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What differences and relationships are between randomized algorithms and nondeterministic algorithms?

From Wikipedia

A randomized algorithm is an algorithm which employs a degree of randomness as part of its logic. The algorithm typically uses uniformly random bits as an auxiliary input to guide its behavior, in the hope of achieving good performance in the "average case" over all possible choices of random bits. Formally, the algorithm's performance will be a random variable determined by the random bits; thus either the running time, or the output (or both) are random variables.

a nondeterministic algorithm is an algorithm that can exhibit different behaviors on different runs, as opposed to a deterministic algorithm. There are several ways an algorithm may behave differently from run to run. A concurrent algorithm can perform differently on different runs due to a race condition. A probabalistic algorithm's behaviors depends on a random number generator. An algorithm that solves a problem in nondeterministic polynomial time can run in polynomial time or exponential time depending on the choices it makes during execution.

Are randomized algorithms and probablistic algorithms the same concept?

If yes, so are randomized algorithms just a kind of nondeterministic algorithms?

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these are different concepts, non deterministic is like pattern of walking of you in one day, and randomize is like a way you choose to go out of maze, in the first sample you don't know how is your walk pattern for today, it's non deterministic and depends to environment changes, in the second sample, your goal is going out of maze, so at first you try some ways then you will find your way, actually in second sample your algorithm is deterministic, means the output is your freedom which is unique in all runs but your startup walks to finding this way are selected by randomize algorithm. –  user742 Oct 11 '12 at 5:52
    
@Saeed You should post this as an answer! –  Yuval Filmus Oct 11 '12 at 5:56
    
@SaeedAmiri: Thanks! (1) I guess it is a good analogy, but I don't understand it. May you try to rephrase it a little? (2) If I understand you correctly, nondeterminsitic algorithm won't always output the correct output, but randomized algorithms will? Is that what you meant by saying randomized algorithms are in fact determinsitic? (3) By "first sample" and "second sample", do you mean nondeterministic algorithms and randomized algorithms respectively? –  Tim Oct 11 '12 at 6:37
    
@Tim, I think Yuval answer is good for your purpose, but note that randomized algorithm can be deterministic or non deterministic. non-deterministic algorithm can be randomize or non randomize, these are different concepts, I think I should explain this in more samples but my current physical situation doesn't let me to type more, I think if you ask Yuval to explain it with more examples you can get it in the right way. –  user742 Oct 11 '12 at 7:07
    
Unfortunately I think wikipedia's phrasing is muddying the waters a bit –  Joe Oct 12 '12 at 19:04

4 Answers 4

Non-deterministic algorithms are very different from probabilistic algorithms.

Probabilistic algorithms are ones using coin tosses, and working "most of the time". As an example, randomized variants of quicksort work in time $\Theta(n\log n)$ in expectation (and with high probability), but if you're unlucky, could take as much as $\Theta(n^2)$. Probabilistic algorithms are practical, and are used for example by your computer when generating RSA keys (to test that the two factors of your secret key are prime). Probabilistic algorithm which don't use any coin tosses are sometimes called "deterministic".

Non-deterministic algorithms are ones which "need a hint" but are always correct: they cannot be fooled by being given the wrong hint. As an example, here is a non-deterministic algorithm that factors an integer $n$: guess a factorization of $n$, and verify that all the factors are prime (there is a "fast-in-theory" deterministic algorithm for doing that). This algorithm is very fast, and rejects false hints. Most people think that randomized algorithms can't factor integers that quickly. Clearly this model of computation isn't realistic.

Why do we care about non-deterministic algorithms? There is a class of problems, known as NP, which consists of decision problems which have efficient non-deterministic algorithms. Most people think that the hardest problems in that class, the so-called NP-complete problems, do not have efficient deterministic (or even randomized) algorithms; this is known as the P vs NP question. Since many natural problems are NP-complete, it is interesting to know whether in fact they are not solvable efficiently, in the worst case (in practice, oftentimes the instances which arise in practice are in fact solvable in reasonable time).

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Thanks! (1) I remember NP problems are those that can be solved by an algorithm on a nondeterministic Turing machine in polynomial time. So are "an algorithm on a nondeterministic Turing machine" and "a nondeterministic algorithm on a deterministic Turing machine" equivalent in some sense? (2) Do you think probabalistic algorithm and randomized algorithm are the same concept? (3) Do you also think that Wikipedia says concurrent algorithm and probabalistic algorithm are two kinds of nondeterministic algorithms is wrong? –  Tim Oct 11 '12 at 6:32
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I don't think the certificate flavor of nondeterminism serves to clarify the difference with randomisation. –  Raphael Oct 11 '12 at 7:35
    
(1) Yes, the two are the same. (2) These are two names for the same concept. (3) Wikipedia is giving some examples of other types of algorithms, though the presentation might be misleading. Parallel and probabilistic algorithms are not non-deterministic. –  Yuval Filmus Oct 11 '12 at 12:49
    
(3) Reading the Wikipedia definition, they visibly meant what Alex wrote in his answer below. –  Yuval Filmus Oct 11 '12 at 20:52
    
@YuvalFilmus (1) A deterministic Turing machine technically can't run a non-deterministic algorithm, it doesn't have any means to generate the "hints". –  Joe Oct 12 '12 at 19:08

An algorithm specifies a method to get from a given input to a desired output that has a certain relation with the input. We say that this algorithm is deterministic if at any point, it is specified exactly and unambiguously what the next step in the algorithm is that must be performed as part of that method, potentially dependent on the input or the partial data computed so far, but always uniquely identified.

Nondeterminism means that some part of the algorithm is left under or even unspecified. For example, "int i = an even number between 0 and n" is underspecified. This means there is no unique behavior that is specified at this point.

For this distinction to be useful, you need the (usual) concept of 'correctness' for (deterministic) algorithms, which informally is that "the algorithm always computes what I want it to compute". It then becomes interesting to think about what correctness would mean for nondeterministic algorithms, which must take into account the choices possible in underspecified instructions.

There are two ways of defining correctness for nondeterminism. The first one is rather simple and less interesting, for which correctness means "the algorithm always computes what I want it to compute, for all sequences of choices I am allowed to make". This sometimes occurs if an author of a bit of pseudocode is too lazy to pick a number and says "pick any even number between 0 and n", when "pick 0" would have made the algorithm deterministic. Essentially, by replacing all nondeterminism by the result of some choice you can make the algorithm deterministic.

This is also the 'nondeterminism' referred to in your second paragraph. This is also the nondeterminism in parallel algorithms: in these algorithms you are not quite sure what execution precisely looks like, but you know that it will always work out, no matter what happens exactly (otherwise your parallel algorithm would be incorrect).

The interesting definition of correctness for nondeterministic algorithm is "the algorithm always computes what I want it to compute, for some sequence of choices I am allowed to make". This means that there may be choices that are wrong, in the sense that they make the algorithm produce the wrong answer or even go in an infinite loop. In the example "pick any even number between 0 and n", perhaps 4 and 16 are right choices, but all other numbers are wrong, and these numbers may vary depending on the input, the partial results and the choices made so far.

When used in computer science, nondeterminism is usually limited to nondeterministically choosing either a 0 or a 1. However, if you pick many such bits nondeterministically, you can generate long nondeterministic numbers or other objects, as well as make nondeterministic choices, so this hardly (if ever) limits its applicability - if applicability is limited, nondeterminism was too powerful in the first place.

Nondeterminism is a tool that is exactly as powerful as a certificate-based deterministic algorithm, that is, an algorithm that checks a property given an instance and a certificate for that property. You can simply nondeterministically guess the certificate for one direction, and you can give a certificate that contains all the 'right' answers for the nondeterministic guesses of 0 and 1 of your program for the other direction.

If we throw running time into the mix, then things become even more interesting. The running time of a nondeterministic algorithm is usually taken to be the minimum over all (right) choices. However, other choices may lead to a dramatically worse running time (which can be asymptotically worse or even arbitrarily worse than the minimum), or even an infinite loop. This is why we take the minimum: we do not care about these weird cases.

Now we get to randomized algorithms. Randomized algorithms are like nondeterministic algorithms, but instead of 'allowing' the choice between 0 and 1 at certain points, this choice is determined by a random coin toss at the time that the choice has to be made (which may differ from run to run, or when the same choice has to be made again later on during the execution of the algorithm). This means that the result is 0 or 1 with equal probability. Correctness now becomes either "the algorithm nearly always computes what I want it to compute" or "the algorithm always computes what I want it to compute" (just the deterministic version). In the second case the time the algorithm needs to compute its answer is usually 'nearly always fast', contrasting with a deterministic 'always fast'.

Contrasting the three: deterministic algorithms exactly specify the answer to the choice, nondeterminism leaves it completely open, but tells you a 'right' answer exists, and randomization leaves the answer up to chance. Note that you can just guess the right coin tosses, which gives rise to a hierarchy between these three: nondeterminism is as powerful as randomization, which in turn is as powerful as determinism, or, with respect to polynomial time, $P \subseteq ZPP \subseteq NP$. In this setting, no proofs are known whether any is strictly more powerful than another.

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"The running time of a nondeterministic algorithm is usually taken to be the minimum over all (right) choices." -- I thought so, too, but apparently it's wrong. (It would allow $O(|s|)$ algorithms for all problems, where $s$ is the solution, after all.) –  Raphael Oct 14 '12 at 10:46

In short: non-determinism means to have multiple, equally valid choices of how to continue a computation. Randomisation means to use an external source of (random) bits to guide computation.


In order to understand nondeterminism, I suggest you look at finite automata (FA). For a deterministic FA (DFA), the transition function is, well, a function. Given the current state and the next input symbol, the next state is uniquely defined.

A non-deterministic automaton (NFA), on the other hand, has a transition relation: given the current state and the next input symbol, there are multiple possible next states! For example, consider this automaton for the language $(ab)^*(ac)^*$:

NFA
[source]

The automaton guesses which $a$ marks the border between $(ab)^*$ and $(ac)^*$; a deterministic automaton would have to postpone its decision until after having read the symbol after each $a$.

The key point here is that acceptance is defined as "accept if there is an accepting run" for NFA. This existence criterion can be interpreted as "always guessing right", even though there is no actual guessing.

Note that there are no probabilities here, anywhere. If you were to translate nondeterminism into programming languages, you would have statements that can cause jumps to different other statements given the same state. Such a thing does not exist, except maybe in esoteric programming languages designed to skrew your mind.


Randomisation is quite different. If we break it down, the automaton/program does not have multiple choices for continuing execution. Once the random bit(s) are drawn, the next statement is uniquely defined:

if ( rand() > 5 )
  do_stuff();
else
  do_other_stuff();

In terms of finite automata, consider this:

PFA
[source]

Now every word has a probability, and the automaton defines a probability distribution over $\{a,b,c\}^*$ (differences between $1$ and the sum of outgoing edges is the probability of terminating; words that can not be accepted have probability $0$).

We can view this as a deterministic automaton, given the sequence of random decisions (which models practice quite well, as we usually use no real random sources); this can be modelled as a DFA over $\Sigma \times \Pi$ where $\Pi$ is a sufficiently large alphabet used by the random source.


One final note: we can see that nondeterminism is a purely theoretical concept, it can not be implemented! So why do we use it?

  1. It often allows for smaller representations. You might know that there are NFA for which the smallest DFA is exponentially as large¹. Using the smaller ones is just a matter of simplifying automaton design and technical proofs.

  2. Translation between models is often more straight-forward if nondeterminism is allowed in the target model. Consider, for instance, converting regular expressions to DFA: the usual (and simple) way is to translate it to an NFA and determinise this one. I am not aware of a direct construction.

  3. This may be an academic concern, but it is interesting that nondeterminism can increase the power of a device. This is not the case for finite automata and Turing machines, arguable the most popular machine models devices, but for example deterministic pushdown-automata, Büchi automata and top-down tree automata can accept strictly less languages than their non-deterministic siblings².


  1. See this question on cstheory.SE for an example.
  2. See here, here and here (Proposition 1.6.2), respectively.
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You should be aware that there are two different definitions of nondeterminism being thrown around here.

  1. As wikipedia defines it, pretty much "not determinism", that is, any algorithm that doesn't always have the same behavior on the same inputs. Randomized algorithms are a special case of "not deterministic" algorithms, because they fit the definition as I just gave it.

  2. Nondeterministic models of computation (like nondeterministic turing machines) are theoretical models of computation. They may have multiple possible paths of execution and they "accept" if any of those paths accept. You should note they aren't real. There is no way to physically run an algorithm that is nondeterministic in this sense, although you can simulate it with a randomized or deterministic one.

In CS, nondeterminism usually means (2), so Wikipedia's definition you gave (which is (1)) is misleading. Most of the answers given so far explain (2), not (1).

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According to 1), randomised Quicksort is a deterministic algorithm; I'm not sure that is useful terminology. I guess 1) could be described as "black-box" view while 2) actually inspects the algorithm/machine at hand. Arguably, CS is all about 2); I'd assign viewpoint 1) to (modular) software engineering. –  Raphael Feb 2 at 17:45
    
@Raphael, Good point, I should fix (1) to say "have the same behavior on the same inputs". Agreed about preferring (2) over (1). –  usul Feb 2 at 18:42
    
"Behaviour" is ambiguous, exactly in the black- vs white-box way. :) –  Raphael Feb 2 at 18:44
    
Sure, but, I guess I view the important distinction as between a formal, precisely defined nondeterministic Turing Machine (2) and vague/ambiguous "not determinism" (1), which could include randomness (whereas an NTM doesn't). So that's all I wanted to say.... –  usul Feb 2 at 18:51

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