BIG-O

# BIG-O - Big-O Notation Analysis of Algorithms(how fast does...

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Big-O Notation Analysis of Algorithms (how fast does an algorithm grow with respect to N) (Note: Best recollection is that a good bit of this document comes from C++ For You++, by Litvin & Litvin) The time efficiency of almost all of the algorithms we have discussed can be characterized by only a few growth rate functions: I. O(l) - constant time This means that the algorithm requires the same fixed number of steps regardless of the size of the task. Examples (assuming a reasonable implementation of the task): A. Push and Pop operations for a stack (containing n elements); B. Insert and Remove operations for a queue. II. O(n) - linear time This means that the algorithm requires a number of steps proportional to the size of the task. Examples (assuming a reasonable implementation of the task): A. Traversal of a list (a linked list or an array) with n elements; B. Finding the maximum or minimum element in a list, or sequential search in an unsorted list of n elements; C. Traversal of a tree with n nodes;

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BIG-O - Big-O Notation Analysis of Algorithms(how fast does...

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