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COP 3530 Spring2012 Data Structures & Algorithms Discussion Session Week 5 Outline ã Growth of functions – Big Oh – Omega – Theta – Little Oh Growth of Functions Gives a simple view of the algorithm’s efficiency. Allows us to compare the relative performance of alternative algorithms. f1(n) is O(n) f2(n) is O(n^2) Growth of Functions Exact running time of an algorithm is usually hard to compute, and it’s unnecessary. For large enough inputs, the lower-order terms of an exact running tim
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  COP 3530 Spring2012 Data Structures & Algorithms Discussion Session Week 5  Outline ã Growth of functions  – Big Oh  – Omega  – Theta  – Little Oh  Growth of Functions  Gives a simple view of the algorithm’s efficiency. Allows us to compare the relative performance of alternative algorithms. f1(n) is O(n) f2(n) is O(n^2)  Growth of Functions  Exact running time of an algorithm is usually hard to compute, and it’s unnecessary. For large enough inputs, the lower-order terms of an exact running time are dominated by high-order terms.  f  (n) = n^2 + 5n + 234 n^2 >> 5n + 234, when n is large enough
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