1. Introduction
At the end of his contribution to a panel discussion on logic for the twentyfirst century [5, page 175]
, Richard Shore posed, as the last of three “probably pieinthesky” problems, the following:
Find, and argue conclusively for, a formal definition of algorithm and the appropriate analog of the ChurchTuring thesis. Here we want to capture the intuitive notion that, for example, two particular programs in perhaps different languages express the same algorithm, while other ones that compute the same function represent different algorithms for the function. Thus we want a definition that will up to some precise equivalence relation capture the notion that two algorithms are the same as opposed to just computing the same function.
The purpose of this paper is to support Shore’s “pieinthesky” assessment of this problem by arguing that there is no satisfactory definition of the sort that he wanted. That is, one cannot give a precise equivalence relation capturing the intuitive notion of “the same algorithm.” We describe several difficulties standing in the way of any attempt to produce such a definition, and we give examples indicating that the intuitive notion is not sufficiently welldefined to be captured by a precise equivalence relation.
Following the terminology in Shore’s statement of the problem, we use “program” for an element of the set on which the desired equivalence relation is to be defined and we use “algorithm” for an equivalence class of programs (or for the abstract entity captured by an equivalence class) with respect to an equivalence relation of the sort requested by Shore.
Shore’s question has been addressed in a limited context by Yanofsky [21]. He considers programs only in the form of (fairly) standard constructions of primitive recursive functions, and he defines an equivalence relation on these programs by listing a number of situations in which two programs should be considered equivalent. He does not, however, claim that the equivalence relation generated by these situations completely captures the intuitive notion of equivalence of programs. In fact, he explicitly says “it is doubtful that we are complete,” and he anticipates one of our objections to the “equivalence class of programs” view of algorithms by saying that “whether or not two programs are the same … is really a subjective decision.” By considering only a limited class of programs and limited sorts of equivalence between them, Yanofsky obtains a notion of algorithm with pleasant properties from the point of view of category theory, but not what Shore asked for.
We shall also consider only a limited class of algorithms, namely deterministic, smallstep, noninteractive algorithms. But for our purposes, such a limitation strengthens our argument. If no suitable equivalence relation can be found in this limited case, then the situation is all the more hopeless when more algorithms (distributed ones, interactive ones, etc.) enter the picture. Any suitable equivalence relation on a large class of programs would restrict to a suitable equivalence relation on any subclass.
Smallstep algorithms are those that proceed in a discrete sequence of steps, performing only a bounded amount of work per step. (The bound depends on the algorithm but not on the input or on the step in the computation.) Noninteractive algorithms^{1}^{1}1It is argued in [11, Section 9] that nondeterminism is a special case of interaction. Thus, “noninteractive” implicitly entails “deterministic.” of this sort were characterized axiomatically in [11]
. They include traditional models of computation such as Turing machines and register machines. Most people, when they hear the word “algorithm”, think of deterministic, smallstep, noninteractive algorithms unless a wider meaning is explicitly suggested. And until the 1960’s, essentially all algorithms in the literature were of this sort. Because Shore posed his question in the context of traditional recursion theory, it is reasonable to suppose that he had deterministic, smallstep, noninteractive algorithms in mind. If so, then it is not only reasonable but in a sense necessary to argue our case in the limited context of such algorithms. For if an argument against the existence of the desired equivalence relation depended on other sorts of algorithms (for example, interactive ones or massively parallel ones), then that argument might be regarded as missing the point of Shore’s question.
In Sections 2 and 3, we discuss preliminary issues concerning the choice of programming languages and their semantics. In Section 4, we describe situations where people can reasonably disagree as to whether two programs implement the same algorithm. In Section 5, we discuss how the intended use of algorithms can affect whether they should count as the same. Section 6 contains examples where two quite different algorithms are connected by a sequence of small modifications, so small that one cannot easily say where the difference arises. Finally, in Section 7, we mention several other domains in which analogous issues arise.
2. Programs
In order to even hope to capture the notion of algorithm by a precise equivalence relation on the set of programs, it is necessary first to have a precise set of programs to work with. The present section addresses this preliminary issue. We argue that there is a precise set of programs, at least for smallstep, noninteractive algorithms, but that this fact is not as obvious as one might think at first.
The mention, in Shore’s question, of “programs in perhaps different languages” suggests that the set of programs should be rather wide, encompassing programs written in a variety of languages. But caution is needed here, in order to keep the set of programs welldefined. We cannot allow arbitrary programming languages, because new ones are being created (and perhaps old ones are falling out of use) all the time. Even languages that persist over time tend to gradually acquire new dialects. We should work instead with a specific set of stable languages.
Not only should the languages be stable, so as not to present a moving target for an equivalence relation, but they should have a precise, unambiguous semantics. For example, in some languages, such as C, the order of evaluation of subexpressions in an expression may be left to the discretion of the compiler, and whether this ambiguity matters in a particular program is, in general, undecidable. If deterministic algorithms are to be captured by equivalence classes of programs, then ambiguity in the programs is unacceptable. This consideration excludes a lot of languages. And pseudocode, despite being widely used to describe algorithms, is even worse from the point of view of precision.
For many purposes in theoretical computer science, especially in complexity theory, it is traditional to use, as the official framework, lowlevel models of computation, such as Turing machines or register machines, that have an unambiguous semantics. Such models of computation are, however, inadequate to serve as the domain of an equivalence relation capturing the general notion of algorithm. The problem is that they can express algorithms only at a low level of abstraction, with many implementation details that are irrelevant to the algorithm.
At first sight, it seems that this difficulty can be circumvented by a suitable choice of equivalence relation on, say, Turing machine programs. Programs that differ only in the irrelevant details of implementation should be declared equivalent. There remains, however, the problem that, by including such details, a Turing machine program may make the intended algorithm unrecognizable. It is not in general easy to reverse engineer a Turing machine program and figure out what algorithm it implements. In fact, it may be impossible; here is an example illustrating the problem (as well as some other issues to be discussed later).
Example 1.
The Euclidean algorithm for computing the greatest common divisor (g.c.d.) of two positive integers is described by the following abstract state machine (ASM), but readers unfamiliar with ASM notation should be able to understand it as if it were pseudocode. In accordance with the usual conventions [10] for ASMs, the program is to be executed repeatedly until a step leaves the state unchanged. We assume for simplicity that the numbers whose g.c.d. is sought are the values, in the initial state, of dynamic nullary function symbols and . We use for the remainder function; is the remainder when is divided by .
if rem then output else do in parallel rem, enddo endif
Here is a variant, using subtraction instead of division. In fact, this variant corresponds more closely to what Euclid actually wrote; see [12, Propositions VII.2 and X.3].
if then elseif then else output endif
Notice that the second version in effect uses repeated subtraction (rather than division) to compute in several steps the remainder function used in a single step of the first version.
Are these two versions the same algorithm? As we shall discuss later, this issue is debatable. The point we wish to make here, before entering any such debate, is that the use of lowlevel programs like Turing machines might cut off the debate prematurely by making the two versions of Euclid’s algorithm identical. Consider programming these versions of Euclid’s algorithm to run on a Turing machine, the input and output being in unary notation. The most natural way to implement division in this context would be repeated subtraction. With this implementation of division, any difference between the two versions disappears.
If we were to use Turing machines (or similar lowlevel programs) as our standard, then to avoid letting them preempt any discussion of whether the two versions of Euclid’s algorithm are the same, we would have to implement at least one of the two versions in a special way. For example, we might implement division by converting the inputs to decimal notation and having the Turing machine perform division as we learned to do it in elementary school. But then the question arises whether the preprocessing from unary to decimal notation has altered the algorithm. And once preprocessing enters the picture, there are arbitrary choices to be made (decimal or binary?), which further complicate the picture.
Motivated by examples like the preceding one, we refuse to restrict ourselves to lowlevel programming languages. We want a language (or several languages) capable of expressing algorithms at their natural level of abstraction, not forcing the programmer to decide implementation details. This desideratum, combined with the earlier one that the language(s) should have a precise, unambiguous semantics, severely restricts the supply of candidate languages. In fact, the only such language that we are aware of is the abstract state machine language, which, in our present context, means smallstep (also called sequential) ASMs, as presented in [11].
Remark 2.
To make this paper more selfcontained, we give the following brief description of sequential ASMs. For more details, see [6] or [10] or [11]. The rest of this paper does not depend on the exact definition of ASMs; a reader willing to read ASM programs as pseudocode should have no difficulty with their use here.
An ASM program describes operations to be performed on a state. The state is a structure in the usual sense of firstorder logic. The computation progresses in discrete steps, changing the interpretations of certain function symbols in the state. The basic units from which ASM programs are built are update rules of the form , whose meaning is that the value of (the interpretation of) the ary function symbol at the tuple consisting of (the values of) is to be changed to (the value of) . ASM programs are built from update rules by two constructors. One is parallel composition;
means to make all the state changes given by the rules . (If two of those changes clash, prescribing different updates of the same function at the same arguments, then no changes are to be made.) The other constructor is the conditional
with the obvious meaning; here is a quantifierfree firstorder formula. This concludes our rough description of ASM programs.
We note here another advantage of ASMs: They can deal directly with structures, such as graphs, as input and output. This contrasts with the need, in most other programming languages, to code structures. In the case of graphs, coding would involve not only deciding, for example, to represent graphs as adjacency matrices, but also choosing, for each particular input graph, an ordering of its vertices, because an adjacency matrix presupposes a particular ordering.
ASMs also make it possible to write programs that use powerful, highlevel operations. This is important for our purposes because we do not want the programming language to prejudge whether an algorithm using such operations is equivalent to one that implements those operations by means of lowerlevel ones. To avoid prejudging, the language must be able to formulate separately the highlevel and lowlevel versions, not simply use the latter as a surrogate for the former. The possibility of writing programs at different levels is one of the key strengths of ASMs.
In view of these considerations, we shall use ASMs as our standard programs in this paper. We emphasize, however, that this decision is not essential for most of our arguments. The difficulties facing an attempt to define the “right” equivalence relation on programs are intrinsic difficulties, not caused by our decision to use ASMs. In fact, the difficulties would be even worse if we were to use programming languages that lack a precise semantics or programming languages that cannot work at multiple levels of abstraction.
3. Behavioral Equivalence
A proper understanding of the use of ASMs to describe algorithms requires a look at the concept of behavioral equivalence, which plays a central role in the theory of ASMs. It is based on an abstract view of algorithms presented in [11]. Before discussing this point of view, it is necessary to resolve a terminological ambiguity, because the word “algorithm”, which plays a crucial role in Shore’s question, is also used as an abbreviation for “sequential algorithm” (as defined in [11]) when no other sorts of algorithms are under consideration. We shall be careful to abstain from this abbreviation. “Sequential algorithm” will mean what is defined in [11], namely an entity to which are associated a set of states, a set of initial states, and a transition function, subject to certain postulates;^{2}^{2}2A rough summary of the postulates is that states are firstorder structures, the transition function involves only a bounded part of the state, and everything is invariant under isomorphisms of structures. “algorithm” without “sequential” will mean what Shore asked for (and what we claim admits no precise characterization), essentially an equivalence class of programs with respect to the pieinthesky equivalence relation. The terminology is somewhat unfortunate in that (1) it suggests that “sequential algorithms” are a special case of “algorithms”, whereas the two concepts are actually separate and (2) the word “sequential” that is the difference between the two names is not a difference between the concepts, since we retain our earlier convention that the only algorithms (in Shore’s sense) that we consider here are smallstep, noninteractive ones. Nevertheless, it seems reasonable to use the expressions “sequential algorithm” and “algorithm” to match the terminology of the sources, [11] and [5] respectively.
Behavioral equivalence, defined in [11] (but there called simply “equivalence”) is a very fine equivalence relation on sequential algorithms, requiring that they have the same states, the same initial states, and the same transition function. The main theorem in [11] is that every sequential algorithm is behaviorally equivalent to a (sequential) ASM. As was pointed out in [11], behavioral equivalence seems excessively fine for many purposes, but this circumstance only makes the main theorem of [11] stronger.
For our present purposes, it is useful, though not absolutely necessary, to know that behavioral equivalence is fine enough, that is, that two behaviorally equivalent sequential algorithms are equivalent in Shore’s sense. This knowledge is useful because it ensures that, by using ASMs as our standard programs, we have representatives for all of the algorithms in Shore’s sense. If it were possible for behaviorally equivalent programs to be inequivalent in Shore’s sense, then there might be algorithms that, although behaviorally equivalent to ASMs, are not Shoreequivalent to any ASMs, and such algorithms would be overlooked in our discussion. This would not be a disaster – if we convince the reader that there is no precise definition for Shoreequivalence on ASMs, then there is surely no such definition for an even wider class of programs (this is why we described the knowledge above as not absolutely necessary)—but it is desirable to know that we are dealing with the whole class of algorithms that we intended to deal with.
Is this desirable knowledge in fact correct? Must behaviorally equivalent algorithms be equivalent in the sense that Shore asked for? In the rest of this section, we argue that the answer is yes, as long as we do not read into ASMs more information than they are intended to convey. In brief, the point is that ASMs are intended to describe sequential algorithms and, since these are not specified beyond their states, initial states, and a transition function,^{3}^{3}3The definition of “sequential algorithm” [11] says that they are entities associated with the three items mentioned, but the postulates concern only these three items. No further role is played by the entities themselves. there is no room for any distinction between behaviorally equivalent ASMs or sequential algorithms.
Because the ASM syntax is readable as pseudocode, however, there is a temptation to consider an ASM as providing additional information beyond the sequential algorithm that it defines. Here is an example to illustrate the situation.
Example 3.
We describe here, in ASM form, a binary decision tree algorithm. The vocabulary has 0ary relation symbols (also known as propositional symbols)
for every sequence (i.e., every sequence of at most 5 binary digits), it has constant symbols for , and it has one additional 0ary symbol . (Visualize the symbols as attached to the internal nodes of a binary tree of height 7 and the symbols as attached to the leaves.) The only dynamic symbol is . States are arbitrary structures for this vocabulary. The ASM program we want is best described by reverse induction on binary strings of length . If has length 6, let be the programIf has length , let be
The program we are interested in is where is the empty sequence. Its natural, intuitive interpretation is as a computation that begins by looking up ; depending on the answer, it looks up for or ; depending on the answer, it looks up for an appropriate of length 2; and so forth, until, after 6 queries, it sets to the appropriate . In other words, we have a standard binary decision tree.
The algorithm does all this work in one step. (If it were allowed to run for another step, nothing would change in the state.) Notice that the program has length of the order of magnitude and that its natural interpretation as a computational process looks up 7 values, namely 6 ’s and one . (To make precise sense of “order of magnitude” here and similar notions below, pretend that 6 is a variable.)
Now let us apply the proof of the main theorem in [11] to the sequential algorithm defined by this ASM. The proof produces another ASM, , behaviorally equivalent to our . This ASM (see [11, Lemma 6.11]) is a parallel combination of guarded updates, one for each of the possible systems of values of the ’s. So has length of the order of magnitude , exponentially greater than . Furthermore, in each run of the natural, intuitive interpretation of as a computational process, it looks up the values of all 63 of the ’s.
In view of the vastly greater work done by in each run (and its vastly greater length), it would be difficult to convince people that it represents the same algorithm as . Yet, all the complexity of is hidden if one looks only at states and transition functions, and so is behaviorally equivalent to . We assert that and should be regarded as defining the same algorithm, that is, as equivalent programs in Shore’s sense. The apparent differences between them arise from regarding the ASM programs not merely as descriptions of sequential algorithms but as telling in detail how the transition function is to be computed. It is tempting to assign this sort of detailed operational semantics to ASMs, but this conflicts with our intention to use them, as in [11], simply to describe sequential algorithms.
We do not claim that one should ignore the difference between an algorithm that looks up seven values and performs six tests on them and an algorithm that looks up 63 values and performs approximately tests on them. But we do claim that, if these properties of the algorithms are to be taken into account, then the ASMs representing the algorithms should be designed to make these properties visible in the states and transition functions. That this can be done is part of the ASM thesis: Any algorithm can be represented at its natural level of abstraction by an ASM. In the case at hand, the level of abstraction could involve keeping track of which ’s the algorithm has used. This can be done, for example, by having additional dynamic symbols (with value or in initial states) with the intended meaning “ has been used” and by including in both and updates setting to whenever is used.^{4}^{4}4Another way to make the evaluation of ’s visible for the purposes of behavioral equivalence is to make the ’s external function symbols, so that evaluating them involves a query to the environment. In this paper, however, we prefer to consider only noninteractive algorithms. Similarly, if we wish to take into account the huge number of guards (of conditional rules) evaluated in the computation intuitively described by , then we should write the ASM at that level of abstraction, that is, we should include updates whereby the algorithm keeps a record of all this work.
For the rest of this paper, we shall take for granted, with the support of considerations like the preceding paragraph, that, whatever Shore’s pieinthesky equivalence relation ought to be, it will be at least as coarse as behavioral equivalence.
Remark 4.
Udi Boker [personal communication] has suggested that, once it is clear that ASMs are to serve as descriptions of what is to happen as a result of a single step, not caring about how it is accomplished within the step, then behavioral equivalence captures the intuitive notion of algorithm. We feel that, although behavioral equivalence is a natural and important equivalence relation, it need not capture the intution. In Example 1 above and the related Example 5 below, we have algorithms that are not behaviorally equivalent but can nevertheless be reasonably viewed as the same algorithm. In other words, the intuitive notion of “the same algorithm” seems not to require the computations to match step by step. Nor does it require the vocabulary of the states to be the same; a reasonable renaming of identifiers in a program should not change the algorithm (but see also Example 13 below).
Given that Shore’s pieinthesky equivalence relation should be at least as coarse as behavioral equivalence, the main theorem of [11] assures us that ASMs are adequate for representing all sequential algorithms in the sense of [11]. These are exactly the smallstep, noninteractive algorithms that we intend to treat in this paper. We therefore have an adequate set of programs, the ASMs, with a precisely defined semantics. That is, we have a set of programs on which it makes sense to try to define an equivalence relation of the sort Shore asked for. Now that this prerequisite for Shore’s question is satisfied, we turn to the question itself.
4. Subjectivity
The formulation of Shore’s question, to “capture the notion that two algorithms are the same,” presupposes that there is such a notion. And indeed, people do seem to have such a notion, to use it, and to understand each other when they use it. But it is not clear that they all use it the same way. Will two competent computer scientists (or mathematicians or programmers) always agree whether two algorithms are the same? We have already quoted Yanofsky’s doubt about this point: equivalence of algorithms is a subjective notion.
There are situations where disagreement is almost guaranteed. Suppose X has invented an algorithm and Y later invents a somewhat modified version of it. X will be more likely than Y to say that the algorithms are really the same.
Even when ulterior motives are not involved, there can easily be disagreements about whether two algorithms are the same. Consider, for example, the two versions of the Euclidean algorithm in Example 1, one using division and the other using repeated subtraction. Are they the same?
One can argue that they are different. The version with subtraction is slower. It can be implemented on simpler processors, for instance, children who have learned how to subtract but not how to divide. It is not behaviorally equivalent to the division version because it takes several steps to do what division did in one step.
Despite all these differences, it seems that most mathematicians (and computer scientists?) would, when confronted with either of these algorithms, call it the Euclidean algorithm. Furthermore, if one just asks, “What is the Euclidean algorithm?”, the answer is usually the division version (see for example [1, Section 8.8] or [7]), even though Euclid gave the subtraction version. To call the division version “Euclidean” strongly suggests that it is considered “the same” as what Euclid did.
The subjectivity of the situation is further emphasized by the following variant.
Example 5.
For positive integers and , let be the multiple of that is closest to (possibly below and possibly above, and in case of a tie then below), and let
be the distance from to the nearest multiple of . Now replace by in the division version of the Euclidean algorithm. Does that change the algorithm? Notice that it may happen that, in runs of the and versions of the algorithm on the same input, only the initial and final states coincide; all the intermediate states may be different (and there may be different numbers of intermediate states). Nevertheless, both versions are called the Euclidean algorithm.
Finally, what about the Euclidean algorithm applied not to integers but to polynomials in one variable (over a field)? It proceeds just like the division version in Example 1, but it uses division of polynomials. When and are polynomials, there are unique polynomials and such that and has strictly smaller degree than does. (We regard the zero polynomial as having degree .) Let denote this , and replace by in the division version above to get the Euclidean algorithm for polynomials. Is that a different algorithm?
Remark 6.
There is a more abstract, nonalgorithmic proof that every two positive integers have a g.c.d. It consists of showing that the smallest positive element of the ideal serves as the g.c.d. The main point of the proof is to show that this divides both and . That proof implicitly contains an algorithm for finding the g.c.d. . As before, we take the initial state to have and , the two numbers whose g.c.d. is sought, but this time and are static; there is one dynamic function symbol , initially equal to .
if then elseif then else output endif
It seems that this algorithm is really different from the Euclidean one; it uses a different idea. But we are not prepared to guarantee that no one will regard it as just another form of the Euclidean algorithm. Nor do we guarantee that “the same idea” has a clear meaning.
Here are some more examples where it is debatable whether two algorithms should be considered the same.
Example 7.
The first is actually a whole class of examples. Consider two algorithms that do the same calculations but then format the outputs differently. Are they the same algorithm? Our description of the situation, “the same calculations” suggests an affirmative answer, but what if the formatting involves considerable work?
Example 8.
Is a sorting algorithm, like quicksort or mergesort a single algorithm, or is it a different algorithm for each domain and each linear ordering? This question is similar to the earlier one about the Euclidean algorithm for numbers and for polynomials, and it is clear that one could generate many more examples of the same underlying question.
Notice that there are some situations that can be regarded as falling under both of the preceding two examples. Suppose one algorithm sorts an array of numbers into increasing order and a second algorithm sorts the same inputs into decreasing order. They can be viewed as the same algorithm with different formatting of the output, or they can be viewed as the same algorithm applied to two different orderings of the natural numbers.
Example 9.
Quicksort begins by choosing a pivot element from the array to be sorted. One might always choose the first element, but a better approach is to choose an element at random. A variant uses the median of three elements (for example, the first, middle, and last entries of the array) as the pivot. (See, for example, [1, pages 94 and 95]
.) Are these different algorithms? What if one increases “three” to a larger odd number?
5. Purpose
In the preceding section, we attributed variations in the notion of “the same algorithm” to people’s different opinions and subjective judgments. There is, however, a more important source of variation, even between the notions used by the same person on different occasions, namely the intended purpose.
For someone who will implement and run the algorithm, differences in running time are likely to be important. A difference in storage requirements may be very important on a small device but not on a device with plenty of space. Of course, the distinction between a small device and one with plenty of space depends on the nature of the computation; even a supercomputer is a small device when one wants to predict next week’s weather. Thus, when one considers a specific device and asks whether an algorithm’s space requirements are an essential characteristic of the algorithm, that is, whether one should count two algorithms as different just because one uses much more space than the other, then answer is likely to be “yes” once the space requirements are large enough but “no” if they are small. Thus, two programs may well count as the same algorithm for someone programming fullsize computers but not for someone programming cellphones.
Although we generally consider only noninteractive algorithms in this paper, let us digress for one paragraph to consider (a limited degree of) interaction. If an algorithm is to be used as a component in a larger system, additional aspects become important, which may be ignored in standalone algorithms. And in this respect, even just the interaction between an algorithm and the operating system can be important for some purposes. Two programs that do essentially the same internal work but ask for different allocations of memory space (or ask for the same allocation at different stages of the computation) or that make different use of library calls will be usefully considered as different algorithms for some purposes but probably not for all. Similarly, if an algorithm’s output is to be used as the input of some other calculation, then formatting (see Example 7) is more important than it otherwise would be.
Returning to the standard situation of noninteractive, smallstep algorithms, we present an example which, although it arose in a different context (complexity theory), helps to show how one’s purpose can influence whether one considers two algorithms the same.
Example 10.
Suppose we have programs and , computing functions and , respectively, say from strings to strings, and each requiring only logarithmic space and linear time (in a reasonable model of computation). Can we combine and to compute the composite function in linear time and log space? The simplest way to compute on input would be to first use on input to compute and then use on input to compute . This combined algorithm runs in linear time but not in log space; storing the intermediate result will in general require linear space.
There is an alternative method to compute . Begin by running , intending to use as its input but not actually computing beforehand. Whenever tries to read a character in the string , run on input to compute that character and provide it to . But make no attempt to remember that computation of . When needs another character from , run again to provide it. Because this version of the algorithm does not try to remember , it can run in log space. But because it needs to restart every time wants a character from , it will run in general in quadratic rather than linear time.
So we have two programs that combine and to compute . One runs in linear time, and the other runs in log space, but neither manages to do both simultaneously. Do these two programs represent the same algorithm?
In an application where strict complexity restrictions like linear time and log space are important, these programs should be considered different. But in more relaxed situations, for example if quadratic time is fast enough, then the difference between these two programs might be regarded as a mere implementation detail.
So far, the purposes we have discussed, which may influence the decision whether two algorithms are the same, have involved implementing and running the algorithms. But people do other things with algorithms, besides implementing and running them. For someone who will extend the algorithm to new contexts, or for a mathematician who will appeal to the algorithm in a proof, the idea underlying the algorithm is of primary importance. Furthermore, in both of these situations the presuppositions of the algorithm will play an important role. Thus, for example, it would be an essential characteristic of (the division form of) the Euclidean algorithm that it uses a measure of “size” on the relevant domain (numerical value or absolute value in the case of integers, degree in the case of polynomials) such that one can always^{5}^{5}5Or almost always; division by zero should be excluded. divide and obtain a remainder “smaller” than the divisor.
It is reasonable to suppose that any worthwhile notion of sameness of algorithms will be adapted to some more or less specific purpose, and that different purposes will yield different notions of sameness. A global, allpurpose notion of “the same algorithm” is indeed pie in the sky.
6. Equivalence
Is the relation, between programs, of expressing the same algorithm really an equivalence relation? The preceding sections suggest that the relation depends on individual judgment and goals, but suppose we fix an individual and a goal, and use the resulting judgment; call two algorithms the same if and only if this particular person regards them as the same for this particular purpose. (And don’t give him an opportunity to change his mind.) Now can we expect to have an equivalence relation? Shore’s question explicitly asks for an equivalence relation, but is the intuitive notion of “the same algorithm” necessarily an equivalence relation?
6.1. Reflexivity
It seems obvious that every program defines the same algorithm as itself, that is, that no program defines two algorithms. But in fact, we had to take some precautions to ensure this obvious fact. Recall the example of the division form and the subtraction form of the Euclidean algorithm which, when programmed on a Turing machine with unary notation, can become the same program. One of our reasons for not adopting Turing machines as our standard programs was precisely this possible failure of reflexivity.
Our insistence that ASMs be understood as defining only sequential algorithms and in fact as defining only their states, initial states, and transition functions, not the details of what happens within a step, is also related to reflexivity. That is because the semantics of ASMs is defined only at the level of sequential algorithms. There is no guarantee that an ASM program cannot be regarded, by two readers, as describing two different processes within a step; the only guarantee is that the ultimate outcome of the step must be the same.
We believe that we have taken enough precautions to ensure reflexivity. In fact, our discussion of behavioral equivalence in Section 3 was intended to support the thesis that an ASM program defines the same algorithm as any behaviorally equivalent ASM, in particular itself.
6.2. Symmetry
The colloquial usage of “the same algorithm” seems to admit some mild failures of symmetry. For example, given a program at a rather abstract level and a detailed implementation of it, a person reading the text of and suddenly understanding what is really going on amid the details might express his understanding by exclaiming “Oh, is just the same as ”, while the symmetric claim “ is just the same as ” is unlikely.
Nevertheless, the intuitive notion of “the same algorithm” underlying Shore’s question is clearly a symmetric one. Shore’s description of the intended intuition refers to the unordered pair of programs, using the phrases “two particular programs” and “two algorithms” with no distinction between a first and a second.
Furthermore, someone exclaiming, in the situation described above, “ is just the same as ,” would probably, if pressed, concede that he didn’t really mean that they are “the same” but that is merely an implementation of , i.e., that the idea behind is the same as that behind , but contains more details and thereby hides the idea.
6.3. Transitivity
In view of the preceding brief discussion of reflexivity and symmetry, we regard the question “Do we have an equivalence relation?” as coming down to “Do we have transitivity?”, and here things are considerably less clear. Might there be, for example, finite (but long) sequences of programs in which each program is essentially the same as its neighbors (and so should express the same algorithm), yet the first and last programs would not be considered the same algorithm? Here are some examples to indicate the sorts of things that can happen. The first example is a continuation of the binary decision tree example.
Example 11.
Consider the programs and described in Example 3, and modify them, as described earlier, to make explicit the differences in the work they do within a step. For example, have them update Boolean variables with the meaning “ was used.” These modifications, call them and , can reasonably be regarded as different algorithms.
There is, however a “continuous” (in a discrete sense) transition between them. An intermediate stage of this transition would be an algorithm that begins by looking up the values of for the first sequences (in order of increasing length and lexicographically for equal lengths, i.e., for the first few levels of the decision tree and for some initial segment of the next level). Using the answers, the algorithm finds (by a single parallel block of guarded updates, i.e., by a table lookup in a table of size ) the path through the part of the tree about which it asked. It reaches a node for which it knows the value of but didn’t look up for the children of . The value of tells the algorithm which of the two children of is relevant, so it looks up the value of that . This value tells which child of is relevant, so that determines the next to evaluate, and so forth. (The algorithm also updates the ’s as above, to indicate which ’s it evaluated.) When , this algorithm is . When , it is . These look different. But is there a significant difference between the algorithms obtained for two consecutive values of ? It looks like just a minor bookkeeping difference. So we have a sorites situation, going from one algorithm to an arguably different one in imperceptible steps—imperceptible in the sense that one could reasonably consider the and versions mere variant implementations of the same algorithm.
Example 12.
Similarly, in Example 9
, the quicksort algorithm could reasonably be considered unchanged by a minor change in the number
of elements whose median determines the first pivot. But a succession of such minor changes can lead to a situation where becomes as large as the entire array to be sorted. Then finding the median, to serve as the first pivot, is practically as hard as sorting the whole array. So at this stage, the algorithm is much less likely to be regarded as the same as traditional quicksort.The next example involves what may be, at least for logicians, the archetypal example of a trivial syntactic change that makes no real difference, namely renaming bound variables (also called equivalence). The particular choice of bound variables is so unimportant that they are often eliminated altogether, for example by means of de Bruijn indices in the calculus [4] or by the boxesandlinks scheme of Bourbaki [3].
Example 13.
Sequential noninteractive ASMs, which we have chosen as our standard programs, don’t have bound variables. They can, however, and often do have variables that resemble bound variables in that the algorithm shouldn’t care what those variables are (as long as they are distinct and don’t clash with other variables). For example, many sequential ASMs have a dynamic symbol , serving as a sort of program counter, whose value in every initial state is a particular element (named by) and whose value at the end of every computation is . Neither the name nor the names of its intermediate values make any real difference to the computation. Nevertheless, changing these names produces a different ASM, one not behaviorally equivalent to the original because it has a different set of states (for a different vocabulary). At first sight, it seems clear that two programs that differ only by such a renaming should be equivalent; the underlying algorithm is the same.
But for certain purposes, there may be a real difference, especially if the identifiers become very long strings, for example the entire text of War and Peace. Then an actual implementation will involve reading the identifiers and (at least) checking which of them are the same and which are not. If the identifiers get too long, a realworld interpreter or compiler will crash. (For such reasons, compiler expert Gerhard Goos likes to say that every compiler is almost always wrong, meaning that it is correct on only finitely many programs.) Even if the system (interpreter or compiler) is willing to handle very long identifiers, it must do additional work to distinguish them. Suppose, for example, that we take a program written with normalsized identifiers and modify it by attaching a particular long random string to the beginning of all the identifiers. Then, if the compiler reads from left to right, it will have to read and compare long strings just to tell whether two occurrences are of the same identifier. (We could also attach another long random string, of possibly different length than the first, to the end of each identifier, so as to defeat compilers that don’t just read from left to right.)
In accordance with the discussion in Section 3, if we want to take into account the work involved in reading and comparing identifiers, then we should write the ASM in a way that makes this work explicit. Suppose then that this rewriting has been done. Now the example leads to a sorites paradox. Lengthen the identifiers gradually; at what point do you get a different algorithm? For a fixed compiler (or interpreter), there may be a welldefined boundary, but the boundary moves when the compiler changes.
Finally, here is an example arising from how the first author, after grading exam papers, sorts them into alphabetical order for recording the grades and returning the papers to the class; see also [15].
Example 14.
With exams, first find the power of 2, say , such that is at least 3 but less than 6. (We don’t have classes with fewer than 3 students, so is a nonnegative integer.) Partition the given, randomly ordered stack of exams into substacks of size (rounded up to an integer in some stacks and down in others), and sort each substack by insertion sort (i.e., by inspection, since the stacks are so small). Merge these stacks in pairs to produce larger, sorted stacks. Then merge these in pairs, and so forth, until all the papers are in one sorted stack. (Implementation detail: If desk space is limited, then don’t do all the insertion sorts first and then all the merging. Rather, the stacks that would eventually be merged by the algorithm above should be actually merged as soon as they become available. This keeps the number of sorted stacks bounded by instead of by . Although it’s irrelevant to the purpose of this example, the variation raises again the question whether it changes the algorithm.) The decision to have between 3 and 6 is just a matter of convenience, so that insertion sort can be easily applied to the stacks of size . In principle, the bounds could be any and . For a fixed , varying gives a chain of algorithms connecting insertion sort (when ) and mergesort (when ), such that each two consecutive algorithms in the chain are intuitively, at least for some people’s intuition, not really different algorithms.
If is not fixed, then there is an infinite sequence of algorithms as above, indexed by the natural numbers . But the sequence, which starts at mergesort, doesn’t reach insertion sort. Instead, there is another sequence, indexed by increasing (in the notation above), that starts at insertion sort (when ) and heads toward mergesort. Between the two sequences, there are other algorithms (or other versions of the same algorithm), for example one that adjusts (and therefore ) to do half of the work by insertion and half by merging. (It may be useful to think of this chain of algorithms in a nonstandard model of arithmetic, where can be infinite, and we have an internally finite but really infinite chain of algorithms, indexed by , connecting insertion sort to mergesort.)
Example 15.
Similar comments apply to the usual implementation of quicksort, where, as described in [13, page 116], “Subfiles of or fewer elements are left unsorted until the very end of the procedure; then a single pass of straight insertion is used to produce the final ordering. Here is a parameter ….” A small change in the parameter can be viewed as a mere implementation detail, not a change in the algorithm, but a succession of such changes can lead, for files of a fixed size, from quicksort to insertion sort. The situation is just like that for mergesort except that insertion sort is used at the end of quicksort and at the beginning of mergesort.
In all the preceding examples, we had a finite sequence of programs such that a reasonable person might call each particular consecutive pair of algorithms equivalent but might not want to call the first and last programs of the sequence equivalent. There is no clear transition from equivalent to inequivalent, just as in the classical sorites paradox there is no clear transition from a pile of sand to a nonpile (or from a nonbald head to a bald one). That situation casts doubt on the transitivity of the supposed equivalence relation.
Vopěnka and his coworkers have developed an interesting approach to such situations, using the socalled alternative set theory; see [20], especially Chapter III. This theory distinguishes sets from classes, but the distinction is not just a matter of size as in the traditonal von NeumannBernaysGödel or KelleyMorse theories of sets and classes. Rather, classes are to be viewed as having somewhat more vague membership conditions than sets do. As a result, a subclass of a set need not be a set.
The axiomatic basis of this theory allows a distinction between natural numbers (in the usual settheoretic sense) and finite natural numbers. If a set contains 0 and is closed under successor, then it contains all the natural numbers, but a class with the same properties might contain only the finite ones. The latter can be regarded as formally modeling the notion of a feasible natural number. If an equivalence relation is a set (of ordered pairs), then its transitivity property can be iterated any natural number number of times, but if it is a class of ordered pairs, then only a feasible number of iterations will work. Thus, sorites paradoxes disappear if one accepts that feasibly many of grains of sand do not constitute a pile and that a man with feasibly many hairs is bald. These ideas can be similarly applied to our examples above. (We thereby add credence to the first author’s claim that the number of exams he has to grade is not feasible.)
7. Analogies
The issues involved in defining equivalence of algorithms are similar to issues arising in some other contexts, and we briefly discuss a few of those here.
7.1. Clustering
The basic problem of saying when two things (in our case, programs) should be treated as the same is very similar to the basic problem of clustering theory, namely saying which elements of a given set are naturally grouped together. Indeed, if we had a reasonable metric on the set of programs, then we might consider applying the techniques of clustering theory to it. But before doing so, we should take into account a comment of Papadimitriou [17]:
There are far too many criteria for the ‘goodness’ of a clustering … and far too little guidance about choosing among them…. The criterion for choosing the best clustering scheme cannot be determined unless the decisionmaking framework that drives it is made explicit.
And that brings us back to our earlier comment that the notion of “same algorithm” that one wants to use will depend on what one intends to do with it and with the algorithms.
We should also note that Papadimitriou’s discussion is in the context where the metric is given and the issue is to choose a criterion for goodness of clustering. Our situation is worse, in that we do not have a natural metric on the set of programs.
7.2. Proofs
Another analog of “When are two algorithms the same?” is “When are two proofs the same?” The role played by the set of programs in our discussion of algorithms would be played by the set of formal deductions in some axiomatic system. The problem is to say when two formal deductions represent the same proof. There are many axiomatic systems that one might consider, ranging from propositional calculus up to systems like ZermeloFraenkel set theory that provide a foundation for almost all of mathematics. But the question of which deductions represent the same proof makes sense at each level, and it leads to many of the same issues that we have discussed in connection with algorithms.
Equality of proofs has been studied in considerable detail but usually only at the level of elementary logic; see for example [14] and [9]. Renaming bound variables is regarded as not changing a proof; the same goes for cancelling the introduction of a connective or quantifier and an immediately following elimination of that connective or quantifier. (This formulation refers to the introduction and elimination rules of natural deduction systems, but there are analogous notions for sequent calculi. Hilbertstyle calculi are not used for studies of equality of proofs, because they require circumlocutions that make formal deductions bear little resemblance to the intuitive proofs they should express.) But such equivalence relations between deductions are too fine to capture the intuitive notion of proofs being the same. In this respect, they resemble the equivalence relation described in [21]. In fact there is another similarity here, namely that in both cases the equivalence relations are designed to look nice from a categorytheoretic viewpoint.
Considerably larger rearrangements of the material in a deduction would be recognized by mathematicians as not changing the proof that the deduction represents. Other changes, however, are considered essential, for example the difference between an analytic and an elementary proof in number theory, or the difference between a bijective proof and a manipulation of generating functions in combinatorics. Between the “clearly the same” and “clearly different” cases, there is a gray area that looks quite analogous to what we see in the case of algorithms.
The analogy between proofs and algorithms has been given mathematical content by the propositionsastypes or CurryHoward correspondence. Here formal deductions in certain systems correspond exactly to programs in certain (usually functional) programming languages. So there may be reason to think that, if we thoroughly understood sameness in one of the two contexts, then we could transfer that understanding (or at least part of it) to the other. Unfortunately, the existing notions of equivalence of proofs translate into equivalence relations on algorithms that are too fine; they count algorithms as equivalent only if they are so for trivial reasons.
In addition to the CurryHoward correspondence, there is another connection between proofs and computation. If we take an algorithm for computing a function and we run it on input obtaining output , then the record of the computation can be regarded as a proof, in an appropriate formal system, of the equation . (This is the essential point behind the theorem that all recursive functions on the natural numbers are representable in (very weak) systems of arithmetic like Robinson’s ; for details see [19, Section 2.4] and note that what is nowadays called representable was there called definable.) If we had a good notion of equivalence of proofs in such formal systems, then we could use it to define a notion of equivalence of algorithms. Call two algorithms, for computing the same function , equivalent if, for each input , not only do they produce the same output , but the resulting proofs of are equivalent. Unfortunately, it seems that, just as in the case of the CurryHoward correspondence, existing notions of equivalence of proofs are too fine and therefore so are the resulting notions of equivalence of algorithms.
7.3. Other analogous questions
We already mentioned, in Remark 6, that certain algorithms are considered different because they are based on different ideas. So we ask: What exactly does it mean for two ideas to be different? What is the “right” equivalence relation on ideas? This problem looks even worse than the original question about algorithms, partly because of its great vagueness and partly because the only analog for the set of programs, a set of things that can express ideas, would seem to be something like the set of all (English?) texts, and the ways in which a text can express an idea seem to be entirely out of control. A related question about texts is “What does it mean for a text in one language to be a translation of a text in another language?” Is expressing the same idea a necessary condition? Is it a sufficient condition? Does it depend on the chosen notion of “same idea”’?
Moschovakis [16] has argued that the meaning of a term in English (or other natural languages) is the algorithm for computing its denotation. With this identification of meanings with algorithms, the question of when two ideas are the same would be not merely analogous to the question of when two algorithms are the same; the former would become a special case of the latter, at least for those ideas that occur as the meanings of terms.
Similar issues arise in mathematics. What does it mean to say that two theorems are equivalent? Material equivalence, meaning equality of truth values, is certainly not the intention. The intuitive idea is that each of the theorems follows easily from the other – at least more easily than proving either theorem from scratch. But “easy” and “from scratch” are subjective notions.
Reverse mathematics (see [8, 18]) provides a notion of equivalence of theorems, namely provable equivalence over some weak base theory. This notion is, however, much coarser than the intuitive notion, since the proofs of equivalence may be highly nontrivial, often more difficult (because of the weakness of the base theory) than the usual proofs (in strong theories like ZermeloFraenkel set theory) of the theorems themselves. Indeed, much of the fascination of reverse mathematics comes from the equivalence between theorems from very different fields of mathematics, theorems that at first seem to have nothing to do with each other. Another way to view the coarseness of the notion of equivalence given by reverse mathematics is that it looks only at one aspect of theorems, namely the setexistence assumptions that underlie them, whereas the intuitive notion of equivalence would look rather at the (intuitive) content of the theorems.
We trust that the reader can extend this list of examples.
References
 [1] Alfred Aho, John Hopcroft, and Jeffrey Ullman, The Design and Analysis of Computer Algorithms, AddisonWesley (1974).
 [2] Andreas Blass and Yuri Gurevich, “Ordinary interactive smallstep algorithms,” A. C. M. Trans. Computational Logic Part I, 7 (2006) 363–419; Parts II and III, to appear. (A preliminary version of Parts II and III is Microsoft Research Tech report MSRTR200488.)
 [3] Nicolas Bourbaki, Eléments de Mathématique XVII, Première partie: Les structures fondamentales de l’analyse. Livre I. Théorie des ensembles, Hermann & Cie (1954). English translation Theory of Sets Hermann and AddisonWesley (1968), reprinted by SpringerVerlag (2004).
 [4] Nicolaas de Bruijn, “Lambda calculus notation with nameless dummies, a tool for automatic formula manipulation, with application to the ChurchRosser theorem,” Nederl. Akad. Wetensch. Proc. Ser. A 75 = Indag. Math. 34 (1972) 381–392.
 [5] Samuel Buss, Alexander Kechris, Anand Pillay, and Richard Shore, “The prospects for mathematical logic in the twentyfirst century,” Bull. Symbolic Logic 7 (2001) 169–196.
 [6] Nachum Dershowitz and Yuri Gurevich, “A natural axiomatization of computability and proof of Church’s thesis,” Bull. Symbolic Logic 14 (2008) 299–350.
 [7] “Euclidean algorithm,” Wikipedia, web site, http://www.answers.com/topic/euclideanalgorithm?cat=technology (Viewed on January 27, 2008.) (2008).
 [8] Harvey Friedman, “Some systems of secondorder arithmetic and their use,” Proc. International Congress of Mathematicians, Vancouver 1974, vol. I (1975) 235–242.
 [9] JeanYves Girard, “Linear logic,” Theoret. Comput. Sci. 50 (1987) 1–101.
 [10] Yuri Gurevich, “Evolving algebras: Lipari guide,” in Specification and Validation Methods, ed. E. Börger, Oxford Univ. Press (1995) 9–36.
 [11] Yuri Gurevich, “Sequential abstract state machines capture sequential algorithms,” A. C. M. Trans. Computational Logic 1 (2000) 77–111.
 [12] David Joyce, “Euclid’s Elements,” web page at http://aleph0.clarku.edu/~djoyce/java/elements/elements.html (Viewed on January 27, 2008.)
 [13] Donald E. Knuth, The Art of Computer Programming, vol. 3, Sorting and Searching, AddisonWesley (1973).
 [14] Joachim Lambek, “Deductive systems and categories. I. Syntactic calculus and residuated categories,” Math. Systems Theory 2 (1968) 287–318.
 [15] “Merge sort,” Literate programs, web site, http://en.literateprograms.org/Merge_sort_(C) .
 [16] Yiannis Moschovakis, “English as a programming language,” Tarski Lecture 2, (March 5, 2008) available at http://www.math.ucla.edu/~ynm/lectures.htm .

[17]
Christos Papadimitriou, “Algorithms, games, and the
internet,”
Proc. 33rd Annual ACM Symposium on Theory of Computing (STOC),
ACM (2001) 749–753.  [18] Stephen Simpson, Subsystems of Second Order Arithmetic, Perspectives in Mathematical Logic, SpringerVerlag (1999).
 [19] Alfred Tarski, with the collaboration of Andrzej Mostowski and Raphael Robinson, Undecidable Theories, NorthHolland (1953).
 [20] Petr Vopěnka, Mathematics in the Alternative Set Theory, Teubner Verlag (1979)
 [21] Noson Yanofsky, “Towards a definition of an algorithm,” preprint, arXiv:math/0602053.
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