# Can an algorithm be faster than O(1)?

This week we had a bright interviewee who claimed that array has constant search time and map has even faster than that search time. Now to me if some algorithm has O(1) time complexity the only way for another equivalent algorithm to be faster is to have a smaller constant coefficient in O(1) estimate (like one algorithm takes at most 230 primitive operations and another takes at most 50 primitive operations and is therefore faster although both have O(1) complexity).

Is it possible for an algorithm to be faster than O(1) except having a smaller coefficient in the estimate?

• I think that you answered your own question. Commented Jul 22, 2011 at 13:42
• there are function which are $o(1)$ like $1 \over n$, but not suitable for an algorithm's running time since number of steps that a halting algorithm takes before halting is a positive natural number (a discrete entity). Commented Jul 22, 2011 at 13:47
• If the algorithm has access to a time machine... Commented Jul 22, 2011 at 14:01
• I suppose you could artificially create a program that gets faster at the input gets larger? for example it could take an integer n as input and then "wait 1/n seconds"... would that be considered "faster than o(1)"? It would certainly not be useful in any case though. Commented Jul 22, 2011 at 14:08
• @Vhailor: you first need to read the input to see what is $n$ and that takes more than constant time. Commented Jul 22, 2011 at 15:09

It is both reasonable and common to assume that any algorithm needs at least a positive constant amount of time for every input. For example, any useful algorithm should answer something (e.g. YES/NO or some number, string, etc.), and it is reasonable to assume that doing so takes at least some constant amount of time. Under this assumption, no algorithm can have a subconstant time complexity.

(In actual computers, this constant minimum amount of time may become smaller by advance in science and technology, but no matter how fast computers become, it is still there as a constant which does not depend on the input size.)

Vhailor comments that a hypothetical algorithm which waits 1/n seconds, where n is the input length, would satisfy the condition. The argument in the above assumes that no such algorithm exists. To justify this, I would argue that it is unreasonable to assume that a machine can wait 1/n seconds for arbitrary large n, because that would require faster and faster information processing as n grows.

Sometimes you may hear “subconstant-time operations,” but before it freaks you out, check what it really means. Usually, it means that the required time divided by some other parameter is subconstant, not that the time itself is subconstant.

• I don't see how your reasoning rules out algorithms that take $1+\frac{1}{n}$ seconds for an input of size $n$. Commented Jul 22, 2011 at 16:04
• @Alex B.:O(1+1/n) is not faster than O(1), it's slower than O(1).
– user13618
Commented Jul 22, 2011 at 16:25
• Well, O(1+1/n) is the same as O(1). Commented Jul 22, 2011 at 16:49
• @Alex: I did not rule them out, but as Ben and ShreevatsaR explained, that is not a subconstant time. Commented Jul 22, 2011 at 16:52
• @ShreevatsaR: Oops, thanks for the correction!
– user13618
Commented Jul 22, 2011 at 18:02

Others have already argued that in the context of sequential algorithms and classical models of computation (e.g., Turing machines or the RAM model), a running time of $0$ makes little sense. However, other models exist.

In the context of distributed algorithms, we usually define that the running time of an algorithm equals the number of communication rounds.

With this definition, we have got not only non-trivial graph algorithms with running time $O(1)$, but also some graph algorithms with running time $0$.

As a simple example, the randomised 2-approximation algorithm for maximum cut has running time $0$ in this model: there is no need to communicate with the neighbours; each node (simultaneously in parallel) just flips a coin and produces its own local output based on the random coin flip.

So it is not just the case that one could, hypothetically speaking, define a model of computation in which a running time of $0$ would make sense, if we abuse some contrived definition of "time".

We do have such models, they are actively studied, and the definition of "time" is natural for these purposes. In particular, with precisely this definition, time (number of communication rounds) and distance (shortest-path distance in the graph) become more or less equivalent concepts.

The Big-O of an algorithm is the Big-O of the number of steps a Turing machine takes before halting. So consider the trivial Turing machine where the initial state is in the set of final states.

$q_0 \in F$

The number of steps it takes before halting is 0.

Now consider the formal definition of Big-O:

$f(x) = O(g(x))$ if and only if $\exists M>0, x_o$ such that $|f(x)|\leq M|g(x)|$ for all $x > x_o$

The Big-O of the trivial Turing machine is then $O(0)$ which is "faster" than $O(1)$ in some sense.

• es, but it depends on how we define the number of steps exactly, I would say that the trivial TM takes 1 step before halting. Commented Jul 23, 2011 at 13:03

I think Qiaochu Yuan was probably joking, but is actually on the right track, in the sense that the answer to the question really depends on what theoretical, physical, or practical model of computation you have in mind.

It's not unreasonable to imagine models in which a computation can take 0 time. For example, you could have a machine made of gears and levers where you set up the gears in a certain position as an input. To get the output, you turn a crank until, after k revolutions, it freezes up and won't turn anymore. The number k is both the output and the running time. It's possible that k is zero, so the running time is zero. Of course this doesn't count the time it takes to walk into the room, set up the inputs, grasp the crank, etc. But it's not completely unreasonable to imagine a useful mathematical model where you don't count those things as running time.

It's also not unreasonable to imagine that you could have faster and faster information processing as n grows. Sometimes more information makes your task easier. If I'm a book editor and my job is to take a pile of submissions, select the best one, and then write the author a request for the necessary revisions, my job could get easier when the pile is taller, because I can afford to be more picky, I'll probably get a better manuscript, and it won't require as many revisions.

However, I think there is a more fundamental issue that prevents anything from being faster than O(1), which is that we're talking about digital computing, not analog. Whether it's a Turing machine or a desktop computer or the system of gears and levers described above, a digital computer typically operates in discrete cycles. You can get a result in 0 cycles, or in 1 cycle, but you can't have half a cycle. If you really want to have O(1/n) performance, then there has to be some $n_o$ such that for $n \ge n_o$ the running time is no more than 1/2 a cycle. This means that for $n \ge n_o$ the computation always terminates in 0 cycles. The example of the levers and gears shows that it is possible for a computation to terminate in 0 cycles, but there can't be any such thing as a nontrivial computation that always terminates in 0 cycles for large n. For a computation like that, you simply wouldn't need to use the machine for $n \ge n_o$. Another way of putting it is that if your computation always terminates in 0 cycles for $n \ge n_o$, then your computation isn't just faster than O(1), it's O(0), which means it's trivial.

So I think the answer is that performance faster than O(1) is not reasonable for a digital computer, and since big-O notation is really designed for digital computers, it's not obvious how to pose the question in the case of analog computers.

• Actually, my take on this is completely opposite of yours. In my view, ruling out the possibility of “faster and faster information processing as n grows” is a more fundamental assumption than the assumption that the computational device is digital. Commented Jul 22, 2011 at 16:56
• Given your examples, e.g. the gears model, a suitable measure of complexity is needed (e.g. number of revolutions). I believe the issue that all algorithms are $\omega(1)$ is simple as stating that all algorithms must output at least 1 bit of information. As a sidenote, big-oh notation was there half a century before modern digital computers. Commented Jul 22, 2011 at 17:49
• Your analog computation is still $O(1)$ due to relativistic considerations. From a physical perspective, since the observable universe has finite usable energy, it will always take time bounded away from zero to output the result of a non-null algorithm. Commented Jul 23, 2011 at 8:30
• @Ben: In your hypothetical (and non-relativistic) computational device, there is no natural notion of “input length.” While I stand behind my words that I view the assumption of computational devices being digital as less fundamental than the assumption of fixed information processing technology, your example made me realize that I do assume that the computational task has some natural notion of “input length” when talking about time complexity. Commented Jul 23, 2011 at 11:18
• Let's assume that the particle model you describe is indeed less than constant in running time. My point is that under any model, you must observe the output somehow, therefore you need at least 1 step. As a small correction, the omega in my previous comment was supposed to be capital. Furthermore, we must consider not what model are possible, but rather what models better fit computation as it is in our universe. So is there a computational procedure that takes less than constant time and if yes, does it matter to us? Commented Jul 23, 2011 at 13:27