MAT 47, Fall 207, CRN: 766 Real Analysis: A First Course Prerequisites: MAT 263 & MAT 300 Instructor: Daniel Cunningham What is Real Analysis? Real Analysis is the important branch of mathematics that investigates the properties of the real numbers and presents the theory behind calculus, differential equations, probability, and much more. The study of real analysis allows for an appreciation of the many interconnections with other mathematical areas as well. To teach well, a teacher of mathematics needs to know a great deal of mathematics. For a teacher of high school mathematics this means having a strong foundation and understanding of the following branches of mathematics: (a) algebra, (b) analysis, (c) statistics, and (d) geometry. MAT 47 provides a transition from elementary calculus to real analysis and is designed for students preparing for secondary mathematics teaching, and/or preparing for graduate courses in mathematics, applied mathematics, or statistics. MAT 300 offers an introduction to mathematical proof which is required for this course. Major objectives of the course A. Students will gain a deeper understanding of the foundations of the real number system and its axioms. proofs of theorems covering limits, functions, continuity, sequences, differentiation, and the Riemann integral. B. Students will further develop their skills in constructing proofs. C. Students who plan to teach mathematics in secondary schools will obtain a sound background in mathematical analysis so that they will be qualified to teach calculus.
Table of Contents Preface.............................................. 5 Proof, Sets, and Functions 6. Proofs............................................ 6.. Important Sets in Mathematics.......................... 7..2 How to Prove an Equation............................ 9..3 How to Prove an Inequality............................ 9..4 Important Properties of Absolute Value..................... 2.2 Sets............................................. 4.2. Basic Definitions of Set Theory.......................... 4.2.2 Set Operations................................... 4.2.3 Indexed Families of Sets.............................. 5.2.4 Generalized Unions and Intersections....................... 6.2.5 Unindexed Families of Sets............................ 9.3 Functions.......................................... 2.3. Real-Valued Functions............................... 2.3.2 One-To-One Functions and Onto Functions................... 22.3.3 Composition of Functions............................. 22.3.4 Inverse Functions.................................. 22.3.5 Functions Acting on Sets............................. 22.4 Mathematical Induction.................................. 26.4. The Well-Ordering Principle........................... 26.4.2 Proof by Mathematical Induction......................... 26 2 Axioms for the Real Numbers 29 2. R is an Ordered Field................................... 29 2.2 The Completeness Axiom................................. 32 2.2. Proofs on the Supremum of a Set......................... 33 2.2.2 Proofs on the Infimum of a Set.......................... 34 2.2.3 Alternative Proof Strategies............................ 37 2.3 The Archimedean Property................................ 4 2.3. The Density of the Rational Numbers...................... 42 2.4 Nested Intervals Theorem................................. 43 3 Sequences 45 3. Convergence......................................... 45 3.2 Limit Theorems for Sequences............................... 56 3.2. Order Preservation Theorems........................... 60 2
3.3 Subsequences........................................ 62 3.4 Monotone Sequences.................................... 64 3.4. The Monotone Subsequence Theorem...................... 66 3.5 Bolzano Weierstrass Theorems.............................. 68 3.6 Cauchy Sequences..................................... 70 3.7 Infinite Limits........................................ 72 3.8 Limit Superior and Limit Inferior............................. 74 3.8. The Limit Superior of a Bounded Sequence................... 74 3.8.2 The Limit Inferior of a Bounded Sequence.................... 76 4 Continuity 79 4. Continuous Functions................................... 79 4.2 Continuity and Sequences................................. 86 4.3 Limits of Functions..................................... 87 4.4 Consequences of Continuity................................ 90 4.5 Uniform Continuity..................................... 93 5 Differentiation 97 5. The Derivative....................................... 97 5.. The Rules of Differentiation............................ 97 5..2 The Chain Rule.................................. 99 5.2 The Mean Value Theorem................................. 0 5.2. The Intermediate Value Theorem for Derivatives................ 05 5.2.2 The Inverse Function Theorem.......................... 06 5.3 Taylor s Theorem...................................... 08 6 Riemann Integration 0 6. The Riemann Integral................................... 0 6.. Partitions and Darboux Sums........................... 0 6..2 Basic Lemmas regarding Partitions and Darboux Sums............ 6..3 The Definition of the Riemann Integral..................... 2 6..4 A Necessary and Sufficient Condition...................... 3 6.2 Properties of the Riemann Integral............................ 7 6.2. Linearity Properties................................ 7 6.2.2 Order Properties.................................. 20 6.2.3 Integration over Subintervals........................... 20 6.2.4 Composition Property............................... 22 6.3 Families of Riemann Integrable Functions........................ 24 6.3. Continuous Functions............................... 24 6.3.2 Monotone Functions................................ 25 6.4 The Fundamental Theorem of Calculus.......................... 27 6.4. Evaluating Riemann Integrals........................... 27 6.4.2 Continuous Functions have Antiderivatives................... 28 7 Infinite Series 3 7. Convergence and Divergence................................ 3 7.2 Convergence Tests..................................... 36 7.3 Power Series......................................... 44 3
4 8 Sequences and Series of Functions 47 8. Pointwise and Uniform Convergence........................... 47 8.2 Preservation Theorems................................... 49 A A Proof of Theorem 6.2.3 53 B Topology on the Real Numbers 56 B. Open and Closed Sets................................... 56 B.2 Compact Sets........................................ 60 C Review of Proof and Logic 65 D The Greek Alphabet 72 List of Symbols 73 Index 75
One of the main goals (though not the only one) that we will accomplish in MAT 47 is to cover the proofs that were omitted in calculus. A typical calculus book proves a few of the easiest theorems. But if you look carefully, you will see that your calculus textbook omits the proofs of many theorems. The reason for this omission is simple: the omitted proofs are much more difficult than the ones included in your calculus book. More difficult does not mean longer and more complicated computations. In fact, the omitted proofs require very little computation, but these proofs do require more advanced and abstract concepts. A typical proof in your calculus text involves only equations and inequalities, but at least half of any proof in MAT 47 will consist of statements in English together with new mathematical concepts and notation. These new concepts include the supremum, the completeness property and uniform continuity. Calculus books use many pictures that support the intuition; but these pictures do not constitute rigorous proofs. Many calculus textbook do not prove many (most) of the important theorems in the calculus. The Intermediate Value Theorem, the Extreme Value Theorem, the integrability of continuous functions on a closed bounded interval, etc. these results are all crucial to calculus and to higher analysis, and they can be stated in terms that a calculus student can understand, but an actual proof cannot be accomplished without answering one very basic question: What are the important properties of the set of real numbers? MAT 47 provides an answer to this question. We should point out that the lack of proofs does not rob the calculus course of all value. The freshman and sophomore calculus courses follow the style of 7th century mathematics. Newton and Leibniz did not know the proofs either, but they knew how to carry out computations. Their computations and ideas were based on a number system that was not well understood by many mathematicians, including Newton and Leibniz. Satisfactory explanations were not found until the work of Cauchy in the early 9th century, and Weierstrass, Dedekind, and Riemann in the late 9th century; their ideas and proofs are those that we will cover in MAT 47. 5
Chapter Proof, Sets, and Functions. Proofs How to Read a Proof While a proof may look like a short story, it is often more challenging to read than a short story. Usually some of the equations or inequalities will not seem clear, and you will have to figure out why they are valid. Some of the arguments will not be immediately understandable and will require some thinking. Many of the steps will seem completely strange and may appear very mysterious. Basically, before you can understand a proof you must unravel it. First, identify the main ideas and steps of the proof. Then see how they fit together to allow one to conclude that the result is correct. One important word of advice while reading a proof. Try to remember what it is that has to be proved. Before reading the proof decide what it is exactly that must be proven. Always ask yourself, What would I have to show in order to prove that? How to Write a Proof Practice! We learn to write proofs by writing proofs. Start by just copying, nearly word for word, a proof in these lecture notes that you find interesting. Vary the wording by using your own phrases. Write out the proof using more steps and more details than you found in the original proof. Try to find a different proof of the same statement and write out your new proof. Try to change the order of the argument, if it is possible. If it is not possible, you will soon see why. All mathematicians first learned how to write proofs by going through this process of imitation. A review of logic and proof is presented in Appendix C on page 65 of these notes. The most important proof strategies that will be used in this course can be found in the appendix starting on page 68 of these notes. Conjecture + Proof = Theorem A conjecture is a statement that you think is plausible but whose truth has not been established. In mathematics one never accepts a conjecture as true until a mathematical proof of the conjecture has been given. Once a mathematical proof of the conjecture is produced we then call the conjecture a theorem. On the other hand, to show that a conjecture is false one must find a particular assignment of values (an example) making the statement of the conjecture false. Such an assignment is called a counterexample to the conjecture. MAT 300 covers proof and logic. 6
.. PROOFS 7 The Proof Is Completed It is convenient to have a mark which signals the end of a proof. Mathematicians in the past, would end their proofs with letters Q.E.D., an abbreviation for the Latin expression quod erat demonstrandum. So in English, we interpret Q.E.D. to mean that which was to be demonstrated. In current times, mathematicians typically use the symbol to let the reader know that the proof has been completed. In these notes we shall do the same... Important Sets in Mathematics Certain sets are frequently used in mathematics. The most commonly used ones are the sets of whole numbers, natural numbers, integers, rational and real numbers. These sets will be denoted by the following symbols:. N = {, 2, 3,... } is the set of natural numbers. 2. Z = {..., 3, 2,, 0,, 2, 3,... } is the set of integers. 3. Q is the set of rational numbers; that is, the set of numbers r = a b b 0. So, 3 2 Q. 4. R is the set of real numbers and so, π R. for integers a, b where In these notes we do not consider 0 to be a natural number. For each of the sets Z, Q and R, we may add + or as a superscript. The + (or ) superscript indicates that only the positive (or negative) numbers will be allowed. For example,. Q + = {x : x is a positive rational number}. 2. Z = {x : x is a negative integer}. 3. R + = {x : x is a positive real number}. For sets A and B we write A B to mean that the set A is a subset of the set B, that is, every element of A is also an element of B. For example, N Z. Example. Consider the set of integers Z. We evaluate the following truth sets:. {x Z : x is a prime number} = {2, 3, 5, 7,,... }. 2. {x Z : x is divisible by 3} = {..., 2, 9, 6, 3, 0, 3, 6, 9, 2,... }. 3. {z Z : z 2 } = {, 0, }. 4. {x Z : x 2 } = {, 0, }. Interval Notation In mathematics, an interval is a set consisting of all the real numbers that lie between two given real numbers a and b, where a b. The numbers a and b are referred to as the endpoints of the interval. A point in an interval that is not an endpoint is called an interior point. An interval may or may not include its endpoints.. The open interval (a, b) is defined to be (a, b) = {x R : a < x < b}. 2. The closed interval [a, b] is defined to be [a, b] = {x R : a x b}. 3. The left-closed interval [a, b) is defined to be [a, b) = {x R : a x < b}. 4. The right-closed interval [a, b) is defined to be (a, b] = {x R : a < x b}.
8 CHAPTER. PROOF, SETS, AND FUNCTIONS Finally, for any real number a we now define the following unbounded intervals.. The interval (a, ) is defined to be (a, ) = {x R : a < x}. 2. The interval [a, ) is defined to be [a, ) = {x R : a x}. 3. The interval (, a) is defined to be (, a) = {x R : x < a}. 4. The interval (, a] is defined to be (, a] = {x R : x a}. The symbol denotes infinity and it is not a number. The notation it is just a useful symbol that allows us to represent intervals that are without an end. Similarly, the notation is used to denote an interval without a beginning. Problem. Using interval notation, evaluate the following truth sets: () {x R : x 2 < 3}. (2) {x R + : (x ) 2 > }. (3) {x R : x > x }. Solution. () We first solve the inequality x 2 < 3 for x 2 obtaining x 2 < 4. The solution to this latter inequality is 2 < x < 2. Thus, {x R : x 2 < 3} = ( 2, 2). (2) We are looking for all the positive real numbers x that satisfy the inequality (x ) 2 >. We see by inspection, that the solution consists of all real numbers x > 2. So, {x R + : (x ) 2 > } = (2, ). (3) We need to find all the negative real numbers x that satisfy x > x. We conclude x2 <. So, we must have < x < 0. So, {x R : x > x } = (, 0). Remark... An interval is said to be proper if it contains more than one point; otherwise, the interval is called improper. For example, the intervals (a, a) and [a, a] are improper. Definition. A positive rational number m n common factors. is in reduced form if m N and n N have no Example. 4 3 is in reduced form, 2 9 is not in reduced form because 2 and 9 have a common factor. Clearly every rational number can be put into reduced form. Lemma..2. Let a, b Z. If p is a prime and p divides ab, then either p divides a or p divides b. Theorem..3. Let p N be a prime number. Then p is an irrational number. Proof. Assume p N is prime. We prove that p is irrational. Assume, for a contradiction, that p is rational. Thus, (i) p = m n for some m, n N and n 0. We shall assume that m n has been put into reduced form. By squaring both sides of (i) we obtain p = m2. Hence, we conclude that (ii) n 2 m 2 = pn 2. Hence, p evenly divides m 2. Since p is is a prime, p evenly divides m by Lemma..2. So, m = pk for some k N. After substituting m = pk in (ii), we conclude p 2 k 2 = pn 2. Therefore, n 2 = pk 2. Thus, p evenly divides n 2, and so, p evenly divides n. Hence, m and n have p as a common factor. It follows that m n is not in reduced form. Contradiction.
.. PROOFS 9..2 How to Prove an Equation Equations play a critical role in modern mathematics. In this text we will establish many theorems that will require us to know how to correctly prove an equation. Because this knowledge is so important and fundamental, our first proof strategy presents two correct methods that we shall use when proving equations. Proof Strategy..4. To prove a new equation ϕ = ψ there are two approaches: (a) Start with one side of the equation and derive the other side. (b) Perform operations on the given equations to derive the new equation. We now apply strategy..4(a) to prove an well known algebraic identity. Theorem..5. Let a and b be arbitrary real numbers. Then (a + b)(a b) = a 2 b 2. Proof. We 2 will start with the left hand side (a+b)(a b) and derive the right hand side as follows: (a + b)(a b) = a(a b) + b(a b) by the distribution property Thus, we have that (a + b)(a b) = a 2 b 2. = a 2 ab + ba b 2 by the distribution property = a 2 b 2 by algebra. We now apply strategy..4(b) to prove a new equation from some given equations. Theorem..6. Let m, n, i, j be integers. Suppose that m = 2i + 5 and n = 3j. Then mn = 6ij + 5j. Proof. We are given that m = 2i + and n = 2j. By multiplying corresponding sides of these two equation, we obtain mn = (2i + 5)(3j). Thus, by algebra, we conclude that mn = 6ij + 5j. Remark..7. To prove that an equation ϕ = ψ is true, it is not a correct method of proof to assume the equation ϕ = ψ and then work on both sides of this equation to obtain an identity. The method described in Remark..7 is a fallacious one and if applied, can produce false equations. For example, this fallacious method can be used to derive the equation =. To illustrate this, let us assume the equation =. Now square both sides, obtaining ( ) 2 = 2 which results in the true equation =. The method cited in Remark..7 would allow us to conclude that = is a true equation. This is complete nonsense. We never want to apply a method that can produce false equations!..3 How to Prove an Inequality To prove a new inequality from some given inequalities is a little more difficult than proving equations. The key difference is that you have to correctly use the Laws of Inequality. Laws of Inequality..8. For all a, b, c, d R the following hold:. Exactly one of the following relations holds: a < b or a = b or a > b. (Trichotomy) 2 Most mathematicians use the term we in their proofs. This is considered polite and is intended to include the reader in the discussion.
0 CHAPTER. PROOF, SETS, AND FUNCTIONS 2. If a < b and b < c, then a < c. (Transitivity Law) 3. If a < b, then a + c < b + c. (Adding on both sides) 4. If a < b and c > 0, then ac < bc. (Multiplying by a positive) 5. If a < b and c < 0, then ac > bc. (Multiplying by a negative) 6. if a < b and c < d, then a + c < b + d. (Additivity) We write a > b when b < a, and a b states that a < b or a = b. Similarly, a b means that a > b or a = b. The Trichotomy Law allows us to assert that if a b, then a b. It should be noted that one can actually prove laws 5 and 6 from laws -4. Furthermore, one can also prove that 0 < and < 0. Problem. Solving inequalities.. Let c < 0. Solve 2cx 0 > 2c 0 for x. 2. Let c <. Solve x 3 c > c + for x. Theorem..9. Let a, b, c be a real numbers and suppose that a < b. Then a c < b c. Proof. Let a, b, c be a real numbers and suppose that () a < b. From the inequality law 3 we obtain a + ( c) < b + ( c). Thus, by algebra, we conclude that a c < b c. The following principles of inequality follow from the Laws of Inequality..8. Substitution Principles of Inequality..0. Let a, p, x, y real numbers. Then the following hold: () Given the sum a + x, you can conclude that a + x < a + y, if x < y. (Replacing a summand with a larger value yields a larger sum.) (2) Given the sum a + x, you can conclude that a + x > a + y, if x > y. (Replacing a summand with a smaller value yields a smaller sum.) (3) Given the product px where p > 0, you can conclude that px < py, if x < y. (Replacing a factor with a larger value yields a larger product.) (4) Given the product px where p > 0, you can conclude that px > py, if x > y. (Replacing a factor with a smaller value yields a smaller product.) Principle () holds for as well (i.e., upon replacing both occurrences of < in () with ). The above (2) also holds for. Moreover, (3) holds for when p 0; and (4) holds for when p 0. We will provide two proofs of Theorems....3 below. The first prove uses the Laws of Inequality..8. The second proof uses the Substitution Principles of Inequality..0. Theorem... If x 3, then x 2 > 2x +. First Proof. Assume that x 3. Since x >, we have that 2x + x > 2x +. So 3x > 2x +. As x 3, we see that xx 3x. So x 2 3x. Thus, x 2 3x > 2x +. Hence, x 2 > 2x +. Second Proof. Assume that x 3. We show that x 2 > 2x + as follows: Therefore, x 2 > 2x +. x 2 = xx by algebra 3x as x 3 (see..0(4)) = 2x + x by algebra > 2x + because x > (see..0()).
.. PROOFS Theorem..2. Let a, b, c, d be a real numbers and suppose that a < b and c < d. a + c < b + d. Then First Proof. Let a, b, c, d be a real numbers satisfying () a < b and (2) c < d. We prove that a + c < b + d. From () and law 3 of the Laws of Inequality..8, we obtain a + c < b + c. From (2) and law 3 again, we conclude that b+c < b+d. So, a+c < b+c < b+d. Therefore, a+c < b+d. Second Proof. Let a, b, c, d be a real numbers satisfying a < b and c < d. We show that a+c < b+d as follows: Therefore, a + c < b + d. a + c < b + c as a < b (see..0()) < b + d as c < d (see..0()). Theorem..3. Suppose a and b are real numbers. If 0 < a < b, then a 2 < b 2. First Proof. Assume 0 < a < b. We show that a 2 < b 2. Since 0 < a < b, we conclude that a < b and that a, b are positive numbers. Multiplying both sides of the inequality a < b by the positive a gives the inequality (i) a 2 < ab, and multiplying both sides of the inequality a < b by the positive b gives the inequality (ii) ab < b 2. Thus, (i) and (ii) give a 2 < ab < b 2. Thus, a 2 < b 2. Therefore, if 0 < a < b, then a 2 < b 2. Second Proof. Assume 0 < a < b. We show that a 2 < b 2 as follows: Therefore, a 2 < b 2. a 2 = aa by algebra < ab as a < b (see..0(3)) < bb as a < b (see..0(3)) = b 2 by algebra. Theorem..4. Suppose a and b are real numbers. If 0 < a < b, then a < b. Proof. Suppose 0 < a < b. We will prove that a < b. Suppose, for a contradiction, that b a. If b = a, then b = ( b) 2 = ( a) 2 = a. Contradiction. If b < a, then b = ( b) 2 < ( a) 2 = a by Theorem..3, and so b < a. Contradiction. Theorem..5. Let a, b, c, d be positive real numbers satisfying a < b and c < d. Then ac < bd. Proof. Let a, b, c, d be positive real numbers satisfying () a < b and (2) c < d. We shall prove that ac < bd. From () we conclude that ac < bc because c > 0. From (2) we obtain bc < bd because b > 0. So, ac < bc < bd. Therefore, ac < bd. Theorem..6. Suppose a, b, x, y are all positive real numbers, that is, suppose a, b, x, y > 0. If a b and x y, then ax by. Proof. Suppose a, b, x, y > 0, a b and x y. We prove that ax by. There are several cases to consider. Suppose a = b and x = y. Then ax = by and so, ax by. Suppose a = b and x < y. Then ax < ay = by and so, ax by. Suppose a < b and x = y. Then ax < bx = by and so, ax by. Suppose a < b and x < y. Then ax < by by Theorem..5. So, ax by.
2 CHAPTER. PROOF, SETS, AND FUNCTIONS..4 Important Properties of Absolute Value Given a real number x, the absolute value of x, denoted by x, is defined by { x, if x 0 x = x, if x < 0. For all a, b, x, c R, where c > 0, we have. x < c if and only if c < x < c 2. x > c if and only if x < c or x > c 3. x = x 4. x x and x x 5. ab = a b 6. a + b a + b (triangle inequality) 7. a b a b (backward triangle inequality) 8. a b a b 9. b a a b We will prove the following three theorems in class: Theorem. Let δ > 0. If x < δ, then 3x 3 < 3δ. Theorem. Let δ > 0. If x < δ, then 3x + 5 < 3δ + 8. Theorem. If x < 2, then 4 < x + 5. We now present two theorems with complete proofs. Theorem. For all ε > 0, if x < ε 3 then (3x + 2) 5 < ε. Proof. Let ε > 0. Assume ( ) x < ε 3. We prove that (3x + 2) 5 < ε as follows: (3x + 2) 5 = 3(x ) by algebra = 3 x by property of absolute value < 3 ε 3 by ( ) and property of inequality = ε by arithmetic. Therefore, (3x + 2) 5 < ε. Theorem. For all ε > 0 there exists a δ > 0 such that if x < δ then (3x + 2) 5 < ε. Proof. Let ε > 0. Let δ = ε. Assume ( ) x < δ. We prove that (3x + 2) 5 < ε as follows: 3 Therefore, (3x + 2) 5 < ε. (3x + 2) 5 = 3(x ) by algebra = 3 x by property of absolute value < 3δ by ( ) and property of inequality = 3 ε 3 since δ = ε 3 = ε by arithmetic.
.. PROOFS 3 Exercises.. Let x and y be real numbers. Prove that (x y)(x 2 + xy + y 2 ) = x 3 y 3. 2. Let x and y be real numbers. Prove that (x + y)(x 2 xy + y 2 ) = x 3 + y 3. 3. Let x and y be real numbers. Prove that (x + y) 2 = x 2 + 2xy + y 2. 4. Let x and y be real numbers. Using exercise 3, prove that (x + y) 3 = x 3 + 3x 2 y + 3xy 2 + y 3. 5. Let ϕ be the positive real number satisfying the equation ϕ 2 ϕ = 0. Prove that ϕ = ϕ. 6. Let ϕ be as in exercise 5. Let a b be real numbers satisfying b a = ϕ. Prove that a b a = ϕ. 7. Let x be a real number such that x >. Prove that x 2 > x. 8. Let x be a real number where x < 0. Prove that x 2 > 0. 9. Let x be a real number where x > 0. Prove that x 2 > 0. 0. Let x be a real number where x 0. Using exercises 8 and 9, prove that x 2 > 0.. Let a and b be real numbers where a b. Using exercise 0 prove that a 2 + b 2 > 2ab. 2. Let x be a real number so that x 2 > x. Must we conclude that x >? 3. Let x be a real number satisfying 0 < x <. Prove that x 2 < x. 4. Let x be a real number where x 2 < x. Must we conclude that 0 < x <? 5. Let a and b are real numbers where a < b. Prove that a > b. 6. Let a, b be positive real numbers and let c, d be negative real numbers. Suppose a < b and c < d. Prove that ad > bc. 7. Find a counterexample showing that the following conjecture is false: Let a, b, c, d be whole numbers satisfying a b c d. Then a c and b d. 8. Find a counterexample showing that the following conjecture is false: Let m 0 and n 0 be integers. Then m + n m n. 9. Find a counterexample showing that the following conjecture is false: Let x 0 and y 0 be real numbers. Then x + y = x + y. 20. Let a, b, c, d be real numbers. Suppose that a + b = c + d and a c. Prove that d b. [Hint: x y if and only if x y 0.] 2. Let a > 0 be a real number. Prove that a > 0. 22. Let x and y be real numbers where x > 0. Using Exercise 2, prove that If xy > 0, then y > 0. 23. Let δ > 0. Prove that if x 5 < δ 3, then 3x 5 < δ. 24. Let δ > 0. Prove that if x 5 < δ, then x + 3 < δ + 8. 25. Prove that if x + 5 <, then < x + 3. 26. Let δ > 0. Prove that if x 3 < δ, then x 2 9 < δ(δ + 6). 27. Prove that for every real number x > 3, there exists a real number y < 0 such that x = 3y 2+y. 28. Prove that for all real numbers x, if x > then 0 < x <. 29. Using interval notation, evaluate the following truth sets: (a) {x R + : x > x }. (b) {x R : x 2 > x }. (c) {x R+ : x > x and x > 2}. (d) {x R : x > x and x 2 }.
4 CHAPTER. PROOF, SETS, AND FUNCTIONS.2 Sets In modern mathematics, many of the most important ideas are expressed in term of sets. A set is just a collection of objects. These objects are referred to as the elements of the set. These elements can be numbers, ordinary objects, words, other sets, functions, etc. An object a may or may not belong to a given set A. If a belongs to the set A then we say that a is an element of A, and we write a A. Otherwise, a is not an element of A and we write a / A..2. Basic Definitions of Set Theory Definition.2.. The following set notation is used throughout mathematics.. For sets A and B, we write A = B to mean that both sets have exactly the same elements. 2. For sets A and B, we write A B to assert that the set A is a subset of the set B, that is, every element of A is also an element of B. 3. For sets A and B, we write A B to state that A is a proper subset of the set B; that is, A B and A B. 4. We write for the empty the set, that is, the set with no members. 5. If A is a finite set, then A represents the number of elements in A. Venn diagrams are geometric shapes that are used to depict sets and their relationships. In Figure. we present a Venn diagram which illustrates the subset relation, a very important concept in set theory and mathematics. A B Figure.: Venn diagram of A B.2.2 Set Operations The language of set theory is used in the definitions of nearly all of mathematics. There are three important and fundamental operations on sets that we shall now discuss: the intersection, the union and the difference of two sets. We illustrate these four set operations in Figure.2 using Venn diagrams. Shading is used to focus one s attention on the result of each set operation. Definition.2.2. Given sets A and B we can build new sets using the set operations:. A B = {x : x A or x B} is the union of A and B. 2. A B = {x : x A and x B} is the intersection of A and B. 3. A \ B = {x : x A and x / B} is the set difference of A and B (also stated in English as A minus B). 4. Given a universe of objects U and A U, the set A c = U \ A = {x U : x / A} is called the complement of A. Example. Let A = {, 2, 3, 4, 5, 6} and B = {2, 4, 6, 8, 0, 2}. Then. A B = {, 2, 3, 4, 5, 6, 8, 0, 2}.
.2. SETS 5 A. Venn diagram of A B B A 2. Venn diagram of A B B A c A A 3. Venn diagram of A \ B B U Figure.2: Set Operations 4. Venn diagram of A c 2. A B = {2, 4, 6}. 3. A \ B = {, 3, 5}, and B \ A = {8, 0, 2}. Problem. Recalling the notation (see page 7) for intervals on the real line, evaluate the result of the following set operations:. ( 3, 2) (, 3). 2. ( 3, 4) (0, ). 3. ( 3, 2) \ [, 3). Solution. While reading the solution to each of these items, it may be helpful to sketch the relevant intervals on the real line.. Since x ( 3, 2) (, 3) if and only if x ( 3, 2) and x (, 3), we see that x is in this intersection precisely when x satisfies both (a) 3 < x < 2 and (b) < x < 3. We see that the only values for x that satisfies both (a) and (b) are those such that < x < 2. Thus, ( 3, 2) (, 3) = (, 2). 2. Since x ( 3, 4) (0, ) if and only if x ( 3, 4) or x (0, ), we see that x is in this union precisely when x satisfies either (a) 3 < x < 4 or (b) 0 < x. We see that the only values for x that satisfies either (a) or (b) are those such that 3 < x. Thus, ( 3, 4) (0, ) = ( 3, ). 3. Since x ( 3, 2) \ [, 3) if and only if x ( 3, 2) and x / [, 3), we see that x is in this set difference precisely when x satisfies (a) 3 < x < 2 and (b) not ( x < 3). We see that the only values for x that satisfies both (a) and (b) are those such that 3 < x <. Thus, ( 3, 2) \ [, 3) = ( 3, )..2.3 Indexed Families of Sets Given a property P (x) we can form the truth set {x : P (x)} when the universe is understood. There is another way to construct sets. For example, consider the set S of all perfect squares, that is, the set of all numbers of the form n 2 for some natural number n. We can define S in two ways:. S = {x : ( n N) (x = n 2 )} = {, 4, 9, 6, 25, }. 2. S = {n 2 : n N} = {, 4, 9, 6, 25, }. In item, we have expressed S as a truth set. Item 2 offers an alternative method for constructing the same set S. This alternative method is a special case of the following technique for constructing
6 CHAPTER. PROOF, SETS, AND FUNCTIONS sets from the set N of natural numbers. Suppose for each i N we have that n i is some object. Then we can form the set S = {n i : i N} of all such objects. In this case, the set N is called the index set and the set S is called an indexed set or indexed family. Since this concept is used so often in mathematics, we will now formulate this idea in terms of a general definition. Definition.2.3. let I be any set and for each i I let x i be some object. Then we can form the set S = {x i : i I}. The set I is called the index set and the set S is called an indexed set or an indexed family We can describe the set S in Definition.2.3 in two ways: As a truth set and as an indexed set, respectively: S = {x : ( i I) (x = x i )} and S = {x i : i I}. Problem 2. Explain what the the following statements mean.. y {sin(x) : x Q}. 2. {x i : i I} A. 3. {x i : i I} A. Solution. The first statement y {sin(x) : x Q} means that y = sin(x) for some x Q. The second statement {x i : i I} A means that x i A for every i I. Finally, the third statement {x i : i I} A means that x i / A for some i I. Definition.2.4. A set F, whose elements are sets, is called a family of sets. Definition.2.5. Let I be any set and for each i I let C i be a set. Then we can form the set F = {C i : i I}. The set I is called the index set and F is called an indexed family of sets. Example 2. Suppose for each natural number n we define the set A n = {0,, 2,..., n}. Then F = {A n : n N} = {A, A 2, A 3,... } is an indexed family of sets, where the set N of natural numbers is the index set. Example 3. For each real number x > 0, let B x = {y R : x < y < x + }, that is, B x = ( x, x + ). Define the indexed family of sets by F = {B x : x R + }, where R + is the index set. Note that B 2 B 5 = ( 2, 3) ( 5 2 2, 9 2 ) = ( 2, 3). Example 4. Let I = {i R : i > }, the set of all real numbers greater than. Suppose that for each real number i I we let B i = [ i, i ], that is, B i = {x R : i x i }. Define the indexed family of sets by F = {B i : i I}. Note that B 2 B 5 = [ 2, 2 2 ] [ 5 2, 2 5 ] = [ 2, 2 5 ]..2.4 Generalized Unions and Intersections Given two sets A and B we can form the union A B and the intersection A B of these sets. In mathematics we often need to form the union and intersection of many more than just two sets. To see how this is done, we need to generalize the operations of union and intersection so that they will apply to more than just two sets. We will first extend the notions of union and intersection to a finite number of sets, and then to an infinite number of sets. We know that x A B means that x is in at least one of the two sets A and B. This notion of union can be easily extended to more than two sets. For finitely many sets, say A, A 2,... A n, we shall say that x is in the union A A 2 A n
.2. SETS 7 when x is in at least one of the sets A, A 2,... A n ; that is, x A i for some i n. There is simpler way to denote this finite union. Using I = {, 2,..., n} as an index set, we shall write A i = A A 2 A n and so, x A i means that x A i for some i I. i I i I We also know that x A B means that x is in both of the two sets A and B. We will extend this operation to more than two sets. For finitely many sets, say A, A 2,... A n, we shall say that x is in the intersection A A 2 A n when x is in every one one of the sets A, A 2,... A n ; that is, x A i for every i n. There is easier way to express this finite intersection. Using I = {, 2,..., n} as an index set, we shall write A i = A A 2 A n and so, x A i means that x A i for every i I. i I i I Similarly, we can form the union and intersection of any indexed family of sets {C i : i I}, where I can be finite or infinite. Definition.2.6. Let {C i : i I} be an indexed family of sets. The union C i is the set of i I elements x such that x C i for at least one i I; that is, C i = {x : x C i for some i I}. Definition.2.7. Let {C i : i I} be an indexed family of sets. The intersection C i is the i I set of elements x such that x C i for every i I; that is, C i = {x : x C i for every i I}. Problem 3. For each n N let C n be the closed interval C n = [, + n]. Then {Cn : n N} is an indexed family of sets. Evaluate the sets C n and C n. Solution. We shall evaluate the union Hence, n N intersection Thus, n N x n N n N n N C n as follows: i I i I n N C n iff x C n for some n N by def. of iff x [, + ] n for some n N by def. of C n. C n = [, 2] because C = [, 2] and C n [, 2] for all n N. We now evaluate the n N C n as follows: x n N C n iff x C n for every n N by def. of iff x [, + ] n C n = {} because + n gets closer and closer to. for every n N by def. of C n.
8 CHAPTER. PROOF, SETS, AND FUNCTIONS Problem 4. Suppose that {C i : i I} is an indexed family of sets. Explain why the the following four statements are true. () x C i means that x C i for some i I. i I (2) x / C i means that x / C i for every i I. i I (3) x C i means that x C i for every i I. i I (4) x / C i means that x / C i for some i I. i I Solution. We first note that the assertion x / C i in (2) is the negation of that in (). Similarly, i I the assertion x / C i in (4) is the negation of that in (3). i I () Clearly, x C i means x C i for some i I, by Definition.2.6. i I x C i iff ( i I)(x C i ). i I (2) From our solution to (), we observed that x C i i I iff ( i I)(x C i ). Thus, x / i I C i iff ( i I)(x C i ) iff ( i I)(x / C i ). We conclude that So, x / C i means ( i I)(x / C i ), that is, x / C i for every i I. i I (3) From Definition.2.7, we see that x C i means x C i for every i I. i I C i iff ( i I)(x C i ). i I (4) In our solution to (3) we noted that x C i i I iff ( i I)(x C i ). Hence, So, x x / i I C i iff ( i I)(x C i ) iff ( i I)[x / C i ]. So, x / C i means ( i I)[x / C i ], that is, x / C i for some i I. i I De Morgan s Laws for Families of Sets Theorem.2.8. Suppose that A is a set and that {B i : i I} is an indexed family of sets. Then () A \ B i = (A \ B i ). i I i I (2) A \ B i = (A \ B i ). i I i I Proof. We shall prove only () and leave (2) as an exercise. We prove that A \ B i = (A \ B i ). i I i I
.2. SETS 9 ( ). First we prove that A \ B i (A \ B i ). To do this, let x A \ B i. We must prove i I i I i I that x (A \ B i ). We do this as follows: i I x A \ i I B i 3 x A and x / i I B i by the definition of \ x A and x / B i for every i I by the definition of x A \ B i for every i I by the definition of \ x i I(A \ B i ) by the definition of. Therefore, A \ B i (A \ B i ). i I i I ( ). We now prove that (A \ B i ) A \ B i. To do this, let x (A \ B i ). We must prove i I i I i I that x A \ B i. We do this as follows: i I x (A \ B i ) x A \ B i for every i I by the definition of i I x A and x / B i for every i I by the definition of \ x A and x / B i by the definition of i I x A \ i I B i by the definition of \. Therefore, (A \ B i ) A \ B i. Thus, the proof of () is complete. i I i I In the proof of Theorem.2.8, the annotations ( ) and ( ) are added as a courtesy to the reader. The notation ( ) is used to make it clear to the reader that we are proving that first set is a subset of the second set. The notation ( ) indicates that we are proving that the second set is a subset of the first set..2.5 Unindexed Families of Sets Indexed families of sets occur frequently in mathematics. Moreover, mathematicians also deal with families of sets (see Definition.2.4) that are not described as an indexed set. Fortunately, by a simple change in notation, every family of sets can be expressed as an indexed set. Let F be a family of sets. Then F = {C A : A F} where F is the index set and C A = A for each A F. Since every family F of sets can be expressed as an indexed family of sets, it follows that all of the operations and theorems we presented on indexed sets also apply to families of sets. When F is a family of sets, the union F is the set of elements x such that x C for some C F; that is, F = {x : x C for some C F}. The intersection F is the set of elements x such that x C for all C F; that is, F = {x : x C for every C F}. 2 The arrow is used to abbreviate the word implies.
20 CHAPTER. PROOF, SETS, AND FUNCTIONS For example, let F be the family of sets defined by F = {{, 2, 9}, {2, 9}, {4, 9}}. Then F = {, 2, 4, 9} and F = {9}. We have the following unindexed version of De Morgan s Theorem.2.8. Theorem.2.9. Suppose that A is a set and that F is a family of sets. Then () A \ F = {A \ B : B F}, (2) A \ F = {A \ B : B F}. Exercises.2. Recalling our discussion on interval notation on page 7, evaluate the following set operations: (a) ( 2, 0) (, 2). (b) ( 2, 4) (, 2). (c) (, 0] \ (, 2]. (d) R \ (2, ). (e) (R \ (, 2]) (, ). 2. Let I = {2, 3, 4, 5}, and for each i I let C i = {i, i +, i, 2i}. (a) For each i I, list the elements of C i. (b) Find C i. i I (c) Find C i. i I 3. For each n N let O n be the open interval O n = (, + n). Then {On : n N} is an indexed family of sets. Evaluate the sets: O n, and O n. n N 4. Let I = {i R : i} = [, ) and for each i I, let A i = {x R : i x 2 i }. Express A i in interval notation, if possible. Express A i in interval notation, if possible. i I 5. Prove Theorem.2.8(2). 6. Prove the following theorems: n N (a) Theorem. Let {A i : i I} and {B i : i I} be two indexed families of sets with indexed set I. Suppose A i B i for all i I. Then A i B i and A i B i. (b) Theorem. Let {A i : i I} and {B j : j J} be two indexed families of sets. Suppose there is an i 0 I such that A i0 B j for all j J. Then A i B j. (c) Theorem. Suppose that A is a set and that {B i : i I} is an indexed family of sets. Then A B i = (A B i ). i I i I (d) Theorem. Suppose that A is a set and that {B i : i I} is an indexed family of sets. Then A B i = (A B i ). i I i I (e) Theorem. Suppose that A is a set and that {B i : i I} is an indexed family of sets. Then A \ B i = (A \ B i ). i I i I 7. Let {B x : x R + } be the family of sets in Example 3. Evaluate B x and B x. x R + x R + i I i I i I i I i I j J i I
.3. FUNCTIONS 2 8. Let {B i : i I} be the family of sets in Example 4. Evaluate B i and B i. i I i I.3 Functions Definition.3.. We write f : A B to mean that f is a function from the set A to the set B, that is, for every element x A there is exactly one element f(x) in B. The value f(x) is called f of x, or the image of x under f. The set A is called the domain of the function f and the set B is called the co-domain of the function f. In addition, we shall say that x A is an input for the function f and that f(x) is the resulting output. We will also say that x gets mapped to f(x). Remark.3.2. If f : A B then every x A is assigned exactly one element f(x) in B. We say that f is single-valued. Thus, for every x A and z A, if x = z then f(x) = f(z). Definition.3.3. Given a function f : A B the range of f, denoted by ran(f), is the set ran(f) = {f(a) : a A} = {b B : b = f(a) for some a A}. The range of a function is the set of all output values produced by the function. Question. Let h : X Y be a function. What does it mean to say that b ran(h)? Answer: b ran(h) means that b = f(x) for some x A. Example. Let f : R R be the function in Figure.3 defined by the formula f(x) = x 2 x. Then ran(f) = {f(x) : x R} = {x 2 x : x R} = [ 4, ). y = f(x) 4 Figure.3: Graph of f(x) = x 2 x.3. Real-Valued Functions Real-valued functions are the focus in a calculus course and they will be the focus in this course as well. A real-valued function is one that has the form f : D R, that is, the output values of the function f are real numbers. In this book, the domain of a real-valued function will typically be a set of real numbers. A polynomial function of degree n is a real-valued function of the form f(x) = a n x n + a n x n + a x + a 0, where a n,..., a, a 0 are real number constants, n is a natural number, and a n 0. A constant function has the form f(x) = a where a is a constant. A rational function is one that is defined as the ratio of polynomials.
22 CHAPTER. PROOF, SETS, AND FUNCTIONS.3.2 One-To-One Functions and Onto Functions Definition. A function f : X Y is said to be one-to-one (or an injection), if distinct elements in X get mapped to distinct elements in Y ; that is, for all a, b X, if a b then f(a) f(b), or equivalently, for all a, b X, if f(a) = f(b) then a = b. Definition. A function f : X Y is said to be onto (or a surjection), if for each y Y there is an x X such that f(x) = y. Definition. A function f : X Y is said to be one-to-one and onto (or a bijection), if f is both one-to-one and onto..3.3 Composition of Functions Definition. Given two functions f : X Y and g : Y Z, one forms the composition function (g f): X Z by defining (g f)(x) = g(f(x)) for all x X. Theorem.3.4. If f : X Y and g : Y Z are one-to-one, then (g f): X Z is one-to-one. Theorem.3.5. If f : X Y and g : Y Z are onto, then (g f): X Z is onto..3.4 Inverse Functions Theorem.3.6. Given a one-to-one function f : X Y, let R = ran(f). Then there is a function f : R X defined as follows: For each y R, f (y) is defined to be the unique element in X such that f(x) = y. That is, for all y R f (y) = x iff f(x) = y. (.) Definition. Given a one-to-one function f : X Y, let R = ran(f). The function f : R X, satisfying equation (.) for all y R, is the inverse function of f. Theorem.3.7. Let f be any one-to-one function f : X Y. Let R = ran(f) and let f : R X be the inverse of f. Then (f f): X X and (f f ): R R. Moreover, the following hold: (a) f : X R is one-to-one and onto. (b) f : R X is one-to-one and onto. (c) (f f)(x) = x, for all x X. (d) (f f )(y) = y, for all y R..3.5 Functions Acting on Sets There are times when we are more interested in what a function does to an entire subset of its domain, rather than how it affects an individual element in the domain. Understanding this behavior on sets can allow one to better understand the function itself and can reveal some properties concerning it s domain and range. The concept of a function acting on a set, is one that appears in every branch of mathematics.
.3. FUNCTIONS 23 f : X Y x S y f[s] X Y Figure.4: Starting with S X we construct the new set f[s] Y. Definition.3.8 (Image of a Set). Let f : X Y be a function. Let S X. The set f[s], called the image of S, is defined by f[s] = {f(x) : x S} = {y Y : y = f(x) for some x S}. Figure.4 illustrates Definition.3.8. The square S represents a subset of the domain of the function f. The image f[s] is represented by a rectangle. Note that f[x] = ran(f). Example 2. Given the function f : R R defined by f(x) = x and S = { 2, 3, 2, 3}. Then the image of S is f[s] = {f(x) : x S} = { x : x S} = {2, 3, 2}. Given a subset S of the domain of a function, Definition.3.8 allows us to construct a subset f[s] of the co-domain of this function. We will now turn this process around. Our next definition will allow us to start with a subset T of the co-domain and then construct a subset of the domain. Definition.3.9 (Inverse Image of a Set). Let f : X Y be a function. Let T Y, that is, let T be a subset of Y. The set f [T ] is the subset of X defined by f [T ] = {x X : f(x) T }. The set f [T ] is called the inverse image of T. A depiction of Definition.3.9 is given in Figure.5. The circle T represents a subset of the co-domain of the function f. The inverse image f [T ] is represented by an ellipse. f : X Y x f [T ] X T y Y Figure.5: Starting with T Y we construct the new set f [T ] X. Example 3. Consider the function f : R R defined by f(x) = x and let T = { 8, 2, 3}. Then the inverse image of T is f [T ] = {x R : f(x) T } = {x R : x T } = { 3, 2, 2, 3}. The notation f used in Definition.3.9, should not be confused with that of an inverse function. Theorem.3.6 implies that the inverse function exists if and only if the original function is one-to-one and onto. Definition.3.9 applies to all functions, even those that are not one-to-one and onto. Given a subset of the co-domain of any function f, the inverse image of this subset always defines a new subset of the function s domain. The following remark states four observations that can be very useful when working with the image, or the inverse image, of a set. Remark.3.0. Let f : X Y, S X, T Y, a X and b Y.
24 CHAPTER. PROOF, SETS, AND FUNCTIONS. If a S, then f(a) f[s]. 2. b f[s] if and only if b = f(x) for some x S. 3. If a f [T ], then f(a) T. 4. If f(a) T, then a f [T ]. Image Warning: If f(a) f[s] then we can conclude that f(a) = f(x) for some x S, by item 2 of Remark.3.0. Furthermore, if f(a) f[s], we cannot necessarily conclude that a S. In Example 2, we have that f(2) f[s] and yet 2 / S. Theorem.3.. Let f : X Y be a function. Let S be a subset of X, and let T be a subset of Y. Then f[s] T if and only if for all x S we have f(x) T. Theorem.3.2. Let f : X Y be a function. Let C, D be subsets of X, and let U, V be subsets of Y. Then (a) f[c D] f[c] f[d] (b) f[c D] = f[c] f[d] (c) f [U V ] = f [U] f [V ] (d) f [U V ] = f [U] f [V ]. Proof. We shall prove only (a) and (d). Let f : X Y be a function. Let C, D be subsets of X and let U, V be subsets of Y. (a). We prove f[c D] f[c] f[d]. Let y f[c D]. We will prove that y f[c] f[d]. Since y f[c D], there is an x C D such that y = f(x) (this follows from the definition of the image of a set). Because x C D, we see that x C and x D. Therefore, y = f(x) f[c] and y = f(x) f[d]. Thus, y f[c] f[d]. (d). We prove f [U V ] = f [U] f [V ]. ( ). First we prove f [U V ] f [U] f [V ]. Let x f [U V ]. We prove x f [U] f [V ] as follows: x f [U V ] f(x) U V Therefore, f [U V ] f [U] f [V ]. by definition of inverse image. f(x) U or f(x) V by definition of. x f [U] or x f [V ] by definition of inverse image. x f [U] f [V ] by definition of. ( ). Now we prove that f [U] f [V ] f [U V ]. Let x f [U] f [V ]. We prove that x f [U V ] as follows: x f [U] f [V ] x f [U] or x f [V ] by definition of. f(x) U or f(x) V by def. of inverse image. f(x) U V by definition of. x f [U V ] by def. of inverse image. Therefore, f [U] f [V ] f [U V ]. This completes the proof of (d). Theorem.3.3. Let f : X Y be a function. Let C, D be subsets of X. If f is one-to-one, then f[c D] = f[c] f[d].
.3. FUNCTIONS 25 Proof. Let f : X Y be a function. Let C, D be subsets of X. Assume that f is one-to-one. We prove that f[c D] = f[c] f[d]. By Theorem.3.2(a), f[c D] f[c] f[d]. We will now show that f[c] f[d] f[c D]. Let y f[c] f[d]. We will prove that y f[c D]. Since y f[c] f[d], we see that y f[c] and y f[d]. Because y f[c], there is an x C such that f(x ) = y. Also, since y f[d], there is an x 2 D such that f(x 2 ) = y. Hence, y = f(x ) = f(x 2 ). Since f is one-to-one, we have x = x 2. Thus, x D. So, x C D and therefore, y = f(x ) f[c D]. We can now conclude that f[c D] = f[c] f[d]. Remark.3.4. Suppose that f : R R and let [a, b] be an interval where a < b. We shall write the image of [a, b] under f as f([a, b]) rather than f[[a, b]]. Exercises.3. Using Definitions.3.8 and.3.9, explain why items -4 of Remark.3.0 are true. 2. Prove Theorem.3.. 3. Prove item (b) of Theorem.3.2. 4. Prove item (c) of Theorem.3.2. 5. Given a, b R with a > 0, define the function f : R R by f(x) = ax + b. Let U = [2, 3]. Using interval notation, evaluate f[u] and f [U]. 6. Define the function f : R R by f(x) = x 2 and let U = [, 4]. Show that (a) f[f [U]] U, (b) f [f[u]] U, (c) f[f [U]] f [f[u]]. 7. Let f : X Y be a function and let A X and B X. Prove that if A B then f[a] f[b]. 8. Let f : R R be the function defined in Example 2. Find A R and B R such that f[a] f[b] and A B. 9. Suppose f : X Y is one-to-one and let A X and B X. Prove that if f[a] f[b] then A B. 0. Let f : X Y be a function and let C Y and D Y. Prove that if C D then f [C] f [D].. Let f : R R be the function defined in Example 3. Find C R and D R such that f [C] f [D] and C D. 2. Suppose f : X Y is onto and let C Y and D Y. Prove if f [C] f [D] then C D. 3. Let f : X Y be a function. Let A be a subset of X. Prove that A f [f[a]]. 4. Suppose f : X Y is one-to-one. Let A X and x X. Prove if f(x) f[a], then x A. 5. Suppose that f : X Y is one-to-one. Let A X. Prove that A = f [f[a]]. 6. Let f : X Y. Suppose A = f [f[a]] for all finite subsets A of X. Prove f is one-to-one. 7. Let f : X Y be a function. Let C be a subset of Y. Prove that f[f [C]] C. 8. Assume that f : X Y is an onto function. Let C Y. Prove that f[f [C]] = C. 9. Given a, b R with a > 0, define the function f : R R by f(x) = ax + b. Using exercises 5 and 8, prove that f[f [U]] = f [f[u]] for every U R.