[英]Finding the Big O time complexity
i = 1;
while (i <= n) {
j = n - i;
while (j >= 2) {
for (k = 1; k <= j; k++) {
s = s + Arr[k];
}
j = j - 2;
}
i = i + 1;
}
The part that confuses me is where it says 让我困惑的部分是它所说的地方
j = n - i;
while(j >= 2){
I'm not really sure how to show my work on that part. 我不太确定该如何展示我的作品。 I'm pretty sure the algorthim is O(n^3) though. 我很确定算法虽然是O(n ^ 3)。
You can simplify it a bit in order to see things more clearly: 您可以稍微简化一下,以便更清楚地看到内容:
for(i = 1; i <= n; i++)
{
for(j = n - i; j >= 2; j -= 2)
{
for(k = 1; k <= j; k++)
{
s = s + Arr[k];
}
}
}
Now things should be simpler 现在事情应该更简单
for(i = 1; i <= n; i++)
: O(n) [executes exactly n times, actually] for(i = 1; i <= n; i++)
: O(n) [实际上执行了n次] for(j = n - i; j >= 2; j -= 2)
: (n-1)/2
in 1st iteration, (n-3)/2
in the 2nd and so on... O(n) for(j = n - i; j >= 2; j -= 2)
:( (n-1)/2
在第一次迭代中, (n-3)/2
在第二次迭代中,依此类推... O(n) for(k = 1; k <= j; k++)
n-2
in 1st iteration, n-3
in the 2nd and so on... O(n) for(k = 1; k <= j; k++)
在第1次迭代中为n-2
在第n-2
次迭代中为n-3
,依此类推... O(n) s = s + Arr[k];
[simple operation] : O(1) [简单操作]: O(1) Multiply every step and you get O(n^3) 乘以每一步,您将得到O(n ^ 3)
If you are still having trouble with it, I would suggest you run a few simulations of this code with varying n
values and a counter inside the loops. 如果您仍然遇到问题,建议您使用循环内的n
值和一个计数器运行此代码的一些模拟。 Hopefully you'll be able to see how the O(n)
is the complexity for each loop 希望您能够看到O(n)
是每个循环的复杂度
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