmanFord algorithm Shortest paths
More informationUndirected Graphs. DSA  lecture 6  T.U.ClujNapoca  M. Joldos 1
Undirected Graphs Terminology. Free Trees. Representations. Minimum Spanning Trees (algorithms: Prim, Kruskal). Graph Traversals (dfs, bfs). Articulation points & Biconnected Components. Graph Matching
More informationUNIT 5 GRAPH. Application of Graph Structure in real world: Graph Terminologies:
UNIT 5 CSE 103  Unit V Graph GRAPH Graph is another important nonlinear data structure. In tree Structure, there is a hierarchical relationship between, parent and children that is onetomany relationship.
More informationCS/COE 1501
CS/COE 1501 www.cs.pitt.edu/~nlf4/cs1501/ Graphs 5 3 2 4 1 0 2 Graphs A graph G = (V, E) Where V is a set of vertices E is a set of edges connecting vertex pairs Example: V = {0, 1, 2, 3, 4, 5} E = {(0,
More informationUCS406 (Data Structure) Lab Assignment1 (2 weeks)
UCS40 (Data Structure) Lab Assignment (2 weeks) Implement the following programs in C/C++/Python/Java using functions a) Insertion Sort b) Bubble Sort c) Selection Sort d) Linear Search e) Binary Search
More informationModule 9 : Numerical Relaying II : DSP Perspective
Module 9 : Numerical Relaying II : DSP Perspective Lecture 36 : Fast Fourier Transform Objectives In this lecture, We will introduce Fast Fourier Transform (FFT). We will show equivalence between FFT and
More informationModern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu. AAddisonWesley
Modern C++ Design Generic Programming and Design Patterns Applied Andrei Alexandrescu f AAddisonWesley Boston San Francisco New York Toronto Montreal London Munich Paris Madrid Capetown Sydney Tokyo Singapore
More informationModern C++ Design. Generic Programming and Design Patterns Applied. Andrei Alexandrescu. .~AddisonWesley
Modern C++ Design Generic Programming and Design Patterns Applied Andrei Alexandrescu.~AddisonWesley Boston " San Francisco " New York " Toronto " Montreal London " Munich " Paris " Madrid Capetown "
More informationA Survey of Mathematics with Applications 8 th Edition, 2009
A Correlation of A Survey of Mathematics with Applications 8 th Edition, 2009 South Carolina Discrete Mathematics Sample Course Outline including Alternate Topics and Related Objectives INTRODUCTION This
More informationGIAN Course on Distributed Network Algorithms. Spanning Tree Constructions
GIAN Course on Distributed Network Algorithms Spanning Tree Constructions Stefan Schmid @ TLabs, 2011 Spanning Trees Attactive infrastructure : sparse subgraph ( loopfree backbone ) connecting all nodes.
More informationCSCE 321/3201 Analysis and Design of Algorithms. Prof. Amr Goneid. Fall 2016
CSCE 321/3201 Analysis and Design of Algorithms Prof. Amr Goneid Fall 2016 CSCE 321/3201 Analysis and Design of Algorithms Prof. Amr Goneid Course Resources Instructor: Prof. Amr Goneid Email: goneid@aucegypt.edu
More informationPARALLEL OPTIMIZATION
PARALLEL OPTIMIZATION Theory, Algorithms, and Applications YAIR CENSOR Department of Mathematics and Computer Science University of Haifa STAVROS A. ZENIOS Department of Public and Business Administration
More informationAgenda. Graph Representation DFS BFS Dijkstra A* Search BellmanFord FloydWarshall Iterative? Noniterative? MST Flow EdmondKarp
Graph Charles Lin genda Graph Representation FS BFS ijkstra * Search BellmanFord FloydWarshall Iterative? Noniterative? MST Flow EdmondKarp Graph Representation djacency Matrix bool way[100][100];
More informationChapter 4. Matrix and Vector Operations
1 Scope of the Chapter Chapter 4 This chapter provides procedures for matrix and vector operations. This chapter (and Chapters 5 and 6) can handle general matrices, matrices with special structure and
More informationDesign Patterns. An introduction
Design Patterns An introduction Introduction Designing objectoriented software is hard, and designing reusable objectoriented software is even harder. Your design should be specific to the problem at
More informationFinal Examination CSE 100 UCSD (Practice)
Final Examination UCSD (Practice) RULES: 1. Don t start the exam until the instructor says to. 2. This is a closedbook, closednotes, nocalculator exam. Don t refer to any materials other than the exam
More informationInternetworking With TCP/IP
Internetworking With TCP/IP Vol II: Design, Implementation, and Internals SECOND EDITION DOUGLAS E. COMER and DAVID L. STEVENS Department of Computer Sciences Purdue University West Lafayette, IN 47907
More informationCS 170 Second Midterm ANSWERS 7 April NAME (1 pt): SID (1 pt): TA (1 pt): Name of Neighbor to your left (1 pt):
CS 170 Second Midterm ANSWERS 7 April 2010 NAME (1 pt): SID (1 pt): TA (1 pt): Name of Neighbor to your left (1 pt): Name of Neighbor to your right (1 pt): Instructions: This is a closed book, closed calculator,
More informationIntroduction to the ThreadX Debugger Plugin for the IAR Embedded Workbench CSPYDebugger
CSPY plugin Introduction to the ThreadX Debugger Plugin for the IAR Embedded Workbench CSPYDebugger This document describes the IAR CSPY Debugger plugin for the ThreadX RTOS. The ThreadX RTOS awareness
More information1) What is the role of Information Technology in modern business? 2) Define computer? Explain the Block Diagram of computer with a neat diagram?
(DMCA 101) ASSIGNMENT  1, DEC  2014. PAPER I : INFORMATION TECHNOLOGY 1) What is the role of Information Technology in modern business? 2) Define computer? Explain the Block Diagram of computer with
More informationShortest Path Algorithm
Shortest Path Algorithm Shivani Sanan* 1, Leena jain 2, Bharti Kappor 3 *1 Assistant Professor, Faculty of Mathematics, Department of Applied Sciences 2 Associate Professor & Head MCA 3 Assistant Professor,
More informationLecture 10 Graph Algorithms
Lecture 10 Graph Algorithms Euiseong Seo (euiseong@skku.edu) 1 Graph Theory Study of the properties of graph structures It provides us with a language with which to talk about graphs Keys to solving problems
More informationShort Term Courses (Including Project Work)
Short Term Courses (Including Project Work) Courses: 1.) Microcontrollers and Embedded C Programming (8051, PIC & ARM, includes a project on Robotics) 2.) DSP (Code Composer Studio & MATLAB, includes Embedded
More informationMon Tue Wed Thurs Fri
In lieu of recitations 320 Office Hours Mon Tue Wed Thurs Fri 8 Cole 9 Dr. Georg Dr. Georg 10 Dr. Georg/ Jim 11 Ali Jim 12 Cole Ali 1 Cole/ Shannon Ali 2 Shannon 3 Dr. Georg Dr. Georg Jim 4 Upcoming Check
More informationChapter 2  Part 1. The TCP/IP Protocol: The Language of the Internet
Chapter 2  Part 1 The TCP/IP Protocol: The Language of the Internet Protocols A protocol is a language or set of rules that two or more computers use to communicate 2 Protocol Analogy: Phone Call Parties
More informationAll Shortest Paths. Questions from exercises and exams
All Shortest Paths Questions from exercises and exams The Problem: G = (V, E, w) is a weighted directed graph. We want to find the shortest path between any pair of vertices in G. Example: find the distance
More informationCOSC 2007 Data Structures II Final Exam. Part 1: multiple choice (1 mark each, total 30 marks, circle the correct answer)
COSC 2007 Data Structures II Final Exam Thursday, April 13 th, 2006 This is a closed book and closed notes exam. There are total 3 parts. Please answer the questions in the provided space and use back
More informationMultidimensional Arrays & Graphs. CMSC 420: Lecture 3
Multidimensional Arrays & Graphs CMSC 420: Lecture 3 MiniReview Abstract Data Types: List Stack Queue Deque Dictionary Set Implementations: Linked Lists Circularly linked lists Doubly linked lists XOR
More informationConfidence Level Red Amber Green
Maths Topic Foundation/ 1 Place Value 2 Ordering Integers 3 Ordering Decimals 4 Reading Scales 5 Simple Mathematical Notation 6a Interpreting RealLife Tables Time 6b Interpreting RealLife Tables Timetables
More informationQuestions from the material presented in this lecture
Advanced Data Structures Questions from the material presented in this lecture January 8, 2015 This material illustrates the kind of exercises and questions you may get at the final colloqium. L1. Introduction.
More informationMicrosoft Visual C# Step by Step. John Sharp
Microsoft Visual C# 2013 Step by Step John Sharp Introduction xix PART I INTRODUCING MICROSOFT VISUAL C# AND MICROSOFT VISUAL STUDIO 2013 Chapter 1 Welcome to C# 3 Beginning programming with the Visual
More informationALGORITHM DESIGN GREEDY ALGORITHMS. University of Waterloo
ALORITHM DSIN RDY ALORITHMS University of Waterloo LIST OF SLIDS  List of Slides reedy Approaches xample: Making Change 4 Making Change (cont.) 5 Minimum Spanning Tree 6 xample 7 Approaches that Don t
More informationSOME ASSEMBLY REQUIRED
SOME ASSEMBLY REQUIRED Assembly Language Programming with the AVR Microcontroller TIMOTHY S. MARGUSH CRC Press Taylor & Francis Group CRC Press is an imprint of the Taylor & Francis Croup an Informa business
More informationCS350: Data Structures Dijkstra s Shortest Path Alg.
Dijkstra s Shortest Path Alg. James Moscola Department of Engineering & Computer Science York College of Pennsylvania James Moscola Shortest Path Algorithms Several different shortest path algorithms exist
More informationMathMatrix IEC Library for ACSELERATOR RTAC Projects
MathMatrix IEC 61131 Library for ACSELERATOR RTAC Projects SEL Automation Controllers Contents 1 Introduction 5 1.1 Special Considerations................................. 5 2 Supported Firmware Versions
More informationLecture Summary CSC 263H. August 5, 2016
Lecture Summary CSC 263H August 5, 2016 This document is a very brief overview of what we did in each lecture, it is by no means a replacement for attending lecture or doing the readings. 1. Week 1 2.
More informationCUDA 6.0 Performance Report. April 2014
CUDA 6. Performance Report April 214 1 CUDA 6 Performance Report CUDART CUDA Runtime Library cufft Fast Fourier Transforms Library cublas Complete BLAS Library cusparse Sparse Matrix Library curand Random
More informationSingle Source Shortest Path (SSSP) Problem
Single Source Shortest Path (SSSP) Problem Single Source Shortest Path Problem Input: A directed graph G = (V, E); an edge weight function w : E R, and a start vertex s V. Find: for each vertex u V, δ(s,
More information1. The Internet 2. Principles 3. Ethernet 4. WiFi 5. Routing 6. Internetworking 7. Transport 8. Models 9. WiMAX & LTE 10. QoS 11. Physical Layer 12.
Lecture Slides 1. The Internet 2. Principles 3. Ethernet 4. WiFi 5. Routing 6. Internetworking 7. Transport 8. Models 9. WiMAX & LTE 10. QoS 11. Physical Layer 12. Additional Topics 1.1. Basic Operations
More informationCOMPUTER AIDED GEOMETRIC DESIGN. Thomas W. Sederberg
COMPUTER AIDED GEOMETRIC DESIGN Thomas W. Sederberg January 31, 2011 ii T. W. Sederberg iii Preface This semester is the 24 th time I have taught a course at Brigham Young University titled, Computer Aided
More informationUser Datagram Protocol (UDP):
SFWR 4C03: Computer Networks and Computer Security Feb 25 2004 Lecturer: Kartik Krishnan Lectures 1315 User Datagram Protocol (UDP): UDP is a connectionless transport layer protocol: each output operation
More informationSMD149  Operating Systems
SMD149  Operating Systems Roland Parviainen November 3, 2005 1 / 45 Outline Overview 2 / 45 Process (tasks) are necessary for concurrency Instance of a program in execution Next invocation of the program
More informationModule 5 Graph Algorithms
Module 5 Graph lgorithms Dr. Natarajan Meghanathan Professor of Computer Science Jackson State University Jackson, MS 97 Email: natarajan.meghanathan@jsums.edu 5. Graph Traversal lgorithms Depth First
More informationProblem set 2. Problem 1. Problem 2. Problem 3. CS261, Winter Instructor: Ashish Goel.
CS261, Winter 2017. Instructor: Ashish Goel. Problem set 2 Electronic submission to Gradescope due 11:59pm Thursday 2/16. Form a group of 23 students that is, submit one homework with all of your names.
More informationSupporting Materials
Preface p. xxiii Introduction p. xxiii Key Features p. xxiii Chapter Outlines p. xxiv Supporting Materials p. xxvi Acknowledgments p. xxvii Java Fundamentals p. 1 Bits, Bytes, and Java p. 2 The Challenge
More information