# Tag Info

36

The usual meaning of algorithm is a program that always halts. Under this definition, no algorithm has a running time of $\Theta(\mathit{BB}(n))$, or indeed $\Omega(\mathit{BB}(n))$. Indeed, such an algorithm could be used to solve the halting problem (assuming you knew a constant $c>0$ such that the running time is at least $c\mathit{BB}(n)$), since you ...

36

Yes: $n+m \le n+n=2n$ which is $O(n)$, and thus $O(n+m)=O(n)$ For clarity, this is true only under the assumption that $m\le n$. Without this assumption, $O(n)$ and $O(n+m)$ are two different things - so it would be important to write $O(n+m)$ instead of $O(n)$.

28

Is what I wrote about big-O correct? Yes. How do big-Theta sets relate to each other, if they relate at all? They are a partition of the space of functions. If $\Theta(f)\cap \Theta(g)\not = \emptyset$, then $\Theta(f)=\Theta(g)$. Moreover, $\Theta(f)\subseteq O(f)$. Why do they relate to each other the way they do? The explanation is probably ...

21

I was wondering, if there are variations of those in reality such as $O(2n^2)$ or $O(\log (n^2))$, or if those are mathematically incorrect. Yes, $O(2n^2)$ or $O(\log (n^2))$ are valid variations. However, you will see them rarely if you would see them at all, especially in the end results. The reason is that $O(2n^2)$ is $O(n^2)$. Similarly, $O(\log (n^2)... 18 We have $$\frac{n!}{(n/2)!(n/4)!\cdots 2!} = \frac{n!}{(n/2)!(n/2)!} \frac{(n/2)!}{(n/4)!(n/4)!} \cdots \frac{4!}{2!2!} \frac{2!}{1!1!} = \\ \binom{n}{n/2} \binom{n/2}{n/4} \cdots \binom{4}{2} \binom{2}{1} \leq 2^n 2^{n/2} \cdots 2^4 2^2 = 2^{n+n/2+\cdots+2+1} <2^{2n} = 4^n.$$ Using Stirling's approximation we can get more refined asymptotics, but we ... 15 Remember your definitions! As$n \to \infty$(the use in CS, almost always)$O(\cdot)$is an upper bound (within a constant multiple, for large$n$),$\Omega(\cdot)$is a lower bound (within a constant multiple, for large$n$), and$\Theta(\cdot)$both of the previous. To see the difference, as$n \to \infty$you have:$\begin{align*} 1 + \lvert \sin n \...

13

You are always free to not use this notation at all. That is, you can determine a function $f(n)$ as precisely as possible, and then try to improve on that. For example, you might have a sorting algorithm that makes $f(n)$ comparisons, so you could try to come up with another sorting algorithm that only does $g(n)$ comparisons. Of course, all kinds of ...

13

vonbrand's answer is correct in general, but let me add that if $\boldsymbol{f(n)}$ is the running time of an algorithm, then you are correct, $\boldsymbol{O(1)}$ and $\boldsymbol{\Theta(1)}$ are the same. This is because running times of algorithms are positive integers, so the example $f(n) = e^{-n}$ is impossible. As you said, intuitively, nothing grows ...

13

You have mistake in $(2.1)^n \cdot n^2<2^n \cdot n^3$, because it is equivalent $\left(\frac{2.1}{2}\right)^n<n$

11

Yes, since $n + m \leq 2n$ the algorithm is $O(n)$. However, you may wish to write $O(m + n)$ because it clearly shows which variables the algorithm depends on, and what each variable does to the complexity.

8

"On the order of" is an informal statement which really only means "approximately". Big O notation is a precise mathematical formulation which expresses asymptotic behavior, not approximate values of a function (e.g., $10n \in O(n)$, despite $10n$ being 10 times as larger as $n$). They can hardly be considered the same things. What the lecturer is trying to ...

8

Usually we call statement $A$ stronger than $B$ when $A$ implies $B$: $A \Rightarrow B$ (weaker-stronger). In other words, $B$ is weaker than $A$. When the presenter is speaking about linear time for partition, this is a stronger statement than $O(n)$ time. All linear functions are in $O(n)$, but it also contains non-linear functions. For example: $\sin n, \... 7 While the accepted answer is quite good, it still doesn't touch at the real reason why$O(n) = O(2n)$. Big-O Notation describes scalability At its core, Big-O Notation is not a description of how long an algorithm takes to run. Nor is it a description of how many steps, lines of code, or comparisons an algorithm makes. It is most useful when used to describe ... 7 You should be very careful when summing up a variable number of terms in asymptotic notation, as the result actually depends on the hidden constants. Consider the following example:$f_i(n) = i\cdot n$for all integers$i$and$n$. Then, for any integer$i$,$f_i(n) \in O(n)$. If you are not careful, you could end up writing something like: $$\sum_{i=1}^n ... 7 Observe that ab=\frac{1}{2}\left((a+b)^2-a^2-b^2\right), hence multiplication requires three squaring operations and 3 additions/subtractions (division by 2 is easy), which means squaring is asymptotically as hard as multiplying. 6 Doc Brown answered your first question perfectly. Let me answer your second question. The expression O(i) is a placeholder for a function which is bounded by Ci for some C > 0. Therefore$$ \sum_{i=1}^n O(i) \leq C \sum_{i=1}^n i = C \frac{n(n+1)}{2} = O(n^2). $$Furthermore, this bound is tight, in the sense that n^2 cannot be replaced with any ... 6 The difference is IMHO well explained in that book, the part you are referring to says The number of anonymous functions in an expression is understood to be equal to the number of times the asymptotic notation appears. So this$$ \sum_{i=1}^n O(i) $$means a placeholder for$$ \sum_{i=1}^n f(i), $$where f(i) is an anonymous function from the set O(... 6 If the O-complexity given is true, then f(x)\leq c_2n\log n will not always be true. So in this case, how is \Theta-complexity different from O-complexity? Yuval has covered the quicksort aspects of your question but you have a couple of fundamental misunderstandings about asymptotics. There is no such thing as "\Theta-complexity" or "O-... 5 You can write O(f) for any function f and it makes perfect sense. As per the definition, g(n)=O(f(n)) if there is some constant c such that g(n)\leq c\,f(n) for all large enough n. Nothing in that definition says that f must be some sort of "nice" function. But, as other answers have pointed out, g(n)=O(f(n)) and g(n)=O(2f(n)) ... 5 From this post, you can approximate \log(1+x) with x for little values of x. Hence,$$ f(n) \sim \frac{1}{\frac{1}{2^n-1}} = 2^n-1 $$Therefore, you can't find any constant c, such that f(n) = O(n^c), as it is \Theta(2^n). 5 This was shown by Hardy in his monograph Orders of Infinity. 5 The argument looks correct. Also notice that you can get a better (but still loose) upper bound as follows:$$ \binom{k}{p-1} \le \sum_{i=0}^{k} \binom{k}{i} = 2^k $$Where the equality \sum_{i=0}^{k} \binom{k}{i} = 2^k follows from the fact that the summation on the left is counting the number of possible subsets of a set with k elements, grouped by ... 4 It may be helpful to understand that Big-O describes a set of functions. That is O(f(n)) = \lbrace g(n) | \exists n,c>0: \forall m > n: c\times g(m) \le f(m)\rbrace The usage of = is kind of unfortunate and using \in would make that relationship a lot clearer. but the set notation symbols are a bit difficult to type so now we are stuck with the ... 4 Look at the definition of O(f(n)), and you see that for example O(2n^2) and O(n^2) are exactly the same. Changing an algorithm from 5n^2 to 3n^2 operations is a 40 percent improvement. Changing from O(5n^2) to O(3n^2) isn’t actually any change, they are the same. Again, read the definition of O(f(n)). 4 How to calculate Big O of T(n) = aT(n^b) + f(n) with 0<b<1? The powerful technique you are searching for is variable substitution. Let S(m)=T(2^m). Then$$S(m)=T(2^m)=aT(2^{mb}) + f(2^m)=aS(mb)+g(m),$$where g(m)=f(2^m). Now that we have a recurrence relation about S(m), to which we might be able to apply the master's theorem. Here are ... 4 It runs in time O(n). Remember that a function only returns once. In each call to f, the for loop is immediately terminated at i=0 by the return statement, so the function body is equivalent to if n < 100000 return 0; else return f(n-1); However, your answer of O((n!)^2) is not wrong: (n!)^2 is a huge overestimate of the running time,... 4 Given your link, you seem to be interested in data structures supporting the following operations: Create(m): create a new instance with room for m elements. Size(): return the number of elements currently stored in the instance. Insert(k): insert an element with priority k. ExtractMax(): return the maximal priority currently stored, and remove it. Since ... 4 The worst-case running time of quicksort is \Theta(n^2), and therefore quicksort always runs in O(n^2), and this bound is tight (that is, best possible). The average-case running time of quicksort is \Theta(n\log n). The best-case running time of quicksort is also \Theta(n\log n), and therefore quicksort always runs in time \Omega(n\log n), and ... 4 An obvious function is$$ f(n) = \begin{cases} n \log n & \text{if$n$is even}, \\ 0 & \text{if$n$is odd}. \end{cases}$$It's in$O (n \log n)$, it's not in$o (n \log n)$and not in$\Theta(n \log n)$. By the way: Every function in$\Theta(f(n)))$is in$O(f(n))$, just take the larger constant from the$\Theta\$ definition. And every function ...

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