Discrete Mathematics COMS 3203 Zeph Grunschlag Copyright © Zeph Grunschlag, 2001-2002. Agenda Course policies Quick Overview Logic Lecture 1 2 About me 3rd year at Columbia Graduated from Berkeley in ’99 Mathematics PhD. Thesis title: Algorithms in Geometric Group Theory Research areas: Lecture 1 Group theory and topology Formal languages Computer vision 3 Course Policies, Schedule, Syllabus Handout explaining required text, overview of course, grading breakdown, policies, etc. –The “contract” Available from course homepage: http://www.cs.columbia.edu/~zeph/3203 All homework, and most other lecture material will be available online Lecture 1 4 Lectures and Notes Lectures are electronic and will be available online after class. There will usually be a low-tech component (blackboard + chalk) which you are responsible for. Lecture 1 5 Quick Overview Discrete Math is essentially that branch of mathematics that does not depend on limits; in this sense, it is the antithesis of Calculus. As computers are discrete object operating one jumpy, discontinuous step at a time, Discrete Math is the right framework for describing precisely Computer Science concepts. Lecture 1 6 Quick Overview The conceptual center of computer science is the ALGORITHM. Lecture 1 7 Quick Overview Discrete Math helps provide… …the machinery necessary for creating sophisticated algorithms …the tools for analyzing their efficiency …the means of proving their validity Lecture 1 8 Quick Overview Although the point of 3203 is to provide the tools for creating and analyzing sophisticated algorithms, we won’t focus on the algorithmic aspect, with some exceptions. To see the algorithmic applications take almost any 4000-level CS course or 3137/9 (Data Structures). Lecture 1 9 Quick Overview - Topics Logic and Sets Make notions you’re already used to from programming a little more rigorous (operators) Fundamental to all mathematical disciplines Useful for digital circuits, hardware design Elementary Number Theory Lecture 1 Get to rediscover the old reliable number and find out some surprising facts Very useful in crypto-systems 10 Quick Overview - Topics Proofs (especially induction) If you want to debug a program beyond a doubt, prove that it’s bug-free Proof-theory has recently also been shown to be useful in discovering bugs in pre-production hardware Counting and Combinatorics Lecture 1 Compute your odds of winning lottery Important for predicting how long certain computer program will take to finish Useful in designing algorithms 11 Quick Overview - Topics Graph Theory Many clever data-structures for organizing information and making programs highly efficient are based on graph theory Very useful in describing problems in Lecture 1 Databases Operating Systems Networks EVERY CS DISCIPLINE!!!! 12 Section 1.1: Logic Axiomatic concepts in math: Equals Opposite Truth and falsehood Statement Objects Collections Lecture 1 13 Section 1.1: Logic We intuitively know that Truth and Falsehood are opposites. That statements describe the world and can be true/false. That the world is made up of objects and that objects can be organized to form collections. The foundations of logic mimic our intuitions by setting down constructs that behave analogously. Lecture 1 14 False, True, Statements Axiom: False is the opposite to Truth. A statement is a description of something. Examples of statements: I’m 31 years old. I have 17 children. I always tell the truth. I’m lying to you. Q’s: Which statements are True? False? Both? Neither? Lecture 1 15 False, True, Statements True: I’m 31 years old. False: I have 17 children. I always tell the truth. Both: IMPOSSIBLE, by our Axiom. Lecture 1 16 False, True, Statements Neither: I’m lying to you. (If viewed on its own) HUH? Well suppose that S = “I’m lying to you.” were true. In particular, I am actually lying, so S is false. So it’s both true and false, impossible by the Axiom. Okay, so I guess S must be false. But then I must not be lying to you. So the statement is true. Again it’s both true and false. In both cases we get the opposite of our assumption, so S is neither true nor false. Lecture 1 17 Propositions To avoid painful head-aches, we ban such silly non-sense and avoid the most general type of statements limiting ourselves to statements with valid truth-values instead: DEF: A proposition is a statement that is true or false. Lecture 1 18 Propositions Propositional Logic is a static discipline of statements which lack semantic content. E.G. p = “Clinton was the president.” q = “The list of U.S. presidents includes Clinton.” r = “Lions like to sleep.” All p and q are no more closely related than q and r are, in propositional calculus. They are both equally related as all three statements are true. Semantically, however, p and q are the same! Lecture 1 19 Propositions So why waste time on such matters? Propositional logic is the study of how simple propositions can come together to make more complicated propositions. If the simple propositions were endowed with some meaning –and they will be very soon– then the complicated proposition would have meaning as well, and then finding out the truth value is actually important! Lecture 1 20 Compound Propositions In Propositional Logic, we assume a collection of atomic propositions are given: p, q, r, s, t, …. Then we form compound propositions by using logical connectives (logical operators) to form propositional “molecules”. Lecture 1 21 Logical Connectives Operator Negation Conjunction Disjunction Exclusive or Conditional Biconditional Lecture 1 Symbol Usage Java not ! and && or || xor (p||q)&&(!p||!q) if,then p?q:true iff (p&&q)||(!p&&!q) 22 Compound Propositions: Examples p = “Cruise ships only go on big rivers.” q = “Cruise ships go on the Hudson.” r = “The Hudson is a big river.” r = “The Hudson is not a big river.” pq = “Cruise ships only go on big rivers and go on the Hudson.” pq r = “If cruise ships only go on big rivers and go on the Hudson, then the Hudson is a big river.” Lecture 1 23 Negation This just turns a false proposition to true and the opposite for a true proposition. EG: p = “23 = 15 +7” p happens to be false, so p is true. In Java, “!” plays the same role: !(23 == 15+7) has the boolean value true whenever evaluated. Lecture 1 24 Negation – truth table Logical operators are defined by truth tables –tables which give the output of the operator in the right-most column. Here is the truth table for negation: p F T Lecture 1 p T F 25 Conjunction Conjunction is a binary operator in that it operates on two propositions when creating compound proposition. On the other hand, negation is a unary operator (the only non-trivial one possible). Lecture 1 26 Conjunction Conjunction is supposed to encapsulate what happens when we use the word “and” in English. I.e., for “p and q ” to be true, it must be the case that BOTH p is true, as well as q. If one of these is false, than the compound statement is false as well. Lecture 1 27 Conjunction EG. p = “Clinton was the president.” q = “Monica was the president.” r = “The meaning of is is important.” Assuming p and r are true, while q false. Out of pq, pr, qr only pr is true. Java: x==3 && x!=3 Evaluates to false for any possible value of x. Lecture 1 28 Conjunction – truth table Lecture 1 p q p q T T F F T F T F T F F F 29 Disjunction – truth table Conversely, disjunction is true when at least one of the components is true: Lecture 1 p q p q T T F F T F T F T T T F 30 Disjunction – caveat Note: English version of disjunction “or” does not always satisfy the assumption that one of p/q being true implies that “p or q ” is true. Q: Can someone come up with an example? Lecture 1 31 Disjunction – caveat A: The entrée is served with soup or salad. Most restaurants definitely don’t allow you to get both soup and salad so that the statement is false when both soup and salad is served. To address this situation, exclusive-or is introduced next. Lecture 1 32 Exclusive-Or – truth table p q p q T T F F T F T F F T T F Note: in this course any usage of “or” will connote the logical operator as opposed to the exclusive-or. Lecture 1 33 Conditional (Implication) This one is probably the least intuitive. It’s only partly akin to the English usage of “if,then” or “implies”. DEF: p q is true if q is true, or if p is false. In the final case (p is true while q is false) p q is false. Semantics: “p implies q ” is true if one can mathematically derive q from p. Lecture 1 34 Conditional -- truth table Lecture 1 p q p q T T F F T F T F T F T T 35 Conditional Q: Does this makes sense? Let’s try examples for each row of truth table: 1. If pigs like mud then pigs like mud. 2. If pigs like mud then pigs can fly. 3. If pigs can fly then pigs like mud. 4. If pigs can fly then pigs can fly. Lecture 1 36 Conditional A: 1. If pigs like mud then pigs like mud. True: nothing about this statement is false. 2. If pigs like mud then pigs can fly. False: seems to assert falsehood 3. If pigs can fly then pigs like mud. True: argument for –only care about end-result. Argument against –counters common English hyperbole. 4. If pigs can fly then pigs can fly. True. WAIT! By first reasoning in 3, when “if” part is false, should only care about “then” part!!!!! On other hand, standard English hyperbole. Lecture 1 37 Conditional: why FF is True Remember, all of these are mathematical constructs, not attempts to mimic English. Mathematically, p should imply q whenever it is possible to derive q by from p by using valid arguments. For example consider the mathematical analog of no. 4: If 0 = 1 then 3 = 9. Q: Is this true mathematically? Lecture 1 38 Conditional: why FF is True A: YES mathematically and YES by the truth table. Here’s a mathematical proof: 1. 0 = 1 (assumption) Lecture 1 39 Conditional: why FF is True A: YES mathematically and YES by the truth table. Here’s a mathematical proof: 1. 0 = 1 (assumption) 2. 1 = 2 (added 1 to both sides) Lecture 1 40 Conditional: why FF is True A: YES mathematically and YES by the truth table. Here’s a mathematical proof: 1. 0 = 1 (assumption) 2. 1 = 2 (added 1 to both sides) 3. 3 = 6 (multiplied both sides by 3) Lecture 1 41 Conditional: why FF is True A: YES mathematically and YES by the truth table. Here’s a mathematical proof: 1. 0 = 1 (assumption) 2. 1 = 2 (added 1 to both sides) 3. 3 = 6 (multiplied both sides by 3) 4. 0 = 3 (multiplied no. 1 by 3) Lecture 1 42 Conditional: why FF is True A: YES mathematically and YES by the truth table. Here’s a mathematical proof: 1. 0 = 1 (assumption) 2. 1 = 2 (added 1 to both sides) 3. 3 = 6 (multiplied both sides by 3) 4. 0 = 3 (multiplied no. 1 by 3) 5. 3 = 9 (added no. 3 and no. 4) Lecture 1 QED 43 Conditional: why FF is True As we want the conditional to make sense in the semantic context of mathematics, we better define it as we have! Other questionable rows of the truth table can also be justified in a similar manner. Lecture 1 44 Conditional: synonyms There are many ways to express the conditional statement p q : If p then q. p implies q. If p, q. p only if q. p is sufficient for q. Some of the ways reverse the order of p and q but have the same connotation: q if p. q whenever p. q is necessary for p. To aid in remembering these, I suggest inserting “is true” after every variable: EG: “p is true only if q is true” Lecture 1 45 Bi-Conditional -- truth table For p q to be true, p and q must have the same truth value. Else, p p q pq T T F F T F T F T F F T Q : Which operator is Lecture 1 q is false: the opposite of? 46 Bi-Conditional A: has exactly the opposite truth table as . This means that we could have defined the bi-conditional in terms of other previously defined symbols, so it is redundant. In fact, only really need negation and disjunction to define everything else. Extra operators are for convenience. Q: Could we define all other logical operations using only negation and exclusive or? Lecture 1 47 Bi-Conditional A: No. Notice that negation and exclusive-or each maintain parity between truth and false: No matter what combination of these symbols, impossible to get a truth table with four output rows consisting of 3 T’s and 1 F (such as implication and disjuction). Lecture 1 48 Bit Strings Electronic computers achieve their calculations inside semiconducting materials. For reliability, only two stable voltage states are used and so the most fundamental operations are carried out by switching voltages between these two stable states. In logic, only two truth values are allowed. Thus propositional logic is ideal for modeling computers. High voltage values are modeled by True, which for brevity we call the number 1, while low voltage values are modeled by False or 0. Lecture 1 49 Bit Strings Thus voltage memory stored in a computer can be represented by a sequence of 0’s and 1’s such as 01 1011 0010 1001 Another portion of the memory might look like 10 0010 1111 1001 Each of the number in the sequence is called a bit, and the whole sequence of bits is called a bit string. Lecture 1 50 Bit Strings It turns out that the analogs of the logical operations can be carried out quite easily inside the computer, one bit at a time. This can then be transferred to whole bit strings. For example, the exclusive-or of the previous bit strings is: 01 1011 0010 1001 Lecture 1 10 0010 1111 1001 11 1001 1101 0000 51 Blackboard Exercises for 1.1 Worked out on the black-board. 1.1.6.c, 1.1.22.d, 1.1.39: 1. q = “You miss the final exam.” r = “You pass the course.” Express q r in English. 2. Construct a truth table for p q. 3. Can one determine relative salaries of F (Fannie), J (Janice) and M (Maggie) from the following? 1. 2. Lecture 1 If F is not highest paid, then J is. If J is not lowest paid, then M is highest paid. 52

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