CSE20: Discrete Mathematics

Size: px
Start display at page:

Download "CSE20: Discrete Mathematics"

Transcription

1 Spring 2018

2 Summary Today: Induction, Program Correctness Reading: Chap. 5

3 Division Theorem Theorem: For every integer a and positive integer d 1, there exist integers q, r such that a = qd + r and 0 r < d Which of the following statements express the division theorem? 1 a. d 1. q. r.[(a = qd + r) (0 r < d)] 2 d 1. a. r. q.[(a = qd + r) (0 r < d)] 3 a. q. d 1. r[(a = qd + r) (0 r < d)] 4 d 1. a. q.[(a qd) {0,..., d 1}] (A) 1; (B) 1,2; (C) 1,2,3; (D) 1,2,3,4; (E) 1,2,4

4 Proof of the Division Theorem Theorem: d 1. a. q.(a qd) {0,..., d 1} Proof: Let d 1 be any positive integer, and D = {0,..., d 1}. WTP: a. q.(a qd) D We first prove it for a 0, by (strong) induction on a. Base case (a < d): Let q = 0. So, a qd = a D. Inductive Step (a d): Inductive Hypothesis: Assume a {0,..., a 1}. q.(a q d) D WTP: q.(a qd) D. Let a = a d. Notice: a 0 and a < a. By I.H. q.(a q d) D So, q = q + 1 such that a qd = (a + d) (q + 1)d = a q d D.

5 Proof of the Division Theorem (cont.) What we proved so far: Lemma: a 0. q.(a qd) D WTP: a < 0. q.(a qd) D Proof: Let a = (a + 1) 0. By Division Lemma, q.(a q d) D Let q = (q + 1). Then, a qd = a 1 + (q + 1)d = (d 1) (a q d) D. Homework: give alternative proof for a < 0 by induction on a.

6 Division Algorithm (python) def divlemma(a,d): # a,d: integers, a>=0, d>0 if (a < d): return 0 else: return (1 + divlemma(a-d,d))

7 Division Algorithm (python) def divlemma(a,d): # a,d: integers, a>=0, d>0 if (a < d): return 0 else: return (1 + divlemma(a-d,d)) def divthm(a,d): # a,d: integers, d>0 if (a>=0): return (divlemma(a,d)) else: return (-(1+divLemma(-(a+1),d)))

8 Division Algorithm (haskell) divlemma :: (Integer,Integer) -> Integer divlemma (a,d) = if (a < d) then 0 else 1 + divlemma(a-d,d) divthm :: (Integer,Integer) -> Integer divthm (a,d) = if (a >= 0) then divlemma(a,d) else -(1 + divlemma(-(a+1),d))

9 Correctness of (Recursive) Programs divlemma :: (Integer,Integer) -> Integer divlemma (a,d) = if (a < d) then 0 else 1 + divlemma(a-d,d) Claim: For all d and a 0, divlemma(a,d) returns an integer q such that a qd {0,..., d 1}. Proof: By induction on a Base case (a < d): Inductive Step (a d):

10 Correctness of divthm divthm :: (Integer,Integer) -> Integer divthm (a,d) = if (a >= 0) then divlemma(a,d) else -(1 + divlemma(-(a+1),d)) Assume divlemma is correct. Claim: For all d and a, divthm(a,d) returns an integer q such that a qd {0,..., d 1}.

11 Correctness of divthm divthm :: (Integer,Integer) -> Integer divthm (a,d) = if (a >= 0) then divlemma(a,d) else -(1 + divlemma(-(a+1),d)) Assume divlemma is correct. Claim: For all d and a, divthm(a,d) returns an integer q such that a qd {0,..., d 1}. Question: What proof method would you use? (A) Proof by induction (B) Proof by contraposition (C) Proof by cases (D) Test the program on some random inputs

12 Another program f :: Integer -> Integer f n = if (n > 0) then 1+f(n-2) else 0 What function does the program compute? (A) f (n) = 1 + (n 2) (B) f (n) = n/2 (C) f (n) = n/2 (D) f (n) = max( n/2, 0) (E) f (n) = max( n/2, 0)

13 Let s prove it f :: Integer -> Integer f n = if (n > 0) then 1+f(n-2) else 0 Claim: f (n) = max( n/2, 0)

14 Let s prove it f :: Integer -> Integer f n = if (n > 0) then 1+f(n-2) else 0 Claim: f (n) = max( n/2, 0) Proof: By (strong) induction on n Base case: If n 0 then f(n) = 0 and max( 0/2, 0) = max(0, 0) = 0 Inductive Step (n 1): Assume f (k) = max( k/2, 0) for all k n f (n + 1) =...

15 Imperative programs def f(n,m): # n,m: integers, n,m >=0, k=0; while (n > 0): n := n-1 k := k + m return k What function does f(n,m) compute? (A) f (n, m) = n + m (B) f (n, m) = n 1 (C) f (n, m) = n m (D) None of the above

16 Program correctness Prog: some program or code fragment with variables x, y, z,... p, q: predicates on variables x, y, z,... p{prog}q: Prog is (partially) correct with respect to initial assertion p and final assertion q if p is true before executing Prog, and Prog terminates, then q is true after executing Prog

17 Program correctness: examples Prog: x :=1 ; z := y + x p : y = 1 q : z = 3 p{prog}q? (A) True; (B) False; (C) Don t know

18 Program correctness: examples Prog: x :=1 ; z := y + x p : y = 1 q : z = 3 p{prog}q? (A) True; (B) False; (C) Don t know p : T (true) q : z > y p{prog}q? (A) True; (B) False; (C) Don t know

19 Program correctness: conditionals Prog = if condition then Prog1 else Prog2 (p condition){prog1}q, (p condition){prog2}q p{prog}q

20 Program correctness: loops Prog = while condition do Prog1 (p condition){prog1}p p{prog}(p condition)

21 How to analyze imperative programs def f(n,m): # n,m: integers, n,m >=0, k=0; c=n; while (c > 0): c := c-1 k := k + m return k Loop Invariant: L(n, m, k, c) L(n, m, k, c) is satisfied before entering the loop If (c > 0) and L(n, m, k, c) are satisfied before entering the loop, then L(n, m, k, c ) is satisfied after executing the loop body once. Claim: L(n, m, k, c) is satisfied at each iteration of the loop Proof: By induction on the number of iterations.

22 Loop Invariants # { L(x,y,z) } while T(x,y,z): Step1(x,y,z) Step2(x,y,z) Step3(x,y,z) # { L(x,y,z) and not(t(x,y,z)) } Assume L(x, y, z) is a loop invariant, and it is satisfied before entering the loop. Then upon exiting the loop we have L(x, y, z) (T (x, y, z)). If the loop terminates, then upon termination we have L(x, y, z) (T (x, y, z)).

23 Loop Invariant Example def f(n,m): # n,m: integers n>=0 k=0; c=n; while (c > 0): c := c-1 k := k + m return k L : (k + c m = n m) (c 0) L is a loop invariant Upon termination we have: (k + c m = n m) (c 0) (c 0) c = 0 and the program outputs k = k + c m = n m.

C241 Homework Assignment 7

C241 Homework Assignment 7 C24 Homework Assignment 7. Prove that for all whole numbers n, n i 2 = n(n + (2n + The proof is by induction on k with hypothesis H(k i 2 = k(k + (2k + base case: To prove H(, i 2 = = = 2 3 = ( + (2 +

More information

Dynamic Semantics. Dynamic Semantics. Operational Semantics Axiomatic Semantics Denotational Semantic. Operational Semantics

Dynamic Semantics. Dynamic Semantics. Operational Semantics Axiomatic Semantics Denotational Semantic. Operational Semantics Dynamic Semantics Operational Semantics Denotational Semantic Dynamic Semantics Operational Semantics Operational Semantics Describe meaning by executing program on machine Machine can be actual or simulated

More information

Induction. Induction. Induction. Induction. Induction. Induction 2/22/2018

Induction. Induction. Induction. Induction. Induction. Induction 2/22/2018 The principle of mathematical induction is a useful tool for proving that a certain predicate is true for all natural numbers. It cannot be used to discover theorems, but only to prove them. If we have

More information

Recursion: Introduction and Correctness

Recursion: Introduction and Correctness Recursion: Introduction and Correctness CSE21 Winter 2017, Day 7 (B00), Day 4-5 (A00) January 25, 2017 http://vlsicad.ucsd.edu/courses/cse21-w17 Today s Plan From last time: intersecting sorted lists and

More information

Hoare Logic: Part II

Hoare Logic: Part II Hoare Logic: Part II COMP2600 Formal Methods for Software Engineering Jinbo Huang Australian National University COMP 2600 Hoare Logic II 1 Factorial {n 0} fact := 1; i := n; while (i >0) do fact := fact

More information

Mathematical Induction. How does discrete math help us. How does discrete math help (CS160)? How does discrete math help (CS161)?

Mathematical Induction. How does discrete math help us. How does discrete math help (CS160)? How does discrete math help (CS161)? How does discrete math help us Helps create a solution (program) Helps analyze a program How does discrete math help (CS160)? Helps create a solution (program) q Logic helps you understand conditionals

More information

In this episode of The Verification Corner, Rustan Leino talks about Loop Invariants. He gives a brief summary of the theoretical foundations and

In this episode of The Verification Corner, Rustan Leino talks about Loop Invariants. He gives a brief summary of the theoretical foundations and In this episode of The Verification Corner, Rustan Leino talks about Loop Invariants. He gives a brief summary of the theoretical foundations and shows how a program can sometimes be systematically constructed

More information

Arithmetic Algorithms, Part 1

Arithmetic Algorithms, Part 1 Arithmetic Algorithms, Part 1 DPV Chapter 1 Jim Royer EECS January 18, 2019 Royer Arithmetic Algorithms, Part 1 1/ 15 Multiplication à la Français function multiply(a, b) // input: two n-bit integers a

More information

CSE 20 DISCRETE MATH SPRING

CSE 20 DISCRETE MATH SPRING CSE 20 DISCRETE MATH SPRING 2016 http://cseweb.ucsd.edu/classes/sp16/cse20-ac/ Today's learning goals Describe computer representation of sets with bitstrings Define and compute the cardinality of finite

More information

SEQUENCES, MATHEMATICAL INDUCTION, AND RECURSION

SEQUENCES, MATHEMATICAL INDUCTION, AND RECURSION CHAPTER 5 SEQUENCES, MATHEMATICAL INDUCTION, AND RECURSION Copyright Cengage Learning. All rights reserved. SECTION 5.4 Strong Mathematical Induction and the Well-Ordering Principle for the Integers Copyright

More information

Program verification. Hoare triples. Assertional semantics (cont) Example: Semantics of assignment. Assertional semantics of a program

Program verification. Hoare triples. Assertional semantics (cont) Example: Semantics of assignment. Assertional semantics of a program Program verification Assertional semantics of a program Meaning of a program: relation between its inputs and outputs; specified by input assertions (pre-conditions) and output assertions (post-conditions)

More information

Imperative Insertion Sort

Imperative Insertion Sort Imperative Insertion Sort Christian Sternagel April 17, 2016 Contents 1 Looping Constructs for Imperative HOL 1 1.1 While Loops............................ 1 1.2 For Loops.............................

More information

ICS141: Discrete Mathematics for Computer Science I

ICS141: Discrete Mathematics for Computer Science I ICS141: Discrete Mathematics for Computer Science I Dept. Information & Computer Sci., Jan Stelovsky based on slides by Dr. Baek and Dr. Still Originals by Dr. M. P. Frank and Dr. J.L. Gross Provided by

More information

Discrete Math in Computer Science Solutions to Practice Problems for Midterm 2

Discrete Math in Computer Science Solutions to Practice Problems for Midterm 2 Discrete Math in Computer Science Solutions to Practice Problems for Midterm 2 CS 30, Fall 2018 by Professor Prasad Jayanti Problems 1. Let g(0) = 2, g(1) = 1, and g(n) = 2g(n 1) + g(n 2) whenever n 2.

More information

NOTE: You have 2 hours, please plan your time. Problems are not ordered by difficulty.

NOTE: You have 2 hours, please plan your time. Problems are not ordered by difficulty. EXAM 2 solutions (COT3100, Sitharam, Spring 2017) NAME:last first: UF-ID Section NOTE: You have 2 hours, please plan your time. Problems are not ordered by difficulty. (1) Are the following functions one-to-one

More information

CSCE 222 Discrete Structures for Computing. Review for Exam 2. Dr. Hyunyoung Lee !!!

CSCE 222 Discrete Structures for Computing. Review for Exam 2. Dr. Hyunyoung Lee !!! CSCE 222 Discrete Structures for Computing Review for Exam 2 Dr. Hyunyoung Lee 1 Strategy for Exam Preparation - Start studying now (unless have already started) - Study class notes (lecture slides and

More information

CSC B36 Additional Notes sample induction and well-ordering proofs. c Nick Cheng

CSC B36 Additional Notes sample induction and well-ordering proofs. c Nick Cheng CSC B36 Additional Notes sample induction and well-ordering proofs c Nick Cheng Introduction We present examples of induction proofs here in hope that they can be used as models when you write your own

More information

Mathematical Structures Combinations and Permutations

Mathematical Structures Combinations and Permutations Definitions: Suppose S is a (finite) set and n, k 0 are integers The set C(S, k) of k - combinations consists of all subsets of S that have exactly k elements The set P (S, k) of k - permutations consists

More information

COT 2104 Homework Assignment 1 (Answers)

COT 2104 Homework Assignment 1 (Answers) 1) Classify true or false COT 2104 Homework Assignment 1 (Answers) a) 4 2 + 2 and 7 < 50. False because one of the two statements is false. b) 4 = 2 + 2 7 < 50. True because both statements are true. c)

More information

Top Down Design. Gunnar Gotshalks 03-1

Top Down Design. Gunnar Gotshalks 03-1 Top Down Design 03-1 Top Down Description Top down is also known as step wise refinement and functional decomposition Given an operation, there are only the following three choices for refinement» Sequence

More information

MATH10040: Numbers and Functions Homework 1: Solutions

MATH10040: Numbers and Functions Homework 1: Solutions MATH10040: Numbers and Functions Homework 1: Solutions 1. Prove that a Z and if 3 divides into a then 3 divides a. Solution: The statement to be proved is equivalent to the statement: For any a N, if 3

More information

Stepwise Refinement! Top Down Design!

Stepwise Refinement! Top Down Design! Stepwise Refinement Top Down Design 11-1 On Top Down Design Useful in creating a function or algorithm when the input and output data structures correspond» If the input and output data structures do not

More information

Discrete Mathematics & Mathematical Reasoning Predicates, Quantifiers and Proof Techniques

Discrete Mathematics & Mathematical Reasoning Predicates, Quantifiers and Proof Techniques Discrete Mathematics & Mathematical Reasoning Predicates, Quantifiers and Proof Techniques Colin Stirling Informatics Some slides based on ones by Myrto Arapinis Colin Stirling (Informatics) Discrete Mathematics

More information

CmSc 250 Intro to Algorithms. Mathematical Review. 1. Basic Algebra. (a + b) 2 = a 2 + 2ab + b 2 (a - b) 2 = a 2-2ab + b 2 a 2 - b 2 = (a + b)(a - b)

CmSc 250 Intro to Algorithms. Mathematical Review. 1. Basic Algebra. (a + b) 2 = a 2 + 2ab + b 2 (a - b) 2 = a 2-2ab + b 2 a 2 - b 2 = (a + b)(a - b) CmSc 250 Intro to Algorithms Mathematical Review 1. Basic Algebra (a + b) 2 = a 2 + 2ab + b 2 (a - b) 2 = a 2-2ab + b 2 a 2 - b 2 = (a + b)(a - b) a/x + b/y = (ay + bx)/xy 2. Exponents X n = XXX..X, n

More information

C241 Homework Assignment 9

C241 Homework Assignment 9 C41 Homework Assignment 9 1. The language L and functions R, A, and T defined below are the same as in Section 7.6. L {a, b, } + 1. L a. u L au L b. u L bu L 3. n. e. A: L L R: L L T : L L 1. A(, v) =

More information

1. (16 points) Circle T if the corresponding statement is True or F if it is False.

1. (16 points) Circle T if the corresponding statement is True or F if it is False. Name Solution Key Show All Work!!! Page 1 1. (16 points) Circle T if the corresponding statement is True or F if it is False. T F The sequence {1, 1, 1, 1, 1, 1...} is an example of an Alternating sequence.

More information

Proofs of Correctness: Introduction to Axiomatic Verification

Proofs of Correctness: Introduction to Axiomatic Verification Proofs of Correctness: Introduction to Axiomatic Verification Introduction Weak correctness predicate Assignment statements Sequencing Selection statements Iteration 1 Introduction What is Axiomatic Verification?

More information

Normal Forms of Propositional Logic

Normal Forms of Propositional Logic Normal Forms of Propositional Logic Bow-Yaw Wang Institute of Information Science Academia Sinica, Taiwan September 12, 2017 Bow-Yaw Wang (Academia Sinica) Normal Forms of Propositional Logic September

More information

C241 Homework Assignment 8

C241 Homework Assignment 8 C241 Homework Assignment 8 1. Estimate the performance of the Bubble Sort program for i from 1 to N 1 by 1 do for j from 1 to i 1 by 1 do if A[j ] A[j + 1] then skip; else t := A[j ] ; A[j ] := A[j + 1]

More information

Review (11.1) 1. A sequence is an infinite list of numbers {a n } n=1 = a 1, a 2, a 3, The sequence is said to converge if lim

Review (11.1) 1. A sequence is an infinite list of numbers {a n } n=1 = a 1, a 2, a 3, The sequence is said to converge if lim Announcements: Note that we have taking the sections of Chapter, out of order, doing section. first, and then the rest. Section. is motivation for the rest of the chapter. Do the homework questions from

More information

Hoare Logic: Reasoning About Imperative Programs

Hoare Logic: Reasoning About Imperative Programs Hoare Logic: Reasoning About Imperative Programs COMP1600 / COMP6260 Dirk Pattinson Australian National University Semester 2, 2017 Catch Up / Drop in Lab When Fridays, 15.00-17.00 Where N335, CSIT Building

More information

CSE 311: Foundations of Computing. Lecture 14: Induction

CSE 311: Foundations of Computing. Lecture 14: Induction CSE 311: Foundations of Computing Lecture 14: Induction Mathematical Induction Method for proving statements about all natural numbers A new logical inference rule! It only applies over the natural numbers

More information

COM S 330 Homework 08 Solutions. Type your answers to the following questions and submit a PDF file to Blackboard. One page per problem.

COM S 330 Homework 08 Solutions. Type your answers to the following questions and submit a PDF file to Blackboard. One page per problem. COM S 0 Homework 08 Solutions Type your answers to the following questions and submit a PDF file to Blackboard. One page per problem. Problem 1. [10pts] Let M = (S, T, s 0 ) be the state machine where

More information

CSC 344 Algorithms and Complexity. Proof by Mathematical Induction

CSC 344 Algorithms and Complexity. Proof by Mathematical Induction CSC 344 Algorithms and Complexity Lecture #1 Review of Mathematical Induction Proof by Mathematical Induction Many results in mathematics are claimed true for every positive integer. Any of these results

More information

Path Testing and Test Coverage. Chapter 9

Path Testing and Test Coverage. Chapter 9 Path Testing and Test Coverage Chapter 9 Structural Testing Also known as glass/white/open box testing Structural testing is based on using specific knowledge of the program source text to define test

More information

The natural numbers. Definition. Let X be any inductive set. We define the set of natural numbers as N = C(X).

The natural numbers. Definition. Let X be any inductive set. We define the set of natural numbers as N = C(X). The natural numbers As mentioned earlier in the course, the natural numbers can be constructed using the axioms of set theory. In this note we want to discuss the necessary details of this construction.

More information

Lecture 7 Feb 4, 14. Sections 1.7 and 1.8 Some problems from Sec 1.8

Lecture 7 Feb 4, 14. Sections 1.7 and 1.8 Some problems from Sec 1.8 Lecture 7 Feb 4, 14 Sections 1.7 and 1.8 Some problems from Sec 1.8 Section Summary Proof by Cases Existence Proofs Constructive Nonconstructive Disproof by Counterexample Nonexistence Proofs Uniqueness

More information

Path Testing and Test Coverage. Chapter 9

Path Testing and Test Coverage. Chapter 9 Path Testing and Test Coverage Chapter 9 Structural Testing Also known as glass/white/open box testing Structural testing is based on using specific knowledge of the program source text to define test

More information

CS 4407 Algorithms Lecture 2: Iterative and Divide and Conquer Algorithms

CS 4407 Algorithms Lecture 2: Iterative and Divide and Conquer Algorithms CS 4407 Algorithms Lecture 2: Iterative and Divide and Conquer Algorithms Prof. Gregory Provan Department of Computer Science University College Cork 1 Lecture Outline CS 4407, Algorithms Growth Functions

More information

CVO103: Programming Languages. Lecture 2 Inductive Definitions (2)

CVO103: Programming Languages. Lecture 2 Inductive Definitions (2) CVO103: Programming Languages Lecture 2 Inductive Definitions (2) Hakjoo Oh 2018 Spring Hakjoo Oh CVO103 2018 Spring, Lecture 2 March 13, 2018 1 / 20 Contents More examples of inductive definitions natural

More information

Programming Languages and Compilers (CS 421)

Programming Languages and Compilers (CS 421) Programming Languages and Compilers (CS 421) Sasa Misailovic 4110 SC, UIUC https://courses.engr.illinois.edu/cs421/fa2017/cs421a Based in part on slides by Mattox Beckman, as updated by Vikram Adve, Gul

More information

x P(x) x P(x) CSE 311: Foundations of Computing announcements last time: quantifiers, review: logical Inference Fall 2013 Lecture 7: Proofs

x P(x) x P(x) CSE 311: Foundations of Computing announcements last time: quantifiers, review: logical Inference Fall 2013 Lecture 7: Proofs CSE 311: Foundations of Computing Fall 2013 Lecture 7: Proofs announcements Reading assignment Logical inference 1.6-1.7 7 th Edition 1.5-1.7 6 th Edition Homework #2 due today last time: quantifiers,

More information

MATH 55 - HOMEWORK 6 SOLUTIONS. 1. Section = 1 = (n + 1) 3 = 2. + (n + 1) 3. + (n + 1) 3 = n2 (n + 1) 2.

MATH 55 - HOMEWORK 6 SOLUTIONS. 1. Section = 1 = (n + 1) 3 = 2. + (n + 1) 3. + (n + 1) 3 = n2 (n + 1) 2. MATH 55 - HOMEWORK 6 SOLUTIONS Exercise Section 5 Proof (a) P () is the statement ( ) 3 (b) P () is true since ( ) 3 (c) The inductive hypothesis is P (n): ( ) n(n + ) 3 + 3 + + n 3 (d) Assuming the inductive

More information

CSC236H Lecture 2. Ilir Dema. September 19, 2018

CSC236H Lecture 2. Ilir Dema. September 19, 2018 CSC236H Lecture 2 Ilir Dema September 19, 2018 Simple Induction Useful to prove statements depending on natural numbers Define a predicate P(n) Prove the base case P(b) Prove that for all n b, P(n) P(n

More information

Name CMSC203 Fall2008 Exam 2 Solution Key Show All Work!!! Page (16 points) Circle T if the corresponding statement is True or F if it is False.

Name CMSC203 Fall2008 Exam 2 Solution Key Show All Work!!! Page (16 points) Circle T if the corresponding statement is True or F if it is False. Name CMSC203 Fall2008 Exam 2 Solution Key Show All Work!!! Page ( points) Circle T if the corresponding statement is True or F if it is False T F GCD(,0) = 0 T F For every recursive algorithm, there is

More information

Finite Automata Theory and Formal Languages TMV027/DIT321 LP Recap: Logic, Sets, Relations, Functions

Finite Automata Theory and Formal Languages TMV027/DIT321 LP Recap: Logic, Sets, Relations, Functions Finite Automata Theory and Formal Languages TMV027/DIT321 LP4 2017 Formal proofs; Simple/strong induction; Mutual induction; Inductively defined sets; Recursively defined functions. Lecture 3 Ana Bove

More information

Math 230 Final Exam, Spring 2009

Math 230 Final Exam, Spring 2009 IIT Dept. Applied Mathematics, May 13, 2009 1 PRINT Last name: Signature: First name: Student ID: Math 230 Final Exam, Spring 2009 Conditions. 2 hours. No book, notes, calculator, cell phones, etc. Part

More information

The following techniques for methods of proofs are discussed in our text: - Vacuous proof - Trivial proof

The following techniques for methods of proofs are discussed in our text: - Vacuous proof - Trivial proof Ch. 1.6 Introduction to Proofs The following techniques for methods of proofs are discussed in our text - Vacuous proof - Trivial proof - Direct proof - Indirect proof (our book calls this by contraposition)

More information

Semantics and Verification of Software

Semantics and Verification of Software Semantics and Verification of Software Thomas Noll Software Modeling and Verification Group RWTH Aachen University http://moves.rwth-aachen.de/teaching/ss-15/sv-sw/ The Denotational Approach Denotational

More information

ICS141: Discrete Mathematics for Computer Science I

ICS141: Discrete Mathematics for Computer Science I ICS141: Discrete Mathematics for Computer Science I Dept. Information & Computer Sci., Jan Stelovsky based on slides by Dr. Baek and Dr. Still Originals by Dr. M. P. Frank and Dr. J.L. Gross Provided by

More information

Predicate Logic - Undecidability

Predicate Logic - Undecidability CS402, Spring 2016 Undecidable Problems Does the following program halts? (1) N : n, total, x, y, z (2) n GetUserInput() (3) total 3 (4) while true (5) for x 1 to total 2 (6) for y 1 to total x 1 (7) z

More information

directed weighted graphs as flow networks the Ford-Fulkerson algorithm termination and running time

directed weighted graphs as flow networks the Ford-Fulkerson algorithm termination and running time Network Flow 1 The Maximum-Flow Problem directed weighted graphs as flow networks the Ford-Fulkerson algorithm termination and running time 2 Maximum Flows and Minimum Cuts flows and cuts max flow equals

More information

Analysis of Algorithms

Analysis of Algorithms September 29, 2017 Analysis of Algorithms CS 141, Fall 2017 1 Analysis of Algorithms: Issues Correctness/Optimality Running time ( time complexity ) Memory requirements ( space complexity ) Power I/O utilization

More information

means is a subset of. So we say A B for sets A and B if x A we have x B holds. BY CONTRAST, a S means that a is a member of S.

means is a subset of. So we say A B for sets A and B if x A we have x B holds. BY CONTRAST, a S means that a is a member of S. 1 Notation For those unfamiliar, we have := means equal by definition, N := {0, 1,... } or {1, 2,... } depending on context. (i.e. N is the set or collection of counting numbers.) In addition, means for

More information

Mathematics for Decision Making: An Introduction. Lecture 8

Mathematics for Decision Making: An Introduction. Lecture 8 Mathematics for Decision Making: An Introduction Lecture 8 Matthias Köppe UC Davis, Mathematics January 29, 2009 8 1 Shortest Paths and Feasible Potentials Feasible Potentials Suppose for all v V, there

More information

Lecture Notes on Software Model Checking

Lecture Notes on Software Model Checking 15-414: Bug Catching: Automated Program Verification Lecture Notes on Software Model Checking Matt Fredrikson André Platzer Carnegie Mellon University Lecture 19 1 Introduction So far we ve focused on

More information

Mathematics 220 Homework 4 - Solutions. Solution: We must prove the two statements: (1) if A = B, then A B = A B, and (2) if A B = A B, then A = B.

Mathematics 220 Homework 4 - Solutions. Solution: We must prove the two statements: (1) if A = B, then A B = A B, and (2) if A B = A B, then A = B. 1. (4.46) Let A and B be sets. Prove that A B = A B if and only if A = B. Solution: We must prove the two statements: (1) if A = B, then A B = A B, and (2) if A B = A B, then A = B. Proof of (1): Suppose

More information

Floyd-Hoare Style Program Verification

Floyd-Hoare Style Program Verification Floyd-Hoare Style Program Verification Deepak D Souza Department of Computer Science and Automation Indian Institute of Science, Bangalore. 9 Feb 2017 Outline of this talk 1 Overview 2 Hoare Triples 3

More information

Show Your Work! Point values are in square brackets. There are 35 points possible. Some facts about sets are on the last page.

Show Your Work! Point values are in square brackets. There are 35 points possible. Some facts about sets are on the last page. Formal Methods Name: Key Midterm 2, Spring, 2007 Show Your Work! Point values are in square brackets. There are 35 points possible. Some facts about sets are on the last page.. Determine whether each of

More information

Foundations of Discrete Mathematics

Foundations of Discrete Mathematics Foundations of Discrete Mathematics Chapter 0 By Dr. Dalia M. Gil, Ph.D. Statement Statement is an ordinary English statement of fact. It has a subject, a verb, and a predicate. It can be assigned a true

More information

Fermat's Little Theorem

Fermat's Little Theorem Fermat's Little Theorem CS 2800: Discrete Structures, Spring 2015 Sid Chaudhuri Not to be confused with... Fermat's Last Theorem: x n + y n = z n has no integer solution for n > 2 Recap: Modular Arithmetic

More information

CS558 Programming Languages

CS558 Programming Languages CS558 Programming Languages Winter 2017 Lecture 2b Andrew Tolmach Portland State University 1994-2017 Semantics Informal vs. Formal Informal semantics Descriptions in English (or other natural language)

More information

Mid-Semester Quiz Second Semester, 2012

Mid-Semester Quiz Second Semester, 2012 THE AUSTRALIAN NATIONAL UNIVERSITY Mid-Semester Quiz Second Semester, 2012 COMP2600 (Formal Methods for Software Engineering) Writing Period: 1 hour duration Study Period: 10 minutes duration Permitted

More information

Homework #1. Denote the sum we are interested in as To find we subtract the sum to find that

Homework #1. Denote the sum we are interested in as To find we subtract the sum to find that Homework #1 CMSC351 - Spring 2013 PRINT Name : Due: Feb 12 th at the start of class o Grades depend on neatness and clarity. o Write your answers with enough detail about your approach and concepts used,

More information

CS173 Lecture B, September 10, 2015

CS173 Lecture B, September 10, 2015 CS173 Lecture B, September 10, 2015 Tandy Warnow September 11, 2015 CS 173, Lecture B September 10, 2015 Tandy Warnow Examlet Today Four problems: One induction proof One problem on simplifying a logical

More information

Axiomatic Semantics. Lecture 9 CS 565 2/12/08

Axiomatic Semantics. Lecture 9 CS 565 2/12/08 Axiomatic Semantics Lecture 9 CS 565 2/12/08 Axiomatic Semantics Operational semantics describes the meaning of programs in terms of the execution steps taken by an abstract machine Denotational semantics

More information

Inductive Definitions with Inference Rules 1 / 27

Inductive Definitions with Inference Rules 1 / 27 Inductive Definitions with Inference Rules 1 / 27 Outline Introduction Specifying inductive definitions Inference rules in action Judgments, axioms, and rules Reasoning about inductive definitions Direct

More information

Midterm Exam. CS 3110: Design and Analysis of Algorithms. June 20, Group 1 Group 2 Group 3

Midterm Exam. CS 3110: Design and Analysis of Algorithms. June 20, Group 1 Group 2 Group 3 Banner ID: Name: Midterm Exam CS 3110: Design and Analysis of Algorithms June 20, 2006 Group 1 Group 2 Group 3 Question 1.1 Question 2.1 Question 3.1 Question 1.2 Question 2.2 Question 3.2 Question 3.3

More information

n n P} is a bounded subset Proof. Let A be a nonempty subset of Z, bounded above. Define the set

n n P} is a bounded subset Proof. Let A be a nonempty subset of Z, bounded above. Define the set 1 Mathematical Induction We assume that the set Z of integers are well defined, and we are familiar with the addition, subtraction, multiplication, and division. In particular, we assume the following

More information

Department of Computer Science University at Albany, State University of New York Solutions to Sample Discrete Mathematics Examination I (Spring 2008)

Department of Computer Science University at Albany, State University of New York Solutions to Sample Discrete Mathematics Examination I (Spring 2008) Department of Computer Science University at Albany, State University of New York Solutions to Sample Discrete Mathematics Examination I (Spring 2008) Problem 1: Suppose A, B, C and D are arbitrary sets.

More information

15.082J & 6.855J & ESD.78J. Algorithm Analysis

15.082J & 6.855J & ESD.78J. Algorithm Analysis 15.082J & 6.855J & ESD.78J Algorithm Analysis 15.082 Overview of subject Importance of Algorithm Analysis Importance of homework Midterms Moving forward 2 Overview of lecture Proof techniques Proof by

More information

IS 709/809: Computational Methods for IS Research. Math Review: Algorithm Analysis

IS 709/809: Computational Methods for IS Research. Math Review: Algorithm Analysis IS 709/809: Computational Methods for IS Research Math Review: Algorithm Analysis Nirmalya Roy Department of Information Systems University of Maryland Baltimore County www.umbc.edu Topics Proof techniques

More information

Proving Programs Correct

Proving Programs Correct Proving Programs Correct Page 1 of 9 Proving Programs Correct How can we be sure that a piece of code does what we want it to do? One way is to try testing the code on a large group of data. Another is

More information

CS156: The Calculus of Computation

CS156: The Calculus of Computation Page 1 of 61 CS156: The Calculus of Computation Zohar Manna Winter 2010 Chapter 5: Program Correctness: Mechanics Page 2 of 61 Program A: LinearSearch with function specification @pre 0 l u < a @post rv

More information

CISC-102 Winter 2016 Lecture 11 Greatest Common Divisor

CISC-102 Winter 2016 Lecture 11 Greatest Common Divisor CISC-102 Winter 2016 Lecture 11 Greatest Common Divisor Consider any two integers, a,b, at least one non-zero. If we list the positive divisors in numeric order from smallest to largest, we would get two

More information

Axiomatic Semantics: Verification Conditions. Review of Soundness of Axiomatic Semantics. Questions? Announcements

Axiomatic Semantics: Verification Conditions. Review of Soundness of Axiomatic Semantics. Questions? Announcements Axiomatic Semantics: Verification Conditions Meeting 18, CSCI 5535, Spring 2010 Announcements Homework 6 is due tonight Today s forum: papers on automated testing using symbolic execution Anyone looking

More information

Loop Convergence. CS 536: Science of Programming, Fall 2018

Loop Convergence. CS 536: Science of Programming, Fall 2018 Solved Loop Convergence CS 536: Science of Programming, Fall 2018 A. Why Diverging programs aren t useful, so it s useful to know how to show that loops terminate. B. Objectives At the end of this lecture

More information

Axiomatic Semantics. Semantics of Programming Languages course. Joosep Rõõmusaare

Axiomatic Semantics. Semantics of Programming Languages course. Joosep Rõõmusaare Axiomatic Semantics Semantics of Programming Languages course Joosep Rõõmusaare 2014 Direct Proofs of Program Correctness Partial correctness properties are properties expressing that if a given program

More information

Imperative Insertion Sort

Imperative Insertion Sort Imperative Insertion Sort Christian Sternagel October 11, 2017 Contents 1 Looping Constructs for Imperative HOL 1 1.1 While Loops............................ 1 1.2 For Loops.............................

More information

Program verification. 18 October 2017

Program verification. 18 October 2017 Program verification 18 October 2017 Example revisited // assume(n>2); void partition(int a[], int n) { int pivot = a[0]; int lo = 1, hi = n-1; while (lo

More information

Math 242: Principles of Analysis Fall 2016 Homework 1 Part B solutions

Math 242: Principles of Analysis Fall 2016 Homework 1 Part B solutions Math 4: Principles of Analysis Fall 0 Homework Part B solutions. Let x, y, z R. Use the axioms of the real numbers to prove the following. a) If x + y = x + z then y = z. Solution. By Axiom a), there is

More information

Why do we need math in a data structures course?

Why do we need math in a data structures course? Math Review 1 Why do we need math in a data structures course? To nalyze data structures and algorithms Deriving formulae for time and memory requirements Will the solution scale? Quantify the results

More information

Computer Science 280 Spring 2002 Homework 2 Solutions by Omar Nayeem

Computer Science 280 Spring 2002 Homework 2 Solutions by Omar Nayeem Computer Science 280 Spring 2002 Homework 2 Solutions by Omar Nayeem Part A 1. (a) Some dog does not have his day. (b) Some action has no equal and opposite reaction. (c) Some golfer will never be eated

More information

Semantic Metatheory of SL: Mathematical Induction

Semantic Metatheory of SL: Mathematical Induction Semantic Metatheory of SL: Mathematical Induction Preliminary matters: why and when do we need Mathematical Induction? We need it when we want prove that a certain claim (n) holds for all n N (0, 1, 2,

More information

G54FOP: Lecture 17 & 18 Denotational Semantics and Domain Theory III & IV

G54FOP: Lecture 17 & 18 Denotational Semantics and Domain Theory III & IV G54FOP: Lecture 17 & 18 Denotational Semantics and Domain Theory III & IV Henrik Nilsson University of Nottingham, UK G54FOP: Lecture 17 & 18 p.1/33 These Two Lectures Revisit attempt to define denotational

More information

Tight Timing Estimation With the Newton-Gregory Formulae

Tight Timing Estimation With the Newton-Gregory Formulae CPC 2003 p.1/30 Tight Timing Estimation With the Newton-Gregory Formulae Robert van Engelen Kyle Gallivan Burt Walsh Florida State University CPC 2003 p.2/30 Introduction Worst-case execution time (WCET)

More information

CSCE 222 Discrete Structures for Computing. Proofs. Dr. Hyunyoung Lee. !!!!! Based on slides by Andreas Klappenecker

CSCE 222 Discrete Structures for Computing. Proofs. Dr. Hyunyoung Lee. !!!!! Based on slides by Andreas Klappenecker CSCE 222 Discrete Structures for Computing Proofs Dr. Hyunyoung Lee Based on slides by Andreas Klappenecker 1 What is a Proof? A proof is a sequence of statements, each of which is either assumed, or follows

More information

CSE 20. Final Review. CSE 20: Final Review

CSE 20. Final Review. CSE 20: Final Review CSE 20 Final Review Final Review Representation of integers in base b Logic Proof systems: Direct Proof Proof by contradiction Contraposetive Sets Theory Functions Induction Final Review Representation

More information

CS 4407 Algorithms Lecture 3: Iterative and Divide and Conquer Algorithms

CS 4407 Algorithms Lecture 3: Iterative and Divide and Conquer Algorithms CS 4407 Algorithms Lecture 3: Iterative and Divide and Conquer Algorithms Prof. Gregory Provan Department of Computer Science University College Cork 1 Lecture Outline CS 4407, Algorithms Growth Functions

More information

Mathematical Induction. Rosen Chapter 4.1,4.2 (6 th edition) Rosen Ch. 5.1, 5.2 (7 th edition)

Mathematical Induction. Rosen Chapter 4.1,4.2 (6 th edition) Rosen Ch. 5.1, 5.2 (7 th edition) Mathematical Induction Rosen Chapter 4.1,4.2 (6 th edition) Rosen Ch. 5.1, 5.2 (7 th edition) Motivation Suppose we want to prove that for every value of n: 1 + 2 + + n = n(n + 1)/2. Let P(n) be the predicate

More information

Deductive Verification

Deductive Verification Deductive Verification Mooly Sagiv Slides from Zvonimir Rakamaric First-Order Logic A formal notation for mathematics, with expressions involving Propositional symbols Predicates Functions and constant

More information

CSE 20: Discrete Mathematics

CSE 20: Discrete Mathematics Spring 2018 Summary Last time: Today: Logical connectives: not, and, or, implies Using Turth Tables to define logical connectives Logical equivalences, tautologies Some applications Proofs in propositional

More information

Chapter 2: The Basics. slides 2017, David Doty ECS 220: Theory of Computation based on The Nature of Computation by Moore and Mertens

Chapter 2: The Basics. slides 2017, David Doty ECS 220: Theory of Computation based on The Nature of Computation by Moore and Mertens Chapter 2: The Basics slides 2017, David Doty ECS 220: Theory of Computation based on The Nature of Computation by Moore and Mertens Problem instances vs. decision problems vs. search problems Decision

More information

Heaps Induction. Heaps. Heaps. Tirgul 6

Heaps Induction. Heaps. Heaps. Tirgul 6 Tirgul 6 Induction A binary heap is a nearly complete binary tree stored in an array object In a max heap, the value of each node that of its children (In a min heap, the value of each node that of its

More information

Solutions Manual. Selected odd-numbers problems from. Chapter 3. Proof: Introduction to Higher Mathematics. Seventh Edition

Solutions Manual. Selected odd-numbers problems from. Chapter 3. Proof: Introduction to Higher Mathematics. Seventh Edition Solutions Manual Selected odd-numbers problems from Chapter 3 of Proof: Introduction to Higher Mathematics Seventh Edition Warren W. Esty and Norah C. Esty 5 4 3 2 1 2 Section 3.1. Inequalities Chapter

More information

Lecture Notes: Axiomatic Semantics and Hoare-style Verification

Lecture Notes: Axiomatic Semantics and Hoare-style Verification Lecture Notes: Axiomatic Semantics and Hoare-style Verification 17-355/17-665/17-819O: Program Analysis (Spring 2018) Claire Le Goues and Jonathan Aldrich clegoues@cs.cmu.edu, aldrich@cs.cmu.edu It has

More information

INDUCTION AND RECURSION. Lecture 7 - Ch. 4

INDUCTION AND RECURSION. Lecture 7 - Ch. 4 INDUCTION AND RECURSION Lecture 7 - Ch. 4 4. Introduction Any mathematical statements assert that a property is true for all positive integers Examples: for every positive integer n: n!

More information

Math 10850, fall 2017, University of Notre Dame

Math 10850, fall 2017, University of Notre Dame Math 10850, fall 2017, University of Notre Dame Notes on first exam September 22, 2017 The key facts The first midterm will be on Thursday, September 28, 6.15pm-7.45pm in Hayes-Healy 127. What you need

More information

At least one of us is a knave. What are A and B?

At least one of us is a knave. What are A and B? 1. This is a puzzle about an island in which everyone is either a knight or a knave. Knights always tell the truth and knaves always lie. This problem is about two people A and B, each of whom is either

More information

CS 4104 Data and Algorithm Analysis. Recurrence Relations. Modeling Recursive Function Cost. Solving Recurrences. Clifford A. Shaffer.

CS 4104 Data and Algorithm Analysis. Recurrence Relations. Modeling Recursive Function Cost. Solving Recurrences. Clifford A. Shaffer. Department of Computer Science Virginia Tech Blacksburg, Virginia Copyright c 2010,2017 by Clifford A. Shaffer Data and Algorithm Analysis Title page Data and Algorithm Analysis Clifford A. Shaffer Spring

More information