Semantics of a Sequential Language for Exact RealNumber Computation.
ABSTRACT We study a programming language with a builtin ground type for real numbers. In order for the language to be sufficiently expressive but still sequential, we consider a construction proposed by Boehm and Cartwright. The nondeterministic nature of the construction suggests the use of powerdomains in order to obtain a denotational semantics for the language. We show that the construction cannot be modelled by the Plotkin or Smyth powerdomains, but that the Hoare powerdomain gives a computationally adequate semantics. As is well known, Hoare semantics can be used in order to establish partial correctness only. Since computations on the reals are infinite, one cannot decompose total correctness into the conjunction of partial correctness and termination as it is traditionally done. We instead introduce a suitable operational notion of strong convergence and show that total correctness can be proved by establishing partial correctness (using denotational methods) and strong convergence (using operational methods). We illustrate the technique with a representative example.

Article: Antrag auf Forderung einer wissenschaftlichen Zusammenarbeit zwischen Deutschland und Grobritannien
 SourceAvailable from: informatics.sussex.ac.uk
Article: Firstorder definability of LRT
 SourceAvailable from: Amin Farjudian[Show abstract] [Hide abstract]
ABSTRACT: We adapt the concept of dataflow process network to suit computation with real numbers and other data types that require approximation, providing a framework for com positional distributed exact computation with these data types. Our processes communicate approximations with each other in a lazy requestresponse manner, which is ad vantageous for geometrical objects and functions where the locality of approximations can be directed by requests. We do not provide a process calculus, instead, starting at the level of event traces, we derive a domaintheoretical semantics. Thanks to such generality, many di erent data types and representations fit into the framework. Our domaintheoretical semantics is an abstraction of the requestresponse behaviour, combining a denotational semantics at the data type level with a generalised concept of convergence rate. We show that a safe estimate of this semantics can be derived compositionally without referring to the event trace level except for atomic processes.
Page 1
Semantics of a Sequential Language for Exact
RealNumber Computation
J. Raymundo MarcialRomeroa,∗,1,
Mart´ ın H. Escard´ oa
aUniversity of Birmingham, Birmingham B15 2TT, England
Abstract
We study a programming language with a builtin ground type for real numbers. In order
for the language to be sufficiently expressive but still sequential, we consider a construction
proposed by Boehm and Cartwright. The nondeterministic nature of the construction sug
gests the use of powerdomains in order to obtain a denotational semantics for the language.
We show that the construction cannot be modelled by the Plotkin or Smyth powerdomains,
but that the Hoare powerdomain gives a computationally adequate semantics. As is well
known, Hoare semantics can be used in order to establish partial correctness only. Since
computations on the reals are infinite, one cannot decompose total correctness into the
conjunction of partial correctness and termination as it is traditionally done. We instead in
troduce a suitable operational notion of strong convergence and show that total correctness
can be proved by establishing partial correctness (using denotational methods) and strong
convergence (using operational methods). We illustrate the technique with a representative
example.
Key words: exact realnumber computation, sequential computation, semantics,
nondeterminism, PCF.
1Introduction
This is a contribution to the problem of sequential computation with real numbers,
where real numbers are taken in the sense of constructive mathematics [2]. It is fair
∗Corresponding author.
Email addresses: jrm@cs.bham.ac.uk(J. Raymundo MarcialRomero),
mhe@cs.bham.ac.uk(Mart´ ın H. Escard´ o).
1Present address: Divisi´ on de Computaci´ on, UAEM, Ciudad Universitaria S/N, 50040,
Toluca, Estado de M´ exico, M´ exico
Preprint submitted to Elsevier Science 20 November 2006
Page 2
to say that the computability issues are well understood [35]. Here we focus on the
issue of designing programming languages with a builtin, abstract data type of real
numbers. Recent research, discussed below, has shown that it is notoriously diffi
cult to obtain sufficiently expressive languages with sequential operational seman
tics and corresponding denotational semantics which articulate the dataabstraction
requirement. Based on ideas arising from constructive mathematics, Boehm and
Cartwright [3], however, proposed a compelling operational solution to the prob
lem. Yet, theirproposalfalls shortof providingafull solutionto thedata abstraction
problem, as it is not immediately clear what the corresponding denotational inter
pretation would be. A partially successful attempt at solving this problem has been
developed by Potts [29] and Edalat, Potts and S¨ underhauf [6], as discussed below.
In light of the above, the purpose of this paper is twofold: (1) to establish the
intrinsic difficulties of providing a denotational model of Boehm and Cartwright’s
operationalapproach,and (2)to showhowitispossibleto copewiththedifficulties.
Before elaborating on this research programme, we pause to discussprevious work.
Di Gianantonio [14], Escard´ o [11], and Potts et al. [28] have introduced various
extensions of the programming language PCF with a ground type for real num
bers. Each of these authors interprets the real numbers type as a variation of the
interval domain introduced by Scott [30]. In the presence of a certain parallel con
ditional [26], all computable firstorder functions on the reals are definable in the
languages [14,8]. By further adding Plotkin’s parallel existential quantifier [26], all
computable functions of all orders become definable in the languages [14,7,10]. In
the absence of the parallel existential quantifier, the expressivity of the languages
at secondorder types and beyond is not known. Partial results in this direction are
developed by Normann [24].
It is natural to ask whether the presence of such parallel constructs is an artifact of
the languages or whether they are needed for intrinsic reasons. Escard´ o, Hofmann
and Streicher [9] have shown that, in the interval domain models, the parallelism
is in fact unavoidable: weak parallelor is definable from addition and other mani
festly sequential unary functions, which indicates that addition, in these models, is
an intrinsically parallel operation. Moreover, Farjudian [12] has shown that if the
parallel conditional is removed from the language, only piecewise affine functions
on the reals are definable.
Essentially, the problem is as follows. Because computable functions on the reals
are continuous (see e.g. [35]), and because the real line is a connected space, any
computable booleanvalued function on the reals is constantly true or constantly
false unless it diverges for some inputs. Hence, definitions using the sequential
conditionalproduceeitherconstanttotalfunctionsorpartialfunctions.Ifoneallows
the booleanvalued functions to diverge at some inputs, then nontrivial predicates
are obtained, and this, together with the parallel conditional, allow us to define the
nontrivial total functions [11].
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This phenomenon had been anticipated by Boehm and Cartwright [3], who also
proposed a solution to the problem. In this paper we develop the proposed solution
and study its operational and denotational semantics. The idea is based on the fol
lowing observations. In classical mathematics, the trichotomylaw “x < y, x = y or
x > y” holds for any pair of real numbers x and y, but, as is well known, it fails in
constructive(and in classical recursive) mathematics. However, the followingalter
native cotransitivity law holds in constructive settings: for any two numbers a < b
and any number x, at least one of the relations a < x or x < b holds. Equivalently,
one has that (−∞,b) ∪ (a,∞) = R. Boehm and Cartwright’s idea is to consider a
language construct rtesta,b, for a < b rational, such that:
(1) rtesta,b(x) evaluates to true or to false for every real number x,
(2) rtesta,b(x) may evaluate to true iff x < b, and
(3) rtesta,b(x) may evaluate to false iff a < x.
It is important here that evaluation never diverges for a convergent input. If the real
number x happens to be in the interval (a,b), then the specification of rtesta,b(x)
allows it to evaluate to true or alternatively to false. The particular choice will de
pend on the particular implementation of the real number x and of the construct
rtesta,b(cf. [20]), and is thus determined by the operational semantics.
As application of the construction, we givean example of a recursive definition of a
sequential program for addition, which is singlevalued at total inputs, as required,
but multivalued at partial inputs. Thus, by allowing the output to be multivalued
at partial inputs, we are able to overcome the negative results of Escard´ o, Hofmann
and Streicher mentioned above.
We take the view that the denotational value of rtesta,b(x) lives in a suitable pow
erdomain of the booleans. Thus (1) if a < x < b then the denotational value would
be the set {true,false}, (2) if a ?< x and x < b then it would be the set {true}, and
(3) if a < x and x ?< b then it would be the set {false}. Technically, one has to be
careful regarding which subsets of the powerset are allowed, but this is tackled later
in the body of the paper. One of our main results is that the Hoare powerdomain
gives a computationally adequate denotational semantics. We also show that the
Plotkin and Smyth powerdomains do not render the rtest construction continuous
and hence cannot be used as models. These and other examples of powerdomains
are discussed in the body of the paper.
As is well known, Hoare semantics can be used in order to establish partial correct
ness only. Because computations on the reals are infinite, one cannot decompose
total correctness into the conjunction of partial correctness and termination, as is
usually done for discrete data types. Instead, we introduce a suitable operational
notion of strong convergence and show that total correctness can be proved by es
tablishing partial correctness (using denotational methods) and strong convergence
(using operational methods). The technique is illustrated by a proof of total correct
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ness of our sequential program for addition. Further applications are discussed in
the concluding section.
1.1Related work.
Potts [29] considers a redundant if operator (rif) for his programming language
LAR (an extension of PCF with linear fractional transformations), defined as
rif : ICK × ICF2× (ICK → t)2→ t
rif x < (I,J); then f else g =
f(x),
g(x),
if I ≪ x;
if J ≪ x.
where K ∈ ICR∞and F is a dense subset of K. He uses the Hoare powerdomain
to develop a denotational semantics for his language and prove computational ad
equacy. Our work justifies this choice. Potts considers a deterministic onestep re
duction relation, while we consider a nondeterministicrelation so as to have a pre
cise match as possible with the denotational semantics in the case of multivalued
terms.
Edalat, Potts and S¨ underhauf [6] had previously considered the denotational coun
terpart of Boehm and Cartwright’s operational solution. However, they restrict at
tentionto what can be referred to as singlevalued,total computations.In particular,
their computational adequacy result for their denotational semantics is restricted to
this special case. Although it is indeed natural to regard this case as the relevant
one, we have already met compelling examples, such as the fundamental opera
tion of addition, in which sequentiality cannot be achieved unless one allows, for
example, multivalued outputs at partial inputs.
For their denotational semantics, they consider the Smyth powerdomain of a topo
logical space of real numbers (which they refer to as the upper powerspace). Thus,
they consider possibly nondeterministic computations of total real numbers, re
stricting their attention to those which happen to be deterministic. In the work re
ported here, we insteadconsidernondeterministiccomputationsof totaland partial
real numbers. In other words, instead of considering a powerdomain of a space of
real numbers, we consider a powerdomain of a domain of partial real numbers. Our
computational adequacy result holds for general computations, total or partial, and
whether deterministic or not. For our domain of partial real numbers, we consider
the interval domain proposed by Scott [30], but the present findings are expected to
apply to many possible notions of domain of partial real numbers.
Farjudian [13] has developed a programming language, which he called SHRAD,
which satisfies the three requirements mentioned at the beginning of the paper: se
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quentiality, data abstraction and expressivity. In his work, he defines a sequential
language in which all computable first order functions are definable. However ex
tensionalityistradedoffforsequentiality,inthesensethatallcomputablefirst order
functions are extensional over total real numbers but not over partial real numbers.
Hence functions such as the rounding functions, which are frequently used in prac
tice, cannot be defined in SHRAD.
Di Gianantonio [15] also discusses the problem of sequential realnumber compu
tation in the presence of data abstraction, with some interesting negativeresults and
translations of parallel languages into sequential ones.
In order to characterize computable functions on the real numbers, Brattka [4] in
troduces a class of relations that includes a contruction which is essentially the
same as Boehm and Cartwright’s multivalued test discussed above. The main dif
ference is that we articulate relations as functions with values on a powerdomain.
With this, we are able to capture highertype computation. Moreover, as discussed
above, we take a powerdomain of the interval domain, not of the real line, and
hence we are able to distinguish partiality from multivaluedness: an interval gives
a partially specified real number, and a set of intervals collects the possible (total
or partial) outputs of a nondeterministic computation.
1.2 Organization.
Section 2 presents a running example that motivates the technical development that
follows. Section 3 introduces some background. Section 4 studies the rtest con
struction from the point of view of powerdomains. Section 5 develops a program
ming language with the rtest construction and establishes computational adequacy
for the denotational semantics developed in Section 4. Section 6 applies this to de
velop techniques for correctness proofs and gives sample applications. Section 7
summarizes the main results and discusses open problems and further work.
2 Running example
In order to motivate the use of the multivalued construction discussed in the in
troduction, we give an example showing how it can be used to avoid the parallel
constructions used in previous works on realnumber computation. We take the
opportunity to introduce some basic concepts and constructions studied in the tech
nical development that follows.
In the programming language considered in [11], the average operation
(− ⊕ −): [0,1] × [0,1] → [0,1]
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defined by
x ⊕ y = (x + y)/2
can be implemented as follows:
x ⊕ y= pif x < c
then pif y < c
then consL(tailL(x) ⊕ tailL(y))
else consC(tailL(x) ⊕ tailR(y))
else pif y < c
then consC(tailR(x) ⊕ tailL(y))
else consR(tailR(x) ⊕ tailR(y)).
Here
c = 1/2,L = [0,c],C = [1/4,3/4],R = [c,1],
the function consa: [0,1] → [0,1] is the unique increasing affine map with image
the interval a, i.e.,
consL(x) = x/2,
consC(x) = x/2 + 1/4,
consR(x) = x/2 + 1/2,
and the function taila: [0,1] → [0,1] is a left inverse, i.e.
taila(consa(x)) = x.
More precisely, the following left inverse is taken, where κais the length of a and
µais the left endpoint of a:
taila(x) = max(0,min(κax + µa,1)).
Because equality on real numbers is undecidable, the relation x < c is undefined
(or diverges, or denotes ⊥) if x = c. In order to compensate for this, one uses a
parallel conditional such that
pif ⊥ then z else z = z.
The intuition behind the above program is the following. If both x and y are in the
interval L, then we know that x ⊕ y is in the interval L, if both x and y are in the
interval R, then we know that x ⊕ y is in the interval R, and so on. The boundary
cases are taken care of by the parallel conditional. For example, 1/2 is both in L
and R, and an unfolding of the program for x = y = 1/2 gives
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1/2 ⊕ 1/2 = pif ⊥
then pif ⊥
then consL(1 ⊕ 1)
else consC(1 ⊕ 0)
else pif ⊥
then consC(0 ⊕ 1)
else consR(0 ⊕ 0).
All branches of the conditionals evaluate to 1/2, but in an infinite number of steps.
This can be seen as follows. A repeated unfolding of 1⊕1 gives the infinite expres
sion consR(consR(consR(...))). Denotationally speaking, the program computes
the unique fixed point of consR, which is 1. Operationally speaking, the first un
folding says that the result of the computation, whatever it is, lives in the interval
R, because, by definition, the image of consRis R; the second unfolding says that
the result is in the right half of the interval R, i.e. in the interval [3/4,1]; the third
unfolding tells us that the result is in the interval [7/8,1], and so on. Thus, the
operational semantics applied to 1 ⊕ 1 produces a shrinking sequence of intervals
converging to 1. The other cases are analogous.
Of course, a drawback of such a recursive definition is that, during evaluation, the
number of parallel processes grows exponentially in the number of unfoldings. In
order to overcome this, we switch back to the usual sequential conditional, and we
replace the partial lessthan test by the multivalued test discussed in the introduc
tion:
Average(x,y)= if rtestl,r(x)
then if rtestl,r(y)
then consL(Average(tailL(x),tailL(y)))
else consC(Average(tailL(x),tailR(y)))
else if rtestl,r(y)
then consC(Average(tailR(x),tailL(y)))
else consR(Average(tailR(x),tailR(y))),
where c of the previous program splits into two points
l = 1/4,r = 3/4.
and this time we choose
L = [0,r],C = [1/8,7/8],R = [l,1].
The intuition behind this program is similar. What is interesting is that, despite the
use of the multivalued construction rtest, the overall result of the computation
is single valued. In other words, different computation paths will give different
shrinking sequences of intervals, but all of them will shrink to the same number. A
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proof of this fact and of correctness of the program is provided in Section 6, using
the techniques developed below. For further examples see [22].
3Background
For domaintheoretic concepts, the reader is referred to [1,27], and for topological
concepts to [33,34] (see also [16]). Here we briefly summarize thenotions and facts
that are relevant to our purposes.
3.1Continuous Domains
Let P be a set with a preorder ⊑. For a subset X of P and an element x ∈ P we
write
↓X = {y ∈ P  y ⊑ x for some x in X},
↑X = {y ∈ P  x ⊑ y for some x in X},
↓x = ↓{x},↑x = ↑{x}.
We also say that X is a lower set iff X = ↓X, and that X is an upper set iff
X = ↑X.
Let x and y be elements of a directed complete partial order (dcpo) D. We say that
x is waybelow or approximates y, denoted x ≪ y, if for every directed subset A of
D,y ⊑?Aimplies∃a ∈ Awithx ⊑ a. Wesaythatxiscompact ifitapproximates
{x ∈ D  x is compact}. We say that a subset B of a dcpo D is a basis for D, if for
every element x of D the set↓↓x ∩ B contains a directed subset with supremum x.
A dcpo is called a continuous domain or simply a domain if it has a basis. A dcpo
is called an algebraic domain if it has a basis of compact elements. An example
of an algebraic domain is the domain T⊥= {⊥,false,true} of booleans, ordered
by ⊥ ⊑ false,⊥ ⊑ true. A function f from a domain D to a domain E is Scott
continuos if it is monotone and f(?A) =?f(A) for all directed subset A of D. A
We say that a Scott closed set is finitely generated if it is the lower set of a finite
set. The following is easily established:
itself. We define↑↑x = {y ∈ D  x ≪ y},↓↓x = {y ∈ D  y ≪ x} and K(D) =
Scott closed subset of a domain D is a lower set closed under directed supremum.
Lemma 3.1 If D is a continuousdomain,C a finitelygeneratedScott closed subset
of D and f : D → D Scott continuous then
↓{f(x)  x ∈ C} = cl{f(x)  x ∈ C}.
where cl denotes topological (Scott) closure.
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3.2The Interval Domains R and I
The set R of nonempty compact subintervals of the Euclidean real line ordered by
reverse inclusion,
x ⊑ y iff x ⊇ y,
is a continuous domain, referred to as the interval domain. Here intervals are re
garded as “partial numbers”, with the singleton intervals playing the role of “total
numbers”. If we add a bottom element to R, then R becomes a bounded complete
continuous domain R⊥. For any interval x ∈ R, we write
x = inf x and x = sup x
so that x = [x,x]. Its length is defined by
κx= x − x.
A subset A ⊆ R has a least upper bound iff it has nonempty intersection, and in
this case
?A =
The waybelow relation of R is given by
?A =
?
sup
a∈Aa, inf
a∈Aa
?
.
x ≪ y iff x < y and y < x.
A basis for R is given by the intervals with distinct rational (alternatively dyadic)
endpoints.
The set I of all nonempty closed intervals contained in the unit interval [0,1]
is a bounded complete, countably based continuous domain, referred as the unit
interval domain. The bottom element of I is the interval [0,1].
3.3 Powerdomains
Powerdomains[25,31,32]areusuallyconstructedas idealcompletions[18]offinite
subsets of basis elements. For our purposes, it is more convenient to work with
their topological representations [27,1,19], which we now summarize. It is enough
for our purposes to restrict attention to ωcontinuous dcpos, which we refer to as
domains in this subsection.
A subset A of a dcpo D is called Scott closed if it is closed in the Scott topology,
that is, if it is a lower set and is closed under the formation of suprema of directed
subsets. We use the notation cl(A) for the topological closure of A, i.e. the smallest
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Scott closed set containing A. A lense is a nonempty set that arises as the intersec
tion of a Scottclosed set and a Scott compact upper subset.Here the notionof Scott
compact set is to be understood in the topological sense (every cover consisting of
Scottopen setshasafinitesubcover).On thesetoflensesofadcpoD,wedefinethe
topological EgliMilner ordering, ⊑TEMby K ⊑TEML if L ⊆ ↑K and K ⊆ cl(L).
Notice that in a finite domain such as the flat domain of booleans, the lenses are
just orderconvex sets, and that the topological EgliMilner order coincides with
the usual ordertheoretical one [16]. This is because in a finite domain the closed
sets are precisely the lower sets, and all sets are compact.
The Plotkin powerdomain PPD of a domain D consists of the lenses of D under
the EgliMilner order, and the formalunion operation A ∪ B is given by actual
union A ∪ B followed by topological convex closure (intersection of all convex
closed sets containing it). There is a natural topological embedding η: D → PPD
given by x ?→ {x}.
The Smyth powerdomain PSD consists of the set of nonempty Scottcompact up
per subsets ordered by reverse inclusion, with formal union given by actual union.
In this case, we have a natural topological embedding η: D → PSD given by
x ?→ ↑x
The Hoare powerdomain PHD consists of all nonempty Scottclosed subsets of
D ordered by inclusion. Because we use this to obtain a denotational model of our
language, we consider it in more detail. Least upper bounds are given by
?
i∈I
Ai= cl
?
i∈I
Ai.
The construction is the functor part of a monad, with action on continuous maps
given by
?f : PHD → PHE
A ?→ clf[A]
for any f : D → E. Its unit is given by
ηD: D → PHD
x ?→ ↓x,
which is also a topological embedding. Instead of considering multiplication, one
can equivalently consider the extension operator [21, Proposition 2.14], in this case
given by
¯f : PHD →
?→ cl?
for any continuous map f : D → PHE. Finally, formal unions are given by actual
PHE
A
a∈Afa
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unions as in the case of the Smyth powerdomain:
A ∪ B = A ∪ B.
4 Semantics of the Multivalued Construction
In orderto makethedevelopmentoftheintroductionprecise, weassumethatweare
given a functorial powerdomain construction P, in a suitable category of domains,
with a natural embedding
ηD: D → PD
and a continuous formalunion operation
(− ∪ −): PD × PD → PD
for every domain D. Then the definition of the function rtesta,b: R → PT, where
a < b are real numbers, can be formulated as
rtesta,b(x) =
η(true),
η(true) ∪ η(false),
η(false),
if x ∈ (−∞,a],
if x ∈ (a,b),
if x ∈ [b,∞).
Because in our language there will be computations on the reals that diverge or
fail to fully specify a real number, we need to embed the real line into a domain
of total and partial real numbers. We choose to work with the domain R⊥, where
R is the interval domain introduced in Section 3. Similarly, as usual, we enlarge
the domain T of booleans with a bottom element. Hence we have to work with an
extension R⊥→ PT⊥of the above function, which we denote by the same name:
R
?
R⊥
rtesta,b
− − − − → PT
?
rtesta,b
− − − − → PT⊥
For the moment, we do not insist on any particular extension. However, in order for
a powerdomain construction to qualify for a denotational model of the language,
the minimum requirement is that it makes the rtesta,bfunction continuous.
Lemma 4.1 If rtesta,b: R⊥ → PT⊥ is a continuous extension of the function
rtesta,b: R → PT, then the inequalities
η(true) ⊑ η(true) ∪ η(false),
η(false) ⊑ η(true) ∪ η(false)
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must hold in the powerdomain PT⊥
PROOF. Because the embedding R ֒→ R⊥is continuous when R is endowed
with its usual topology and R⊥with its Scott topology, so is its composition with
the function rtesta,b: R⊥ → PT⊥, which we denote by r: R → PT⊥. (This is
the diagonal of the above commutative square). In any dcpo, the relation d ⊑ e
holds if and only if every neighbourhood of d is a neighbourhood of e. Let V be a
neighbourhood of t := η(true). We have to show that n := η(true) ∪ η(false) ∈ V .
The set U := r−1(V ) is open in R by continuity of r : R → PT. Because r(a) =
t ∈ V , we have that a ∈ r−1(V ) = U. Hence, because U is open in R, there is
an open interval (u,v) with a ∈ (u,v) ⊆ U. Choose x such that a < x < v and
x < b, that is, such that x ∈ (a,b) ∩ (u,v) ⊆ U. By construction, r(x) = n. But
x ∈ r−1(V ), which shows that n ∈ V and hence that t ⊑ n, which amounts to the
first inequality. The second inequality is obtained in the same way.
2
Thus, any powerdomain not satisfying the above two inequalities does not qualify
for a model. In particular, this rules out the Plotkin and Smyth powerdomains. In
fact, for the Plotkin powerdomain one has that η(true) = {true} and η(false) =
{false}, and their formal union is {true,false} because this set is orderconvex,
but the sets {true} and {true,false} are incomparable in the EgliMilner order.
For the Smyth powerdomain, the same sets are obtained by the embedding, formal
union is given by actual union, and hence the inequalities do not hold because the
order is given by reverse inclusion. We omit routine proofs of the fact that e.g. the
mixed [17] and the sandwich [5] powerdomains also fail to satisfy the inequalities
and hence to make the rtesta,bconstruction continuous.
On the other hand, for the Hoare powerdomain, the inequalities do hold. In fact,
η(true) = {true,⊥} and η(false) = {false,⊥}, their formal union is their actual
union {true,false,⊥}, and the ordering is given by inclusion. Moreover:
Proposition 1 There is a continuous extension rtestH
tion rtesta,b: R → PT.
a,b: R⊥→ PHT⊥of the func
PROOF. The functions f,g: R⊥→ PT⊥defined by
f(x) =
η(true),
⊥,
if x ⊆ (−∞,b),
otherwise,
g(x) =
η(false),
⊥,
if x ⊆ (a,∞),
otherwise,
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{true,false, }
{true, } {false, }
{ }
{true,false, }
{true, } {false, }
{ }
{false}
{true,false}
{true}
Smyth:
(Reverse Inclusion order)
{true,false, }
(Egli−Milner order)
{true,false}
{true} {false}
(Inclusion order)
Plotkin:
Hoare:
Fig. 1. Powerdomains of T⊥.
areeasilyseentobecontinuous,andtheyareconsistentbecauseη(true)andη(false)
are consistent elements. Hence their join
rtestH
a,b= f ⊔ g
is welldefined and continuous. An easy verification shows that this function has
the required extension property.
2
As we want to match our model with the operational semantics of the construction,
it would be desirable to distinguish between the elements {true} and {true,⊥} in
themodel.However,theHoare powerdomaindoes notdistinguishthem, and,on the
other hand, as we have just seen, other powerdomains do not give a continuous in
terpretation of our construction. In order to overcome this problem when the Hoare
powerdomain is used as a denotational model, one usually decomposes proofs of
program correctness into partial correctness and termination. A related approach is
considered in Section 6.
From now on, we denote rtestH
cations, we are only interested in the situation 0 < a < b < 1 and the restriction
of this function to the domain I of closed subintervals of the interval [0,1], again
written rtesta,b: I → PT⊥.
a,b: R⊥→ PHT⊥simply by rtesta,b. In our appli
4.0.0.1
main goal of this paper, we briefly digress to discuss a natural variation rtest′
Remark on the boundary cases of rtest.
Before proceeding to the
a,b:
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R → PT of the rtesta,bconstruction, defined by
rtest′
a,b(x) =
η(true),
η(true) ∪ η(false),
η(false),
if x ∈ (−∞,a),
if x ∈ [a,b],
if x ∈ (b,∞).
With a proofsimilarto that of Lemma4.1, we conclude that if rtest′
then
a,bis continuous
η(true) ∪ η(false) ⊑ η(true)
η(true) ∪ η(false) ⊑ η(false).
This rules out the Plotkin and Hoare powerdomains, but not the Smyth powerdo
main. However, it is not clear what the operational counterpart of this function
would be. The function rtesta,bis operationally computable because, for any argu
ment x given intensionally as a shrinking sequence of intervals, the computational
rules systematicallyestablishone of thesemidecidableconditionsa < x and x < b.
However, the conditions a ≤ x and x ≤ b are not semidecidable, and hence it is
not immediately apparent what a computationally adequate operational semantics
for rtest′would be. But it is interesting, as pointed out by one of the referees, that
thecotransitivitylawgivenintheintroductionasaconstructivejustificationofrtest
can be equivalently formulated as “a ≤ x or x ≤ b whenever a < b”. In any case,
it is not clear to us, at the time of writing, whether or how this reformulation of the
cotransitivity law would lead to a computational mechanism for rtest′.
5 A Programming Language for Sequential RealNumber Computation
We introduce the language LRT for the rtest construction, which amounts to the
language considered by Escard´ o [11] with the parallel conditional removed and a
constantfor rtesta,badded. Weremark that thisis acallbynamelanguage. Because
realnumber computations are infinite, and there are no canonical forms for partial
realnumbercomputations,itisnotclearwhatacallbyvalueoperationalsemantics
ought to be. We leave this as an open problem.
5.1Syntax
The language LRT is an extension of PCF with a ground type for real numbers and
suitable primitive functions for realnumber computation. Its raw syntax is given
by
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x ∈ V ariable,
t::=nat  bool  I  t → t,
P ::=x  n  true  false  (+1)(P)  (−1)(P) 
(= 0)(P)  ifP thenP elseP  consa(P) 
taila(P)  rtesta,b(P)  λx : t.P  PP  YP,
where the subscripts of the constructs cons, tail are rational intervals and those
of rtest are rational numbers. (We apologize for using the letters a and b to denote
numbers and intervals in different contexts.) Terms of ground type I are intended
to compute real numbers in the unit interval.
It is convenient for our purposes to first define the denotational and then the opera
tional semantics.
5.2Denotational Semantics.
The ground types bool,nat and I are interpreted as the Hoare powerdomainof the
domains of booleans, natural numbers and intervals, respectively. Function types
are interpreted as function spaces in the category of dcpos:
?bool? = PHT⊥,?nat? = PHN⊥,?I? = PHI,
?σ → τ? = ?σ? → ?τ?.
This reflects the fact that we are considering a callbyname language.
The interpretation of constants in LRT is defined as follows:
?true? = η(true),?false? = η(false),?n? = η(n),
?(+1)? =?
(+1),?(−1)? =?
(−1),?(= 0)? =?
(= 0),
?consa? = ?
consa,?taila? =?
?Y?(F) =
taila,
?
?rtesta,b? = rtesta,b,
n≥0
Fn(⊥),
?if?(B,X,Y ) =
X,
Y,
X ∪ Y,
⊥,
if B = η(true),
if B = η(false),
if B = η(true) ∪ η(false),
if B = ⊥.
Herethesymbolsη,?,
are defined in Section 2, and the function rtesta,bis defined in Section 4.
aredefinedasinSection3.3,thefunctions(+1),(−1),(= 0)
are the standard interpretations in the Scott model of PCF, the functions consa,taila
15
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