Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
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Chapter 4.6, Problem 2E
Program Plan Intro
To show that the master recurrence has solution
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Let f (f(n) and g(n)) be asymptotically nonnegative functions. Using the basic definition of Θ notation, prove that max(f(n), g(n)) = Θ(f(n) + g(n)),
Use the master method to give tight asymptotic bounds for the following recurrence
T(n) = 2T(n/4) + nº.5
(nº.5Ign)
e(nº.5)
e(n)
○ e(n²)
Solve the first-order linear recurrence
T(n) = 3T(n − 1) +8, T(0) = 6
by finding an explicit closed formula for T(n) and enter your answer in the box below.
T(n) =
Chapter 4 Solutions
Introduction to Algorithms
Ch. 4.1 - Prob. 1ECh. 4.1 - Prob. 2ECh. 4.1 - Prob. 3ECh. 4.1 - Prob. 4ECh. 4.1 - Prob. 5ECh. 4.2 - Prob. 1ECh. 4.2 - Prob. 2ECh. 4.2 - Prob. 3ECh. 4.2 - Prob. 4ECh. 4.2 - Prob. 5E
Ch. 4.2 - Prob. 6ECh. 4.2 - Prob. 7ECh. 4.3 - Prob. 1ECh. 4.3 - Prob. 2ECh. 4.3 - Prob. 3ECh. 4.3 - Prob. 4ECh. 4.3 - Prob. 5ECh. 4.3 - Prob. 6ECh. 4.3 - Prob. 7ECh. 4.3 - Prob. 8ECh. 4.3 - Prob. 9ECh. 4.4 - Prob. 1ECh. 4.4 - Prob. 2ECh. 4.4 - Prob. 3ECh. 4.4 - Prob. 4ECh. 4.4 - Prob. 5ECh. 4.4 - Prob. 6ECh. 4.4 - Prob. 7ECh. 4.4 - Prob. 8ECh. 4.4 - Prob. 9ECh. 4.5 - Prob. 1ECh. 4.5 - Prob. 2ECh. 4.5 - Prob. 3ECh. 4.5 - Prob. 4ECh. 4.5 - Prob. 5ECh. 4.6 - Prob. 1ECh. 4.6 - Prob. 2ECh. 4.6 - Prob. 3ECh. 4 - Prob. 1PCh. 4 - Prob. 2PCh. 4 - Prob. 3PCh. 4 - Prob. 4PCh. 4 - Prob. 5PCh. 4 - Prob. 6P
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- Find a tight bound solution for the following recurrence: T(n) = 4 T(n/2) + c n (c is a positive constant) That is, find a function g(n) such that T(n) ∊ Θ(g(n)). For convenience, you may assume that n is a power of 2, i.e., n=2k for some positive integer k. Justify your answer. [Note: Read question 1-(2) first before writing your answer] Prove your answer in 1-(1) either using the iteration method or using the substitution method.arrow_forwardLet f(n) and g(n) be asymptotically nonnegative increasing functions. Prove: (f(n) + g(n))/2 = ⇥(max{f(n), g(n)}), using the definition of ⇥ .arrow_forwardLet T(n) be defined by the first-order linear recurrence T(n) = 2T(n-1) +8 Suppose it is given that T(2) = c. Compute T(0) by iterating backwards and express your answer in terms of c. T(0) =arrow_forward
- Use the master method to give tight asymptotic bounds for the following recurrence T(n) = 2T(n/4) + n e(n²) Đ(n5) e(n) (nº.5Ign)arrow_forwardFind the solution for each of the following recurrences, and then give tight bounds (i.e., in Θ(·)) for T (n). (a) T (n) = T (n − 1) + 1/n with T (0) = 0. (b) T (n) = T (n − 1) + cn with T (0) = 1, where c > 1 is some constant (c) T (n) = 2 T (n − 1) + 1 with T (0) = 1arrow_forwardPlease solve using iterative method: Solve the following recurrences and compute the asymptotic upper bounds. Assume that T(n) is a constant for sufficiently small n. Make your bounds as tight as possible. a. T(n) = T(n − 2) + √n b.T(n) = 2T(n − 1) + carrow_forward
- Apply a suitable approach to compare the asymptotic order of growth forthe following pair of functions. Prove your answer and conclude by telling if f(n) ?Ѳ(g(n)), f(n) ?O(g(n)) or f(n) ?Ω(g(n)). f(n) = 100n2+ 20 AND g(n) = n + log narrow_forwardSolve this recurrence equation T(1) = 1 T(n) = T(n/2) + bnlogn, n >1 (b being a constant)arrow_forward4. Practice with the iteration method. We have already had a recurrence relation ofan algorithm, which is T(n) = 4T(n/2) + n log n. We know T(1) ≤ c.(a) Solve this recurrence relation, i.e., express it as T(n) = O(f(n)), by using the iteration method.Answer:(b) Prove, by using mathematical induction, that the iteration rule you have observed in 4(a) is correct and you have solved the recurrence relation correctly. [Hint: You can write out the general form of T(n) at the iteration step t, and prove that this form is correct for any iteration step t by using mathematical induction.Then by finding out the eventual number of t and substituting it into your generalform of T(n), you get the O(·) notation of T(n).]arrow_forward
- Prove by induction that, if T(n) ≤T(5n/6) + O(n), then T(n) = O(n). Assume the base case isconstant, i.e., that T(n) = Θ(1) for all n≤cfor some constant c. Then, prove this result againusing the DC Recurrence Theoremarrow_forward1. Solve the following recurrences and prove that your solutions are correct. for n-1 T(n – 1) +7 for n22 for n=1 T(n) = 27(n – 1) for n22 for n-1 + 1)T(n – 1) for n22 T(n) =arrow_forwardUse Master Method to deduce the time complexity of the following recurrence equations: T(n) = 4T(n/2) + n T(n) = 4T(n/2) + nlognarrow_forward
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