An implementation of hash table for classification of combinatorial objects

Size: px
Start display at page:

Download "An implementation of hash table for classification of combinatorial objects"

Transcription

1 An implementation of hash table for classification of combinatorial objects Mariya Dzhumalieva-Stoeva, Venelin Monev Faculty of Mathematics and Informatics, Veliko Tarnovo University Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 1 / 15

2 Outline The classification problem Implementation of hash tables Experimental results Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 2 / 15

3 Classification problem Ω - finite set of combinatorial objects, concerned with an equivalence relation =. Classification problem Finding a subset T Ω, such that: 1 a Ω, b T : a = b, 2 a, b T a b. isomorphism problem isomorph free generation recorded objects Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 3 / 15

4 Canonical representative map Definition A canonical representative map is a function ρ : Ω Ω which satisfies the following properties: 1 for all X Ω it holds that ρ(x ) = X, 2 for all X,Y Ω it holds that X = Y implies ρ(x ) = ρ(y ). The object X is in canonical form if ρ(x ) = X. Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 4 / 15

5 Basic algorithm Procedure RejectionByRecordCan Input: U -set of binary matrices Output: T -set of binary matrices in canonical form 1] T 2] for( a U ) do 3] obtain ρ(a) 4] if( ρ(a) / T ) 5] T T {ρ(a)} 6] end if 7] end for 8] T T end procedure Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 5 / 15

6 Basic algorithm look up fast whether ρ(a) / T store element ρ(a) to the set T possible implementation single-linked list hash table Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 6 / 15

7 Hash table Each element is represented in the hash table via two fields: key and value. The key is calculated by specific hash function: Definition A hash function h is any mathematical function of the form h : Ω {0, 1,..., s 1}, A hash table of size s can be implemented as one dimensional array HT [0... s 1] of slots. The key h(a) is the index of an element a. The slot HT [h(a)] contains an essential information. Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 7 / 15

8 Hash table Main points: to chose an appropriate hash function to select a method for collision resolution Collision - two distinct elements a 1 a 2 have the same hash value h(a 1 ) = h(a 2 ). Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 8 / 15

9 Implementation of the hash table The hash table is separated into two structures: one-dimensional integer array HT [0..size 1] an object a is represented in the slot HT [h(a)] each slot contains position of the corresponding file record random access file of records each record consists of pointer field and matrix field collision resolution is implemented by chaining via linked list structure Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 9 / 15

10 Procedure HashTableLookUp Input: Output: A - matrix in canonical form ρ(a) boolean value stored: true or false 1] key h(a); 2] if( slot HT[key] is empty ) 3] append A to file; 4] HT[key] position of A in the file; 5] return stored true; 6] else... Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 10 / 15

11 Procedure HashTableLookUp... 6] else 7] currentrec record in position HT[key]; 8] while( currentrec.next end of list ); 9] if( currentrec.matrix is equal to A ) 10] return stored false; 11] currentrec currentrec.next; 12] end while 13] if( currentrec.next is end of list ) 14] append A to file; 15] currentrec.next position of A in the file; 16] return stored true; 17] end if 18] end else Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 11 / 15

12 Remarks The algorithm is implemented in C programming language on a computer with CPU Intel Core i7-6700, 3,40GHz, 16 GB RAM memory, having an ordinary HDD. The input file contains set of random binary matrices. The used hash function is FNV-1a, which hashes strings. Each matrix is written row by row and converted to string of printable ASCII characters. Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 12 / 15

13 Experimental results matrices size n m memory cells collisions Running time sec n, m sec n, m sec n, m sec sec sec sec sec 19 min Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 13 / 15

14 Conclusion The procedure HashTableLookUp is called for each input matrix. The number of collisions depends on the hash table sizes and the type of the objects. The operation deleting is not implemented. There are no empty records in the output file. More than one file could be used for the implementation of the hash table. The algorithm can be easily implemented for the non binary case. Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 14 / 15

15 References I. Bouyukliev (2007) About the code equivalence. Advances in Coding Theory and Cryptology, T. Shaska, W.C. Huffman, D. Joyner, V. Ustimenko, Series on Coding Theory and Cryptology, World Scientific Publishing, Hackensack, NJ, I. Bouyukliev and M. Dzhumalieva-Stoeva (2014) Representing Equivalence Problems For Combinatorial Objects. Serdica Journal of Computing 8,No 4, , Th. H. Cormen, Ch. E. Leiserson, R. L. Rivest, Cl. Stein (2009) Introduction to algorithms, Third edition. The MIT Press Cambridge, Massachusetts London, England, P. Kaski and P. R. J. Östergård (2006) St. Skiena (2008) The Algorithm Design Manual, Second Edition. Springer-Verlag London, Fowler-Noll-Vo hash funciton FNV-1a alternate algorithm. ( Mariya Dzhumalieva-Stoeva, Venelin Monev ( Faculty Anof implementation Mathematics and of hash Informatics, table Veliko Tarnovo University ) 15 / 15

A Method for Construction of Orthogonal Arrays 1

A Method for Construction of Orthogonal Arrays 1 Eighth International Workshop on Optimal Codes and Related Topics July 10-14, 2017, Sofia, Bulgaria pp. 49-54 A Method for Construction of Orthogonal Arrays 1 Iliya Bouyukliev iliyab@math.bas.bg Institute

More information

About parallelization of an algorithm for the maximum clique problem 1

About parallelization of an algorithm for the maximum clique problem 1 Seventh International Workshop on Optimal Codes and Related Topics September 6-12, 2013, Albena, Bulgaria pp. 53-58 About parallelization of an algorithm for the maximum clique problem 1 Iliya Bouyukliev

More information

PAijpam.eu A NOTE ON KNUTH S IMPLEMENTATION OF EXTENDED EUCLIDEAN GREATEST COMMON DIVISOR ALGORITHM Anton Iliev 1, Nikolay Kyurkchiev 2, Angel Golev 3

PAijpam.eu A NOTE ON KNUTH S IMPLEMENTATION OF EXTENDED EUCLIDEAN GREATEST COMMON DIVISOR ALGORITHM Anton Iliev 1, Nikolay Kyurkchiev 2, Angel Golev 3 International Journal of Pure and Applied Mathematics Volume 118 No. 1 2018, 31-37 ISSN: 1311-8080 (printed version); ISSN: 1314-3395 (on-line version) url: http://www.ijpam.eu doi: 10.12732/ijpam.v118i1.3

More information

Introduction to Algorithms Third Edition

Introduction 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 information

Grouping Genetic Algorithm with Efficient Data Structures for the University Course Timetabling Problem

Grouping Genetic Algorithm with Efficient Data Structures for the University Course Timetabling Problem Grouping Genetic Algorithm with Efficient Data Structures for the University Course Timetabling Problem Felipe Arenales Santos Alexandre C. B. Delbem Keywords Grouping Genetic Algorithm Timetabling Problem

More information

Web page recommendation using a stochastic process model

Web page recommendation using a stochastic process model Data Mining VII: Data, Text and Web Mining and their Business Applications 233 Web page recommendation using a stochastic process model B. J. Park 1, W. Choi 1 & S. H. Noh 2 1 Computer Science Department,

More information

Block Method for Convex Polygon Triangulation

Block Method for Convex Polygon Triangulation ROMANIAN JOURNAL OF INFORMATION SCIENCE AND TECHNOLOGY Volume 15, Number 4, 2012, 344 354 Block Method for Convex Polygon Triangulation Predrag S. STANIMIROVIĆ, Predrag V. KRTOLICA, Muzafer H. SARAČEVIĆ,

More information

SYLLABUS Type of evaluation

SYLLABUS Type of evaluation SYLLABUS 1. Information regarding the programme 1.1 Higher education Babeș-Bolyai University, Cluj-Napoca institution 1.2 Faculty Faculty of Mathematics and Computer Science 1.3 Department Department of

More information

Fast Implementation of the ANF Transform

Fast Implementation of the ANF Transform Fast Implementation of the ANF Transform Valentin Bakoev Faculty of Mathematics and Informatics, Veliko Turnovo University, Bulgaria MDS OCRT, 4 July, 27 Sofia, Bulgaria V. Bakoev (FMI, VTU) Fast Implementation

More information

Efficient and Effective Practical Algorithms for the Set-Covering Problem

Efficient and Effective Practical Algorithms for the Set-Covering Problem Efficient and Effective Practical Algorithms for the Set-Covering Problem Qi Yang, Jamie McPeek, Adam Nofsinger Department of Computer Science and Software Engineering University of Wisconsin at Platteville

More information

Just Sort. Sathish Kumar Vijayakumar Chennai, India (1)

Just Sort. Sathish Kumar Vijayakumar Chennai, India (1) Just Sort Sathish Kumar Vijayakumar Chennai, India satthhishkumar@gmail.com Abstract Sorting is one of the most researched topics of Computer Science and it is one of the essential operations across computing

More information

Applications of Succinct Dynamic Compact Tries to Some String Problems

Applications of Succinct Dynamic Compact Tries to Some String Problems Applications of Succinct Dynamic Compact Tries to Some String Problems Takuya Takagi 1, Takashi Uemura 2, Shunsuke Inenaga 3, Kunihiko Sadakane 4, and Hiroki Arimura 1 1 IST & School of Engineering, Hokkaido

More information

How Efficient Can Fully Verified Functional Programs Be - A Case Study of Graph Traversal Algorithms

How Efficient Can Fully Verified Functional Programs Be - A Case Study of Graph Traversal Algorithms How Efficient Can Fully Verified Functional Programs Be - A Case Study of Graph Traversal Algorithms Mirko Stojadinović Faculty of Mathematics, University of Belgrade Abstract. One approach in achieving

More information

Data Structures and Algorithms. Chapter 7. Hashing

Data Structures and Algorithms. Chapter 7. Hashing 1 Data Structures and Algorithms Chapter 7 Werner Nutt 2 Acknowledgments The course follows the book Introduction to Algorithms, by Cormen, Leiserson, Rivest and Stein, MIT Press [CLRST]. Many examples

More information

CSI33 Data Structures

CSI33 Data Structures Outline Department of Mathematics and Computer Science Bronx Community College November 30, 2016 Outline Outline 1 Chapter 13: Heaps, Balances Trees and Hash Tables Hash Tables Outline 1 Chapter 13: Heaps,

More information

The Further Mathematics Support Programme

The Further Mathematics Support Programme Degree Topics in Mathematics Groups A group is a mathematical structure that satisfies certain rules, which are known as axioms. Before we look at the axioms, we will consider some terminology. Elements

More information

ALGORITHMS FOR GENERATING NEAR-RINGS ON FINITE CYCLIC GROUPS

ALGORITHMS FOR GENERATING NEAR-RINGS ON FINITE CYCLIC GROUPS ALGORITHMS FOR GENERATING NEAR-RINGS ON FINITE CYCLIC GROUPS Angel Golev Abstract. In the present work are described the algorithms that generate all near-rings on finite cyclic groups of order 16 to 29.

More information

CSL 201 Data Structures Mid-Semester Exam minutes

CSL 201 Data Structures Mid-Semester Exam minutes CL 201 Data tructures Mid-emester Exam - 120 minutes Name: Roll Number: Please read the following instructions carefully This is a closed book, closed notes exam. Calculators are allowed. However laptops

More information

Determinant Computation on the GPU using the Condensation AMMCS Method / 1

Determinant Computation on the GPU using the Condensation AMMCS Method / 1 Determinant Computation on the GPU using the Condensation Method Sardar Anisul Haque Marc Moreno Maza Ontario Research Centre for Computer Algebra University of Western Ontario, London, Ontario AMMCS 20,

More information

Homework3: Dynamic Programming - Answers

Homework3: Dynamic Programming - Answers Most Exercises are from your textbook: Homework3: Dynamic Programming - Answers 1. For the Rod Cutting problem (covered in lecture) modify the given top-down memoized algorithm (includes two procedures)

More information

Dictionaries and Hash Tables

Dictionaries and Hash Tables Dictionaries and Hash Tables Nicholas Mainardi Dipartimento di Elettronica e Informazione Politecnico di Milano nicholas.mainardi@polimi.it 14th June 2017 Dictionaries What is a dictionary? A dictionary

More information

COMP Analysis of Algorithms & Data Structures

COMP Analysis of Algorithms & Data Structures COMP 3170 - Analysis of Algorithms & Data Structures Shahin Kamali Topic 1 - Introductions University of Manitoba Picture is from the cover of the textbook CLRS. COMP 3170 - Analysis of Algorithms & Data

More information

Efficient subset and superset queries

Efficient subset and superset queries Efficient subset and superset queries Iztok SAVNIK Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Glagoljaška 8, 5000 Koper, Slovenia Abstract. The paper

More information

Introduction to Algorithms October 12, 2005 Massachusetts Institute of Technology Professors Erik D. Demaine and Charles E. Leiserson Quiz 1.

Introduction to Algorithms October 12, 2005 Massachusetts Institute of Technology Professors Erik D. Demaine and Charles E. Leiserson Quiz 1. Introduction to Algorithms October 12, 2005 Massachusetts Institute of Technology 6.046J/18.410J Professors Erik D. Demaine and Charles E. Leiserson Quiz 1 Quiz 1 Do not open this quiz booklet until you

More information

Data Structures and Algorithms. Roberto Sebastiani

Data Structures and Algorithms. Roberto Sebastiani Data Structures and Algorithms Roberto Sebastiani roberto.sebastiani@disi.unitn.it http://www.disi.unitn.it/~rseba - Week 07 - B.S. In Applied Computer Science Free University of Bozen/Bolzano academic

More information

Relational Database: The Relational Data Model; Operations on Database Relations

Relational Database: The Relational Data Model; Operations on Database Relations Relational Database: The Relational Data Model; Operations on Database Relations Greg Plaxton Theory in Programming Practice, Spring 2005 Department of Computer Science University of Texas at Austin Overview

More information

Tweaking for Better Algorithmic Implementation

Tweaking for Better Algorithmic Implementation Tweaking for Better Algorithmic Implementation Zirou Qiu, Ziping Liu, Xuesong Zhang Computer Science Department Southeast Missouri State University Cape Girardeau, MO, U. S. A. zqiu1s@semo.edu, zliu@semo.edu,

More information

DATA STRUCTURES AND ALGORITHMS

DATA STRUCTURES AND ALGORITHMS LECTURE 1 Babeş - Bolyai University Computer Science and Mathematics Faculty 2017-2018 Overview Course organization 1 Course organization 2 3 4 Course Organization I Guiding teachers Lecturer PhD. Marian

More information

CLASSIFICATION OF REGULAR DIGRAPHS, NORMALLY REGULAR DIGRAPHS, AND STRONGLY REGULAR DIGRAPHS

CLASSIFICATION OF REGULAR DIGRAPHS, NORMALLY REGULAR DIGRAPHS, AND STRONGLY REGULAR DIGRAPHS International Journal of Pure and Applied Mathematics Volume 78 No. 3 2012, 379-393 ISSN: 1311-8080 (printed version) url: http://www.ijpam.eu PA ijpam.eu CLASSIFICATION OF REGULAR DIGRAPHS, NORMALLY REGULAR

More information

Lecture 1. Introduction

Lecture 1. Introduction Lecture 1 Introduction 1 Lecture Contents 1. What is an algorithm? 2. Fundamentals of Algorithmic Problem Solving 3. Important Problem Types 4. Fundamental Data Structures 2 1. What is an Algorithm? Algorithm

More information

REDUCING GRAPH COLORING TO CLIQUE SEARCH

REDUCING GRAPH COLORING TO CLIQUE SEARCH Asia Pacific Journal of Mathematics, Vol. 3, No. 1 (2016), 64-85 ISSN 2357-2205 REDUCING GRAPH COLORING TO CLIQUE SEARCH SÁNDOR SZABÓ AND BOGDÁN ZAVÁLNIJ Institute of Mathematics and Informatics, University

More information

Mobile Robot Path Planning Software and Hardware Implementations

Mobile Robot Path Planning Software and Hardware Implementations Mobile Robot Path Planning Software and Hardware Implementations Lucia Vacariu, Flaviu Roman, Mihai Timar, Tudor Stanciu, Radu Banabic, Octavian Cret Computer Science Department, Technical University of

More information

Lecture 8. Dynamic Programming

Lecture 8. Dynamic Programming Lecture 8. Dynamic Programming T. H. Cormen, C. E. Leiserson and R. L. Rivest Introduction to Algorithms, 3rd Edition, MIT Press, 2009 Sungkyunkwan University Hyunseung Choo choo@skku.edu Copyright 2000-2018

More information

Exam in Algorithms & Data Structures 3 (1DL481)

Exam in Algorithms & Data Structures 3 (1DL481) Exam in Algorithms & Data Structures 3 (1DL481) Prepared by Pierre Flener Tuesday 15 March 2016 from 08:00 to 13:00, in Polacksbacken Materials: This is a closed-book exam, drawing from the book Introduction

More information

arxiv: v2 [math.co] 29 May 2013

arxiv: v2 [math.co] 29 May 2013 arxiv:1301.5100v2 [math.co] 29 May 2013 Bitwise operations related to a combinatorial problem on binary matrices Krasimir Yankov Yordzhev Abstract Some techniques for the use of bitwise operations are

More information

Algorithms and Data Structures. Algorithms and Data Structures. Algorithms and Data Structures. Algorithms and Data Structures

Algorithms and Data Structures. Algorithms and Data Structures. Algorithms and Data Structures. Algorithms and Data Structures Richard Mayr Slides adapted from Mary Cryan (2015/16) with some changes. School of Informatics University of Edinburgh ADS (2018/19) Lecture 1 slide 1 ADS (2018/19) Lecture 1 slide 3 ADS (2018/19) Lecture

More information

Parallel and Distributed Boolean Gröbner Bases Computation in SageMath

Parallel and Distributed Boolean Gröbner Bases Computation in SageMath Parallel and Distributed Boolean Gröbner Bases Computation in SageMath Akira Nagai nagai.akira@lab.ntt.co.jp NTT Secure Platform Laboratories Japan Yosuke Sato ysato@rs.kagu.tus.ac.jp Tokyo University

More information

Dynamic Programming. Design and Analysis of Algorithms. Entwurf und Analyse von Algorithmen. Irene Parada. Design and Analysis of Algorithms

Dynamic Programming. Design and Analysis of Algorithms. Entwurf und Analyse von Algorithmen. Irene Parada. Design and Analysis of Algorithms Entwurf und Analyse von Algorithmen Dynamic Programming Overview Introduction Example 1 When and how to apply this method Example 2 Final remarks Introduction: when recursion is inefficient Example: Calculation

More information

DATA STRUCTURES AND ALGORITHMS

DATA STRUCTURES AND ALGORITHMS LECTURE 11 Babeş - Bolyai University Computer Science and Mathematics Faculty 2017-2018 In Lecture 9-10... Hash tables ADT Stack ADT Queue ADT Deque ADT Priority Queue Hash tables Today Hash tables 1 Hash

More information

CLASS-ROOM NOTES: OPTIMIZATION PROBLEM SOLVING - I

CLASS-ROOM NOTES: OPTIMIZATION PROBLEM SOLVING - I Sutra: International Journal of Mathematical Science Education, Technomathematics Research Foundation Vol. 1, No. 1, 30-35, 2008 CLASS-ROOM NOTES: OPTIMIZATION PROBLEM SOLVING - I R. Akerkar Technomathematics

More information

General Instructions. You can use QtSpim simulator to work on these assignments.

General Instructions. You can use QtSpim simulator to work on these assignments. General Instructions You can use QtSpim simulator to work on these assignments. Only one member of each group has to submit the assignment. Please Make sure that there is no duplicate submission from your

More information

INF2220: algorithms and data structures Series 3

INF2220: algorithms and data structures Series 3 Universitetet i Oslo Institutt for Informatikk A. Maus, R.K. Runde, I. Yu INF2220: algorithms and data structures Series 3 Topic Map and Hashing (Exercises with hints for solution) Issued: 7. 09. 2016

More information

Algorithms and Data Structures

Algorithms and Data Structures Lesson 4: Sets, Dictionaries and Hash Tables Luciano Bononi http://www.cs.unibo.it/~bononi/ (slide credits: these slides are a revised version of slides created by Dr. Gabriele D Angelo)

More information

Different binary search trees can represent the same set of values (Fig.2). Vladimir Shelomovskii, Unitech, Papua New Guinea. Binary search tree.

Different binary search trees can represent the same set of values (Fig.2). Vladimir Shelomovskii, Unitech, Papua New Guinea. Binary search tree. 1 Vladimir Shelomovskii, Unitech, Papua New Guinea, CS411 Binary search tree We can represent a binary search tree by a linked data structure in which each node is an object. Each node contains (Fig.1):

More information

Embedded Subgraph Isomorphism and Related Problems

Embedded Subgraph Isomorphism and Related Problems Embedded Subgraph Isomorphism and Related Problems Graph isomorphism, subgraph isomorphism, and maximum common subgraph can be solved in polynomial time when constrained by geometrical information, in

More information

2 Proposed Implementation. 1 Introduction. Abstract. 2.1 Pseudocode of the Proposed Merge Procedure

2 Proposed Implementation. 1 Introduction. Abstract. 2.1 Pseudocode of the Proposed Merge Procedure Enhanced Merge Sort Using Simplified Transferrable Auxiliary Space Zirou Qiu, Ziping Liu, Xuesong Zhang Department of Computer Science Southeast Missouri State University Cape Girardeau, MO 63701 zqiu1s@semo.edu,

More information

A novel algorithm to determine the leaf (leaves) of a binary tree from its preorder and postorder traversals

A novel algorithm to determine the leaf (leaves) of a binary tree from its preorder and postorder traversals Journal of Algorithms and Computation journal homepage: http://jac.ut.ac.ir A novel algorithm to determine the leaf (leaves) of a binary tree from its preorder and postorder traversals N. Aghaieabiane

More information

Chapter 4 Graphs and Matrices. PAD637 Week 3 Presentation Prepared by Weijia Ran & Alessandro Del Ponte

Chapter 4 Graphs and Matrices. PAD637 Week 3 Presentation Prepared by Weijia Ran & Alessandro Del Ponte Chapter 4 Graphs and Matrices PAD637 Week 3 Presentation Prepared by Weijia Ran & Alessandro Del Ponte 1 Outline Graphs: Basic Graph Theory Concepts Directed Graphs Signed Graphs & Signed Directed Graphs

More information

Ordinary Differential Equation Solver Language (ODESL) Reference Manual

Ordinary Differential Equation Solver Language (ODESL) Reference Manual Ordinary Differential Equation Solver Language (ODESL) Reference Manual Rui Chen 11/03/2010 1. Introduction ODESL is a computer language specifically designed to solve ordinary differential equations (ODE

More information

Genetic Algorithms Based Solution To Maximum Clique Problem

Genetic Algorithms Based Solution To Maximum Clique Problem Genetic Algorithms Based Solution To Maximum Clique Problem Harsh Bhasin computergrad.com Faridabad, India i_harsh_bhasin@yahoo.com Rohan Mahajan Lingaya s University Faridabad, India mahajanr28@gmail.com

More information

Thomas H. Cormen Charles E. Leiserson Ronald L. Rivest. Introduction to Algorithms

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 information

A SIMPLE APPROXIMATION ALGORITHM FOR NONOVERLAPPING LOCAL ALIGNMENTS (WEIGHTED INDEPENDENT SETS OF AXIS PARALLEL RECTANGLES)

A SIMPLE APPROXIMATION ALGORITHM FOR NONOVERLAPPING LOCAL ALIGNMENTS (WEIGHTED INDEPENDENT SETS OF AXIS PARALLEL RECTANGLES) Chapter 1 A SIMPLE APPROXIMATION ALGORITHM FOR NONOVERLAPPING LOCAL ALIGNMENTS (WEIGHTED INDEPENDENT SETS OF AXIS PARALLEL RECTANGLES) Piotr Berman Department of Computer Science & Engineering Pennsylvania

More information

Hash Tables Outline. Definition Hash functions Open hashing Closed hashing. Efficiency. collision resolution techniques. EECS 268 Programming II 1

Hash Tables Outline. Definition Hash functions Open hashing Closed hashing. Efficiency. collision resolution techniques. EECS 268 Programming II 1 Hash Tables Outline Definition Hash functions Open hashing Closed hashing collision resolution techniques Efficiency EECS 268 Programming II 1 Overview Implementation style for the Table ADT that is good

More information

Data Structures And Algorithms

Data Structures And Algorithms Data Structures And Algorithms Hashing Eng. Anis Nazer First Semester 2017-2018 Searching Search: find if a key exists in a given set Searching algorithms: linear (sequential) search binary search Search

More information

Quiz 1 Practice Problems

Quiz 1 Practice Problems Introduction to Algorithms: 6.006 Massachusetts Institute of Technology March 7, 2008 Professors Srini Devadas and Erik Demaine Handout 6 1 Asymptotic Notation Quiz 1 Practice Problems Decide whether these

More information

Quiz 1 Solutions. (a) If f(n) = Θ(g(n)) and g(n) = Θ(h(n)), then h(n) = Θ(f(n)) Solution: True. Θ is transitive.

Quiz 1 Solutions. (a) If f(n) = Θ(g(n)) and g(n) = Θ(h(n)), then h(n) = Θ(f(n)) Solution: True. Θ is transitive. Introduction to Algorithms October 17, 2007 Massachusetts Institute of Technology 6.006 Fall 2007 Professors Ron Rivest and Srini Devadas Quiz 1 Solutions Problem 1. Quiz 1 Solutions Asymptotic Notation

More information

overview overview who practicalities introduction data structures and algorithms lecture 1 sorting insertion sort pseudo code merge sort

overview overview who practicalities introduction data structures and algorithms lecture 1 sorting insertion sort pseudo code merge sort overview data structures and algorithms 2017 09 04 lecture 1 overview who lectures: Femke van Raamsdonk f.van.raamsdonk at vu.nl T446 exercise classes: Paul Ursulean Petar Vukmirovic when and where tests

More information

Parallelizing SAT Solver With specific application on solving Sudoku Puzzles

Parallelizing SAT Solver With specific application on solving Sudoku Puzzles 6.338 Applied Parallel Computing Final Report Parallelizing SAT Solver With specific application on solving Sudoku Puzzles Hank Huang May 13, 2009 This project was focused on parallelizing a SAT solver

More information

The Edge Slide Graph of the 3-cube

The Edge Slide Graph of the 3-cube Rose-Hulman Undergraduate Mathematics Journal Volume 12 Issue 2 Article 6 The Edge Slide Graph of the 3-cube Lyndal Henden Massey University, Palmerston North, New Zealand, lyndal_henden@hotmail.com Follow

More information

1KOd17RMoURxjn2 CSE 20 DISCRETE MATH Fall

1KOd17RMoURxjn2 CSE 20 DISCRETE MATH Fall CSE 20 https://goo.gl/forms/1o 1KOd17RMoURxjn2 DISCRETE MATH Fall 2017 http://cseweb.ucsd.edu/classes/fa17/cse20-ab/ Today's learning goals Explain the steps in a proof by mathematical and/or structural

More information

A PRIMAL-DUAL EXTERIOR POINT ALGORITHM FOR LINEAR PROGRAMMING PROBLEMS

A PRIMAL-DUAL EXTERIOR POINT ALGORITHM FOR LINEAR PROGRAMMING PROBLEMS Yugoslav Journal of Operations Research Vol 19 (2009), Number 1, 123-132 DOI:10.2298/YUJOR0901123S A PRIMAL-DUAL EXTERIOR POINT ALGORITHM FOR LINEAR PROGRAMMING PROBLEMS Nikolaos SAMARAS Angelo SIFELARAS

More information

Efficient Sequential Algorithms, Comp309. Motivation. Longest Common Subsequence. Part 3. String Algorithms

Efficient Sequential Algorithms, Comp309. Motivation. Longest Common Subsequence. Part 3. String Algorithms Efficient Sequential Algorithms, Comp39 Part 3. String Algorithms University of Liverpool References: T. H. Cormen, C. E. Leiserson, R. L. Rivest Introduction to Algorithms, Second Edition. MIT Press (21).

More information

The Tail has the pointer value z.next = NIL. the pointer to the next object x.next, down arrows.

The Tail has the pointer value z.next = NIL. the pointer to the next object x.next, down arrows. 1 Vladimir Shelomovskii, Unitech, Papua New Guinea, CS411 Linked list A linked list is a data structure in which the objects are arranged in a linear order. The order in a linked list is determined by

More information

Journal of Asian Scientific Research MODELLING ENGINEERING NETWORKS BY USING NODAL AND MESH INCIDENCE MATRICES. M.O. Oke. R.A. Raji. Y.O.

Journal of Asian Scientific Research MODELLING ENGINEERING NETWORKS BY USING NODAL AND MESH INCIDENCE MATRICES. M.O. Oke. R.A. Raji. Y.O. Journal of Asian Scientific Research, 0, ():8-6 Journal of Asian Scientific Research journal homepage: http://aessweb.com/journal-detail.php?id=00 MODELLING ENGINEERING NETWORKS BY USING NODAL AND MESH

More information

CSE 332 Spring 2013: Midterm Exam (closed book, closed notes, no calculators)

CSE 332 Spring 2013: Midterm Exam (closed book, closed notes, no calculators) Name: Email address: Quiz Section: CSE 332 Spring 2013: Midterm Exam (closed book, closed notes, no calculators) Instructions: Read the directions for each question carefully before answering. We will

More information

Algorithms and Data Structures, or

Algorithms and Data Structures, or Algorithms and Data Structures, or... Classical Algorithms of the 50s, 60s and 70s Mary Cryan A&DS Lecture 1 1 Mary Cryan Our focus Emphasis is Algorithms ( Data Structures less important). Most of the

More information

This is the author s version of a work that was submitted/accepted for publication in the following source:

This is the author s version of a work that was submitted/accepted for publication in the following source: This is the author s version of a work that was submitted/accepted for publication in the following source: Chowdhury, Israt J. & Nayak, Richi (2) A novel method for finding similarities between unordered

More information

International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July ISSN

International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July ISSN International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-201 971 Comparative Performance Analysis Of Sorting Algorithms Abhinav Yadav, Dr. Sanjeev Bansal Abstract Sorting Algorithms

More information

Hashing. Dr. Ronaldo Menezes Hugo Serrano. Ronaldo Menezes, Florida Tech

Hashing. Dr. Ronaldo Menezes Hugo Serrano. Ronaldo Menezes, Florida Tech Hashing Dr. Ronaldo Menezes Hugo Serrano Agenda Motivation Prehash Hashing Hash Functions Collisions Separate Chaining Open Addressing Motivation Hash Table Its one of the most important data structures

More information

Generalized trace ratio optimization and applications

Generalized trace ratio optimization and applications Generalized trace ratio optimization and applications Mohammed Bellalij, Saïd Hanafi, Rita Macedo and Raca Todosijevic University of Valenciennes, France PGMO Days, 2-4 October 2013 ENSTA ParisTech PGMO

More information

Randomized Algorithms: Element Distinctness

Randomized Algorithms: Element Distinctness Randomized Algorithms: Element Distinctness CSE21 Winter 2017, Day 24 (B00), Day 16-17 (A00) March 13, 2017 http://vlsicad.ucsd.edu/courses/cse21-w17 Element Distinctness: WHAT Given list of positive integers

More information

A Fast Algorithm for Optimal Alignment between Similar Ordered Trees

A Fast Algorithm for Optimal Alignment between Similar Ordered Trees Fundamenta Informaticae 56 (2003) 105 120 105 IOS Press A Fast Algorithm for Optimal Alignment between Similar Ordered Trees Jesper Jansson Department of Computer Science Lund University, Box 118 SE-221

More information

Introducing Hashing. Chapter 21. Copyright 2012 by Pearson Education, Inc. All rights reserved

Introducing Hashing. Chapter 21. Copyright 2012 by Pearson Education, Inc. All rights reserved Introducing Hashing Chapter 21 Contents What Is Hashing? Hash Functions Computing Hash Codes Compressing a Hash Code into an Index for the Hash Table A demo of hashing (after) ARRAY insert hash index =

More information

Reverse Polish notation in constructing the algorithm for polygon triangulation

Reverse Polish notation in constructing the algorithm for polygon triangulation Reverse Polish notation in constructing the algorithm for polygon triangulation Predrag V. Krtolica, Predrag S. Stanimirović and Rade Stanojević Abstract The reverse Polish notation properties are used

More information

The Graph of Simplex Vertices

The Graph of Simplex Vertices wwwccsenetorg/jmr Journal of Mathematics Research Vol 4, No ; February The Graph of Simplex Vertices M El-Ghoul Mathematics Department, Faculty of Science, Tanta University, Tanta, Egypt Tel: 4-684-8 E-mail:

More information

END-TERM EXAMINATION

END-TERM EXAMINATION (Please Write your Exam Roll No. immediately) Exam. Roll No... END-TERM EXAMINATION Paper Code : MCA-205 DECEMBER 2006 Subject: Design and analysis of algorithm Time: 3 Hours Maximum Marks: 60 Note: Attempt

More information

Range Tree Applications in Computational Geometry

Range Tree Applications in Computational Geometry Range Tree Applications in Computational Geometry ANTONIO-GABRIEL STURZU, COSTIN-ANTON BOIANGIU Computer Science Department Politehnica University of Bucharest Splaiul Independentei 313, Sector 6, Bucharest,

More information

Implementation and Experiments of Frequent GPS Trajectory Pattern Mining Algorithms

Implementation and Experiments of Frequent GPS Trajectory Pattern Mining Algorithms DEIM Forum 213 A5-3 Implementation and Experiments of Frequent GPS Trajectory Pattern Abstract Mining Algorithms Xiaoliang GENG, Hiroki ARIMURA, and Takeaki UNO Graduate School of Information Science and

More information

Detecting Data Structures from Traces

Detecting Data Structures from Traces Detecting Data Structures from Traces Alon Itai Michael Slavkin Department of Computer Science Technion Israel Institute of Technology Haifa, Israel itai@cs.technion.ac.il mishaslavkin@yahoo.com July 30,

More information

Heap Order Property: Key stored at the parent is smaller or equal to the key stored at either child.

Heap Order Property: Key stored at the parent is smaller or equal to the key stored at either child. A Binary Heap is a data structure that is an array object that can be viewed as a nearly complete binary tree. The tree is completely filled on all levels except the lowest, which may only be partially

More information

Efficient Sequential Algorithms, Comp309. Problems. Part 1: Algorithmic Paradigms

Efficient Sequential Algorithms, Comp309. Problems. Part 1: Algorithmic Paradigms Efficient Sequential Algorithms, Comp309 Part 1: Algorithmic Paradigms University of Liverpool References: T. H. Cormen, C. E. Leiserson, R. L. Rivest Introduction to Algorithms, Second Edition. MIT Press

More information

CS 157: Assignment 5

CS 157: Assignment 5 Problem : Printing Neatly CS 157: Assignment 5 Douglas R. Lanman 4 April 006 In a word processor or in L A TEX, one routinely encounters the pretty printing problem. That is, how does one transform text

More information

Algorithms. Algorithms 1.4 ANALYSIS OF ALGORITHMS

Algorithms. Algorithms 1.4 ANALYSIS OF ALGORITHMS ROBERT SEDGEWICK KEVIN WAYNE Algorithms ROBERT SEDGEWICK KEVIN WAYNE 1.4 ANALYSIS OF ALGORITHMS Algorithms F O U R T H E D I T I O N http://algs4.cs.princeton.edu introduction observations mathematical

More information

Advances in Programming Languages

Advances in Programming Languages T O Y H Advances in Programming Languages APL8: Multiparameter Type Classes, Constructor Classes Ian Stark School of Informatics The University of Edinburgh Thursday 4 February Semester 2 Week 4 E H U

More information

Winning Positions in Simplicial Nim

Winning Positions in Simplicial Nim Winning Positions in Simplicial Nim David Horrocks Department of Mathematics and Statistics University of Prince Edward Island Charlottetown, Prince Edward Island, Canada, C1A 4P3 dhorrocks@upei.ca Submitted:

More information

DATA STRUCTURES AND ALGORITHMS

DATA STRUCTURES AND ALGORITHMS LECTURE 11 Babeş - Bolyai University Computer Science and Mathematics Faculty 2017-2018 In Lecture 10... Hash tables Separate chaining Coalesced chaining Open Addressing Today 1 Open addressing - review

More information

Algorithms and Data Structures

Algorithms and Data Structures Algorithms and Data Structures or, Classical Algorithms of the 50s, 60s, 70s Richard Mayr Slides adapted from Mary Cryan (2015/16) with small changes. School of Informatics University of Edinburgh ADS

More information

Topos Theory. Lectures 3-4: Categorical preliminaries II. Olivia Caramello. Topos Theory. Olivia Caramello. Basic categorical constructions

Topos Theory. Lectures 3-4: Categorical preliminaries II. Olivia Caramello. Topos Theory. Olivia Caramello. Basic categorical constructions Lectures 3-4: Categorical preliminaries II 2 / 17 Functor categories Definition Let C and D be two categories. The functor category [C,D] is the category having as objects the functors C D and as arrows

More information

SAT BASED ALGORITHMIC APPROACH FOR SUDOKU PUZZLE

SAT BASED ALGORITHMIC APPROACH FOR SUDOKU PUZZLE International Journal of Computer Engineering & Technology (IJCET) Volume 9, Issue 6, November-December 2018, pp. 38 45, Article ID: IJCET_09_06_005 Available online at http://www.iaeme.com/ijcet/issues.asp?jtype=ijcet&vtype=9&itype=6

More information

The Randomized Shortest Path model in a nutshell

The Randomized Shortest Path model in a nutshell Panzacchi M, Van Moorter B, Strand O, Saerens M, Kivimäki I, Cassady St.Clair C., Herfindal I, Boitani L. (2015) Predicting the continuum between corridors and barriers to animal movements using Step Selection

More information

Keccak discussion. Soham Sadhu. January 9, 2012

Keccak discussion. Soham Sadhu. January 9, 2012 Keccak discussion Soham Sadhu January 9, 2012 Keccak (pronounced like Ketchak ) is a cryptographic hash function designed by Guido Bertoni, Joan Daemen, Michaël Peeters and Gilles Van Assche. Keccak is

More information

Section 05: Midterm Review

Section 05: Midterm Review Section 05: Midterm Review 1. Asymptotic Analysis (a) Applying definitions For each of the following, choose a c and n 0 which show f(n) O(g(n)). Explain why your values of c and n 0 work. (i) f(n) = 5000n

More information

Parallelization of Graph Isomorphism using OpenMP

Parallelization of Graph Isomorphism using OpenMP Parallelization of Graph Isomorphism using OpenMP Vijaya Balpande Research Scholar GHRCE, Nagpur Priyadarshini J L College of Engineering, Nagpur ABSTRACT Advancement in computer architecture leads to

More information

Bitwise Operations Related to a Combinatorial Problem on Binary Matrices

Bitwise Operations Related to a Combinatorial Problem on Binary Matrices I.J.Modern Education and Computer Science, 2013, 4, 19-24 Published Online May 2013 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijmecs.2013.04.03 Bitwise Operations Related to a Combinatorial Problem

More information

You must include this cover sheet. Either type up the assignment using theory5.tex, or print out this PDF.

You must include this cover sheet. Either type up the assignment using theory5.tex, or print out this PDF. 15-122 Assignment 5 Page 1 of 11 15-122 : Principles of Imperative Computation Fall 2012 Assignment 5 (Theory Part) Due: Tuesday, October 30, 2012 at the beginning of lecture Name: Andrew ID: Recitation:

More information

2 Fundamentals of data structures

2 Fundamentals of data structures 2.6 Hash tables Learning objectives: Be familiar with the concept of a hash table and its uses. Be able to apply simple hashing algorithms. Know what is meant by a collision and how collisions are handled

More information

Design and Analysis of Algorithms. Comp 271. Mordecai Golin. Department of Computer Science, HKUST

Design and Analysis of Algorithms. Comp 271. Mordecai Golin. Department of Computer Science, HKUST Design and Analysis of Algorithms Revised 05/02/03 Comp 271 Mordecai Golin Department of Computer Science, HKUST Information about the Lecturer Dr. Mordecai Golin Office: 3559 Email: golin@cs.ust.hk http://www.cs.ust.hk/

More information

Final Examination CSE 100 UCSD (Practice)

Final 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 closed-book, closed-notes, no-calculator exam. Don t refer to any materials other than the exam

More information

BRONX COMMUNITY COLLEGE of the City University of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE. Sample Final Exam

BRONX COMMUNITY COLLEGE of the City University of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE. Sample Final Exam BRONX COMMUNITY COLLEGE of the City University of New York DEPARTMENT OF MATHEMATICS AND COMPUTER SCIENCE CSI33 Sample Final Exam NAME Directions: Solve problems 1 through 5 of Part I and choose 5 of the

More information

COMP171. Hashing.

COMP171. Hashing. COMP171 Hashing Hashing 2 Hashing Again, a (dynamic) set of elements in which we do search, insert, and delete Linear ones: lists, stacks, queues, Nonlinear ones: trees, graphs (relations between elements

More information