FIRST AND FOLLOW Example
|
|
|
- Beverly Leonard
- 10 years ago
- Views:
Transcription
1 FIRST AND FOLLOW Example CS Stephen Watt University of Western Ontario Copyright 2007 Stephen M. Watt
2 FIRST/FOLLOW Main Ideas Look at all the grammar rules. Examine the possibilities of all substrings of RHS symbols being nullable. Nullable RHS prefixes imply adding to FIRST sets. Nullable RHS suffixes imply adding to FOLLOW sets. Nullable RHS interior substrings imply adding to FOLLOW sets.
3 Algorithm for FIRST, FOLLOW, nullable for each symbol X FIRST[X] := { }, FOLLOW[X] := { }, nullable[x] := false for each terminal symbol t FIRST[t] := {t} repeat for each production X Y1 Y2 Yk, if all Yi are nullable then nullable[x] := true if Y1..Yi-1 are nullable then FIRST[X] := FIRST[X] U FIRST[Yi] if Yi+1..Yk are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FOLLOW[X] if Yi+1..Yj-1 are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FIRST[Yj] until FIRST, FOLLOW, nullable do not change
4 Example Grammar G = (Σ, V, S, P), where Σ = { a, c, d} V = {X, Y, Z} S = Z P = { Z d, Y ε, X Y, Z X Y Z, Y c, X a }
5 Initialization The initialization loops give FIRST[X] := {} FOLLOW[X] := {} nullable[x] := false FIRST[Y] := {} FOLLOW[Y] := {} nullable[y] := false FIRST[Z] := {} FOLLOW[Z] := {} nullable[z] := false FIRST[a] := {a} FIRST[c] := {c} FIRST[d] := {d}
6 Rules That We Will Iterate for each production X Y1 Y2 Yk, if all Yi are nullable then nullable[x] := true if Y1..Yi-1 are nullable then FIRST[X] := FIRST[X] U FIRST[Yi] if Yi+1..Yk are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FOLLOW[X] if Yi+1..Yj-1 are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FIRST[Yj]
7 How to Interpret Sub-sequences Yi..Yk when i < k has k-i+1 elements Yi..Yk when i = k has k-i+1 = 1 elements Yi..Yk when i = k+1 has k-i+1 = 0 elements Otherwise, not defined. (Some applications actually do define i > k+1 by making a subsequence taking the original elements backwards, which can be interesting and powerful.)
8 Specialize for Z d Taking Z d as the production X Y1..Yk, we have X is Z and Y1 is d. There are no other Yi. The first rule if all Yi are nullable then nullable[x] := true is if d is nullable (it isn t) then nullable[z] := true.
9 Specialize for Z d The second rule if Y1..Yi-1 are nullable then FIRST[X] := FIRST[X] U FIRST[Yi] can be instantiated only for i=1. This gives if Y1..Y0 = ε is nullable (which it is) then FIRST[Z] := FIRST[Z] U { d }
10 Specialize for Z d The third rule if Yi+1..Yk are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FOLLOW[X] can be instantiated only for i=1. This gives if Y2..Y1= ε is nullable then FOLLOW[d] := FOLLOW[d] U FOLLOW[Z] We are not interested in computing FOLLOW of terminals, so we won t bother with this one.
11 Specialize for Z d The fourth rule if Yi+1..Yj-1 are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FIRST[Yj] has no valid values for i and j. The only Yi is Y1, so we would have to have i=j=1. But the subsequence Y2..Y0 does not exist.
12 Rules for X a, Y c, Z d The same logic applies for all of X a, Y c, Z d. This gives: FIRST[X] := FIRST[Z] U {a} FIRST[Y] := FIRST[Z] U {c} FIRST[Z] := FIRST[Z] U {d} There are also some rules to compute FOLLOW of terminals but we don t care about those.
13 Specialize for X Y Taking X Y as the production X Y1..Yk, we have X is X, Y1 is Y, and there are no other Yi. The first rule if all Yi are nullable then nullable[x] := true is if Y is nullable then nullable[x] := true.
14 Specialize for X Y The second rule if Y1..Yi-1 are nullable then FIRST[X] := FIRST[X] U FIRST[Yi] can be instantiated only for i=1. It becomes if Y1..Y0 is nullable then FIRST[X] := FIRST[X] U FIRST[Y] The condition is always satisfied as Y1..Y0 is empty. FIRST[X] := FIRST[X] U FIRST[Y]
15 Specialize for X Y The third rule if Yi+1..Yk are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FOLLOW[X] can be instantiated only for i=1. It becomes if Y2..Y1 is nullable then FOLLOW[Y] := FOLLOW[Y] U FOLLOW[X]. The test is true (the empty string is nullable).
16 Specialize for X Y The fourth rule if Yi+1..Yj-1 are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FIRST[Yj] has no instantiation. The only Y is Y1, so we would have to have i=j=1. But the subsequence Y2..Y0 is not defined.
17 Rules for X Y All the rules together for X Y are if Yi is nullable then, nullable[z] := true. FIRST[X] := FIRST[X] U FIRST[Y] FOLLOW[Y] := FOLLOW[Y] U FOLLOW[X].
18 Specialize for Y ε Taking Y ε as the production X Y1..Yk, we have X is Y, and there are no Yi. The first rule if all Yi are nullable then nullable[x] := true is if all zero of the Y s are nullable (true) then nullable[y] := true.
19 Specialize for Y ε The second rule if Y1..Yi-1 are nullable then FIRST[X] := FIRST[X] U FIRST[Yi] cannot be instantiated as there are no Yi.
20 Specialize for Y ε The third rule if Yi+1..Yk are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FOLLOW[X] cannot be instantiated as there are no Yi.
21 Specialize for Y ε The fourth rule if Yi+1..Yj-1 are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FIRST[Yj] has no instantiation as there are no Yi.
22 Rules for Y ε All the rules together for Y ε are nullable[y] := true.
23 Specialize for Z X Y Z Taking Z X Y Z as the production X Y1..Yk, we have X is Z, Y1 is X, Y2 is Y, Y3 is Z. The first rule if all Yi are nullable then nullable[x] := true is if X, Y, Z are all nullable then nullable[z] := true.
24 Specialize for Z X Y Z The second rule if Y1..Yi-1 are nullable then FIRST[X] := FIRST[X] U FIRST[Yi] can be instantiated for i=1,2 or 3. These give if ε is nullable (which it is) then FIRST[Z] := FIRST[Z] union FIRST[X] if X is nullable then FIRST[Z] := FIRST[Z] union FIRST[Y] if XY is nullable then ou FIRST[Z] := FIRST[Z] union FIRST[Z]
25 Specialize for Z X Y Z The third rule if Yi+1..Yk are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FOLLOW[X] can be instantiated for i=1,2,3. These give if YZ is nullable then FOLLOW[X] := FOLLOW[X] union FOLLOW[Z] if Zisnullable then FOLLOW[Y] := FOLLOW[Y] union FOLLOW[Z] if ε is nullable then FOLLOW[Z] := FOLLOW[Z] union FOLLOW[Z]
26 Specialize for Z XYZ The fourth rule if Yi+1..Yj-1 are all nullable then FOLLOW[Yi] := FOLLOW[Yi] U FIRST[Yj] has instantiations for (i,j) = (1,2), (1,3), (2,3). if ε is nullable then FOLLOW[X] := FOLLOW[X] union FIRST[Y] if Y is nullable then FOLLOW[X] O := FOLLOW[X] O union FIRST[Z] if ε is nullable then FOLLOW[Y] := FOLLOW[Y] union FIRST[Z]
27 Rules for Z X Y Z if X, Y, Z all nullable then nullable[z] := true. if X is nullable then FIRST[Z] := FIRST[Z] union FIRST[Y] if XY is nullable then FIRST[Z] := FIRST[Z] union FIRST[Z] if YZ is nullable then FOLLOW[X] := FOLLOW[X] union FOLLOW[Z] if Z is nullable then FOLLOW[Y] := FOLLOW[Y] union FOLLOW[Z] if Yi is nullable then FOLLOW[X] := FOLLOW[X] union FIRST[Z] FIRST[Z] := FIRST[Z] union FIRST[X] FOLLOW[X] := FOLLOW[X] union FIRST[Y] FOLLOW[Y] := FOLLOW[Y] union FIRST[Z] FOLLOW[Z] := FOLLOW[Z] union FOLLOW[Z]
28 All the Rules We Need to Iterate nullable[x] := true, if Y is nullable nullable[y] := true. nullable[z] := true, if X, Y, Z all nullable FIRST[X] := FIRST[Z] U {a} FIRST[Y] := FIRST[Z] U {c} FIRST[Z] := FIRST[Z] U {d} FIRST[X] := FIRST[X] U FIRST[Y] FIRST[Z] := FIRST[Z] U FIRST[X] FIRST[Z] := FIRST[Z] U FIRST[Y], if X is nullable FIRST[Z] := FIRST[Z] U FIRST[Z], if XY is nullable // Trivial can drop it FOLLOW[X] := FOLLOW[X] U FIRST[Y] FOLLOW[X] := FOLLOW[X] U FIRST[Z], if Y is nullable FOLLOW[Y] := FOLLOW[Y] U FIRST[Z] FOLLOW[X] := FOLLOW[X] U FOLLOW[Z], if YZ is nullable FOLLOW[Y] := FOLLOW[Y] U FOLLOW[Z], if Z is nullable FOLLOW[Y] := FOLLOW[Y] U FOLLOW[X] FOLLOW[Z] := FOLLOW[Z] U FOLLOW[Z]
29 Now Can Iterate to Fill in the Table Z d Y ε X Y Z X Y Z Y c X a X Y Z nullable FIRST FOLLOW false false false nullable FIRST FOLLOW X false a c d Y true c d Z false d nullable FIRST FOLLOW X true a c a c d Y true c a c d Z false a c d
FIRST and FOLLOW sets a necessary preliminary to constructing the LL(1) parsing table
FIRST and FOLLOW sets a necessary preliminary to constructing the LL(1) parsing table Remember: A predictive parser can only be built for an LL(1) grammar. A grammar is not LL(1) if it is: 1. Left recursive,
Logic in Computer Science: Logic Gates
Logic in Computer Science: Logic Gates Lila Kari The University of Western Ontario Logic in Computer Science: Logic Gates CS2209, Applied Logic for Computer Science 1 / 49 Logic and bit operations Computers
Automata and Formal Languages
Automata and Formal Languages Winter 2009-2010 Yacov Hel-Or 1 What this course is all about This course is about mathematical models of computation We ll study different machine models (finite automata,
University of Toronto Department of Electrical and Computer Engineering. Midterm Examination. CSC467 Compilers and Interpreters Fall Semester, 2005
University of Toronto Department of Electrical and Computer Engineering Midterm Examination CSC467 Compilers and Interpreters Fall Semester, 2005 Time and date: TBA Location: TBA Print your name and ID
Bottom-Up Parsing. An Introductory Example
Bottom-Up Parsing Bottom-up parsing is more general than top-down parsing Just as efficient Builds on ideas in top-down parsing Bottom-up is the preferred method in practice Reading: Section 4.5 An Introductory
CS103B Handout 17 Winter 2007 February 26, 2007 Languages and Regular Expressions
CS103B Handout 17 Winter 2007 February 26, 2007 Languages and Regular Expressions Theory of Formal Languages In the English language, we distinguish between three different identities: letter, word, sentence.
Optimizations. Optimization Safety. Optimization Safety. Control Flow Graphs. Code transformations to improve program
Optimizations Code transformations to improve program Mainly: improve execution time Also: reduce program size Control low Graphs Can be done at high level or low level E.g., constant folding Optimizations
Positional Numbering System
APPENDIX B Positional Numbering System A positional numbering system uses a set of symbols. The value that each symbol represents, however, depends on its face value and its place value, the value associated
Fast string matching
Fast string matching This exposition is based on earlier versions of this lecture and the following sources, which are all recommended reading: Shift-And/Shift-Or 1. Flexible Pattern Matching in Strings,
CS143 Handout 08 Summer 2008 July 02, 2007 Bottom-Up Parsing
CS143 Handout 08 Summer 2008 July 02, 2007 Bottom-Up Parsing Handout written by Maggie Johnson and revised by Julie Zelenski. Bottom-up parsing As the name suggests, bottom-up parsing works in the opposite
Database Design and Normal Forms
Database Design and Normal Forms Database Design coming up with a good schema is very important How do we characterize the goodness of a schema? If two or more alternative schemas are available how do
Python Loops and String Manipulation
WEEK TWO Python Loops and String Manipulation Last week, we showed you some basic Python programming and gave you some intriguing problems to solve. But it is hard to do anything really exciting until
Copyright 2013 wolfssl Inc. All rights reserved. 2
- - Copyright 2013 wolfssl Inc. All rights reserved. 2 Copyright 2013 wolfssl Inc. All rights reserved. 2 Copyright 2013 wolfssl Inc. All rights reserved. 3 Copyright 2013 wolfssl Inc. All rights reserved.
Turing Machines, Part I
Turing Machines, Part I Languages The $64,000 Question What is a language? What is a class of languages? Computer Science Theory 2 1 Now our picture looks like Context Free Languages Deterministic Context
Syntaktická analýza. Ján Šturc. Zima 208
Syntaktická analýza Ján Šturc Zima 208 Position of a Parser in the Compiler Model 2 The parser The task of the parser is to check syntax The syntax-directed translation stage in the compiler s front-end
Automata Theory. Şubat 2006 Tuğrul Yılmaz Ankara Üniversitesi
Automata Theory Automata theory is the study of abstract computing devices. A. M. Turing studied an abstract machine that had all the capabilities of today s computers. Turing s goal was to describe the
CSE 308. Coding Conventions. Reference
CSE 308 Coding Conventions Reference Java Coding Conventions googlestyleguide.googlecode.com/svn/trunk/javaguide.html Java Naming Conventions www.ibm.com/developerworks/library/ws-tipnamingconv.html 2
How To Program In Scheme (Prolog)
The current topic: Scheme! Introduction! Object-oriented programming: Python Functional programming: Scheme! Introduction Next up: Numeric operators, REPL, quotes, functions, conditionals Types and values
Negative Integer Exponents
7.7 Negative Integer Exponents 7.7 OBJECTIVES. Define the zero exponent 2. Use the definition of a negative exponent to simplify an expression 3. Use the properties of exponents to simplify expressions
A simple algorithm with no simple verication
A simple algorithm with no simple verication Laszlo Csirmaz Central European University Abstract The correctness of a simple sorting algorithm is resented, which algorithm is \evidently wrong" at the rst
1. Prove that the empty set is a subset of every set.
1. Prove that the empty set is a subset of every set. Basic Topology Written by Men-Gen Tsai email: [email protected] Proof: For any element x of the empty set, x is also an element of every set since
Functional Dependencies and Finding a Minimal Cover
Functional Dependencies and Finding a Minimal Cover Robert Soulé 1 Normalization An anomaly occurs in a database when you can update, insert, or delete data, and get undesired side-effects. These side
15. Functions and Lists. Example 2: Sorting a List of Numbers. Example 1: Computing the Diameter of a Cloud of Points
15. Functions and Lists Example 1: Computing the Diameter of a Cloud of Points Topics: Subscripting Map Searching a list Example 1: Clouds of points Example 2: Selection Sort 500 Points. Which two are
Unix Shell Scripts. Contents. 1 Introduction. Norman Matloff. July 30, 2008. 1 Introduction 1. 2 Invoking Shell Scripts 2
Unix Shell Scripts Norman Matloff July 30, 2008 Contents 1 Introduction 1 2 Invoking Shell Scripts 2 2.1 Direct Interpretation....................................... 2 2.2 Indirect Interpretation......................................
vector calculus 2 Learning outcomes
29 ontents vector calculus 2 1. Line integrals involving vectors 2. Surface and volume integrals 3. Integral vector theorems Learning outcomes In this Workbook you will learn how to integrate functions
Sect 6.1 - Greatest Common Factor and Factoring by Grouping
Sect 6.1 - Greatest Common Factor and Factoring by Grouping Our goal in this chapter is to solve non-linear equations by breaking them down into a series of linear equations that we can solve. To do this,
Derivative Approximation by Finite Differences
Derivative Approximation by Finite Differences David Eberly Geometric Tools, LLC http://wwwgeometrictoolscom/ Copyright c 998-26 All Rights Reserved Created: May 3, 2 Last Modified: April 25, 25 Contents
A Systematic Approach to Factoring
A Systematic Approach to Factoring Step 1 Count the number of terms. (Remember****Knowing the number of terms will allow you to eliminate unnecessary tools.) Step 2 Is there a greatest common factor? Tool
STATUS REPORT ON MAUDE-NPA TOOL
STATUS REPORT ON MAUDE-NPA TOOL Catherine Meadows Santiago Escobar Jose Meseguer September 28, 2006 1 GOAL Extend standard free algebra model of crypto protocol analysis to deal with algebraic properties
2110711 THEORY of COMPUTATION
2110711 THEORY of COMPUTATION ATHASIT SURARERKS ELITE Athasit Surarerks ELITE Engineering Laboratory in Theoretical Enumerable System Computer Engineering, Faculty of Engineering Chulalongkorn University
Reminder: Complexity (1) Parallel Complexity Theory. Reminder: Complexity (2) Complexity-new
Reminder: Complexity (1) Parallel Complexity Theory Lecture 6 Number of steps or memory units required to compute some result In terms of input size Using a single processor O(1) says that regardless of
Automata and Computability. Solutions to Exercises
Automata and Computability Solutions to Exercises Fall 25 Alexis Maciel Department of Computer Science Clarkson University Copyright c 25 Alexis Maciel ii Contents Preface vii Introduction 2 Finite Automata
SOLUTION Trial Test Grammar & Parsing Deficiency Course for the Master in Software Technology Programme Utrecht University
SOLUTION Trial Test Grammar & Parsing Deficiency Course for the Master in Software Technology Programme Utrecht University Year 2004/2005 1. (a) LM is a language that consists of sentences of L continued
Formal Languages and Automata Theory - Regular Expressions and Finite Automata -
Formal Languages and Automata Theory - Regular Expressions and Finite Automata - Samarjit Chakraborty Computer Engineering and Networks Laboratory Swiss Federal Institute of Technology (ETH) Zürich March
Normalisation. Why normalise? To improve (simplify) database design in order to. Avoid update problems Avoid redundancy Simplify update operations
Normalisation Why normalise? To improve (simplify) database design in order to Avoid update problems Avoid redundancy Simplify update operations 1 Example ( the practical difference between a first normal
Lecture 2: Regular Languages [Fa 14]
Caveat lector: This is the first edition of this lecture note. Please send bug reports and suggestions to [email protected]. But the Lord came down to see the city and the tower the people were building.
Relational Database Design
Relational Database Design To generate a set of relation schemas that allows - to store information without unnecessary redundancy - to retrieve desired information easily Approach - design schema in appropriate
Visual Logic Instructions and Assignments
Visual Logic Instructions and Assignments Visual Logic can be installed from the CD that accompanies our textbook. It is a nifty tool for creating program flowcharts, but that is only half of the story.
Factoring - Greatest Common Factor
6.1 Factoring - Greatest Common Factor Objective: Find the greatest common factor of a polynomial and factor it out of the expression. The opposite of multiplying polynomials together is factoring polynomials.
Regular Expressions and Automata using Haskell
Regular Expressions and Automata using Haskell Simon Thompson Computing Laboratory University of Kent at Canterbury January 2000 Contents 1 Introduction 2 2 Regular Expressions 2 3 Matching regular expressions
ASSIGNMENT ONE SOLUTIONS MATH 4805 / COMP 4805 / MATH 5605
ASSIGNMENT ONE SOLUTIONS MATH 4805 / COMP 4805 / MATH 5605 (1) (a) (0 + 1) 010 (finite automata below). (b) First observe that the following regular expression generates the binary strings with an even
Applies to Version 6 Release 5 X12.6 Application Control Structure
Applies to Version 6 Release 5 X12.6 Application Control Structure ASC X12C/2012-xx Copyright 2012, Data Interchange Standards Association on behalf of ASC X12. Format 2012 Washington Publishing Company.
LZ77. Example 2.10: Let T = badadadabaab and assume d max and l max are large. phrase b a d adadab aa b
LZ77 The original LZ77 algorithm works as follows: A phrase T j starting at a position i is encoded as a triple of the form distance, length, symbol. A triple d, l, s means that: T j = T [i...i + l] =
MACM 101 Discrete Mathematics I
MACM 101 Discrete Mathematics I Exercises on Combinatorics, Probability, Languages and Integers. Due: Tuesday, November 2th (at the beginning of the class) Reminder: the work you submit must be your own.
FACTORING ax 2 bx c. Factoring Trinomials with Leading Coefficient 1
5.7 Factoring ax 2 bx c (5-49) 305 5.7 FACTORING ax 2 bx c In this section In Section 5.5 you learned to factor certain special polynomials. In this section you will learn to factor general quadratic polynomials.
STAFF ADDITIONAL COMPENSATION - REJECT RULES
1. District Number must be numeric in the range 01-69 or 71-75 and must be correct for the district submitting the data. -record rejected- The first two records listed below would be loaded to the data
Questions and Answers in Computer Science. By Yonas Tesfazghi Weldeselassie http://www.ictp.trieste.it/ weldesel
Questions and Answers in Computer Science By Yonas Tesfazghi Weldeselassie http://www.ictp.trieste.it/ weldesel The Questions in this material are collected from Computer Science Subject Graduate Record
RDB-MINER: A SQL-Based Algorithm for Mining True Relational Databases
998 JOURNAL OF SOFTWARE, VOL. 5, NO. 9, SEPTEMBER 2010 RDB-MINER: A SQL-Based Algorithm for Mining True Relational Databases Abdallah Alashqur Faculty of Information Technology Applied Science University
TECHNOLOGY Computer Programming II Grade: 9-12 Standard 2: Technology and Society Interaction
Standard 2: Technology and Society Interaction Technology and Ethics Analyze legal technology issues and formulate solutions and strategies that foster responsible technology usage. 1. Practice responsible
Elementary Number Theory and Methods of Proof. CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.
Elementary Number Theory and Methods of Proof CSE 215, Foundations of Computer Science Stony Brook University http://www.cs.stonybrook.edu/~cse215 1 Number theory Properties: 2 Properties of integers (whole
The Halting Problem is Undecidable
185 Corollary G = { M, w w L(M) } is not Turing-recognizable. Proof. = ERR, where ERR is the easy to decide language: ERR = { x { 0, 1 }* x does not have a prefix that is a valid code for a Turing machine
Knuth-Morris-Pratt Algorithm
December 18, 2011 outline Definition History Components of KMP Example Run-Time Analysis Advantages and Disadvantages References Definition: Best known for linear time for exact matching. Compares from
11 Multivariate Polynomials
CS 487: Intro. to Symbolic Computation Winter 2009: M. Giesbrecht Script 11 Page 1 (These lecture notes were prepared and presented by Dan Roche.) 11 Multivariate Polynomials References: MC: Section 16.6
Database Design and Normalization
Database Design and Normalization CPS352: Database Systems Simon Miner Gordon College Last Revised: 9/27/12 Agenda Check-in Functional Dependencies (continued) Design Project E-R Diagram Presentations
CS 1133, LAB 2: FUNCTIONS AND TESTING http://www.cs.cornell.edu/courses/cs1133/2015fa/labs/lab02.pdf
CS 1133, LAB 2: FUNCTIONS AND TESTING http://www.cs.cornell.edu/courses/cs1133/2015fa/labs/lab02.pdf First Name: Last Name: NetID: The purpose of this lab is to help you to better understand functions:
Reading 13 : Finite State Automata and Regular Expressions
CS/Math 24: Introduction to Discrete Mathematics Fall 25 Reading 3 : Finite State Automata and Regular Expressions Instructors: Beck Hasti, Gautam Prakriya In this reading we study a mathematical model
Computer Programming I
Computer Programming I Levels: 10-12 Units of Credit: 1.0 CIP Code: 11.0201 Core Code: 35-02-00-00-030 Prerequisites: Secondary Math I, Keyboarding Proficiency, Computer Literacy requirement (e.g. Exploring
Resolution. Informatics 1 School of Informatics, University of Edinburgh
Resolution In this lecture you will see how to convert the natural proof system of previous lectures into one with fewer operators and only one proof rule. You will see how this proof system can be used
Command Scripts. 13.1 Running scripts: include and commands
13 Command Scripts You will probably find that your most intensive use of AMPL s command environment occurs during the initial development of a model, when the results are unfamiliar and changes are frequent.
Mathematics for Computer Science/Software Engineering. Notes for the course MSM1F3 Dr. R. A. Wilson
Mathematics for Computer Science/Software Engineering Notes for the course MSM1F3 Dr. R. A. Wilson October 1996 Chapter 1 Logic Lecture no. 1. We introduce the concept of a proposition, which is a statement
SQL Server Table Design - Best Practices
CwJ Consulting Ltd SQL Server Table Design - Best Practices Author: Andy Hogg Date: 20 th February 2015 Version: 1.11 SQL Server Table Design Best Practices 1 Contents 1. Introduction... 3 What is a table?...
Programming and Software Development CTAG Alignments
Programming and Software Development CTAG Alignments This document contains information about four Career-Technical Articulation Numbers (CTANs) for Programming and Software Development Career-Technical
A CONSTRUCTION OF THE UNIVERSAL COVER AS A FIBER BUNDLE
A CONSTRUCTION OF THE UNIVERSAL COVER AS A FIBER BUNDLE DANIEL A. RAMRAS In these notes we present a construction of the universal cover of a path connected, locally path connected, and semi-locally simply
Intermediate Math Circles March 7, 2012 Linear Diophantine Equations II
Intermediate Math Circles March 7, 2012 Linear Diophantine Equations II Last week: How to find one solution to a linear Diophantine equation This week: How to find all solutions to a linear Diophantine
Boolean Algebra. Boolean Algebra. Boolean Algebra. Boolean Algebra
2 Ver..4 George Boole was an English mathematician of XIX century can operate on logic (or Boolean) variables that can assume just 2 values: /, true/false, on/off, closed/open Usually value is associated
CSC 180 H1F Algorithm Runtime Analysis Lecture Notes Fall 2015
1 Introduction These notes introduce basic runtime analysis of algorithms. We would like to be able to tell if a given algorithm is time-efficient, and to be able to compare different algorithms. 2 Linear
6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010. Class 4 Nancy Lynch
6.045: Automata, Computability, and Complexity Or, Great Ideas in Theoretical Computer Science Spring, 2010 Class 4 Nancy Lynch Today Two more models of computation: Nondeterministic Finite Automata (NFAs)
#820 Computer Programming 1A
Computer Programming I Levels: 10-12 Units of Credit: 1.0 CIP Code: 11.0201 Core Code: 35-02-00-00-030 Prerequisites: Secondary Math I, Keyboarding Proficiency, Computer Literacy requirement Semester 1
Why? A central concept in Computer Science. Algorithms are ubiquitous.
Analysis of Algorithms: A Brief Introduction Why? A central concept in Computer Science. Algorithms are ubiquitous. Using the Internet (sending email, transferring files, use of search engines, online
NP-Completeness and Cook s Theorem
NP-Completeness and Cook s Theorem Lecture notes for COM3412 Logic and Computation 15th January 2002 1 NP decision problems The decision problem D L for a formal language L Σ is the computational task:
CS101 Lecture 11: Number Systems and Binary Numbers. Aaron Stevens 14 February 2011
CS101 Lecture 11: Number Systems and Binary Numbers Aaron Stevens 14 February 2011 1 2 1 3!!! MATH WARNING!!! TODAY S LECTURE CONTAINS TRACE AMOUNTS OF ARITHMETIC AND ALGEBRA PLEASE BE ADVISED THAT CALCULTORS
Table of Contents. Pointshop Betting. Installation
User Guide Table of Contents Pointshop Betting... 1 Installation... 1 Configuration... 2 Customizing Theme... 2 Customizing Betting and UI Settings... 2 Customizing Chat Notice Settings... 3 Sound Settings...
Unit 3 Boolean Algebra (Continued)
Unit 3 Boolean Algebra (Continued) 1. Exclusive-OR Operation 2. Consensus Theorem Department of Communication Engineering, NCTU 1 3.1 Multiplying Out and Factoring Expressions Department of Communication
Lossless Data Compression Standard Applications and the MapReduce Web Computing Framework
Lossless Data Compression Standard Applications and the MapReduce Web Computing Framework Sergio De Agostino Computer Science Department Sapienza University of Rome Internet as a Distributed System Modern
We will learn the Python programming language. Why? Because it is easy to learn and many people write programs in Python so we can share.
LING115 Lecture Note Session #4 Python (1) 1. Introduction As we have seen in previous sessions, we can use Linux shell commands to do simple text processing. We now know, for example, how to count words.
1 Approximating Set Cover
CS 05: Algorithms (Grad) Feb 2-24, 2005 Approximating Set Cover. Definition An Instance (X, F ) of the set-covering problem consists of a finite set X and a family F of subset of X, such that every elemennt
How To Find Out What A Key Is In A Database Engine
Database design theory, Part I Functional dependencies Introduction As we saw in the last segment, designing a good database is a non trivial matter. The E/R model gives a useful rapid prototyping tool,
Math 53 Worksheet Solutions- Minmax and Lagrange
Math 5 Worksheet Solutions- Minmax and Lagrange. Find the local maximum and minimum values as well as the saddle point(s) of the function f(x, y) = e y (y x ). Solution. First we calculate the partial
On line construction of suffix trees 1
(To appear in ALGORITHMICA) On line construction of suffix trees 1 Esko Ukkonen Department of Computer Science, University of Helsinki, P. O. Box 26 (Teollisuuskatu 23), FIN 00014 University of Helsinki,
each college c i C has a capacity q i - the maximum number of students it will admit
n colleges in a set C, m applicants in a set A, where m is much larger than n. each college c i C has a capacity q i - the maximum number of students it will admit each college c i has a strict order i
AIMMS Function Reference - Set Functions
AIMMS Function Reference - Set Functions This file contains only one chapter of the book. For a free download of the complete book in pdf format, please visit www.aimms.com Aimms 3.13 Set Related Functions
( 1)2 + 2 2 + 2 2 = 9 = 3 We would like to make the length 6. The only vectors in the same direction as v are those
1.(6pts) Which of the following vectors has the same direction as v 1,, but has length 6? (a), 4, 4 (b),, (c) 4,, 4 (d), 4, 4 (e) 0, 6, 0 The length of v is given by ( 1) + + 9 3 We would like to make
Logic in general. Inference rules and theorem proving
Logical Agents Knowledge-based agents Logic in general Propositional logic Inference rules and theorem proving First order logic Knowledge-based agents Inference engine Knowledge base Domain-independent
Regular Expressions with Nested Levels of Back Referencing Form a Hierarchy
Regular Expressions with Nested Levels of Back Referencing Form a Hierarchy Kim S. Larsen Odense University Abstract For many years, regular expressions with back referencing have been used in a variety
COLLEGE ALGEBRA. Paul Dawkins
COLLEGE ALGEBRA Paul Dawkins Table of Contents Preface... iii Outline... iv Preliminaries... Introduction... Integer Exponents... Rational Exponents... 9 Real Exponents...5 Radicals...6 Polynomials...5
Magit-Popup User Manual
Magit-Popup User Manual for version 2.5 Jonas Bernoulli Copyright (C) 2015-2016 Jonas Bernoulli You can redistribute this document and/or modify it under the terms of the GNU General
VHDL Test Bench Tutorial
University of Pennsylvania Department of Electrical and Systems Engineering ESE171 - Digital Design Laboratory VHDL Test Bench Tutorial Purpose The goal of this tutorial is to demonstrate how to automate
Moving from CS 61A Scheme to CS 61B Java
Moving from CS 61A Scheme to CS 61B Java Introduction Java is an object-oriented language. This document describes some of the differences between object-oriented programming in Scheme (which we hope you
If-Then-Else Problem (a motivating example for LR grammars)
If-Then-Else Problem (a motivating example for LR grammars) If x then y else z If a then if b then c else d this is analogous to a bracket notation when left brackets >= right brackets: [ [ ] ([ i ] j,
Computing Orthonormal Sets in 2D, 3D, and 4D
Computing Orthonormal Sets in 2D, 3D, and 4D David Eberly Geometric Tools, LLC http://www.geometrictools.com/ Copyright c 1998-2016. All Rights Reserved. Created: March 22, 2010 Last Modified: August 11,
Lecture 2: Universality
CS 710: Complexity Theory 1/21/2010 Lecture 2: Universality Instructor: Dieter van Melkebeek Scribe: Tyson Williams In this lecture, we introduce the notion of a universal machine, develop efficient universal
Fundamentele Informatica II
Fundamentele Informatica II Answer to selected exercises 1 John C Martin: Introduction to Languages and the Theory of Computation M.M. Bonsangue (and J. Kleijn) Fall 2011 Let L be a language. It is clear
Finite Automata. Reading: Chapter 2
Finite Automata Reading: Chapter 2 1 Finite Automaton (FA) Informally, a state diagram that comprehensively captures all possible states and transitions that a machine can take while responding to a stream
New Techniques for Regular Expression Searching
New Techniques for Regular Expression Searching Gonzalo Navarro Mathieu Raffinot Abstract We present two new techniques for regular expression searching and use them to derive faster practical algorithms.
