Java 6 Illuminated: An Active Learning Approach, 2nd Edition

15.2 Running Time Analysis of Algorithms: Counting Statements 1125

choosing n

0

= 2 and c

0

= a

p

(1 + M).

For n >= n

0

, we have

f (n) <= c

0

n

p

and therefore,

f (n) is O(n

p

), i.e., f(n) is Big-Oh of its most dominant term.

15.2 Running Time Analysis of Algorithms:Counting Statements

One simple method to analyze the running time of a code sequence or a

method is simply to count the number of times each statement is executed

and to calculate a total count of statement executions.

Example 15.1 is a method that calculates the total value of all the elements

of an array of size n and returns the sum.

public static int addElements( int [ ] arr )

{

int sum = 0; // ( 1 ) ...