I have been looking at Big O notation and have come across an operational count 2^n+n^2
. I understand the practice of big O notation is to remove the constants and the low order terms, however I cant figure out which one to make O(n)
. I think it may be 2^n
but have had no luck finding anything to suggest this.
2n2 grows faster than 2n. (Take antilog of both sides.)
When analyzing the Big-O performance of sorting algorithms, n typically represents the number of elements that you're sorting. So, for example, if you're sorting n items with Bubble Sort, the runtime performance in the worst case will be on the order of O(n2) operations.
The answer is O(1) if you use the formula for summation directly. Show activity on this post. the addition is called n times, so the whole code has a time complexity of O(1) * n = O(n). If there is nothing missing in your question, O(n) will be the correct answer to the task.
O(n) is Big O Notation and refers to the complexity of a given algorithm. n refers to the size of the input, in your case it's the number of items in your list. O(n) means that your algorithm will take on the order of n operations to insert an item.
Look at the growth factor over time. For the first eight values of n
, O(n^2)
works out to:
0, 1, 4, 9, 16, 25, 36, 49...
O(2^n)
produces powers of two:
1, 2, 4, 8, 16, 32, 64, 128...
It should be fairly obvious which one is growing faster.
Note that the general rule applies even with different bases and exponents. O(1.1^n)
may have initially lower work than O(n^10)
for smaller n
, but all exponential growth with an exponent greater than 1 will eventually outpace fixed exponent polynomial growth as n
approaches infinity.
By L'Hopital's rule:
lim_{n -> infinity} (n^2 / 2^n )
= 1/log(2) lim_{n -> infinity} (2n / 2^n)
= 1/log(2)^2 lim_{n -> infinity} (2 / 2^n)
= 0
We have n^2 = o(2^n)
which implies n^2 = O(2^n)
.
If this proof doesn't make sense: By definition, f(n) = O(g(n)
if and only if f(n)
is bounded within some constant multiple of g(n)
after n
grows past some constant. And a way to think of f(n) = o(g(n))
is that as n
grows to infinity, g(n)
will continue to outgrow f(n)
faster. In other words:
f(n) = o(g(n))
if and only if the limit f(n)/g(n)
becomes zero as n
goes to infinity.
o(g(n)
is a strictly stronger condition that f(n) = O(g(n))
.
Alternatively, you just need to use the definition directly: Find some a
and b
such that n^2 <= a | 2^n |
for all n >= b
, which is simple algebraic manipulation.
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