Shawn Zhong

Shawn Zhong

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Shawn Zhong

钟万祥
  • Tutorials
  • Mathematics
    • Math 240
    • Math 375
    • Math 431
    • Math 514
    • Math 521
    • Math 541
    • Math 632
    • Abstract Algebra
    • Linear Algebra
    • Category Theory
  • Computer Sciences
    • CS/ECE 252
    • CS/ECE 352
    • Learn Haskell
  • AP Notes
    • AP Microecon
    • AP Macroecon
    • AP Statistics
    • AP Chemistry
    • AP Physics E&M
    • AP Physics Mech
    • CLEP Psycho

Home / 2018 / March / Page 2

Math 521 – 3/19

  • Mar 19, 2018
  • Shawn
  • Math 521
  • No comments yet
Cauchy Sequence • A sequence {p_n } in a metric space X is said to be Cauchy sequence • If ∀ε 0, ∃N∈N s.t. d(p_n,p_m ) ε,∀n,m≥N Diameter • Let E be a nonempty subset of metric space X • Let S be set of all real numbers of the form d(p,q) with p,q∈E • Then diam S≔sup⁡S is called the diameter of E (possibly ∞) • If {p_n } is a sequence in X and E={p_N,p_(N+1),…} • Then {p_n } is a Cauchy sequence if and only if (lim)_(N→∞)⁡〖diam E_N 〗=0 Theorem 3.10 • Statement (a) ○ If E ̅ is the closure of a set E in a metric space X, then diam E ̅=diam E • Proof (a) ○ diam E≤diam E ̅ § This is obvious since E⊂E ̅ ○ diam E ̅≤diam E § Let p,q∈E ̅ § Let ε 0, then ∃p^′,q^′∈E s.t. d(p,p′) ε/2, d(q,q′) ε/2 § d(p,q)≤diam E □ d(p,q)≤d(p,p^′ )+d(p^′,q^′ )+d(q^′,q) □  ε/2+d(p^′,q^′ )+ε/2 □ =ε+d(p^′,q^′ ) □ ≤ε+diam E □ Since ε 0 was arbitrary, d(p,q)≤diam E § So diam E ̅≤diam E ○ Therefore diam E ̅=diam E • Statement (b) ○ If K_n is a sequence of compact sets in X s.t. ○ K_n⊃K_(n+1),∀n and (lim)_(n→∞)⁡〖diam K_n 〗=0 ○ Then ⋂24_(n=1)^∞▒K_n consists of exactly one point • Proof (b) ○ Let K=⋂24_(n=1)^∞▒K_n ○ By Theorem 2.36, K is not empty ○ If K contains more than one point, diam K 0 ○ But K_n⊃K, ∀n∈N, then ○ diam K_n≥diam K 0⇒lim_(n→∞)⁡〖K_n 〗≥diam K 0 ○ This contradicts lim_(n→∞)⁡〖diam K_n 〗=0 ○ There can only be one point in K
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5.3 Recursive Definitions and Structural Induction

  • Mar 19, 2018
  • Shawn
  • Math 240
  • No comments yet
Recursively Defined Functions • Definition ○ A recursive or inductive definition of a function consists of two steps. ○ Basis Step § Specify the value of the function at zero. ○ Recursive Step § Give a rule for finding the at an integer from its values at smaller integers ○ A function f(n) is the same as a sequence a_0,a_1,… where f(i)=a_i ○ This was done using recurrence relations in Section 2.4 • Example 1 ○ Suppose f is defined by § f(0)=3 § f(n+1)=2f(n)+3 ○ Find f(1), f(2), f(3), f(4) § f(1)=2f(0)+3=2∙3+3=9 § f(2)=2f(1)+3=2∙9+3=21 § f(3)=2f(2)+3=2∙21+3=45 § f(4)=2f(3)+3=2∙45+3=93 • Example 2 ○ Give a recursive definition of the factorial function n! ○ f(0) = 1 ○ f(n + 1) = (n + 1)∙ f(n) • Example ○ Give a recursive definition of ∑_(k=0)^n▒a_k ○ The first part of the definition is § ∑_(k=0)^0▒a_k =a_0 ○ The second part is § ∑_(k=0)^(n+1)▒a_k =(∑_(k=0)^n▒a_k )+a_(n+1) Fibonacci Numbers • The Fibonacci numbers are defined as follows: ○ f_0=0 ○ f_1=1 ○ f_n=f_(n−1)+f_(n−2) • Find f_2,f_3,f_4,f_5 ○ f_2=f_1+f_0=1+0=1 ○ f_3=f_2+f_1=1+1=2 ○ f_4=f_3+f_2=2+1=3 ○ f_5=f_4+f_3=3+2=5 • Show that whenever n ≥ 3, f_n   α^(n−2), where α=(1+√5)/2 ○ Let P(n) be the statement f_n   α^(n−2) . ○ Use strong induction to show that P(n) is true whenever n≥3. ○ Basis step § P(3) holds since α   2 = f_3 § P(4) holds since α^2 = (3+√5)/2   3 = f_4 ○ Inductive step § Assume that P(j) holds § i.e., f_j   α^(j−2) for all integers j with 3 ≤ j ≤ k, where k ≥ 4. § Show that P(k+1) holds, i.e., f_(k+1) α^(k−1). § Since α^2= α + 1 (because α is a solution of x^2−x−1=0), § α^(k−1)=α^2⋅α^(k−3)=(α+1)⋅α^(k−3)=α⋅α^(k−3)+1⋅α^(k−3)=α^(k−2)+α^(k−3) § By the inductive hypothesis, because k ≥ 4 we have □ f_(k−1) α^(k−3) □ f_k α^(k−3) § Therefore, it follows that □ f_(k+1)=f_k+f_(k−1) α^(k−2)+α^(k−3)=α^(k−1) § Hence, P(k+1) is true. Lamé’s Theorem • Lamé’s Theorem ○ Let a and b be positive integers with a≥b. ○ Then the number of divisions used by the Euclidian algorithm to find gcd(a,b) ○ is less than or equal to five times the number of decimal digits in b. • Proof ○ When we use the Euclidian algorithm to find gcd(a,b) with a≥b, ○ n divisions are used to obtain (with a=r_0,b=r_1) § r_0=r_1 q_1+r_2, 0 r_2 r_1 § r_1=r_2 q_2+r_3, 0 r_3 r_2 § ⋮ § r_(n−2)=r_(n−1) q_(n−1)+r_n, 0 r_n r_(n−1) § r_(n−1)=r_n q_n ○ Since each quotient q_1,q_2,…,q_(n−1) is at least 1 and q_n≥2 § r_n≥1=f_2 § r_(n−1)≥2r_n≥2f_2=f_3 § r_(n−2)≥r_(r−1)+r_n≥f_3+f_2=f_4 § ⋮ § r_2 r_3+r_4≥f_(n−1)+f_(n−2)=f_n § b=r_1≥r_2+r_3≥f_n+f_(n−1)=f_(n+1) ○ If n divisions are used to find gcd(a,b) with a≥b, then b ≥ f_(n+1) ○ By Example 4, f_(n+1) α^(n−1), for n   2, where α=(1+√5)/2. ○ Therefore, b   α^(n−1). ○ Because log_10⁡α ≈ 0.208   1/5, log_10⁡b   (n−1) log_10⁡α   (n−1)/5 ○ Hence, n−1 5⋅log_10⁡b ○ Suppose that b has k decimal digits. Then b   10k and log_10⁡b k. ○ It follows that n−1 5k and since k is an integer, n≤5k. ○ Therefore, O(log b) divisions are used to find gcd(a,b) whenever a   b. ○ The number of divisions needed is less than or equal to 5⋅(log_10⁡b+1) ○ Since the number of decimal digits in b is less than or equal to log_10⁡b+1 Recursively Defined Sets and Structures • Recursive definitions of sets have two parts: ○ The basis step specifies an initial collection of elements. ○ The recursive step gives the rules for forming new elements in the set from those already known to be in the set. • Sometimes the recursive definition has an exclusion rule, which specifies that ○ the set contains nothing other than those elements specified in the basis step ○ and generated by applications of the rules in the recursive step. • We always assume that the exclusion rule holds, even if it is not explicitly mentioned. • Example: Subset of Integers S ○ Basis step: 3 ∊ S. ○ Recursive step: If x ∊ S and y ∊ S, then x+y is in S. ○ Initially 3 is in S, then 3 + 3 = 6, then 3 + 6 = 9, etc. • Example: The natural numbers N. ○ Basis step: 0 ∊ N. ○ Recursive step: If n is in N, then n + 1 is in N. ○ Initially 0 is in N, then 0 + 1 = 1, then 1 + 1 = 2, etc. Strings • Definition: The set Σ* of strings over the alphabet Σ: ○ Basis step: λ ∊ Σ* (λ is the empty string) ○ Recursive step: If w is in Σ* and x is in Σ, wx∈Σ^∗. • Example ○ If Σ = {0,1} ○ The strings in in Σ* are the set of all bit strings, λ, 0, 1, 00,01,10, 11, etc. • Example ○ If Σ = {a,b}, show that aab is in Σ*. ○ Since λ ∊ Σ* and a ∊ Σ, a ∊ Σ*. ○ Since a ∊ Σ* and a ∊ Σ, aa ∊ Σ*. ○ Since aa ∊ Σ* and b ∊ Σ, aab ∊ Σ*. String Concatenation • Two strings can be combined via the operation of concatenation. • Let Σ be a set of symbols and Σ* be the set of strings formed from the symbols in Σ. • We can define the concatenation of two strings, denoted by ∙, recursively as follows: ○ Basis step § If w∈Σ^∗, then w⋅λ=w ○ Recursive step § If w_1∈Σ^∗ and w_2∈Σ^∗ and x∈Σ^∗ then w_1⋅(w_2 x)=(w_1⋅w_2 )x • Often w_1⋅w_2 is written as w_1 w_2. • If w_1 = abra and w_2 = cadabra, the concatenation w_1 w_2 = abracadabra. Length of a String • Give a recursive definition of l(w), the length of the string w. • The length of a string can be recursively defined by: ○ l(λ)=0 ○ l(wx) = l(w) + 1 if w∊Σ* and x∈Σ Rooted Trees • The set of rooted trees, where a rooted tree consists of ○ a set of vertices containing a distinguished vertex called the root ○ edges connecting these vertices • can be defined recursively by these steps • Basis step ○ A single vertex r is a rooted tree. • Recursive step ○ Suppose that T_1,…,T_n are disjoint rooted trees with roots r_1,…,r_n respectively. ○ Then the graph formed by § start with a root r, which is not in any of the rooted trees T_1,…,T_n § add an edge from r to each of the vertices r_1,…,r_n ○ is also a rooted tree. Full Binary Trees • The set of full binary trees can be defined recursively by these steps. ○ Basis step § There is a full binary tree consisting of only a single vertex r. ○ Recursive step § If T_1 and T_2 are disjoint full binary trees § There is a full binary tree, denoted by T_1∙T_2, consisting of □ a root r □ edges connecting the root to each of the roots of T_1 and T_2 • The height h(T) of a full binary tree T is defined recursively as follows: ○ Basis step § The height of a full binary tree T consisting of only a root r is h(T)=0 ○ Recursive step § If T_1 and T_2 are full binary trees § Then the full binary tree T=T_1⋅T_2 has height § h(T)=1+max⁡(hT_1 ),hT_2 )) • The number of vertices n(T) of a full binary tree T is defined recursively as follows ○ Basis step § n(T)=1 for full binary tree T consisting of only a root r ○ Recursive step § If T_1 and T_2 are full binary trees § Then the full binary tree T=T_1⋅T_2 has the number of vertices § n(T)=1+n(T_1 )+n(T_2 ) Structural Induction • To prove a property of the elements of a recursively defined set, we use structural induction • Basis step ○ Show that The result holds for all elements specified in the basis step • Recursive step ○ Suppose the statement is true for each of the elements used to construct new elements in the recursive step of the definition ○ Show that the result holds for these new elements. • The validity of structural induction can be shown to follow from the principle of mathematical induction. Structural Induction and Binary Trees • If T is a full binary tree, then n(T)≤2^(hT)+1)−1 • Proof: Use structural induction • Basis step ○ The result holds for a full binary tree consisting only of a root ○ n(T) = 1 and h(T) = 0 ○ Hence, n(T) = 1 ≤ 2^(0+1)−1 = 1 • Recursive step ○ Assume n(T_1 )≤2^(hT_1 )+1)−1 and n(T_2 )≤2^(hT_2 )+1)−1 for full binary trees T_1,T_2 ○ n(T)=1+n(T_1 )+n(T_2 ) ○ ≤1+(2^(hT_1 )+1)−1)+(2^(hT_2 )+1)−1) ○ ≤2⋅max⁡(2^(hT_1 )+1),2^(hT_2 )+1) )−1 ○ =2⋅2^max⁡〖(h(T_1 ),hT_2 ))+1〗 −1 ○ =2⋅2^ht) −1 ○ =2^(ht)+1)−1
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Math 521 – 3/16

  • Mar 18, 2018
  • Shawn
  • Math 521
  • No comments yet
Theorem 2.41 (The Heine-Borel Theorem) • For a set E∈Rk, the following properties are equivalent (a) E is closed and bounded (b) E is compact (c) Every infinite subset of E has a limit point in E • Proof (a)⇒(b) ○ If (a) holds, then E⊂I for some k-cell ○ (b) follow from § Theorem 2.40 (I is compact) § Theorem 2.35 (Closed subsets of compact sets are compact) • Proof (b)⇒(c) ○ See Theorem 2.37 • Proof (c)⇒(a) ○ Suppose E is not bounded § Then E contains points (x_n ) ⃗ s.t. |(x_n ) ⃗ | n, ∀n∈N § {(x_n ) ⃗ } is an infinite subset of E with no limit points § This is a contradiction, so E must be bounded ○ Suppose E is not closed § Then ∃(x_0 ) ⃗∈Rk that is a limit point of E but not in E § For n∈N, ∃(x_n ) ⃗∈E s.t. |(x_n ) ⃗−(x_0 ) ⃗ | 1/n § Let S={(x_n ) ⃗ }_(n∈N □ S is infinite □ S has (x_0 ) ⃗ as a limit point § Let y ⃗∈Rk and y ⃗≠(x_0 ) ⃗ □ By triangle inequality □ |(x_n ) ⃗−y ⃗ |≥|(x_0 ) ⃗−y ⃗ |−|(x_n ) ⃗−(x_0 ) ⃗ | □ |(x_n ) ⃗−y ⃗ | |(x_0 ) ⃗−y ⃗ |−1/n □ |(x_n ) ⃗−y ⃗ | 1/2 |(x_0 ) ⃗−y ⃗ | □ For all but finitely many n □ Therefore y ⃗ cannot be a limit point of S, by Theorem 2.20 § Since y ⃗ was arbitrary, nothing other than (x_0 ) ⃗ is a limit point of S § By (c), (x_0 ) ⃗∈E,which makes a contradiction, so E has to be closed ○ Therefore E is closed and bounded Theorem 2.42 (The Weierstrass Theorem) • Statement ○ Every bounded infinite subset E of Rk has a limit point in Rk • Proof ○ E is bounded, so E⊂I⊂Rk for some k-cell I ○ By Theorem 2.40, I is compact ○ By Theorem 2.37, E has a limit point in I ○ Hence, E has a limit point in Rk Subsequences • Definition ○ Given a sequence {p_n } ○ Consider a sequence {n_k }⊂N with n_1 n_2 n_3 … ○ Then the sequence {p_(n_i ) } is a subsequence of {p_n } ○ If {p_(n_i ) } converges, its limit is called a subsequential limit of {p_n } • Example ○ Let {p_n }=1/n={1, 1/2,1/3,1/4,1/5,…} ○ One subsequence is{1, 1/4,1/6,1/7,1/38,1/101,1/135,…} ○ But{1/19,1/18,1/2,1/237,1/12,1/59,1/32,…} is not a subsequence • Note ○ A subsequential limit might exist for a sequence in the absence of a limit ○ {p_n } converges to p if and only if every subsequence of {p_n } converges to p Theorem 3.6 • Statement (a) ○ If {p_n } is a sequence in a compact metric space X ○ Then some subsequence of {p_n } converges to a point of X • Proof (a) ○ Let E be the range of {p_n } ○ If E is finite § ∃p∈E and a sequence {n_i }⊂N with n_1 n_2 n_3 … s.t. § p_(n_1 )=p_(n_2 )=p_(n_3 )=…=p ○ If E is infinite § By Theorem 2.37, E has a limit point p∈X § By Theorem 2.20, inductively choose n_i s.t. d(p,p_(n_i ) ) 1/i, ∀i∈N § It follows that {p_(n_i ) } converges to p • Statement (b) ○ Every bounded sequences in Rk contains a convergent subsequence • Proof (b) ○ By Theorem 2.41, every bounded subset of Rk is in a compact subset of Rk ○ Result follows by (a)
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Math 521 – 3/14

  • Mar 16, 2018
  • Shawn
  • Math 521
  • No comments yet
Theorem 2.38 (Nested Intervals Theorem) • Statement ○ If {I_n } is a sequence of closed intervals in R s.t. I_n⊃I_(n+1),∀n∈N ○ Then ⋂24_(n=1)^∞▒I_n is nonempty • Intuition • Proof ○ Let I_n≔[a_n,b_n ] ○ Let E≔{a_n }_(n∈N § E is nonempty § E is bounded above by b_1 since b_1≥a_n,∀n∈N § So sup⁡E exists ○ Let x≔sup⁡E ○ For m,n∈N, a_n≤a_(m+n)≤b_(m+n)≤b_m § a_n≤b_m⇒x≤b_n,∀m∈N § x=sup⁡E⇒a_m≤x,∀m∈N ○ So, x∈[a_m,b_m ],∀m∈N ○ Therefore x∈⋂24_(n=1)^∞▒I_n Theorem 2.39 • Statement ○ Let k be a positive integer ○ If {I_n } is a sequence of k-cells s.t. I_n⊃I_(n+1),∀n∈N ○ Then ⋂24_(n=1)^∞▒I_n is nonempty • Proof ○ Let I_n consists of all points x ⃗=(x_1,x_2,…,x_k ) s.t. ○ a_(n,j)≤x_j≤b_(n,j), where 1≤j≤k,n=1,2,3,… ○ Let I_(n,j)=[a_(n,j),b_(n,j) ] ○ For each j, {I_(n,j) } satisfies the hypothesis of Theorem 2.38 ○ Therefore ∃x_j^∗∈⋂24_(n=1)^∞▒I_(n,j) , for 1≤j≤k ○ Let (x^∗ ) ⃗=(x_1^∗,x_2^∗,…,x_k^∗ ) ○ By construction, (x^∗ ) ⃗∈⋂24_(n=1)^∞▒I_n Theorem 2.40 • Statement ○ Every k-cell is compact • Proof ○ Let I={(x_1,x_2,…,x_k )∈Rk│a_j≤x_j≤b_j,1≤j≤k} be a k-cell ○ Let δ=√(∑_(j=1)^k▒(b_j−a_j )^2 ), then |x ⃗−y ⃗ |≤δ,∀x ⃗,y ⃗∈I ○ Suppose {G_α } is an open cover of I with no finite subcover ○ Build sequence {I_n } § Let c_j=(a_j+b_j)/2 § Consider intervals [a_j,c_j ] and [c_j,b_j ] § Those intervals describes 2^k k-cells Q_i whose union is I § Since the number of Q_i is finite, and {G_α } has no finite subcover § ∃Q_i not covered by a finite subcover of {G_α }; call this I_1 § Repeat this process on I_1 to obtain I_2,I_3,… § We can build a sequence {I_n } ○ {I_n } is a sequence of k-cells s.t. § I⊃I_1⊃I_2⊃… § I_n is not covered by any finite sub-collection of {G_α } § If x ⃗,y ⃗∈I_n, then |x ⃗−y ⃗ |≤δ/2^n ○ By Theorem 2.38, ∃x ⃗^∗∈I_n,∀n∈N ○ Then (x^∗ ) ⃗∈G_α, for some G_α § G_α is open § i.e. ∃r 0 s.t. |y ⃗−(x^∗ ) ⃗ | r⇒y ⃗∈G_α § By Archimedean Property, ∃n∈N s.t. δ/2^n r § In this case, I_n⊂G_α, which is impossible, since § I_n is not covered by any finite sub-collection of {G_α } § So no such open cover {G_α } exists ○ So every open cover of I have a finite subcover ○ Therefore I is compact
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5.2 Strong Induction and Well-Ordering

  • Mar 14, 2018
  • Shawn
  • Math 240
  • No comments yet
Strong Induction • To prove that P(n) is true for all positive integers n • where P(n) is a propositional function, complete two steps: • Basis Step ○ Verify that the proposition P(1) is true. • Inductive Step ○ Show the conditional statement § [P(1)∧P(2)∧…∧P(k)]→ P(k+1) ○ holds for all positive integers k. • Strong Induction is sometimes called ○ the second principle of mathematical induction ○ complete induction Proof using Strong Induction • Prove that every natural number n 7 can be written as 3p+5q • where p and q are natural numbers • Prove this result using strong induction • Basis Step ○ 8=3×1+5×1 • Inductive Step ○ Inductive hypothesis: The statement is true for any n for k≥n≥8 ○ In particular it is true for k+1−3 (assuming k+1−3≥8) ○ So k+1−3=3p+5q and so k+1=3(p+1)+5q • What happens if k+1=9 or k+1=10? ○ Add those cases into the basis step ○ 9=3×3+5×0 ○ 10=3×0+5×2 Which Form of Induction Should Be Used? • We can always use strong induction instead of mathematical induction. • But there is no reason to use it if it is simpler to use mathematical induction • In fact, the principles of mathematical induction, strong induction, and the well-ordering property are all equivalent. • Sometimes it is clear how to proceed using one of the three methods, but not the other two. Proof of the Fundamental Theorem of Arithmetic • Show that if n is an integer greater than 1 • Then n can be written as the product of primes. • Let P(n) be the proposition that n can be written as a product of primes. • Basis Step ○ P(2) is true since 2 itself is prime. • Inductive Step ○ The inductive hypothesis is P(j) is true for j∈Z with 2≤j≤k ○ To show that P(k+1) must be true under this assumption ○ Two cases need to be considered: ○ If k+1 is prime, then P(k+1) is true. ○ Otherwise, k+1 is composite ○ And it can be written as the product of two positive integers ○ a and b with 2≤a≤b≤k+1 ○ By inductive hypothesis a and b can be written as product of primes ○ Therefore k + 1 can also be written as the product of those primes. • Hence, every integer greater than 1 can be written as product of primes Well-Ordering Property • Well-ordering property ○ Every nonempty set of nonnegative integers has a least element. • The well-ordering property is one of the axioms of the positive integers • The well-ordering property can be used directly in proofs. • The well-ordering property can be generalized. • Definition: A set is well ordered if every subset has a least element. ○ N is well ordered under ≤. ○ The set of finite strings over an alphabet using lexicographic ordering is well ordered. Proof of The Division Algorithm • Use the well-ordering property to prove the division algorithm ○ If a is an integer and d is a positive integer, then ○ there are unique integers q and r with 0≤r d, such that ○ a=dq+4 • Let S be the set of nonnegative integers of the form a=dq, q∈Z. • The set is nonempty since −dq can be made as large as needed • By the well-ordering property, S has a least element r=a−dq_0 • The integer r is nonnegative. • It also must be the case that r d • If it were not, then there would be a smaller nonnegative element in S ○ a−d(q_0+1)=a−dq_0−d=r−d 0 • Therefore, there are integers q and r with 0≤r d
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