Chapter 8: Q33E (page 536)
Use Exercise\(31\)to show that if\(a < {b^d}\), then\(f(n)\)is\(O\left( {{n^{{{\log }_b}a}}} \right)\).
Short Answer
The expression\(f(n) = O\left( {{n^{{{\log }_b}a}}} \right)\)is proved.
Chapter 8: Q33E (page 536)
Use Exercise\(31\)to show that if\(a < {b^d}\), then\(f(n)\)is\(O\left( {{n^{{{\log }_b}a}}} \right)\).
The expression\(f(n) = O\left( {{n^{{{\log }_b}a}}} \right)\)is proved.
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Get started for freeIn this exercise we construct a dynamic programming algorithm for solving the problem of finding a subset S of items chosen from a set of n items where item i has a weight , which is a positive integer, so that the total weight of the items in S is a maximum but does no exceed a fixed weight limit W. Let denote the maximum total weight of the items in a subset of the first j items such that this total weight does not exceed w. This problem is known as the knapsack problem.
a) Show that if, then
b) Show that if , then.
c) Use (a) and (b) to construct a dynamic programming algorithm for determining the maximum total weight of items so that this total weight does not exceed W. In your algorithm store the values as they are found.
d) Explain how you can use the values computed by the algorithm in part (c) to find a subset of items with maximum total weight not exceeding W.
Find the solution of the recurrence relation if , and
How many operations are needed to multiply two \(32 \times 32\) matrices using the algorithm referred to in Example 5?
Show that if and is a power of , then , where and
Use Exercise 48 to solve the recurrence relation\((n + 1){a_n} = (n + 3){a_{n - 1}} + n\) , for \(n \geqslant 1\), with \({a_0} = 1\)
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