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Data Structues and Algorithms Algorithms growth evaluation

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Rate of growth Big theta The statement “f has the same growth rate as g.” A function f has the same growth rate as g (or f has the same order as g) if we can find a number m and two positive constants c and d such that c|g(n)| ≤ |f (n)| ≤ d|g(n)| for all n ≥ m. In this case we write f (n) = Θ (g(n)) and say that f (n) is big theta of g(n). It’s easy to verify that the relation “has the same growth rate as” is an equivalence relation. Proportionality: If two functions f and g are proportional, then f (n) = Θ (g(n)).

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Rate of growth The Log Function. Recall that log functions with different bases are proportional. In other words, if we have two bases a > 1 and b > 1, then log a n = (log a b) (log b n) for all n > 0. So we can disregard the base of the log function when considering rates of growth. In other words, we have log a n = θ ( log b n )

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Rate of growth Some approximations

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Rate of growth A function f has a lower growth rate than g (or f has lower order than g) if In this case we write f (n) = o (g(n)) and say that f is little oh of g. We’ll show that log n = o (n). Since we can write log n = (log e)(loge n), it follows that the derivative of log n is (log e)(1/n). Therefore, we obtain the following equations:

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Rate of growth Big O. Now let’s look at a notation that gives meaning to the statement “the growth rate of f is bounded above by the growth rate of g.” The standard notation to describe this situation is f(n) = O(g(n)), which we read as f (n ) is big oh of g (n ). The precise meaning of the notation f (n) = O (g (n) ) is given by the following definition. The notation f (n) = O (g (n) ) means that there are positive numbers c and m such that |f (n)| ≤ c |g (n)| for all n ≥ m.

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Rate of growth Big O. Now let’s look at a notation that gives meaning to the statement “the growth rate of f is bounded above by the growth rate of g.” The standard notation to describe this situation is f(n) = O(g(n)), which we read as f (n ) is big oh of g (n ). The precise meaning of the notation f (n) = O (g (n) ) is given by the following definition. The notation f (n) = O (g (n) ) means that there are positive numbers c and m such that |f (n)| ≤ c |g (n)| for all n ≥ m.

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Rate of growth Big Ω. Now let’s go the other way. We want a notation that gives meaning to the statement “the growth rate of f is bounded below by the growth rate of g.” The standard notation to describe this situation is f (n) = Ω ( g (n) ), which we can read as f (n ) is big omega of g (n ). The precise meaning of the notation f (n) = Ω ( g (n) ) is given by the following definition. The notation f (n) = Ω ( g (n) ) means that there are positive numbers c and m such that |f (n)| ≥ c |g (n)| for all n ≥ m.

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Rate of growth The four symbols Θ, o, O, and Ω can also be used to represent terms within an expression. For example, the equation h (n) = 4n 3 + O (n 2 ) means that h (n) equals 4n 3 plus a term of order at most n 2. The four symbols Θ, o, O, and Ω can be formally defined to represent sets of functions: Θ (g) is the set of functions with the same order as g; o(g) is the set of functions with lower order than g; O(g) is the set of functions of order bounded above by that of g; Ω (g) is the set of functions of order bounded below by that of g.

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Rate of growth When set representations are used, we can use an expression like f (n) ∈ Θ ( g (n) ) to mean that f has the same order as g. The set representations also give some nice relationships. For example, we have the following relationships, where the subset relation is proper. O ( g (n) ) ⊃ Θ ( g (n) ) ∪ o ( g (n) ), Θ ( g (n) ) = O ( g (n) ) ∩ Ω ( g (n) ).

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CHAPTER 2 ANALYSIS OF ALGORITHMS Part 1. 2 Big Oh and other notations Introduction Classifying functions by their asymptotic growth Theta, Little oh,

CHAPTER 2 ANALYSIS OF ALGORITHMS Part 1. 2 Big Oh and other notations Introduction Classifying functions by their asymptotic growth Theta, Little oh,

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