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105) Big-O basically means that t(n) asymptotically (only for large n) grows not faster than g(n) (give or take a constant factor). The order of growth of t(n) is not larger than g(n).
When put in terms of order of growth, f = O(g) is like "f <= g." When two searching algorithms are compared, both the worst and average time is O(n) for a sequential search and O(log2 n) for the binary search. (Sestoft, p. 110)
Big-O notation is concerned with what happens for very large values of N, therefore only the largest term in a polynomial is needed. All smaller terms are dropped.
For example, the number of operations in some sorts is N2 - N...
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