l1_logic

6.042/18.062J Mathematics for Computer Science February 1, 2005 Srini Devadas and Eric Lehman Lecture Notes Logic It’s really sort of amazing that people manage to communicate in the English language. Here are some typical sentences: 1. “You may have cake or you may have ice cream.” 2. “If pigs can fly, then you can understand the Chernoff bound.” 3. “If you can solve any problem we come up with, then you get an A for the course.” 4. “Every American has a dream.” What precisely do these sentences mean? Can you have both cake and ice cream or must you choose just one desert? If the second sentence is true, then is the Chernoff bound incomprehensible? If you can solve some problems we come up with but not all, then do you get an A for the course? And can you still get an A even if you can’t solve any of the problems? Does the last sentence imply that all Americans have the same dream or might they each have a different dream? Some uncertainty is tolerable in normal conversation. But when we need to formu­ late ideas precisely— as in mathematics— the ambiguities inherent in everyday language become a real problem. We can’t hope to make an exact argument if we’re not sure ex­ actly what the individual words mean. (And, not to alarm you, but it is possible that we’ll be making an awful lot of exacting mathematical arguments in the weeks ahead.) So be­ fore we start into mathematics, we need to investigate the problem of how to talk about mathematics. To get around the ambiguity of English, mathematicians have devised a special mini­ language for talking about logical relationships. This language mostly uses ordinary English words and phrases such as “or”, “implies”, and “for all”. But mathematicians endow these words with definitions more precise than those found in an ordinary dictio­ nary. Without knowing these definitions, you could sort of read this language, but you would miss all the subtleties and sometimes have trouble following along. Surprisingly, in the midst of learning the language of logic, we’ll come across the most important open problem in computer science— a problem whose solution could change the world. 2 Logic 1 Propositions A proposition is a statement that is either true or false. This definition is a little vague, but it does exclude sentences such as, “What’s a surjection, again?” and “Learn logarithms!” Here are some examples of propositions. “All Greeks are human.” “All humans are mortal.” “All Greeks are mortal.” Archimedes spent a lot of time fussing with such propositions in the 4th century BC while developing an early form of logic. These are perfectly good examples, but we’ll be more concerned with propositions of a more mathematical flavor: “2 + 3 = 5” This proposition happens to be true. Sometimes the truth of a proposition is more difficult to determine: 4“a + b4 + c4 = d4 has no solution where a, b, c are positive integers.” Euler made this conjecture in 1769. For 218 years no one knew whether this proposition was true or false. Finally, Noam Elkies who works at the liberal arts school up Mass Ave. found a solution to the equation: a = 2682440, b = 15365639, c = 18796760, d = 20615673. So the proposition is false! There are many famous propositions about primes. Recall that a prime is an integer greater than 1 not divisible by any positive integer other than 1 and itself. The first few primes are 2, 3, 5, 7, 11, and 13. On the other hand, 9 and 77 are not primes, because 9 is divisible by 3 and 77 is divisible by 7. The following proposition is called Goldbach’s Conjecture, after Christian Goldbach who first stated it in 1742. “Every even integer greater than 2 is the sum of two primes.” Even today, no one knows whether Goldbach’s Conjecture is true or false. Every even integer ever checked is a sum of two primes, but just one exception would disprove the claim. For the next while, we won’t be much concerned with the internals of propositions— whether they involve mathematics or Greek mortality— but rather with how propositions are combined and related. So we’ll frequently use variables such as P and Q in place of specific propositions such as “All humans are mortal” and “2+3 = 5”. The understanding is that these variables, like propositions, can take on only the values “true” and “false”. Such true/false variables are sometimes called Boolean variables after their inventor, George something­or­other. Logic 3 1.1 Combining Propositions In English, we can modify, combine, and relate propositions with words such as “not”, “and”, “or”, “implies”, and “if­then”. For example, we can combine three propositions into one like this: If all humans are mortal and all Greeks are human, then all Greeks are mortal. 1.1.1 “Not”, “And” and “Or” We can precisely define these special words using truth tables. For example, if P denotes an arbitrary proposition, then the validity of the proposition “not P” is defined by the following truth table: P not P T F F T The first row of the table indicates that when proposition P is true (T), the proposition “not P” is false (F). The second line indicates that when P is false, “not P” is true. This is probably what you would expect. In general, a truth table indicates the true/false value of a proposition for each possible setting of the variables. For example, the truth table for the proposition “P and Q” has four lines, since the two variables can be set in four different ways: P Q T T T T F F F T F F F F P and Q According to this table, the proposition “P and Q” is true only when P and Q are both true. This is probably reflects the way you think about the word “and”. There is a subtlety in the truth table for “P or Q”: P Q T T T T F T F T T F F F P or Q This says that “P or Q” is true when P is true, Q is true, or both are true. This isn’t always the intended meaning of “or” in everyday speech, but this is the standard definition in mathematical writing. So if a mathematician says, “You may have cake or your may have ice cream”, then you can have both. 4 Logic 1.1.2 “Implies” The least intuitive connecting word is “implies”. Mathematicians regard the propositions “P implies Q” and “if P then Q” as synonymous, so both have the same truth table. (The lines are numbered so we can refer to the them later.) P Q 1. T T T 2. T F F 3. F T T 4. F F T P implies Q, if P then Q Let’s experiment with this definition. For example, is the following proposition true or false? 2“If Goldbach’s Conjecture is true, then x ≥ 0 for every real number x.” Now, we told you before that no one knows whether Goldbach’s Conjecture is true or false. But that doesn’t prevent you from answering the question! This proposition has the 2form P ⇒ Q where P is “Goldbach’s Conjecture is true” and Q is “x ≥ 0 for every real number x”. Since Q is definitely true, we’re on either line 1 or line 3 of the truth table. Either way, the proposition as a whole is true! One of our original examples demonstrates an even stranger side of implications. “If pigs fly, then you can understand the Chernoff bound.” Don’t take this as an insult; we just need to figure out whether this proposition is true or false. Curiously, the answer has nothing to do with whether or not you can understand the Chernoff bound. Pigs do not fly, so we’re on either line 3 or line 4 of the truth table. In both cases, the proposition is true! In contrast, here’s an example of a false implication: “If the moon is white, then the moon is made of white cheddar.” Yes, the moon is white. But, no, the moon is not made of white cheddar cheese. So we’re on line 2 of the truth table, and the proposition is false. The truth table for implications can be summarized in words as follows: An implication is true when the if­part is false or the then­part is true. This sentence is worth remembering; a large fraction of all mathematical statements are of the if­then form! Logic 5 1.1.3 “If and Only If” Mathematicians commonly join propositions in one additional way that doesn’t arise in ordinary speech. The proposition “P if and only if Q” asserts that P and Q are logically equivalent; that is, either both are true or both are false. P Q T T T T F F F T F F F T P if and only if Q The following if­and­only­if statement is true for every real number x: 2“x − 4 ≥ 0 if and only if x ≥ 2” | | For some values of x, both inequalities are true. For other values of x, neither inequality is true . In every case, however, the proposition as a whole is true. The phrase “if and only if” comes up so often that it is often abbreviated “iff”. 1.2 Propositional Logic in Computer Programs Propositions and logical connectives arise all the time in computer programs. For exam­ ple, consider the following snippet, which could be either C, C++, or Java: if ( x > 0 || (x <= 0 && y > 100) ) (further instructions) The symbol || denotes “or”, and the symbol && denotes “and”. The further instructions are carried out only if the proposition following the word if is true. On closer inspection, this big expression is built from two simpler propositions. Let A be the proposition that x > 0, and let B be the proposition that y > 100. Then we can rewrite the condition this way: A or ((not A) and B) A truth table reveals that this complicated expression is logically equivalent to “A or B”. A B T T T T T F T T F T T T F F F F A or ((not A) and B) A or B This means that we can simplify the code snippet without changing the program’s behav­ ior: 6 Logic if ( x > 0 || y > 100 ) (further instructions) Rewriting a logical expression involving many variables in the simplest form is both difficult and important. Simplifying expressions in software might slightly increase the speed of your program. But, more significantly, chip designers face essentially the same challenge. However, instead of minimizing && and || symbols in a program, their job is to minimize the number of analogous physcial devices on a chip. The payoff is potentially enormous: a chip with fewer devices is smaller, consumes less power, has a lower defect rate, and is cheaper to manufacture. 1.3 A Cryptic Notation Programming languages use symbols like && and ! in place of words like “and” and “not”. Mathematicians have devised their own cryptic symbols to represent these words, which are summarized in the table below. English Cryptic Notation “not P” ¬P or P “P and Q” P ∧Q “P or Q” P ∨Q “P implies Q” or “if P then Q” P ⇒ Q “P if and only if Q” P ⇔ Q For example, using this notation, “If P and not Q, then R” would be written: R(P ∧ ¬Q) ⇒ This symbolic language is helpful for writing complicated logical expressions com­ pactly. But in most contexts ordinary words such as “or” and “implies” are much easier to understand than symbols such as ∨ and ⇒. So we’ll use this symbolic language spar­ ingly, and we advise you to do the same. 1.4 Logically Equivalent Implications Are these two sentences saying the same thing? If I am hungry, then I am grumpy. If I am not grumpy, then I am not hungry. 7 Logic We can settle the issue by recasting both sentences in terms of propositional logic. Let P be the proposition “I am hungry”, and let Q be “I am grumpy”. The first sentence says “P implies Q” and the second says “(not Q) implies (not P )”. We can compare these two statements in a truth table: P T T T T T F F F F T T T F F T T Q P implies Q (not Q) implies (not P ) Sure enough, the two statements are precisely equivalent. In general, “(not Q) implies (not P )” is called the contrapositive of “P implies Q”. And, as we’ve just shown, the two are just different ways of saying the same thing. This equivalence is mildly useful in programming, mathematical arguments, and even everyday speech, because you can always pick whichever of the two is easier to say or write. In contrast, the converse of “P implies Q” is the statement “Q implies P”. In terms of our example, the converse is: If I am grumpy, then I am hungry. This sounds like a rather different contention, and a truth table confirms this suspicion: P T T T T T F F T F T T F F F T T Q P implies Q Q implies P Thus, an implication is logically equivalent to its contrapositive, but is not equivalent to its converse. One final relationship: an implication and its converse together are equivalent to an if and only if statement. Specifically, these two statements together: If I am grumpy, then I am hungry. If I am hungry, then I am grumpy. are equivalent to the single statement: I am grumpy if and only if I am hungry. Once again, we can verify this with a truth table: P Q T T T F F T F F (P implies Q) and (Q implies P ) T T F F F F T T Q if and only if P 8 Logic SAT A proposition is satisfiable if some setting of the variables makes the proposition true. For example, P ∧ ¬Q is satisfiable because the expression is true when P is true and Q is false. On the other hand, P ∧ ¬P is not satisfiable because the expression as a whole is false for both settings of P . But determining whether or not a more complicated proposition is satisfiable is not so easy. How about this one? (P ∨Q ∨R) ∧ (¬P ∨ ¬Q) ∧ (¬P ∨ ¬R) ∧ (¬R ∨ ¬Q) The general problem of deciding whether a proposition is satisfiable is called SAT. One approach to SAT is to construct a truth table and check whether or not a T ever appears. But this approach is not very efficient; a proposition with n variables has a truth table with 2n lines. For a proposition with just 30 variables, that’s already over a billion! Is there an efficient solution to SAT? Is there some ingenious procedure that quickly determines whether any given proposition is satifiable or not? No one knows. And an awful lot hangs on the answer. An efficient solution to SAT would immediately imply efficient solutions to many, many other important problems involving packing, scheduling, routing, and circuit verification. This sounds fantastic, but there would also be worldwide chaos. Decrypting coded messages would also become an easy task (for most codes). Online financial transactions would be insecure and secret communications could be read by everyone. At present, though, researchers are completely stuck. No one has a good idea how to either solve SAT more efficiently or to prove that no efficient solution exists. This is the outstanding unanswered question in computer science. 2 Sets and Sequences Propositions of the sort we’ve considered so far are good for reasoning about individual statements, but not so good for reasoning about a collection of objects. Let’s first review a couple mathematical tools for grouping objects and then extend our logical language to cope with such collections. A set is a collection of distinct objects, which are called elements. The elements of a set can be just about anything: numbers, points in space, or even other sets. The conventional way to write down a set is to list the elements inside curly­braces. For example, here are Logic 9 some sets: N = {0, 1, 2, 3, . . .} the natural numbers C = {red, blue,yellow} primary colors D = {Nifty,Friend,Horatio,Pretty­Pretty} dead pets P = {{a, b} , {a, c} , {b, c}} a set of sets The elements of a set are required to be distinct; a set can not contain multiple copies of the same element. The order of elements is not significant, so {x, y} and {y, x} are the same set written two different ways. The expression e ∈ S asserts that e is an element of set S. For example, 7 ∈ N and blue ∈ C, but Wilbur �∈ D yet. Sets are simple, flexible, and everywhere. You’ll find at least one set mentioned on almost every page in these notes. 2.1 Some Popular Sets Mathematicians have devised special symbols to represent some common sets. symbol set elements ∅ the empty set none N natural numbers {0, 1, 2, 3, . . .} Z integers {. . . ,−3,−2,−1, 0, 1, 2, 3, . . .} 1 5Q rational numbers 2 , − 3 , 16, etc. R real numbers π, e, −9, etc. C complex numbers i, 19 2 , √ 2 − 2i, etc. A superscript + restricts a set to its positive elements; for example, R+ is the set of positive real numbers. 2.2 Comparing and Combing Sets The expression S ⊆ T indicates that set S is a subset of set T , which means that every element of S is also an element of T . For example, N ⊆ Z (every natural number is an integer) and Q ⊆ R (every rational number is a real number), but C �⊆ Z (not every complex number is an integer). As a memory trick, notice that the ⊆ points to the smaller set, just like a ≤ sign points to the smaller number. Actually, this connection goes a little further: there is a symbol ⊂ analogous to <. Thus, S ⊂ T means that S is a subset of T , but the two are not equal. So for every set A, A ⊆ A, but A �⊂ A. There are several ways to combine sets. Let’s define a couple for use in examples: X = {1, 2, 3} Y = {2, 3, 4} 10 Logic • The union of sets X and Y (denoted X ∪ Y ) contains all elements appearing in X or Y or both. Thus, X ∪ Y = {1, 2, 3, 4}. • The intersection of X and Y (denoted X ∩ Y ) consists of all elements that appear in both X and Y . So X ∩ Y = {2, 3}. • The difference of X and Y (denoted X − Y ) consists of all elements that are in X , but not in Y . Therefore, X − Y = {1} and Y − X = {4}. Sometimes one is exclusively concerned with subsets of a certain large set. For exam­ ple, we might be focused on subsets of a the real numbers. In this case, we can talk about the complement of a set Z, which consists of all elements not in Z and is denoted Z. For example, when we’re working with the real numbers R, the complement of the positive real numbers is the set of negative real numbers together with zero. 2.3 Sequences Sets provide one way to group a collection of objects. Another way is in a sequence, which is a list of objects called terms or components. Short sequences are commonly described by listing the elements between parentheses; for example, (a, b, c) is a sequence with three terms. While both sets and sequences perform a gathering role, there are several differences. • The elements of a set are required to be distinct, but terms in a sequence can be the same. Thus, (a, b, a) is a valid sequence, but {a, b, a} is not a valid set. • The terms in a sequence have a specified order, but the elements of a set do not. For example, (a, b, c) and (a, c, b) are different sequences, but {a, b, c} and {a, c, b} are the same set. • The empty set is usually denoted ∅, and the empty sequence is typically λ. The product operation is one link between sets and sequences. A product of sets, S1 × S2 × . . . × Sn, is a new set consisting of all sequences where the first component is drawn from S1, the second from S2, and so forth. For example, N × N is the set of all pairs of natural numbers: N × N = {(0, 0), (0, 1), (1, 0), (0, 2), (1, 1), (2, 0), . . .} A product of n copies of a set S is denoted Sn . For example, {0, 1} 3 is the set of all 3­bit sequences: {0, 1} 3 = {(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1)} � Logic 11 3 Predicates A predicate is a proposition whose truth depends on the value of one or more variables. For example, “n is a perfect square” is a predicate whose truth depends on the value of n. The predicate is true for n = 4 since 4 is a prefect square, but false for n = 5 since 5 is not a perfect square. Like other propositions, predicates are often named with a letter. Furthermore, a function­like notation is used to denote a predicate supplied with specific variable val­ ues. For example, we might name our earlier predicate P : P (n) = “n is a perfect square” Now P (4) is true, and P (5) is