2 Notion of Algorithm.docx

# Of an algorithm by implication we also bound the

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of an algorithm, by implication we also bound the running time of the algorithm on any arbitrary inputs as well. For example, the best-case running time of insertion sort is Ω(n), which implies that the running time of insertion sort is Ω(n). 2.3.5 Asymptotic Notations Θ , O and Ω in Equations Example a) . Let consider the formula 2 · n 2 + 3 · n +1 =2 · n 2 + Θ (n) . How do we interpret such a formula?

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In general, however, when asymptotic notation appears in a formula, we interpret it as standing for some anonymous function that we do not care to name. Thus, in the above example we have: 2 · n 2 + 3 · n +1 =2 · n 2 + Θ (n) , where f(n) Θ (n) . We can notice that in this case f(n)= 3 · n +1 , which indeed is Θ (n). Example b) . Using asymptotic notation in this manner can help eliminate inessential detail and clutter in an equation. For example let be the recursive relation: )n()/n(T)n(T Θ+ =22 If we are interested only in the asymptotic behavior of T(n) , there is no point in specifying all the lower-order terms exactly; The lower-order terms are all understood to be included in the anonymous function denoted by the term Θ (n). Example c) . In some cases, asymptotic notation appears on the left-hand side of an equation, as in 2 · n 2 + Θ (n)= Θ (n 2 ) We interpret such equations using the following rule: No matter how the anonymous functions are chosen on the left of the equal sign, there is a way to choose the anonymous functions on the right or the equal sign to make the equation valid. Thus, the meaning of our example is that for any function f(n ) Θ (n), there is some function f(n) Θ (n 2 ), such that 2 · n 2 + f(n) = g(n) for all n . In other words, the right-hand side of an equation provides coarser level of detail than the left- hand side. Example d) . A number of such relationship can be chained together as in: )n()n(nnn 222 2132Θ=Θ+ =+ + We can interpret each equation separately by the rule above. The first equation says that there is some function f(n) Θ (n) such that 2 · n 2 + 3 ·n + 1 = 2 · n 2 + f(n) for all n . The second equation says that for any function g(n) Θ (n) (such as the f(n) just mentioned), there is some function h(n) Θ (n 2 ) such that 2 · n 2 + g(n) = h(n) for all n .
Note that this interpretation implies that 2 · n 2 + 3 ·n + 1 = Θ (n 2 ) , which is what the chaining of equations intuitively gives us. 2.3.6 o-notation The asymptotic upper bound provided by O-notation may or may not be asymptotically tight . The bound 2 · n 2 = O(n) is asymptotically tight, but the bound 2 · n = O(n 2 ) is not. • We use o-notation to denote an upper bound that is not asymptotically tight [CLR92]: ----------------------------------------------------------- o(g(n)) = { f(n) : for any positive constant c>0 there exists a constant n 0 >0 such that 0 ≤ f(n) < c·g(n) for n ≥ n 0 } [2.3.6.a] ----------------------------------------------------------- The main difference is that in f(n) = O(g(n)) , the bound 0 ≤ f(n) ≤ c·g(n) holds for some constant c > 0 , but in f(n) = o(g(n) , the bound 0 ≤ f(n) < c·g(n) holds for all constants c > 0 .

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