Boolean Algebra cont. The digital abstraction

Similar documents
Boolean Algebra. Boolean Algebra

Data Structures LECTURE 10. Huffman coding. Example. Coding: problem definition

Lecture 11 Binary Decision Diagrams (BDDs)

Linear Algebra Introduction

MAT 403 NOTES 4. f + f =

Unit 4. Combinational Circuits

Boolean Algebra. Boolean Algebras

Lecture 2: Cayley Graphs

T b a(f) [f ] +. P b a(f) = Conclude that if f is in AC then it is the difference of two monotone absolutely continuous functions.

M A T H F A L L CORRECTION. Algebra I 2 1 / 0 9 / U N I V E R S I T Y O F T O R O N T O

Lecture 8: Abstract Algebra

Boolean algebra.

CS12N: The Coming Revolution in Computer Architecture Laboratory 2 Preparation

MA10207B: ANALYSIS SECOND SEMESTER OUTLINE NOTES

NFA and regex. the Boolean algebra of languages. non-deterministic machines. regular expressions

Digital Control of Electric Drives

CS 491G Combinatorial Optimization Lecture Notes

Mid-Term Examination - Spring 2014 Mathematical Programming with Applications to Economics Total Score: 45; Time: 3 hours

Solutions for HW9. Bipartite: put the red vertices in V 1 and the black in V 2. Not bipartite!

Chapter 6 Continuous Random Variables and Distributions

CS 330 Formal Methods and Models Dana Richards, George Mason University, Spring 2016 Quiz Solutions

On Implicative and Strong Implicative Filters of Lattice Wajsberg Algebras

MATH 409 Advanced Calculus I Lecture 22: Improper Riemann integrals.

Section 2.3. Matrix Inverses

Bisimulation, Games & Hennessy Milner logic

The University of Nottingham SCHOOL OF COMPUTER SCIENCE A LEVEL 2 MODULE, SPRING SEMESTER MACHINES AND THEIR LANGUAGES ANSWERS

6.5 Improper integrals

Solutions to Assignment 1

POSITIVE IMPLICATIVE AND ASSOCIATIVE FILTERS OF LATTICE IMPLICATION ALGEBRAS

6.1 Definition of the Riemann Integral

Chapter 3. Vector Spaces. 3.1 Images and Image Arithmetic

Technische Universität München Winter term 2009/10 I7 Prof. J. Esparza / J. Křetínský / M. Luttenberger 11. Februar Solution

+ = () i =, find the values of x & y. 4. Write the function in the simplifies from. ()tan i. x x. 5. Find the derivative of. 6.

The Riemann and the Generalised Riemann Integral

Chapter 3 Single Random Variables and Probability Distributions (Part 2)

for all x in [a,b], then the area of the region bounded by the graphs of f and g and the vertical lines x = a and x = b is b [ ( ) ( )] A= f x g x dx

CS344: Introduction to Artificial Intelligence

Algebra in a Category

NON-DETERMINISTIC FSA

More Properties of the Riemann Integral

Probability. b a b. a b 32.

22: Union Find. CS 473u - Algorithms - Spring April 14, We want to maintain a collection of sets, under the operations of:

ENGR 3861 Digital Logic Boolean Algebra. Fall 2007

Co-ordinated s-convex Function in the First Sense with Some Hadamard-Type Inequalities

AUTOMATA AND LANGUAGES. Definition 1.5: Finite Automaton

Lecture 08: Feb. 08, 2019

Solutions to Problem Set #1

Control with binary code. William Sandqvist

Combinational Logic. Precedence. Quick Quiz 25/9/12. Schematics à Boolean Expression. 3 Representations of Logic Functions. Dr. Hayden So.

The Riemann-Stieltjes Integral

Foundations of Computer Science Comp109

I 3 2 = I I 4 = 2A

Data Compression Techniques (Spring 2012) Model Solutions for Exercise 4

CIT 596 Theory of Computation 1. Graphs and Digraphs

#A42 INTEGERS 11 (2011) ON THE CONDITIONED BINOMIAL COEFFICIENTS

CS 573 Automata Theory and Formal Languages

CS 330 Formal Methods and Models

Project 6: Minigoals Towards Simplifying and Rewriting Expressions

Sturm-Liouville Theory

Fast Boolean Algebra

Reasoning and programming. Lecture 5: Invariants and Logic. Boolean expressions. Reasoning. Examples

Grammar. Languages. Content 5/10/16. Automata and Languages. Regular Languages. Regular Languages

Algorithm Design and Analysis

April 8, 2017 Math 9. Geometry. Solving vector problems. Problem. Prove that if vectors and satisfy, then.

Nondeterministic Finite Automata

ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT, OAKLAND UNIVERSITY ECE-2700: Digital Logic Design Fall Notes - Unit 1

Fachgebiet Rechnersysteme1. 1. Boolean Algebra. 1. Boolean Algebra. Verification Technology. Content. 1.1 Boolean algebra basics (recap)

Now we must transform the original model so we can use the new parameters. = S max. Recruits

Part 4. Integration (with Proofs)

ELECTRICAL AND COMPUTER ENGINEERING DEPARTMENT, OAKLAND UNIVERSITY ECE-378: Computer Hardware Design Winter Notes - Unit 1

1 nonlinear.mcd Find solution root to nonlinear algebraic equation f(x)=0. Instructor: Nam Sun Wang

Introduction to Group Theory

SIMPLE NONLINEAR GRAPHS

SECTION A STUDENT MATERIAL. Part 1. What and Why.?

CARLETON UNIVERSITY. 1.0 Problems and Most Solutions, Sect B, 2005

Improper Integrals. Introduction. Type 1: Improper Integrals on Infinite Intervals. When we defined the definite integral.

AT100 - Introductory Algebra. Section 2.7: Inequalities. x a. x a. x < a

Fact: All polynomial functions are continuous and differentiable everywhere.

Arrow s Impossibility Theorem

Section 1.3 Triangles

Algorithm Design and Analysis

Homework 3 Solutions

CHAPTER 4: DETERMINANTS

September 13 Homework Solutions

CHAPTER 1 Regular Languages. Contents

A Study on the Properties of Rational Triangles

Reference : Croft & Davison, Chapter 12, Blocks 1,2. A matrix ti is a rectangular array or block of numbers usually enclosed in brackets.

Necessary and sucient conditions for some two. Abstract. Further we show that the necessary conditions for the existence of an OD(44 s 1 s 2 )

Proportions: A ratio is the quotient of two numbers. For example, 2 3

Coalgebra, Lecture 15: Equations for Deterministic Automata

PROPERTIES OF TRIANGLES

Overview of Today s Lecture:

Lesson 55 - Inverse of Matrices & Determinants

For a, b, c, d positive if a b and. ac bd. Reciprocal relations for a and b positive. If a > b then a ab > b. then

( ) { } [ ] { } [ ) { } ( ] { }

UniversitaireWiskundeCompetitie. Problem 2005/4-A We have k=1. Show that for every q Q satisfying 0 < q < 1, there exists a finite subset K N so that

Chapter 1: Fundamentals

CS311 Computational Structures Regular Languages and Regular Grammars. Lecture 6

Algorithms & Data Structures Homework 8 HS 18 Exercise Class (Room & TA): Submitted by: Peer Feedback by: Points:

Inequalities of Olympiad Caliber. RSME Olympiad Committee BARCELONA TECH

Transcription:

Boolen Alger ont The igitl strtion

Theorem: Asorption Lw For every pir o elements B. + =. ( + ) = Proo: () Ientity Distriutivity Commuttivity Theorem: For ny B + = Ientity () ulity.

Theorem: Assoitive Lw In Boolen lger eh o the inry opertions ( + ) n ( ) is ssoitive. Tht is or every B. + ( + ) = ( + ) +. ( ) = ( )

Proo: () Let A Distriutivity A Commuttivity Distriutivity Distriutivity Iempotent Lw Asorption Lw Asorption Lw

A Commuttivity Distriutivity Distriutivity Iempotent Lw Asorption Lw Commuttivity Asorption Lw

Putting it ll together: A Sme trnsitions eore +

A A () Dulity Also

Theorem : DeMorgn s Lw For every pir o elements B. ( + ) =. ( ) = + Proo: () We irst prove tht (+) is the omplement o. Thus (+) = By the einition o the omplement n its uniqueness it suies to show: (i) (+)+( ) = n (ii) (+)( ) =. () Dulity ( ) = +

Distriutivity Commuttivity Assoitivity n re the omplements o n respetively Theorem: For ny B + = Iempotent Lw

Commuttivity Distriutivity Commuttivity Assoitivity Commuttivity n re the omplements o n respetively Theorem: For ny B = Iempotent Lw

Alger o Sets Consier set S. B = ll the susets o S (enote y P(S)). plus set-union times set-intersetion M P S Aitive ientity element empty set Ø Multiplitive ientity element the set S. Complement o B: S \

Theorem: The lger o sets is Boolen lger. Proo: By stisying the ioms o Boolen lger: B is set o t lest two elements For every non empty set S: S PS B. Closure o ( ) n ( ) over B (untions B B B ). S. P(S) y einition P(S) y einition S n S n P( S) P( S) y einition y einition

A. Cummuttivity o ( ) n ( ). or : or : An element lies in the union preisely when it lies in one o the two sets n. qully n element lies in the union preisely when it lies in one o the two sets n. Hene n : n :

A. Distriutivity o ( ) n ( ).. Let n or I We hve n. Hene I We hve n. Hene or

This n e onute in the sme mnner s. We present n lterntive wy: Deinition o intersetion n Also einition o intersetion einition o union Similrly * **

Tking (*) n (**) we get Distriutivity o union over intersetion n e onute in the sme mnner.

A3. istene o itive n multiplitive ientity element. S. S S S. itive ientity - multiplitive ientity - S A4. istene o the omplement. S B \. S S S B \ \. S S B \ \. Alger o sets is Boolen lger. All ioms re stisie

Boolen epression - Reursive einition: se: B epressions. reursion step: Let n e Boolen epressions. Then ( + ) ( ) Dul trnsormtion - Reursive einition: Dul: epressions epressions se: B\{} reursion step: Let n e Boolen epressions. Then [ul( )] ( + ) [ ul( ) ul( ) ] ( ) [ ul( ) + ul( ) ]

Let e the ul o untion ( n ) Lemm: In swithing lger = ( n ) Proo: Let ( n ) e Boolen epression. We show tht pplying the omplement on the whole epression together with repling eh vrile y it s omplement yiels the ul trnsormtion einition. Inution sis: epressions. n n

Inution hypothesis: Lemm hols or Boolen epressions: n. Tht is: n n n n n n Inution step: show tht it is true or ( + ) ( ) n n hypothesis inution n n Lw Morgn De' n n I then n

I then hypothesis De' Morgn Lw I then inution inution hypothesis

Deinition: A untion is lle sel-ul i = Lemm: For ny untion n ny two-vlue vrile A the untion g = A + A is sel-ul. Proo: (hols or ny Boolen lger) ul g ula A Dul einition Distriutivity Commuttivity ul A ul A ula ul ula ul A A A AA A A A

Distriutivity Commuttivity A A A AA AA A A A A A is the omplement o A A A Ientity A A Commuttivity A A Notie tht the ove epression hs the orm: + + where =A = =.

We now prove stronger lim: B. Ientity is the omplement o Distriutivity Commuttivity Commuttivity Distriutivity Theorem: For ny B + = Ientity

ul g ula A A A A A For emple: v g v v v sel-ul

sier proo () or swithing lger only: (using ul properties) ul g ula A A A Swithing lger n n OR Ientity A A A A

sier proo () or swithing lger only: (se nlysis) ul g ula A A A A = = Ientity Commuttivity Theorem: For ny B = Ientity ul( g) Asorption Lw g

A = ul( g) g

mple o trnser untion or n inverter : stritly eresing in. stritly inresing in onve in the intervl. is onve in the intervl monotone eresing

- - - ontinuous!.!. slope = - slope = -

slope = - high out slope = - low out low in high in true only i: high out high in low in low out

BUT this is not lwys the se. For emple: slope = - high out slope = - low out low in high in high in high out Moreover in this emple it n e prove tht no threshol vlues eist whih re onsistent with einition 3 rom leture notes.

Using the ssumption: there eists suh tht point : () = strt with : high out high in low in low out y y slope < -

set : high in high in () = low out high in y y low in low in high out low in slope < -

slope = - () = high in low out low in high out high out slope = - high out high in low in low out low out true i: low in high in min slope < - set min