# 2 ex log10 n log 10 log2 n base of logarithm is not

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Unformatted text preview: f(n) = O(h(n)) f(n) = Ω (g(n)) & g(n) = Ω (h(n)) ⇒ f(n) = Ω (h(n)) f(n) = o (g(n)) & g(n) = o (h(n)) ⇒ f(n) = o (h(n)) f(n) = ω(g(n)) & g(n) = ω(h(n)) ⇒ f(n) = ω(h(n)) Reflexivity f(n) = Θ(f(n)) f(n) = O(f(n)) f(n) = Ω (f(n)) mp - 21 Comp 122 Properties Symmetry f(n) = Θ(g(n)) iff g(n) = Θ(f(n)) Complementarity f(n) = O(g(n)) iff g(n) = Ω (f(n)) f(n) = o(g(n)) iff g(n) = ω((f(n)) mp - 22 Comp 122 Common Functions March 26, 2014 Comp 122, Spring 2004 Monotonicity f(n) is mp - 24 monotonically increasing if m ≤ n ⇒ f(m) ≤ f(n). monotonically decreasing if m ≥ n ⇒ f(m) ≥ f(n). strictly increasing if m < n ⇒ f(m) < f(n). strictly decreasing if m > n ⇒ f(m) > f(n). Comp 122 Exponentials Useful Identities: 1 a= a (a m ) n = a mn −1 a m a n = a m+ n Exponentials and polynomials nb lim n = 0 n→∞ a ⇒ n b = o( a n ) mp - 25 Comp 122 Logarithms x = logba is the exponent for a = bx. a = b logb a log c (ab) = log c a + log c b log b a = n log b a n Natural log: ln a = logea Binary log: lg a = log2a lg2a = (lg a)2 lg lg a = lg (lg a) log c a log b a = log c b log b (1 / a ) = − log b a 1 log b a = log a b a logb c = c logb a mp - 26 Comp 122 Logarithms and exponentials – Bases If the base of a logarithm is changed from one constant to another, the value is altered by a constant factor. Ex: log10 n * log210 = log2 n. Base of logarithm is not an issue in asymptotic notation. Exponentials with different bases differ by a exponential factor (not a constant factor). Ex: 2n = (2/3)n*3n. mp - 27 Comp 122 Polylogarithms For a ≥ 0, b > 0, lim n→∞ ( lga n / nb ) = 0, so lga n = o(nb), and nb = ω(lga n ) Prove using L’Hopital’s rule repeatedly lg(n!) = Θ(n lg n) Prove using Stirling’s approximation (in the text) for lg( n!). mp - 28 Comp 122 Exercise Express functions in A in asymptotic notation using functions in B. A 5n2 + 100n B 3n2 + 2 A ∈ Θ (B) A ∈ Θ(n2), n2 ∈ Θ(B) ⇒ A ∈ Θ(B) log3(n2) log2(n3) A ∈ Θ (B) logba = logca / logcb; A = 2lgn / lg3, B = 3lgn, A/B =2/(3lg3) A ∈ ω (B) nlg4 3lg n alog b = blog a; B =3lg n=nlg 3; A/B =nlg(4/3) →∞ as n→∞ A ∈ ο (B) lg2n n1/2 lim ( lga n / nb ) = 0 (here a = 2 and b = 1/2) ⇒ A ∈ ο (B) n→∞ mp - 29 Comp 122 Summations – Review March 26, 2014 Comp 122, Spring 2004 Review on Summations Why do we need summation formulas? For computing the running times of iterative constructs (loops). (CLRS – Appendix A) Example: Maximum Subvector Given an array A[1…n] of numeric values (can be positive, zero, and negative) determine the subvector A[i…j] (1≤ i ≤ j ≤ n) whose sum of elements is maximum over all subvectors. 1 mp - 31 -2 2 Comp 122 2 Review on Summations MaxSubvector(A, n) maxsum ← 0; for i ← 1 to n do for j = i to n sum ← 0 for k ← i to j do sum += A[k] maxsum ← max(sum, maxsum) return maxsum...
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## This document was uploaded on 03/26/2014.

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