15.2Running Time Analysis of Algorithms:Counting Statements1125
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 ofits most dominant term.
15.2Running 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 oftimes 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
ofan array ofsize n and returns the sum.
public static int addElements(int [ ] arr )
{
int sum = 0;// ( 1 ) ...
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