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Some Useful Facts

Some Useful Facts

Understand some basic results that come in handy when doing analysis using the big-O notation.

Some handy results

In this lesson, we discuss four results that are useful to know. Note that the size of input for an algorithm is always some discrete value. That’s the reason these results are proven here for integer values of nn only and not real values of nn.

Fact I: n is O(2n){n \text{ is } O(2^n)}.

In fact, 2n2^n is a strict upper bound on nn, for any integer nn.

n<2n   for all n1n \lt 2^n\ \ \text{ for all }n \geq 1

It’s clearly true for n=1n=1, but it’s also true for larger values as for every unit increase in the value of nn, the left-hand side (LHS) increases by 11, but the right-hand side (RHS) doubles.

Here’s another well-known fact.

Fact II:  log2n is O(n)\ \log_2 n \text{ is } O(n).

Once again, we can say something stronger:

log2n<n    for all n1\log_2 n < n\ \ \ \text{ for all }n \geq 1 ...