# 2.7: Properties of Logarithms

Two implications of these properties of logarithms are important to appreciate from an algorithmic perspective:

The base of the logarithm has no real impact on the growth rate - Compare the following three values: log2(1,000,000) = 19.9316, log3(1,000,000) = 12.5754, and log100 (1, 000, 000) = 3. A big change in the base of the logarithm produces little difference in the value of the log. Changing the base of the log from a to c involves dividing by logc a. This conversion factor is lost to the Big Oh notation whenever a and c are constants. Thus we are usually justified in ignoring the base of the logarithm when analyzing algorithms.

Logarithms cut any function down to size - The growth rate of the logarithm of any polynomial function is O(lg n). This follows because loga nb = b·loga n The power of binary search on a wide range of problems is a consequence of this observation. Note that doing a binary search on a sorted array of n2 things requires only twice as many comparisons as a binary search on n things. Logarithms efficiently cut any function down to size.